... area north of GH and south of GB; PSD = Parry Sound District. ... for the three species overlaid on top of one another; barriers with bootstrap support > 0.90 are ...... reduced dispersal due to habitat conversion for agriculture and development.
Conservation Genetics of Freshwater Turtles
by
Christina Maria Davy
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy in Zoology Department of Ecology and Evolutionary Biology University of Toronto
© Copyright by Christina M. Davy, 2013
i
Conservation Genetics of Freshwater Turtles Christina M. Davy Doctor of Philosophy in Zoology Department of Ecology and Evolutionary Biology University of Toronto 2013
Abstract Turtles have long life spans, overlapping generations and promiscuous mating systems. Thus, they are an ideal model system with which to investigate the application of conservation genetics methods and assumptions to long-lived organisms. Turtles are also one of the most threatened groups of vertebrates and conservation genetics studies are essential to effective recovery of turtle species. This thesis has two main objectives: 1) to evaluate some common population genetics assumptions with respect to turtles and other long-lived organisms, and 2) to collect important information on the population genetics of threatened turtles in Ontario, which can be used to inform species recovery. In Chapters Two and Three, I describe the development of novel microsatellite markers for the snapping turtle and spiny softshell. In Chapter Four I demonstrate significant genetic structure in populations of the endangered spotted turtle in Ontario, and find that “bottleneck tests” may fail to detect recent population declines in small turtle populations. I also show that spotted turtles do not show the typical correlation between population size and genetic diversity. In Chapter Five I use microsatellite markers developed in Chapter Two and document population structure in the widespread snapping turtle for the first time. I compare these results with results from Chapter Four to test the traditionally accepted hypothesis that genetic diversity is reduced in small, isolated populations compared to large, connected populations. As in Chapter Four, my results suggest that the usual patterns of genetic ii
structure and loss of diversity may not apply to turtles. In Chapter Six I conduct a conservation genetics study of the endangered Blanding’s turtle. Finally, in Chapter Seven I combine results from spotted, snapping and Blanding’s turtles to test whether vagility predicts population structure, genetic diversity and significant barriers to gene flow in three species sampled across a single landscape. Analyses reveal minimal congruence in barriers to gene flow and the three species show unexpected and contrasting patterns of diversity across the landscape. Discordant patterns among species highlight areas for further research and shed light on possible cryptic behaviour, and I discuss potential further directions for research in the Summary.
iii
Acknowledgments
First of all, thank you to Leif, Katja and the Davy and Einarson families for all your support and “grenzenloses Vertrauen”. I wrote this thesis in the first person because I’ve been informed that that is how one writes a thesis. While I will happily take credit for the work I have done (and of course for anything that requires correction or improvement), I dislike this convention because this research was supported in many ways by a great many wonderful people. If annoyingly long lists of names appear below, it is because I have been fortunate to have help of many kinds from many corners. Dr. Bob Murphy is an incredible supervisor and his generosity towards his students is practically limitless. I need to point out his eccentricities simply because I think it will make him happy. He has many, and they kept life at the ROM entertaining, hilarious and inspiring. Although Bob has worked with virtually every possible “herp” out there, this project represents (to my knowledge) his first foray into the world of North American turtles. I am grateful that he was so open-minded about the direction my thesis took, and I hope he has enjoyed the experience too. I am also grateful for the numerous “extra” opportunities Bob provides to students in his lab; in my case, studies of Ctenosaura and the Seri Indians, whipping frogs from Vietnam, multiple paternities in desert tortoises and some pretty great teaching experiences. I have learned so much, and laughed quite a bit too. Thank you to the rest of my advisory committee, Dr. Deborah McLennan and Dr. Chris Wilson. Chris was my population genetics guru and helped me make some sense out of biogeographic patterns that seemed arbitrary at first. Deborah’s detailed and thoughtful comments on my manuscripts greatly improved the clarity of the final version, and she has inspired me to keep improving my writing. I am grateful to my committee for providing enough guidance to get me going while also giving me room to take a slightly exploratory approach to this project. Bob, Deborah and Chris - thank you also for being so understanding and encouraging when I announced that I would be taking maternity leave.
iv
Thank you to my fellow graduate students at the ROM, especially Christopher Blair, Ida Conflitti, Pedro Bernardo, Pamela Wong and Andre Ngo, for discussions, relevant and otherwise, and for sharing parts of the journey. Amy Lathrop, Kristen Choffe, Oliver Haddrath and Christopher Blair introduced me to molecular laboratory analyses and answered endless questions about reaction recipes and lab methods. While I was working in Mauritius over Christmas 2007, I was chatting with another field technician and asked her if she would consider working on a turtle project in Canada. She (unexpectedly) said yes, and the crazy lady proceeded to join me four summers of field work catching turtles in Ontario. To Suzanne Coombes – thank you. I might have been able to do it without you, but if so, it would have been much less fun. Ashley Leifso joined the party a little later but has been equally wonderful, and kept things running smoothly through buckets of baby softshells and later on in the lab, where we did manage to finish genotyping (10 days before the baby arrived). This project is built on the previous studies of turtles in Ontario, many of which originated in the labs of Dr. Ron Brooks and, more recently, Dr. Jacqueline Litzgus. I thank both of them for their hard work, inspiring research and dedication to the study and conservation of turtles. Dr. Jackie Litzgus, David Seburn, Scott Gillingwater and Joe Cebek provided both encouragement and helpful suggestions during my thesis work, and Dr. Fred Schueler, Dr. Francis Cook and Dr. Frank Ross also provided helpful suggestions and useful information. Dr. Brock Fenton provided much-needed random moments of bat-related distraction and inspiration, and helped me maintain perspective. Irene and Till Davy, Leif Einarson, Dr. Jackie Litzgus, Johnston Miller and David Seburn made helpful comments on previous versions and their suggestions were very much appreciated. Joe Crowley, John Urquhart and James Patterson provided helpful comments and several memorable debates (as a result of which I’m still unsure whether Blanding’s turtles really do or do not occur on the Bruce Peninsula). Thank you to the Litzgus lab – especially Amanda Bennett, James Baxter-Gilbert, Matt Keevil, James Patterson, Megan Rasmussen, Julia Riley and Katherine Yagi – for hours of turtle-talk, laughter, and “adopting” me into their lab at conferences. v
Working with species at risk in Ontario is constrained by a vast tangle of red tape. This is probably necessary, but it can slow things down considerably. When I told Bob that I wanted to work in Ontario, he informed me that he was happy with this, as long as I took care of the permits. I have since learned the reason why...So for helping me to navigate the maze of regulations and obtain the six different pieces of paper I required each year, I thank Amelia Argue, Corina Brdar, Melody Cairns, Stephanie Chan, Sarah Crosgrey, Tammy Dobbie, Sandy Dobbyn, Mike Gatt, Ron Gould, Pud Hunter, Alistair Mackenzie, Andrew Promaine, Emily Slavik, Roxanne St. Martin, Scott Sutton, Scott Taylor and all the other MNR and Canada Parks staff whom I did not contact directly but who helped get the necessary papers processed. Phew. Access to sites and logistical support were generously provided by the Ausable Bayfield Conservation Authority, Jackie Litzgus, Ontario Hydro, Ontario Nature (Mark Carabetta and John Urquhart), Ontario Parks, Parks Canada, Megan Rasmussen, Rick MacArthur, David Seburn, the Nature Conservancy of Canada, South Nation Conservation Authority, Anne and Katherine Yagi. Additional samples were contributed by Brennan Caverhill, Joe Cebek, Scott Gillingwater, Bob Johnson, Jeremy Rouse, David Seburn and Jim Trottier. My work on the south shore of Lake Huron would not have been possible without accommodations generously provided by the Fraser-Green family in 2008, and by Mrs. Stephanie Donaldson in 2009 – 2011. A large portion of this thesis was made possible by the support of Wildlife Preservation Canada. To WPC and to Elaine Williams, my Conservation Fairy Godmother – thank you. I would not have been able to start or complete my studies without the support of an Ontario Graduate Scholarship and a CGS from NSERC. The Blanding’s turtle chapter was funded largely by a SARRFO grant through the Toronto Zoo; thanks to Bob Johnson and Julia Phillips for making this happen. Pedro Bernardo helped get the Blanding’s turtle genotyping done on time. Finally, thanks to the Till Eulenspiegel Foundation for filling in the gaps where necessary. It takes a lot of swanp-walking to find a lot of spotted turtles. Thanks to James Baxter-Gilbert, Christopher Blair, Hope Brock, Mark Carabetta, Suzie Coombes, Laila Copes, Joe Crowley, Eric Davy, Leif Einarson, Kari Jean, Chris Law, Ashley Leifso, Jackie Litzgus, Steve Marks, Melissa Oddie, Karen Paquette, James Patterson, Crystal Roberston, Michelle Scheerder, David Seburn, Will (Kum C.) Shim, Emily Slavik, Dan Storisteanu, John Urquhart, Silu Wang, Amy Whitear, vi
and everyone else who came out and helped with surveys. Thanks also to everyone who has helped or is helping with turtle projects that are not included in this thesis (head-starting projects, righting-time experiments, etc.) – it continues to be an adventure and I am so lucky to have such wonderful people on the “turtle team”. This was not an uncomplicated journey. The details of the various road-blocks I encountered are not important - but all the support I had while overcoming them is. I am privileged to have had the opportunity to spend more than four years immersed in a subject I love. I have learned so much, and I am inspired by how much more there is to learn. To my friends – thank you for helping me to stay grounded. Returning to my parents, Veronika, Eric, my grandmother, my in-laws (and sibling-in-laws!), and especially to Leif – the gratitude I feel for your support is something I would rather express to you directly than share publicly in a thesis, so I’ll do that. Finally, thanks to my parents for introducing me to the turtles... and thanks, of course, to the turtles.
vii
Table of Contents Table of Contents ACKNOWLEDGMENTS
IV
TABLE OF CONTENTS
VIII
LIST OF TABLES
XII
CHAPTER 1 CONSERVATION GENETICS AND FRESHWATER TURTLES: A GENERAL INTRODUCTION
1
1
1
1.1 OUTLINE
1
1.2 CONSERVATION GENETICS: OBJECTIVES AND CHALLENGES
1
1.3 HOW SMALL IS SMALL AND WHAT ARE WE MEASURING?
3
1.4 TURTLES AND CONSERVATION GENETICS OF LONG-LIVED ORGANISMS
4
1.5 CONSERVATION GENETICS OF FRESHWATER TURTLES IN ONTARIO
6
1.6 REFERENCES
8
CHAPTER 2 CHARACTERIZATION OF TEN NOVEL MICROSATELLITE LOCI AND CROSSAMPLIFICATION OF TWO LOCI IN THE SNAPPING TURTLE (CHELYDRA SERPENTINA)
12
2 ABSTRACT
12
2.1 PRIMER NOTE
13
2.2 ACKNOWLEDGMENTS
15
2.3 REFERENCES
15
CHAPTER 3 ISOLATION AND CHARACTERIZATION OF ELEVEN NOVEL POLYMORPHIC MICROSATELLITE LOCI IN THE SPINY SOFTSHELL TURTLE (APALONE SPINIFERA)
18
3 ABSTRACT
18
3.1 PRIMER NOTE
19
3.2 ACKNOWLEDGEMENTS
21
3.3 REFERENCES
21 viii
CHAPTER 4 CONSERVATION GENETICS OF THE ENDANGERED SPOTTED TURTLE DO NOT SUPPORT A RELATIONSHIP BETWEEN GENETIC VARIATION AND POPULATION SIZE.
25
4 ABSTRACT
25
4.1 INTRODUCTION
26
4.2 METHODS
28
4.2.1
SAMPLE COLLECTION AND GENOTYPING
28
4.2.2
POPULATION GENETICS ANALYSES
30
4.2.3
DEVELOPMENT OF GENETIC ASSIGNMENT TESTS FOR CANADIAN CL. GUTTATA
32
4.2.4
ANALYSES OF GENETIC BOTTLENECKS
32
4.3 RESULTS
32
4.4 DISCUSSION
35
4.4.1
BIOGEOGRAPHY AND CONSERVATION GENETICS OF CLEMMYS GUTTATA
36
4.4.2
MANAGEMENT IMPLICATIONS
37
4.4.3
LONG-LIVED ORGANISMS (TURTLES) AND LOSS OF DIVERSITY IN FRAGMENTED POPULATIONS
38
4.4.4
BOTTLENECK TESTS AND LONG-LIVED ORGANISMS
39
4.5 ACKNOWLEDGEMENTS
40
4.6 REFERENCES
41
CHAPTER 5 UNEXPECTED PATTERNS OF GENETIC DIVERSITY IN TWO SYMPATRIC SPECIES OF TURTLE.
59
5 ABSTRACT
59
5.1 INTRODUCTION
60
5.2 METHODS
61
5.2.1
STUDY SPECIES
61
5.2.2
DATA COLLECTION AND ANALYSES – CHELYDRA SERPENTINA
62
5.2.3
DATA COLLECTION AND ANALYSES – CLEMMYS GUTTATA
64
5.2.4
INTERSPECIFIC COMPARISONS
64
5.2.5
DATA ACCESSIBILITY
64
5.3 RESULTS
64
5.3.1
BAYESIAN CLUSTERING ANALYSES
65
5.3.2
POPULATION DIFFERENTIATION
65
5.3.3
INTERSPECIFIC COMPARISON
66
5.4 DISCUSSION
66 ix
5.4.1
SUMMARY
70
5.5 ACKNOWLEDGMENTS
71
5.6 REFERENCES
71
CHAPTER 6 CONSERVATION GENETICS OF BLANDING’S TURTLE (EMYS BLANDINGII) IN ONTARIO, CANADA.
85
6 ABSTRACT
85
6.1 INTRODUCTION
86
6.2 METHODS
88
6.3 RESULTS
90
6.4 DISCUSSION
92
6.5 ACKNOWLEDGMENTS
96
6.6 REFERENCES
96
CHAPTER 7 GENOTYPES AND GHOSTS: COMPARATIVE LANDSCAPE GENETICS REVEALS INCONGRUENT BARRIERS TO GENE FLOW AMONGST THREE SPECIES OF FRESHWATER TURTLE
108
7 ABSTRACT
108
7.1 INTRODUCTION
109
7.2 METHODS
112
7.2.1
STUDY SPECIES AND RELATIVE DISPERSAL ABILITY
112
7.2.2
BAYESIAN DELINEATION OF POPULATION BOUNDARIES
112
7.2.3
BARRIER ESTIMATION WITH MONMONIER’S ALGORITHM
113
7.2.4
ESTIMATION OF MIGRATION AMONG POPULATIONS
113
7.3 RESULTS
114
7.3.1
BAYESIAN DELINEATION OF POPULATION BOUNDARIES
114
7.3.2
BARRIER ESTIMATION WITH MONMONIER’S ALGORITHM
115
7.3.3
ESTIMATION OF MIGRATION AMONG POPULATIONS
115
7.4 DISCUSSION
116
7.4.1
COMPARATIVE LANDSCAPE GENETICS OF FRESHWATER TURTLES
116
7.4.2
LONG-LIVED ORGANISMS AND LANDSCAPE GENETICS
119
7.4.3
CONSERVATION IMPLICATIONS
121
7.5 REFERENCES
123 x
CHAPTER 8 SUMMARY AND CONCLUSIONS
138
8
138
8.1 SUMMARY
138
8.2 SHORT-TERM STUDIES OF LONG-LIVED ORGANISMS – DIRECTIONS FOR FUTURE RESEARCH
140
8.3 REFERENCES
141
COPYRIGHT ACKNOWLEDGEMENTS
144
xi
List of Tables Table 2.1. Characteristics of ten novel and two cross-amplified microsatellite loci for 127 Chelydra serpentina sampled from across southern Ontario. N=number of individuals genotyped; k = number of alleles; Ho = observed heterozygosity; He = expected heterozygosity; PI = probability of identity. Primer sequences shown include a 5’ M13 tail (5’-TGT AAA ACG ACG GCC AGT-3’) on forward primers and a 5’ GTTTCTT pigtail on reverse primers. MteD111 and MteD9 are from Hackler et al. (2006), with M13 tail (F) and pigtail (R) added. Loci which are not in Hardy-Weinberg equilibrium (p < 0.01) are indicated with a *. Table 3.1. Primer sequences and amplification conditions for 11 novel polymorphic loci for Apalone spinifera. Temp = primer-specific annealing temperature (°C). Primer sequences shown include a 5’ M13 tail (5’-TGT AAA ACG ACG GCC AGT-3’) on forward primers and a 5’ pigtail (GTTTCTT) on reverse primers. Table 3.2. Characteristics of 11 novel polymorphic loci for 15 Apalone spinifera from southern Ontario and 30 individuals of unknown origin. N=number of individuals genotyped; k = number of alleles; Ho = observed heterozygosity; He = expected heterozygosity; PI = probability of identity. Table 4.1. Summary statistics for eleven microsatellite loci originally developed for the Glyptemys muhlenbergii (King and Julian 2004) and amplified in 256 Clemmys guttata from southern Ontario. Temp. = annealing temperature (°C) used in PCR amplification. * indicates an initial touchdown of 1°C/cycle from 10°C above the annealing temperature, followed by a constant annealing temperature for the remaining cycles; N = number of individuals amplified at each locus; k = number of alleles; Ne = number of effective alleles; HO = observed heterozygosity; HE = expected heterozygosity; PI = probability of identity, PIsibs = Probability of identity for siblings at a locus. Table 4.2. Number of alleles (private alleles in parentheses), observed and expected heterozygosities (HO and HE), and estimated frequency of a null allele (for each locus across all populations), for 256 Clemmys guttata from southern Ontario, by locus and populations (see Figure 1 for definition of site acronyms). xii
Table 4.3. Genetic diversity (heterozygosity, allelic richness and private allelic richness) of sampled regions, genetic populations and sites for 253 Clemmys guttata genotyped at 11 microsatellite loci. Allelic and private allelic richness are rarefacted to account for variation in sample sizes (Kalinowski 2004). Pop1–5 = genetic clusters supported by both STRUCTURE and TESS analyses.
Georgian Bay is considered independently. HO = observed heterozygosity
averaged across all loci; HE = expected heterozygosity averaged across all loci. See Figure 1 and text for definitions of site acronyms. Table 4.4: Pairwise values FST (below the diagonal) and Dest (Jost 2008, above diagonal) for 13 putative subpopulations of Clemmys guttata sampled across southern ON (N = 253; see Figure 1 for definition of site acronyms). Sites in the Golden Horseshoe, Georgian Bay and the Bruce Peninsula are analyzed together. FST values in italics are not significant (p > 0.05). Table 4.5: Hierarchical analysis of molecular variance (AMOVA; Excoffier et al. 1992) conducted in ARLEQUIN. Each source of variation was significant (p = 0.000). Tested populations were those identified by both STRUCTURE and TESS analyses with GB treated as a separate, sixth population. Subpopulations refer to sampling sites except GB, GH and BP which are treated as single subpopulations. Table 4.6. Assignment of individuals in GENECLASS analysis based on sampling sites; 66.3% of individuals were assigned correctly. Shaded areas indicate clustering of sites in genetic populations supported by both STRUCTURE and TESS. Sampling sites correspond to Figure 1. Table 4.7. Summary of bottleneck tests in published studies of population genetics of tortoises and freshwater turtles. Loci = the number of loci used in bottleneck tests (in some cases this was lower than the total number amplified). Individuals = the maximum-minimum and mean ( ) number of individuals genotyped per tested population. When only populations above a certain size limit were used, only these populations were included in the summary. Where values for loci and individuals are in bold this indicates that the minimum sampling recommendations for tests in BOTTLENECK were met. Table 5.1. Summary statistics for 11 microsatellite loci (Hackler et al. 2007; Davy et al. 2012) amplified in 167 Chelydra serpentina from southern Ontario. N = number of individuals successfully amplified at each locus; k = number of alleles; Ne = number of effective alleles; HO xiii
= observed heterozygosity; HE = expected heterozygosity; PI = probability of identity; PIsibs = Probability of identity for siblings at a locus. Locus MteD111 showed evidence of potential null alleles and was excluded from all multi-locus analyses. Table 5.2. Genetic diversity in 167 Chelydra serpentina sampled across southern Ontario based on 10 microsatellite loci. Populations (Pop) and subpopulations (SP) were identified with Bayesian clustering analyses (see text for details). GH = Golden Horseshoe, EO1 = Eastern Ontario 1. Number of alleles (private alleles in parentheses); HO and HE: observed and expected heterozygosities; N: sample size per tested unit. Estimated frequency of a null allele was calculated for each locus across all populations. Summary statistics are presented for locus MteD111, but this locus was excluded from calculations of mean heterozygosity and allelic richness. Table 5.3. Population differentiation (Dest above the diagonal, FST below) for four subpopulations and two admixed groups of Chelydra serpentina identified by STRUCTURE analysis (Figure 3). FST values in bold are significant (p < 0.05). Subpopulations (SP) are described in the text. GH = Golden Horseshoe. EO = EO1 and EO3. Table 5.4. Hierarchical partitioning of molecular variance with AMOVA (Excoffier et al. 1992). All sources of variation with AMOVA (Excoffier et al. 1992). All sources of variation were significant (p < 0.02). Table 6.1. Genetic diversity at 12 microsatellite loci for 97 Emys blandingii from southern Ontario. Temp. (optimal annealing temperature (°C) determined from temperature gradients of initial PCR reactions) sample size (N), allelic richness (k), observed and expected heterozygosity (HO, HE) and two measures of probability of identify (PI, PISibs) are shown for each locus. Total values show mean ± standard error for N, k, Ne, HO and HE, and PI/PIsibs values with all loci included. Table 6.2. Number of alleles (number of private alleles in parentheses) and observed and expected heterozygosities (HO and HE) for 97 Emys blandingii sampled across southern Ontario and genotyped at 12 microsatellite loci. Loci Gmu– from King and Julian (2004). Loci Eb– from Osentoski et al. (2002). Acronyms for sampling areas are defined in Figure 1. Estimated frequency of a null allele is based on analysis of the entire data set following Brookfield (1996). xiv
No loci showed consistent evidence for null alleles when sampling areas were analyzed independently. HO = observed heterozygosity; HE = expected heterozygosity; Ar = allelic richness; PAr = private allelic richness. Table 6.3. Genetic differentiation of Emys blandingii among sites in Ontario with N ≥ 15. All FST values are significant (p < 0.05). All FST values were significant (p < 0.05). Average historical number of migrants per generation (Nm) was calculated following Barton and Slatkin (1986). Table 6.4. GENECLASS results for Bayesian assignment tests. Values represent the proportion of individuals from each sampled population assigned to each population. Values in bold indicate the proportion of individuals from each sampled population assigned correctly to their source population. Grey shaded areas indicate the two larger genetic clusters identified by TESS and STRUCTURE.
Table 7.1. Life history, distribution and behavioral traits of Clemmys guttata, Chelydra serpentina and Emys blandingii. Global conservation status is determined by the International Union for Conservation of Nature (IUCN); Canadian conservation status is determined by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC). Table 7.2. Mean, range, and maximum and minimum 95% confidence interval of effective population size (Ne) estimated in ONeSAMP (Tallmon et al. 2008) for populations of three freshwater turtles in southern Ontario, Canada. N = number of populations for which estimates were obtained; mean Ne = mean estimated effective population size; s.d. = standard deviation; range = minimum – maximum estimate. N is lower than the number of populations sampled because Ne estimates for sites from which < 20 individuals were sampled were not included (estimated Ne from sites with low sample sizes were all < 25). Table 7.3. Average historic number of migrants per generation (Nm, Barton and Slatkin 1986).
xv
List of Figures Figure 4.1. Approximate location of sampled sites. LE = Lake Erie; LH = Lake Huron; BP = Bruce Peninsula; GB = Georgian Bay; HC = Hastings County; DOR = dead on road; EO = Eastern Ontario. Figure 4.2. A) Population structure inferred by STRUCTURE and TESS for increasing values of K. Colours indicating populations in the K = 5 model (marked with an asterisk) match colours used in Figure 4. B) Estimated ln probability of the data (L(K)) for STRUCTURE analyses at increasing values of K with 8 independent runs at each. C) ∆K (Evanno et al. 2005) calculated from (B). D) TESS
results: decreasing deviance information criterion (DIC) with increasing Kmax.
Figure 4.3. Principal Coordinates Analysis plot based on Dest (Table 4) for populations (a, b) and based on genetic distance for individuals (c). Figure 4.4. Genetic population structure identified by STRUCTURE and TESS with K = 5 (Figure 2). Georgian Bay was assigned to different populations by the two analyses. Hypothesized dispersal routes for Clemmys guttata colonizing Canada after glacial retreat are indicated by the large grey arrows. Figure 5.1. Sampling sites for 167 Chelydra serpentina (blue squares) sampled in this study and 256 Clemmys guttata (yellow squares) sampled in Chapter 3. Bi-coloured squares indicate sites where both species were sampled. Insert shows pairs of sampling areas used for comparisons of genetic diversity between species. LE1 = Lake Erie 1; LE2 = Lake Erie 2; LH1 = Lake Huron 1; LH 2 = Lake Huron 2; BP = Bruce Peninsula; GB = Georgian Bay; GH = Golden Horseshoe; N = North of Golden Horseshoe; Kaw. = Kawartha Lakes area; Alg. = Algonquin Provincial Park; HC = Hastings County; LO = north-east shore of Lake Ontario; EO = Eastern Ontario. Base map modified from http://www.aquarius.geomar.de/omc/make_map.html and used under the GNU Free Documentation license. Figure 5.2. Results of Bayesian clustering analyses for increasing values of K, the number of genetically distinct populations represented in the sample following analyses described in Methods. Structure results for K = 1 – 8 : A) Log likelihood (L(K)) of the data (mean ± standard xvi
deviation); B) ∆K following Evanno et al. (2005). Tess results for Kmax = 2–14; C) Deviance information criterion (mean ± standard deviation) following analyses described in Methods. Figure 5.3. Results of Bayesian clustering analyses for a range of models with increasing values of K inferred using STRUCTURE and TESS. Models shown here are those that best fit the data based on criteria described in Methods. Population structure in Cl. guttata across the same landscape is shown for comparison (from Chapter 3). Colours used for subpopulations in the K = 4 model are consistent with colours used in Figure 4. Figure 5.4. Principle component analysis of genetic distance for 167 Ch. serpentina based on 10 microsatellite loci. A and B: PCoA of populations based on Dest ; C and D: PCoA based on genetic distance among individuals labelled by sampling site. Figure 5.5. Heterozygosity and effective population sizes of Ch. serpentina and Cl. guttata compared across five pairs of sites (Figure 1, inset). HO: observed heterozygosity. HE: expected heterozygosity. Ne: effective population size. Figure 6.1. Approximate location of collection areas for Emys blandingii sampled across southern Ontario. Top right inset indicates species range in North America (shown in red). Sampling was focused on sites indicated with grey squares: LE = Lake Erie; GH = Golden Horseshoe; PSD = Parry Sound District; KAW = Kawartha Lakes; EO = Eastern Ontario. Sample sizes are included in each site marker. Grey triangles indicate extra samples included opportunistically (each triangle represents an individual turtle): LHsouth = south shore of Lake Huron; LHnorth = north shore of Lake Huron; ALG = Algonquin Provincial Park. Variation in sample sizes results from differential sampling effort; differences in sample sizes are not reflective of variation in actual population sizes. Base map modified from http://www.aquarius.geomar.de/omc/make_map.html and used under the GNU Free Documentation license; range map modified from COSEWIC (2005). Figure 6.2. Principal Coordinates analysis of sampling areas (A, B) and individuals (C) for 91 Emys blandingii sampled from across southern Ontario based on 12 microsatellite loci. Figure 6.3. Population structure inferred by Bayesian inference for 91 Emys blandingii collected across southern Ontario. A) TESS results showing decreasing deviance information criterion (DIC) with increasing values of Kmax. B) STRUCTURE results, mean estimated ln probability of xvii
the data (L(K)) for increasing values of K, and ∆K, the second order rate of change of L(K) following Evanno et al. (2005). Site abbreviations are explained in Figure 1. Figure 7.1. Sampling sites and groupings of sites used for BARRIER analysis. Colors indicate the sampled species at each site: yellow = Clemmys guttata, blue = Chelydra serpentina, red = Emys blandingii. Small triangles indicate individual samples from otherwise unsampled sites. Squares with dashed lines indicate the four areas used for comparison of Nm estimates. BARRIER analysis considered genetically continuous samples with N > 12 as sampling units (inset, bottom right, based on STRUCTURE results). ALG = Algonquin Provincial Park; BP = Bruce Peninsula; EO = Eastern Ontario; GB = Georgian Bay; GH = Golden Horseshoe; HC = Hastings County; KAW = Kawartha Lakes; LE = Lake Erie; LH = Lake Huron; LO = Lake Ontario; N = area north of GH and south of GB; PSD = Parry Sound District. SP = subpopulation. Base map modified from http://www.aquarius.geomar.de/omc/make_map.html and used under the GNU Free Documentation license. Figure 7.2. Genetic population structure inferred in A) TESS, and B) STRUCTURE for Clemmys guttata (yellow/brown), Chelydra serpentina (blue), and Emys blandingii (red). SP = subpopulation. See Figure 1 for explanation of site abbreviations. Inferred clusters are plotted on maps to the right of each set of results. Division of Cl. guttata samples under a K = 2 model (implemented in STRUCTURE; see Chapter 3) is shown by a dashed black line on the bar plot and the map for comparison. Figure 7.3. Barriers to gene flow identified with Monmonier’s algorithm. Colored numbers indicate sampling sites; thin green lines indicate boundaries between populations based on Delaunay triangulation. A) Clemmys guttata (yellow; N = 253), B) Chelydra serpentina (blue; N = 167) and C) Emys blandingii (red; N = 91). Estimates are based on 5,000 bootstrap replicates of genetic distance matrices (Nei’s absolute distance). The thickness of each line and the numbers in black text indicate the strength of bootstrap support. D) Barriers and sampling sites for the three species overlaid on top of one another; barriers with bootstrap support > 0.90 are marked with a dashed line. Figure 7.4. Significant barriers (bootstrap support > 0.90) inferred using Monmonier’s algorithm for Clemmys guttata (yellow dashed lines), Chelydra serpentina (blue dashed lines) and Emys blandingii (red dashed lines). xviii
Figure 7.5. Average number of migrants per generation for Clemmys guttata, Chelydra serpentina and E. blandingii estimated following Barton and Slatkin (1986) among four sites at which all three species were sampled.
xix
Chapter 1 Conservation genetics and freshwater turtles: a general introduction
1 1.1 Outline Conservation genetics is essentially the study of population genetics in small populations. The field differs from traditional population genetics because its goal is to inform effective recovery of threatened species, and the importance of genetics informing species recovery is well documented. Much of the theory behind conservation genetics is based on studies of organisms with relatively short life-spans and generation times, because these characteristics make them good models for multi-generational studies. Long-lived organisms with overlapping generation times may show different responses to population fragmentation than these studies predict. Thus, effective conservation genetics studies of long-lived organisms must consider these potential differences. Turtles provide an ideal model group to test the application of conservation genetics theory to long-lived organisms. Because many species of turtles are threatened, turtles are also a group for which conservation genetics studies are desperately needed so that effective conservation actions can be implemented. In Ontario, seven of the eight resident species of turtle are listed as “at-risk”, but no information about genetic structure in these populations is available. In this thesis I develop genetic markers for two species of turtle and undertake conservation genetics studies of three species. I use the results of these studies to test the application of several widely accepted conservation genetics hypotheses to long-lived organisms.
1.2 Conservation genetics: objectives and challenges The field of conservation genetics was explicitly established in the late 1900s, but it is strongly rooted in classical population genetics. In the early 1900s, renewed interest in the work of Mendel (1866) set the stage for advances in the study of quantitative genetics. Significant early models in population genetics, such as “Hardy-Weinberg equilibrium” (Hardy, 1908), still inform contemporary studies of the relationships among populations. The effects of genetic drift on allele frequencies in a population were considered by Fisher (1922), who discussed the possibility of fixation of alleles over time. This line of inquiry continued with investigations of 1
the potential impacts of inbreeding and increased genetic load (Wright 1922; Haldane 1926, 1937; Fisher, 1949). Wright (1931) coined the term “genetic drift” and demonstrated that drift has a much stronger effect on small populations than on large populations. Wright (1931) also introduced the concept of “effective population size” (Ne), the number of breeding individuals in an idealized population that produces the observed loss of heterozygosity from one generation to the next. The importance of natural selection in causing populations to diverge was investigated (Haldane 1924; Fisher 1930), and the impacts of drift and selection on natural populations are still being debated in the literature (Keller and Waller 2002, Sutton et al. 2011). Similarly, the null hypothesis of isolation by distance (Wright 1943, Malécot 1948) still provides a useful framework in which to test hypotheses about genetic structuring of populations. The consideration of genetic changes in small populations in the context of conservation biology began in the late 1900s. Gilpin and Soulé (1986) identified four “extinction vortexes”, biological phenomena that can lead to extinction in small populations. Two of these involved loss of genetic variation through genetic drift in small populations, through inbreeding depression and increased genetic load, or through long-term fixation of alleles by genetic drift leading to reduced adaptive potential. Thus, the field of conservation genetics applies the study of population genetics to small and declining populations (Frankham et al. 2002). The relative importance of evolutionary forces such as genetic drift, mutation, and natural selection change as population sizes decline. Random factors such as genetic drift and demographic stochasticity have a greater impact on genetic diversity in small populations and can cause allele frequencies in such populations to change dramatically over a few generations (Fisher 1922, Wright 1931). Genetic drift in small populations may also cause the random loss of some alleles and the eventual fixation of others. Additionally, random mating in small populations leads to increased inbreeding, which may cause reduced fitness (inbreeding depression, as reviewed by Charlesworth and Willis 2009). When genetic diversity has been significantly reduced in a population fragment, long-term recovery of the population may be difficult even if short-term population growth can be easily achieved (Ewing et al. 2008). For example, recovered populations of the pink pigeon (Columba mayeri) have reduced fitness due to inbreeding (Swinnerton et al. 2004), and lethal recessive alleles have led to a high incidence of chondrodystrophy (a form of dwarfism) in a captive breeding program for the California condor (Gymnogyps californianus) (Ralls et al. 2000). 2
Thus, understanding the distribution of genetic diversity in small populations is essential to effective population management and can facilitate genetic management of populations in especially dire circumstances. Successful genetic management of threatened populations results in a quantifiable increase in fitness and/or population growth. Examples include genetic rescue in adders (Vipera berus), bighorn sheep (Ovis canadensis), Florida panthers (Puma concolor) and lakeside daisies (Hymenoxys herbacea) (Demauro 1994; Madsen et al. 1994; 2004; Land and Lacy 2000; Tallmon et al. 2004; Hogg et al. 2006; Miller et al. 2012). Conversely, attempts to recover species without consideration of genetic management has led, for example, to extreme inbreeding depression in reintroduced populations of koala (Phasolarctos cinereus; Houlden et al. 1996; Sherwin et al. 2000) and outbreeding depression in reintroduced populations of ibex (Capra ibex; Turcek 1951; Grieg 1979). The major distinction between conservation genetics and other population genetics research is the explicit objective of contributing to the preservation and recovery of threatened populations and species (Frankham et al. 2002). Therefore, conservation genetics studies aim to generate recommendations for maintaining genetic diversity in threatened species.
1.3 How small is small and what are we measuring? Studies on the biology and genetics of “small” populations require definition of “small” and of which part of a population is included in the measurement. The maintenance of genetic variation in a population relies not on its census population size (the estimated total number of individuals in the population based on methods such as capture-mark-recapture) but rather on its genetic effective population size (Ne). Wright (1938, 1969) defined genetic Ne as the number of individuals that would cause the observed loss of heterozygosity per generation (1/2N) in an idealized population. Natural populations deviate from the assumptions of Wright’s idealized model, for example, due to non-random mating, unequal sex ratios or high variance in reproductive success among individuals. Thus, Ne in wild populations is usually lower than Nc (the census population size), and Ne is the “population size” of interest in conservation genetics studies (Frankham 1996; Jamieson and Allendorf 2012). Estimates of Ne:Nc ratios in wild populations average 0.10–0.15, but estimates vary from < 0.001 to > 0.30 among species with differing life history strategies (Frankham 1996; Palstra and Ruzannte 2008). In general, Ne:Nc appears to be higher in low-fecundity species than in high3
fecundity species, but the great amount of intraspecific variation in this ratio precludes an accurate prediction (Palstra and Ruzzante 2008; Luikart et al. 2010). Estimates of Nc are usually based on methods such as capture-mark-recapture, while Ne can be estimated from genetic data (e.g. Tallmon et al. 2008; Waples and Do 2008). So how small is “too small”? Franklin (1980) suggested that Ne > 50 is required to avoid the deleterious effects of inbreeding in wild populations, and that Ne > 500 is required for maintaining long-term genetic viability of a population. These estimates were derived based on population genetics theory and Franklin did not suggest that they should apply to all species in all circumstances. Nevertheless, a Google Scholar search for the so-called “50/500 rule” identifies over 2,800 peer-reviewed papers citing, discussing, and criticizing this “rule of thumb,” and the definition of “small” population size is the subject of ongoing debate. There is little consensus on either the number of individuals required to maintain sufficient genetic diversity for long-term population persistence, or even if such a number can be estimated with current methods (e.g. Brook et al. 2011; Flather et al. 2011a, 2011b; Traill et al. 2007; 2010; Jamieson and Allendorf 2012). However, there is some consensus that the minimum number of individuals required for long-term genetic persistence of most species is probably several thousand as this generally predicts Ne > 500 (Traill et al. 2010; Flathers et al. 2011). Loss of genetic variation through genetic drift or inbreeding is not the only (nor necessarily the largest) threat to small populations (Lande 1988). Demographic impacts on genetically healthy populations can cause extirpation and extinction. Thus, mitigating the factors causing population decline is as crucial to species recovery as maintenance of genetic diversity. Nevertheless, genetic diversity is vital to the long-term persistence of a species and must be considered in recovery plans for threatened species (Frankham et al. 2002; Jamieson and Allendorf 2012).
1.4 Turtles and conservation genetics of long-lived organisms Turtles are one of the most threatened groups of vertebrates, with nearly 50% of species listed as threatened by the International Union on the Conservation of Nature (IUCN; http://www.iucnredlist.org/; Turtle Conservation Coalition 2011). However, data on genetic population structure are unavailable for many species (Alacs et al. 2007). This thesis investigates patterns of genetic diversity in three species of turtle in Ontario: the spotted turtle (Clemmys guttata), the snapping turtle (Chelydra serpentina) and the Blanding’s turtle (Emys blandingii). I 4
also develop genetic markers for future studies of the spiny softshell (Apalone spinifera). All four species are considered “at risk” by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) and the Committee on the Status of Species at Risk in Ontario (COSSARO). However, genetic population structure in these species has not been studied. Therefore, the number of genetically distinct populations of each species in Ontario is unknown. As well as providing species-specific information about threatened turtles in Ontario, my thesis uses turtles as a model to test the genetic impacts of population fragmentation on long-lived organisms. Most species of turtles have long generation times, for example > 40 years in E. blandingii (COSEWIC 2005). Most species of turtle are also extremely long-lived; Cl. guttata may live for 110 years (Litzgus 2006) and Ch. serpentina may live > 100 years (R. Brooks, unpublished data, in COSEWIC 2008). Thus, the impacts of current anthropogenic landscape modifications will have strong demographic effects on populations of turtles long before a genetic signature of that effect becomes detectable. Studies of mitochondrial DNA show extremely low intraspecific differentiation in several turtle species in the north-eastern United States and Canada (Amato et al. 2008; McGaugh et al. 2008; Phillips et al. 1996). Fortunately, microsatellite markers are more informative for population genetics research on turtles (Tessier et al. 2005). Recently, several genetic studies of turtles in North America have compared genetic diversity among “isolated” or “fragmented” populations and “continuous” or “unfragmented” populations (e.g. Rubin et al. 2001; Kuo and Janzen 2004; Richtsmeier et al. 2008; Pittman et al. 2011; Banning-Anthonysamy 2012). Other studies have investigated the effects of predefined barriers to gene flow such as dams or urban development (e.g. Bennett et al. 2010). The questions posed by these studies are central to conservation biology. Unfortunately, the results are difficult to interpret without the right context and information, which are unavailable for most species of turtle. Specifically, what is a “normal” level of genetic variation in populations of turtles in North America, and how large, small, fragmented or continuous are most populations of turtles? How does population structure vary among species? Are most species panmictic across large distances or do they show evidence of historical population structure pre-dating major anthropogenic landscape modification? How do mating systems or patterns of paternity affect genetic diversity in turtles and other organisms with overlapping generations?
5
What is clear from the literature is that populations of freshwater turtles may be connected across relatively large distances (> 100km; Bennet et al. 2010; Banning-Anthonysamy et al. 2012), and that even dramatic population declines may not be detectable genetically (Kuo and Janzen 2004; Pittman et al. 2011). Therefore, I do not attempt to define populations a priori in this thesis but rather collect samples across a wide geographic range and use a suite of analyses to determine the number of genetic populations represented in the samples. This approach allows an unbiased assessment of the number of genetic populations present in Ontario, which can be used to improve population management plans. Comparative studies provide an opportunity to test some of the conservation genetics hypotheses discussed above. For example, can factors such as dispersal ability, rarity, fecundity, or population size predict variation in genetic structure among species (Frankham 1996; Mitton 1997)? How does the dispersal ability of different turtle species affect the placement or number of barriers to gene flow across the landscape? Do turtle species share common barriers to gene flow across the landscape? Here, I use data from Cl. guttata, Ch. serpentina and E. blandingii to investigate these questions. All three species share long generation times and life spans, but they differ in their fecundity, vagility and abundance.
1.5 Conservation genetics of freshwater turtles in Ontario In Chapters Two and Three I apply 454 “shotgun” sequencing to characterize and develop primers for polymorphic microsatellite markers for the snapping turtle (Ch. serpentina) and the spiny softshell (Apalone spinifera). These markers can be used for population genetics studies of both species, and for investigations of relatedness and paternity. In Chapter Four I investigate patterns of genetic diversity among populations within a species. I use genetic and field data from populations of the spotted turtle (Cl. guttata) to test the relationship between census population size and genetic diversity. Both theoretical and empirical data show that these factors are correlated across a wide range of taxa, and I test whether or not this correlation is also present in an endangered turtle. I use 11 microsatellite markers to investigate genetic structure in Cl. guttata across southern Ontario. This is the first study of genetic population structure in Cl. guttata, and I propose management units based on the results. The ability of traditionally used “bottleneck tests” to detect recent population declines in Cl.
6
guttata is also tested. The use of these tests in other studies of turtles and tortoises is evaluated in a literature review. In Chapter Five I compare patterns of genetic diversity between two species, Cl. guttata and Ch. serpentina. Variation in fecundity, rarity, vagility and abundance between these two species is used to test several widely accepted hypotheses about the factors affecting genetic diversity. I apply the microsatellite markers developed in Chapter Two to characterize population structure in Ch. serpentina across southern Ontario, and compare the results to those obtained for Cl. guttata in Chapter Four. I also use approximate Bayesian computation of effective population size (Ne) to compare Ne between these species, and consider the effect of variation in Ne on genetic diversity. In Chapter Six I conduct a conservation genetics study of Blanding’s turtle (Emys blandingii) across southern Ontario. Ontario is poorly represented in previous studies of the genetic structure of E. blandingii populations (one south-eastern site at St. Lawrence Islands National Park is sampled by Mockford et al. 2007). I characterize population structure in E. blandingii based on data from 12 microsatellite loci and test the null hypothesis that the “Great Lakes-St. Lawrence” population defined by COSEWIC is a single, panmictic population (COSEWIC 2005). I also compare genetic diversity of Ontario E. blandingii to values reported by Mockford et al. (2007) to further test the hypothesis that genetic variation is greater in the continuous main range of the species than in isolated eastern populations. In Chapter Seven I combine microsatellite data from the previous three chapters to compare relative migration rates, population boundaries and barriers to gene flow among Cl. guttata, Ch. serpentina and E. blandingii. I infer population boundaries using Bayesian clustering analyses and infer barriers to gene flow using Monmonier’s algorithm. Based on these data, I test the hypothesis that vagility can predict genetic population structure. I identify common areas of high gene flow and common barriers to gene flow among species. Finally, in the Conclusions I summarize the major findings of my dissertation and discuss possible future directions for research.
7
1.6 References Alacs EA, Janzen FJ, Scribner KT (2007) Genetic issues in freshwater turtle and tortoise conservation. Chel Res Monogr 4:107–123 Amato ML, Brooks RJ, Fu J (2008) A phylogeographic analysis of populations of the wood turtle (Glyptemys insculpta) throughout its range. Mol Ecol 17:570–581 Banning-Anthonysamy WJ (2012) Spatial ecology, habitat use, genetic diversity, and reproductive success: measures of connectivity of a sympatric freshwater turtle assemblage in a fragmented landscape. PhD dissertation, University of Illinois at UrbanaChampaign. Bennett AM, Keevil M, Litzgus JD (2010) Spatial ecology and population genetics of northern map turtles (Graptemys geographica) in fragmented and continuous habitats in Canada. Chel Conserv Biol 9:185–195 Brook BW, Bradshaw JA, Trail LW, Frankham R (2011) Minimum viable population size: not magic, but necessary. Trends Ecol Evol 26:619–620 Charlesworth D, Willis JH (2009) The genetics of inbreeding depression. Nat Rev Genet 10:78396 COSEWIC (2005) COSEWIC assessment and update status report on the Blanding's Turtle Emydoidea blandingii in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. viii + 40 pp. (www.sararegistry.gc.ca/status/status_e.cfm) COSEWIC (2008) COSEWIC assessment and status report on the snapping turtle Chelydra serpentina in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. vii + 47 pp. (www.sararegistry.gc.ca/status/status_e.cfm) Demauro MM (1994) Development and implementation of a recovery program for the federally threatened lakeside daisy (Hymenoxys acaulis var. glabra). In: Bowles ML, Whelan CJ (eds.) Restoring of endangered species: Conceptual Issues, Planning and Implementation. Cambridge University Press, Cambridge, UK, pp 298-321 Ewing SR, Nager RG, Nicoll MAC, Aumjaud A, Jones CG, Keller LF (2008) Inbreeding and loss of genetic variation in a reintroduced population of Mauritius kestrel. Conserv Biol 22:395–404 Fisher RA (1922) On the dominance ratio. P Roy Soc Edinb 42:321–341 Fisher RA (1930) The genetical theory of natural selection. Clarendon Press Fisher RA (1949) The theory of inbreeding. Oliver and Boyd, Edinburgh. Franklin IR (1980) Evolutionary change in small populations. In: Soulé ME, Wilcox BA (eds.) Conservation Biology: an Evolutionary–Ecological Perspective. Sinauer Associates pp 135–150 Frankham R (1996) Relationship of genetic variation to population size in wildlife. Conserv Biol 10:1500–1508 Frankham R, Ballou JD, Briscoe DA (2002) Introduction to Conservation Genetics. Cambridge University Press, Cambridge, U.K.
8
Frankham R (2010) Challenges and opportunities of genetic approaches to biological conservation Biol Conserv 143:1919–1927 Flather CH, Hayward GD, Beissinger SR, Stephens PA (2011) Minimum viable populations: is there a ‘magic number’ for conservation practitioners? Trends Ecol Evol 26:307–316 Flather CH, Hayward GD, Beissinger SR, Stephens PA (2011) A general target for MVPs: unsupported and unnecessary. Trends Ecol Evol 26:620–622 Gilpin ME, Soulé ME (1986) Minimum viable populations: The processes of species extinctions. In Soulé M (ed.) Conservation biology: The science of scarcity and diversity, pp. 13–34. Sinauer Associates, Sunderland, Massechussets Grieg JC (1979) Principles of genetic conservation in relation to wildlife management in Southern Africa. S Afr J Wildl Res 9:57-78 Haldane JBS (1924) The mathematical theory of natural and artificial selection. Part I. Trans Cambridge Philos Soc 23:19–41 Haldane JBS (1926) A mathematical theory of natural and artificial selection. Math Proc Cambridge 23: 363–372 Haldane JBS (1937) The effect of variation on fitness. Am Nat 71: 337–349 Hardy GH (1908) Mendelian proportions in a mixed population. Science 28: 49–50 Hogg JT, Forbes SH, Steele BM, Luikart G (2006) Genetic rescue of an insular population of large mammals. Proc R Soc Lond B Biol Sci 273:1491–1499 Houlden BA, England PR, Taylor AC, Greville WD, Sherwin WB (1996) Low genetic variability of the koala Phasolarctos cinereus in southeastern Australia. Mol Ecol 5:269– 281 Jamieson IG, Allendorf FW (2012) How does the 50/500 rule apply to MVPs? Trends Ecol Evol 27:578–584 Land DE, Lacy RC (2000) Introgression levels achieved through Florida panther genetic restoration. Endangered Species Updates 17:99–103 Lande R (1988) Genetics and Demography in Biological Conservation. Science 241:1455-1460 Litzgus JD (2006) Sex differences in longevity in the spotted turtle (Clemmys guttata). Copeia 2006:281–288 Luikart G, Ryman N, Tallmon DA, Schwartz MK, Allendorf FW (2010) Estimation of census and effective population sizes: the increasing usefulness of DNA-based approaches. Conserv Genet 11:355–373 Keller LF, Waller DM (2002) Inbreeding effects in wild populations. Trends Ecol Evol 17: 230– 241 Kuo CH, Janzen FJ (2004) Genetic effects of a persistent bottleneck on a natural population of ornate box turtles (Terrapene ornata). Conserv Genet 5:425–437 Madsen T, Shine R, Olsson M, Wittzell H (1999) Restoration of an inbred Adder population. Nature 402:34–35 Madsen T, Ujvari B, Olsson M (2004) Novel genes continue to enhance population growth of an inbred population of Adders (Vipera berus). Biol Conserv 120:145–147 9
Malécot G (1948) Les Mathematiques de l’Hérédité. Masson et Cie, Paris. McGaugh SE, Eckerman CM, Janzen FJ (2008) Molecular phylogeography of Apalone spinifera (Reptilia, Trionychidae). Zool Scr 37:289–304 Mendel G (1866) Versuche über Pflanzen-Hybriden. Reprinted in 1951: J Hered 42: 3–47 Miller JM, Poissant J, Hogg JT, Coltman DW (2012) Genomic consequences of genetic rescue in an insular population of bighorn sheep (Ovis canadensis). Mol Ecol 21:1583–1596 Mitton JB (1997) Selection in natural populations. Oxford University Press: Oxford. Mockford SW, Herman TB, Snyder M, Wright JM (2007) Conservation genetics of Blanding’s turtle and its application in the identification of evolutionarily significant units. Conserv Genet 8:209–219 Palstra FD, Ruzzante PE (2008) Genetic estimates of contemporary effective population size: what can they tell us about the importance of genetic stochasticity for wild population persistence? Mol Ecol 17:3428–3447 Phillips CA, Dimmick WW, Carr JL (1996) Conservation genetics of the common snapping turtle (Chelydra serpentina). Conserv Biol 10:397–405 Pittman SE, King T, Faurby S, Dorcas ME (2011) Genetic and demographic status of an isolated bog turtle (Glyptemys muhlenbergii) population: implications for the conservation of small populations of long-lived animals. Conserv Genet 12:1589–1601 Ralls K, Ballou JD, Rideout BA, Frankham R (2000) Genetic management of chondrodystrophy in California condors. Anim Conserv 3:145–153 Richtsmeier RJ, Bernstein NP, Demastes JW, Black RW (2008) Migration, gene flow, and genetic diversity within and among Iowa populations of ornate box turtles (Terrapene ornata ornata). Chel Conserv Biol 7:3–11 Rubin CS, Warner RE, Bouzat JL, Paige KN (2001) Population genetic structure of Blanding’s turtles (Emydoidea blandingii) in an urban landscape. Biol Conserv 99:323–330 Sherwin WB, Timms P, Wilcken J, Houldne B (2000) Analysis and conservation implications of koala genetics. Conserv Biol 14:639–649 Sutton JT, Nakagawa S, Robertson BC, Jamieson IG (2011) Disentangling the roles of natural selection and genetic drift in shaping variation at MHC immunity genes. Mol Ecol 20: 4408–4420 Swinnerton K, Groombridge JJ, Jones CG, Burn RW, Mungroo Y (2004) Inbreeding depression and founder diversity among captive and free-living populations of the endangered pink pigeon Columba mayeri. Anim Conserv 7:353–364 Tallmon DA, Luikart G, Waples RS (2004) The alluring simplicity and complex reality of genetic rescue. Trends Ecol Evol 19:489–496 Tallmon DA, Koyuk A, Luikart GH, Beaumont MA (2008) ONeSAMP: a program to estimate effective population size using approximate Bayesian computation. Mol Ecol Resour 8: 299–301 Tessier N, Paquette S, Lapointe FJ (2005) Conservation genetics of the wood turtle (Glyptemys insculpta) in Quebec, Canada. Can J Zool 83:765–772 10
Traill LW, Bradshaw CJA, Brook BW (2007) Minimum viable population size: a meta-analysis of 30 years of published estimates. Biol Conserv 139:159–166 Traill LW, Brook BW, Frankham R, Bradshaw CJA (2010) Pragmatic population viability targets in a rapidly changing world. Biol Conserv 143:28–34 Turcek FJ (1951) Effect of introductions on two game populations in Czechoslovakia. J Wildl Manage 15:113–114 Turtle Conservation Coalition. (2011). Turtles in Trouble: The World’s 25+ Most Endangered Tortoises and Freshwater Turtles—2011. Rhodin AGJ, Walde AD, Horne BD, van Dijk PP, Blanck T, Hudson R (eds.): IUCN/SSC Tortoise and Freshwater Turtle Specialist Group, Turtle Conservation Fund, Turtle Survival Alliance, Turtle Conservancy, Chelonian Research Foundation, Conservation International, Wildlife Conservation Society, and San Diego Zoo Global, 54 pp. Lunenburg, MA Waples RS, Do C (2008) ldne: a program for estimating effective population size from data on linkage disequilibrium. Mol Ecol Resour 8:753–756 Wright S (1922) Coefficients of inbreeding and relationship. Am Nat 56: 330–338 Wright S (1931) Evolution in Mendelian populations. Genetics 16: 97–159 Wright S (1943) Isolation by distance. Genetics 28: 114–138 Wright S (1969) Evolution and the Genetics of Populations. Vol. 2. The Theory of Gene Frequencies, University of Chicago Press.
11
Chapter 2 Characterization of ten novel microsatellite loci and crossamplification of two loci in the snapping turtle (Chelydra serpentina)1 Christina M. Davy1,2*, Ashley E. Leifso3, Ida M. Conflitti1,2 and Robert W. Murphy1,2 1 Department of Ecology and Evolutionary Biology, 25 Willcocks St., University of Toronto, Toronto, ON, M5S 3B2, Canada 2 Department of Natural History, Royal Ontario Museum, 100 Queen’s Park, Toronto, Ontario, M5S 2C6, Canada. 3 Wildlife Preservation Canada, RR #5, 5420 Highway 6 North, Guelph, Ontario, N1H 6J2, Canada
2
Abstract
We used 454 (‘‘shotgun’’) sequencing to obtain a partial genomic library for the snapping turtle (Chelydra serpentina). We characterized ten microsatellite loci from these sequences and tested cross-amplification of loci originally developed for the alligator snapping turtle (Macrochelys temminckii). We genotyped 127 individuals from Ontario at twelve loci. The number of alleles per locus ranged from 1 to 14; heterozygosity ranged from 0.157 to 0.850. These loci will be used to study population genetic structure in this long-lived reptile and may cross-amplify in two closely related species. Keywords: microsatellite; Chelydra serpentina; Macrochelys temminckii
1
This chapter is published in Conservation Genetics Resources 4:695–698 (DOI: 10.1007/s12686-012-9624-7). The original publication is available at www.springerlink.com. The co-authors grant permission to include this chapter and its appendix in the thesis, and authorize the use of the thesis by the National Library. 12
2.1 Primer Note The snapping turtle (Chelydra serpentina) is a long-lived species found from southern Canada east of the Rocky Mountains southwards to the Gulf Coast and Florida (Ernst and Lovich 2009). The International Union for the Conservation of Nature (IUCN) lists the species as Least Concern. However, its life history makes it vulnerable to over-harvesting (van Dijk 2011). Reliable microsatellite markers for this species may provide valuable information for potential conservation initiatives. We use 454 sequencing to develop novel microsatellite markers, and cross-amplify nine microsatellite markers previously developed for the alligator snapping turtle (Macrochelys temminckii; Hackler et al. 2006). For microsatellite development, we isolated genomic DNA from snapping turtle blood using phenol–chloroform procedure (Sambrook et al. 1989) and cleaned the DNA using EtOH precipitation. We obtained a partial genomic library by sequencing on a Roche GS Junior (Roche, Branford, CT) at Trent University’s Natural Resources DNA Profiling and Forensics Centre. The run produced 127,778 sequences averaging 423 base pairs, which we searched for tri- and tetra-nucleotide microsatellites using the program MSATCOMMANDER (Faircloth 2008). We examined potential target loci by eye to identify loci with appropriate flanking regions for primer design and designed 40 primer pairs using Primer 3 (Rozen and Skaletsky 2000). We labelled forward primers with a fluorescent 5’ M13 tail and labelled reverse primers with a 5’ pigtail (GTTTCTT; Brownstein et al. 1996) to facilitate adenylation. We collected blood samples from 127 Ch. serpentina from across southern Ontario by caudal venipuncture and stored the blood on FTA cards (Whatman, Inc.). We extracted genomic DNA from each card following the method suggested by Smith and Burgoyne (2004) for samples with nucleated erythrocytes. We ran a temperature gradient with two DNA samples at each locus and used the optimal temperature for all subsequent PCR reactions. Amplification followed the methods of Schuelke (2000), using 4.0 lL of M13-labelled forward primer, 0.66 lL each of pigtailed reverse primer (Eurofins MWG Operon) and a 6-carboxyfluorescein dye (6-FAM; Eurofins MWG Operon) and 1.0 lL of DNA eluate (6–9 ng). We also tested cross-amplification of nine microsatellite loci developed for the alligator snapping turtle (Macrochelys temminckii; Hackler et al. 2006) using a 12.5 lL PCR reaction containing 0.6 lL of forward primer and 1.0 lL each of reverse primer and 6-FAM. Two of the nine alligator snapping turtle loci amplified 13
successfully in the snapping turtle. PCR cycling parameters followed King and Julian (2004) with annealing temperatures adjusted for each locus as summarized in Table 1. We visualized length of the amplified fragments using a 3730 DNA Analyzer (Applied Biosystems) with GS(500) Liz (Applied Biosystems) as a size standard and scored genotypes using GENEMARKER (SoftGenetics, State College, PA). One to two homozygous samples were subsequently sequenced at each locus to confirm identity of the amplified fragments, and five percent of the sampled individuals were genotyped twice at each locus to assess genotyping error. We successfully amplified ten novel loci and cross-amplified two of the nine alligator snapping turtle loci (MteD9 and MteD111). We genotyped 127 Ch. serpentina from Ontario to characterize these 12 loci. We found no ambiguities in the genotypes of the individuals amplified and genotyped multiple times. Sequencing of homozygotes confirmed the identity and motifs of the amplified fragments. We used GENALEX v6.0 (Peakall and Smouse 2006) to quantify the number of alleles per locus (k), calculate observed and expected heterozygosity (HO and HE) and probability of identity (PI) for each locus. We used GENEPOP 4.0.10 (Raymond and Rousset 1995; Rousset 2008) to test for linkage disequilibrium and deviations from Hardy–Weinberg equilibrium (HWE). The number of alleles per locus ranged from 1 to 14. Heterozygosity ranged from 0.157 to 0.850. Table 2.1 summarizes the primer sequences, amplification conditions and characteristics of the 12 characterized loci. None of the 66 pairwise comparisons between loci showed evidence of linkage disequilibrium after Bonferroni correction for multiple comparisons. Two loci (Cs18 and MteD111) showed significant deviations from HWE (p < 0.01). Three alleles at the tri-nucleotide locus Cs16 differed by only one base pair. Sequencing of homozygous individuals and successful replication of these genotypes in independent amplifications both demonstrated that these are unique alleles and are not the result of stutter. One locus (Cs14) was monomorphic in the tested samples. Because they are all from the northern limits of this species’ range low genetic diversity is expected, but it may be variable in southern populations. The family Chelydridae contains two other species (Phillips et al. 1996): the Central American Snapping Turtle (Chelydra rossignoni), listed by the IUCN as Vulnerable, and the South 14
American snapping turtle (C. acutirostris), which remains to be assessed. Cross-amplification of these markers could facilitate conservation genetic analyses of these two closely related and poorly understood species.
2.2 Acknowledgments This project was supported by a Canada Collection grant from Wildlife Preservation Canada (WPC) to CD and a National Science and Engineering Research Council (NSERC) Discovery Grant (A3148) to Robert W. Murphy. Genotyping costs were offset by the generous assistance of the Schad Foundation. Ida Conflitti assisted with sequencing to confirm homology of M. temminckii and Ch. serpentina sequences. CD and IC were funded by Canada Graduate Scholarships from NSERC. Ashley Leifso assisted with microsatellite genotyping and was funded by WPC through a Science Horizons grant from Environment Canada. We thank Dr. C. Kyle, E. Kerr and M. Harnden at the NRDPFC (Trent University) for conducting 454 sequencing. Sample collection was conducted with the permission of the Government of Ontario and Ontario Parks and followed Animal Use Protocol 2010-14 (Royal Ontario Museum).
2.3 References Brownstein MJ, Carpten JD, Smith JR (1996) Modulation of nontemplated nucleotide addition by taq DNA polymerase: primer modifications that facilitate genotyping. BioTech 20:1004–1010. Ernst C, Lovich J (2009) Turtles of the United States and Canada 2nd ed. Johns Hopkins University Press, Baltimore. Faircloth BC (2008) msatcommander: detection of microsatellite repeat arrays and automated, locus-specific primer design. Mol Ecol Resour 8:92–94 Hackler JC, van den Bussche RAV, Leslie DM (2006) Characterization of microsatellite DNA markers for the alligator snapping turtle, Macrochelys temminckii. Mol Ecol Notes 7:474–476 King TL, Julian SE (2004) Conservation of microsatellite DNA flanking sequences across 13 Emydid genera assayed with novel bog turtle (Glyptemys muhlenbergii) loci. Conserv Genet 5:719–725 Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes 6:288–295 Phillips CA, Dimmick WW, Carr JL (1996) Conservation genetics of the common snapping turtle (Chelydra serpentina). Conserv Biol 10:397–405
15
Raymond M, Rousset F (1995) GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J Hered 86:248–249 Rousset F (2008) Genepop'007: a complete reimplementation of the Genepop software for Windows and Linux. Mol Ecol Resour 8:103–106 Rozen S, Skaletsky HJ (2000) Primer3 on the WWW for general users and for biologist programmers. In: Krawetz S, Misener S (eds) Bioinformatics Methods and Protocols: Methods in Molecular Biology. Humana Press, Totowa, NJ, pp 365-386. Source code available at http://fokker.wi.mit.edu/primer3/ Sambrook J, Fritsch EF, Maniatis T (1989) Molecular cloning—a laboratory manual, 2nd edn. Cold Spring Harbor Laboratory Press, New York, NY Schuelke M (2000) An economic method for the fluorescent labeling of PCR fragments. Nat Biotechnol 18:233–234 Smith LM, Burgoyne LA (2004) Collecting, archiving and processing DNA from wildlife samples using FTA® databasing paper. BMC Ecol 4:4: http://www.biomedcentral.com/1472-6785/4/4 van Dijk PP (2011) Chelydra serpentina. In: IUCN 2011. IUCN Red List of Threatened Species. Version 2011.2. . Downloaded on 12 December 2011.
16
Table 2.1. Characteristics of ten novel and two cross-amplified microsatellite loci for 127 Chelydra serpentina sampled from across southern Ontario. N=number of individuals genotyped; k = number of alleles; Ho = observed heterozygosity; He = expected heterozygosity; PI = probability of identity. Primer sequences shown include a 5’ M13 tail (5’-TGT AAA ACG ACG GCC AGT-3’) on forward primers and a 5’ GTTTCTT pigtail on reverse primers. MteD111 and MteD9 are from Hackler et al. (2006), with M13 tail (F) and pigtail (R) added. The “*” indicates loci that are not in Hardy-Weinberg equilibrium (p < 0.01).
Locus Cs08 Cs12 Cs14 Cs16 Cs17 Cs18* Cs19 Cs22 Cs24 Cs25 MteD9 MteD111*
Primer Sequence (5’ – 3’)* F: TGTAAAACGACGGCCAGTGCTGGACATGTACGTGCAAA R: GTTTCTTTGTATATGTCCTTTAGGCATTTATGTG F: TGTAAAACGACGGCCAGTGGCATTTCTAGCCAACAGGA R: GTTTCTTAGCGGTGTGCTTTTCTCAGT F: TGTAAAACGACGGCCAGTCAGACAGGGGCTGTTGAGTC R: GTTTCTTGGCAGCTCTGTGTGTCAGTC F: TGTAAAACGACGGCCAGTTCCAAATGCAGCTCTCTTCA R: GTTTCTTCCTTGCCATCTCGAACAAAT F: TGTAAAACGACGGCCAGTTGGAAACTCTCCTTGTCTGTCC R: GTTTCTTGAGGCACTTTATTAATTCCTACCTCT F: TGTAAAACGACGGCCAGTTGGTGGTTTCTCTGGAAGTTTT R: GTTTCTTTTCTGCTTTTAACCTGCACTCA F: TGTAAAACGACGGCCAGTTTGAGTGGTCTACGGGAACC R: GTTTCTTTGACGGTTTCCTAGCGGTAT F: TGTAAAACGACGGCCAGTCGGCAGAAGATAAGAGGCATT R: GTTTCTTTGGTAGGGTTGCTCATGAAA F: TGTAAAACGACGGCCAGTTGTTTCCATTCCAAACACCTG R: GTTTCTTGCAACACTGCTTCCCTTCAT F: TGTAAAACGACGGCCAGTTGTGTTGTCACAGGGCACTT R: GTTTCTTAAATGGACTGCGGACACTTC F: TGTAAAACGACGGCCAGTCCAGATGCTAGTCTCACACC R: GTTTCTTGCTTACTGGAATTAACCTCATG F: TGTAAAACGACGGCCAGTTCCACAAACTCCCATCTTC R: GTTTCTTCCACACGGAAAAATCTATCTAC
Repeat motif AGAT
Annealing temperature (°C) 54
Size (bp) 157–198
N 127
k 11
Ho
He
PI
0.850
0.859
0.0356
AGAT
58
197 – 250
126
12
0.841
0.808
0.0536
AGG
60
231
57
1
-
-
1.000
AAT
58
193 – 198
127
4
0.701
0.678
0.1645
ACC
60
290 – 305
127
6
0.654
0.691
0.1399
AATT
56
268 – 276
127
3
0.157
0.159
0.7169
AGG
58
194 – 206
127
5
0.472
0.469
0.3158
AAAT
56
321 – 333
127
4
0.378
0.357
0.4339
ACC
61
417 – 432
127
3
0.551
0.575
0.2540
ATC
60
220 – 229
127
3
0.567
0.560
0.2921
TAGA
60
261 – 285
127
6
0.740
0.743
0.1058
TAGA
60
175 – 191
120
14
0.558
0.865
0.0324
17
Chapter 3 Isolation and characterization of eleven novel polymorphic microsatellite loci in the spiny softshell turtle (Apalone spinifera)2 Christina M. Davy1,2*, Ida M. Conflitti1,2, Daniel M.L. Storisteanu3 and Robert W. Murphy1,2 1
Department of Ecology and Evolutionary Biology, University of Toronto, 25 Willcocks St., Toronto, ON, M5S 3B2, Canada
2
3
Department of Natural History, Royal Ontario Museum, 100 Queen’s Park, Toronto, Ontario, M5S 2C6, Canada.
Department of Medicine, Addenbrooke’s Hospital, University of Cambridge, Hills Road, Cambridge, CB2 2QQ, United Kingdom
3
Abstract
We isolated and characterized 11 microsatellite loci for the spiny softshell turtle (Apalone spinifera) from a partial genomic library obtained using 454 sequencing technology. Genotypes of 15 individuals from southern Ontario and 30 individuals of unknown origin contained 6 to 20 alleles per locus and the level of heterozygosity ranged from 0.229 to 0.800. These markers would be useful for population genetics studies and enforcement activities such as assignment of illegally traded individuals to their population of origin. Keywords: microsatellite; next-generation sequencing; turtle
2
This chapter is published in Conservation Genetics Resources 4:759–761 (DOI: 10.1007/s12686-012-9638-1). The original publication is available at www.springerlink.com. The co-authors grant permission to include this chapter and its appendix in the thesis, and authorize the use of the thesis by the National Library. 18
3.1 Primer Note The spiny softshell turtle (Apalone spinifera) is a widely distributed species ranging from southern-eastern Canada to north-eastern Mexico (Ernst and Lovich 2009). The species is listed as Least Concern by the International Union for the Conservation of Nature (IUCN). However, particular populations are considered to be at risk (van Dijk 2011) and the Canadian populations of A. spinifera are listed as Threatened (COSEWIC 2002). Despite lack of data on harvest levels in many parts of the species’ range, recorded exports of A. spinifera from the North America to Asian food markets increased dramatically in recent years and doubled between 2006 and 2008 (IUCN/SSC Tortoise and Freshwater Turtle Specialist Group 2010). Illegal harvest poses a significant threat to at-risk populations of A. spinifera. Here, we present a suite of variable microsatellite markers for A. spinifera that could be used to answer a range of research questions as well as for conservation enforcement (for example, development of assignment tests). We used phenol-chloroform extraction (Sambrook et al. 1989) to isolate genomic DNA from a whole blood sample of A. spinifera stored in 95% EtOH and cleaned the DNA using standard EtOH precipitation. A partial genomic library was obtained by sequencing on a Roche GS Junior (Roche, Branford, CT) using the next generation sequencing facilities at Trent University's Natural Resources DNA Profiling and Forensics Centre. The GS Junior run produced 137,054 sequences averaging 415 base pairs in length. We searched sequences for tri-, tetra- and penta-nucleotide microsatellites with the program MSATCOMMANDER (Faircloth 2008) and designed 40 primer pairs using the software Primer 3 (Rozen and Skaletsky 2000). We added a 5’ M13 tail to forward primers to facilitate fluorescent labelling, and a 5’ pigtail (GTTTCTT; Brownstein et al. 1996) to reverse primers to facilitate adenylation. PCR amplification followed the method of Schuelke (2000) and cycling parameters followed King and Julian (2004) with annealing temperatures adjusted for each locus (Table 3.1). We used a 3730 DNA Analyzer (Applied Biosystems) to visualize length of the amplified fragments by comparison to GS(500) Liz size standard (Applied Biosystems). We scored genotypes using GENEMARKER (SoftGenetics, State College, PA).
19
Eleven of the 40 primer pairs amplified unambiguous, replicable alleles. We used GENALEX v6.0 (Peakall and Smouse 2006) to quantify the number of alleles per locus (k), calculate observed and expected heterozygosity (Ho and He) and probability of identity (PI) for each locus. We used GENEPOP 4.0.10 (Raymond and Rousset 1995; Rousset 2008) to test for linkage disequilibrium and deviations from Hardy-Weinberg equilibrium (HWE). We obtained blood from 15 individual A. spinifera from southern Ontario by caudal venipuncture following approved Animal Use Protocols and stored it on FTA cards (Whatman Inc.). We prepared DNA for PCR following the protocols of Smith and Burgoyne (2004) for processing FTA cards containing blood with nucleated erythrocytes. Because the sampled Ontario population is near the northern limit of the species’ range we expected genetic variation to be relatively low. Thus, we also collected 30 tissue (muscle) samples from A. spinifera carcasses confiscated by Government of Ontario wildlife enforcement staff. We isolated DNA from the muscle samples using phenol-chloroform extraction (Sambrook et al. 1989). The exact origin of these individuals was unknown, but we assumed that they represented a wider geographic distribution than the samples from Ontario and included them to better investigate polymorphism in these markers. We genotyped a total of 45 individuals at 11 loci. We also sequenced alleles from homozygous loci to confirm that the amplified fragments were homologous to those obtained through 454 sequencing. Table 3.2 summarizes the characteristics of each locus for the samples from Ontario and those of unknown origin. The overall number of alleles per locus ranged from 6 to 20 and the level of heterozygosity ranged from 0.229 to 0.800. No linkage disequilibrium was detected when the population from Ontario is analyzed separately. However, when considering the entire dataset, 5 of the 55 pairwise comparisons between loci showed evidence of linkage disequilibrium after Bonferroni correction for multiple comparisons (As18 and As07; As18 and As15; As15 and AsB12; AsB09 and As12; and As15 and As B09). All loci were in HWE in the samples from Ontario (p < 0.05) with the exception of AsB14 (p = 0.048). Only one locus (AsB08) meets the expectations of HWE in the 30 samples of unknown origin (p < 0.05). However, these samples probably do not represent a single population and should not be treated as such. 20
These 11 polymorphic loci will facilitate studies of the population genetics of A. spinifera. Due to the unconventional use of individuals for whom the exact location of origin is not known, these results are not intended to provide a robust genetic profile of any particular population. Rather, the results demonstrate the utility of these variable loci for population genetics studies in this species, including population assignment tests and potential conservation enforcement.
3.2 Acknowledgements This project was made possible by a Canada Collection grant from Wildlife Preservation Canada to C.D. and a National Science and Engineering Research Council (NSERC) Discovery Grant A3148 to Robert W. Murphy. Generous assistance from the Schad Foundation offset genotyping costs. Ida Conflitti performed sequencing to confirm sequences motifs; both CD and IC were funded by NSERC Canada Graduate Scholarships. Daniel Storisteanu assisted with genotyping and was funded by an NSERC Undergraduate Summer Research Award. We thank Dr. Chris Kyle, Emily Kerr and Matthew Harnden at the NRDPFC (Trent University) for conducting 454 sequencing. Sample collection was conducted with the permission of the Government of Ontario following Animal Use Protocol 2010-14 (Royal Ontario Museum, Toronto, Canada). We thank Rick Andrews and the Lake Ontario Enforcement Unit for access to tissues from confiscated turtles.
3.3 References Bacher J, Hennes LF, Gu T, Tereba A, Micka KA, Sprecher CJ, Lins AM, Amiott EA, Rabbach DR, Taylor JA, Helms C, Donis-Keller H, Schumm JW (1999) Pentanucleotide repeats: highly polymorphic genetic markers displaying minimal stutter artifact. In: Proceedings from the ninth international symposium on human identification, Orlando, pp 24–37 Brown DJ, Faralloa VR, Dixon JR, Baccusa JT, Simpson TR, Forstner MRJ (2011) Freshwater Turtle Conservation in Texas: Harvest Effects and Efficacy of the Current Management Regime. J Wildl Manag 75:486–494 Brownstein MJ, Carpten JD, Smith JR (1996) Modulation of nontemplated nucleotide addition by taq DNA polymerase: primer modifications that facilitate genotyping. BioTechnol 20:1004–1010. COSEWIC 2002. COSEWIC assessment and update status report on the spiny softshell turtle Apalone spinifera in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. vii + 17 pp. Ernst C, Lovich J (2009) Turtles of the United States and Canada 2nd ed. Johns Hopkins University Press, Baltimore. 21
Faircloth BC (2008) msatcommander: detection of microsatellite repeat arrays and automated, locus-specific primer design. Mol Ecol Resour 8:92–94 IUCN/SSC Tortoise & Freshwater Turtle Specialist Group. 2010. A study of progress on conservation of and trade in CITES-listed tortoises and freshwater turtles in Asia. In: CoP15, Inf. 22. Convention on international trade in endangered species of wild fauna and flora. Fifteenth meeting of the conference of the parties, Doha (Qatar). 13–25 March. . King TL, Julian SE (2004) Conservation of microsatellite DNA flanking sequences across 13 Emydid genera assayed with novel bog turtle (Glyptemys muhlenbergii) loci. Conserv Genet 5:719–725 Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes 6:288–295 Raymond M, Rousset F (1995) GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J Hered 86:248–249 Rousset F (2008) Genepop'007: a complete reimplementation of the Genepop software for Windows and Linux. Mol Ecol Resour 8:103–106 Rozen S, Skaletsky HJ (2000) Primer3 on the WWW for general users and for biologist programmers. In: Krawetz S, Misener S (eds) Bioinformatics Methods and Protocols: Methods in Molecular Biology. Humana Press, Totowa, NJ, pp 365-386. Source code available at http://fokker.wi.mit.edu/primer3/. Sambrook J, Fritsch EF, Maniatis T (1989) Molecular cloning—a laboratory manual, 2nd edn. Cold Spring Harbor Laboratory Press, New York, NY Schuelke M (2000) An economic method for the fluorescent labeling of PCR fragments. Nat Biotechnol 18:233–234 Smith LM, Burgoyne LA (2004) Collecting, archiving and processing DNA from wildlife samples using FTA® databasing paper. BMC Ecol 4:4: http://www.biomedcentral.com/1472-6785/4/4 van Dijk, P.P. 2011. Apalone spinifera. In: IUCN 2011. IUCN Red List of Threatened Species. Version 2011.2. . Downloaded on 19 December 2011
22
Table 3.1. Primer sequences and amplification conditions for 11 novel polymorphic loci for Apalone spinifera. Motif = repeat motif of microsatellite. Temp = primer-specific annealing temperature (°C). Primer sequences shown include a 5’ M13 tail (5’-TGT AAA ACG ACG GCC AGT-3’) on forward primers and a 5’ pigtail (GTTTCTT) on reverse primers. Locus
Primer Sequence (5’ – 3’)
Motif
Temp
As07
F: TGTAAAACGACGGCCAGTACGACGCCAAAATTTGAGTT
AGAT
52
ATGGT
52
CTTT
60
GATT
58
GTTT
54
GTTT
58
AAC
61
AAC
58
ATC
58
AAT
58
AAT
58
R: GTTTCTTACTTTTGTTCCTCCGGGTTT As12
F: TGTAAAACGACGGCCAGTTGATCATTGTCTCTTGGCAGTC R: GTTTCTTGTGATTGCAGCAGCGAAATA
As13
F: TGTAAAACGACGGCCAGTCCCACTGGGATTGCTAACTT R: GTTTCTTTGGATGAAGAAATTGCATGG
As14
F: TGTAAAACGACGGCCAGTGTGGCTGAAAAGGCAAGACT R: GTTTCTTTGCAAAATGGACCTTGAACA
As15
F: TGTAAAACGACGGCCAGTTGGCCTTAGGCAAGTCTTTT R: GTTTCTTGAGCCTACATCTGCAATGGTT
As18
F: TGTAAAACGACGGCCAGTTTTAATTCCTGAGAGGGACACTG R: GTTTCTTGCAGTAAAGGGCAAAACCAG
AsB07
F: TGTAAAACGACGGCCAGTTTCAGTAAGAAAGTTGTAAATCTTGAA R: GTTTCTTATATGGCCCTTGACCTCACA
AsB08
F: TGTAAAACGACGGCCAGTGCCGCATCAGCTTTGTTAAG R: GTTTCTTTTCCCTGTGCTTACCTGGTC
AsB09
F: TGTAAAACGACGGCCAGTCTGCTTCACCCCTTCTCTGA R: GTTTCTTAGGCATCGGATACAAACAGG
AsB12
F: TGTAAAACGACGGCCAGTTGCCAGAATCTTCAAAAGCA R: GTTTCTTCTCCTGTGAGCCAGGTCAGT
AsB14
F:TGTAAAACGACGGCCAGTTGTTGCAAACACAGTTGGAA R: GTTTCTTTGCCAGAAAGAAATCACCAA
23
Table 3.2. Characteristics of 11 novel polymorphic loci for 15 Apalone spinifera from southern Ontario and 30 individuals of unknown origin. N=number of individuals genotyped; k = number of alleles; Ho = observed heterozygosity; He = expected heterozygosity; PI = probability of identity. confiscated individuals (unknown origin; N=30)
southern Ontario (N=15) Locus As07
Size (bp) 191–307
N 14
k 11
Ho 0.714
He 0.860
PI 0.034
N 27
k 14
Ho 0.556
He 0.767
PI 0.069
As12
243–309
15
5
0.600
0.638
0.178
30
7
0.700
0.733
0.113
As13 As14
172–236 216–224
15 15
5 1
0.600 0.000
0.702 0.000
0.146 1.000
30 26
17 6
0.900 0.308
0.925 0.567
0.011 0.229
As15
262–342
15
9
0.867
0.820
0.056
27
16
0.630
0.833
0.045
As18
252–305
13
5
0.385
0.337
0.453
27
12
0.630
0.871
0.029
AsB07
179–205
15
2
0.333
0.358
0.476
30
9
0.367
0.527
0.250
AsB08
209–230
15
3
0.333
0.380
0.423
28
8
0.821
0.844
0.044
AsB09
142–180
15
3
0.333
0.640
0.206
30
10
0.500
0.814
0.055
AsB12
256–283
15
5
0.667
0.709
0.136
27
5
0.259
0.628
0.197
AsB14
237–252
13
6
0.385
0.589
0.195
30
6
0.667
0.821
0.057
24
Chapter 4 Conservation genetics of the endangered spotted turtle do not support a relationship between genetic variation and population size. Formatted for Biological Conservation.
4
Abstract
The hypothesis that genetic variation is affected by population size is widely accepted in conservation genetics. Here, I test this hypothesis in a particularly long-lived vertebrate, and test the efficacy of “bottleneck” tests in long-lived species. The endangered spotted turtle (Clemmys guttata) is restricted to small, disjunct and declining populations, and can be used to model the application of conservation genetics theory to long-lived organisms with overlapping generations. I genotyped 256 individuals at 11 microsatellite loci and used a suite of conservation genetics analyses to investigate population structure across the Canadian range of Cl. guttata. Within-site allelic richness ranged from 3.18 to 4.49; observed heterozygosity ranged from 0.510 to 0.743. Although allelic richness was correlated with population size, heterozygosity and private allelic richness were not. Bottleneck tests failed to detect population declines in 12 of 13 tested sites. A literature review discovered that bottleneck tests in 17 of 18 studies of tortoises and freshwater turtles had insufficient sampling, potentially resulting in Type I and II errors. Bayesian analyses identified a minimum of five genetic populations and a maximum of 10 genetically differentiated subpopulations which are demographically independent. Genetic population structure of Cl. guttata appeared to reflect patterns of postglacial colonization rather than current landscape modifications. These results can improve management and recovery plans for the endangered spotted turtle and demonstrate that long-
25
lived organisms such as turtles may not show the generally accepted relationship between genetic diversity and population size. Keywords: Clemmys guttata, landscape genetics, microsatellites, Ontario, STRUCTURE, TESS
4.1 Introduction Genetic drift can significantly impact small or fragmented populations (Ewing et al., 2008) by driving the stochastic loss of genetic diversity and increasing inbreeding. These impacts can reduce fitness and evolutionary potential. Conservation genetics aims to mitigate these impacts and maintain adaptability to environmental changes in threatened species by preserving genetic diversity (Frankham, 1996; Frankham et al., 2002). The emerging fields of spatial and landscape genetics emphasize that genetic population structure is shaped by historic and current landscape structure (Manel et al., 2003; Guillot et al., 2009). Spatial analyses can also be integrated into conservation genetics studies to explicitly consider the effects of current and historic landscapes on the status of a species and its potential for recovery. Small populations of organisms with long-life spans, overlapping generations and promiscuous mating systems violate some of the assumptions of conservation genetics theory (Frankham et al., 2002). Such populations may experience the genetic effects of population fragmentation more slowly than typical model organisms such as Drosophila melanogaster or Caenorhabditis elegans. For example, most species of turtle have delayed maturity, long generation times (> 25 years), long life-spans and polygamous or promiscuous mating systems (Congdon et al., 1993; 1994; Litzgus, 2006; Davy et al., 2011). Therefore, turtles are an effective model system to study the effects of population fragmentation on long-lived organisms. Given that almost 50% of turtle species are threatened and require protection (www.iucnredlist.org), population genetics studies of turtles are also a conservation priority (Alacs et al., 2007). Freshwater turtles often show no detectable genetic population structure on small spatial scales (< 80 km; Bennet et al., 2010; Banning-Anthonysamy, 2012), but analyses of population structure on a larger spatial scale can reveal the historical drivers of patterns across the landscape (e.g. Tessier et al., 2005; Pearse et al., 2006; Stepien et al., 2009; Echelle et al., 2010). Taken in 26
the context of the current, modified landscape, such analyses can identify areas that should be prioritized for mitigation measures and facilitate recovery plans to maximize the preservation of genetic diversity. For example, these analyses can identify situations where translocations or the development of wildlife corridors can re-connect fragmented, historically continuous populations. They can also identify cases where increased connectivity across the landscape could be detrimental to species recovery, for example, by leading to outbreeding depression or genetic homogenization among strongly differentiated populations. The genetic effects of recent, anthropogenic habitat and population fragmentation are usually not detectable in turtles (Rubin et al., 2001; Kuo and Janzen, 2004; Marsack and Swanson, 2009; Pittman et al., 2011), even when dispersal is restricted (Bennett et al., 2010). Similarly, small populations of turtles that have declined significantly in recent years often show no evidence of recent genetic bottlenecks (Kuo and Janzen, 2004; Mockford et al., 2005; Marsack and Swanson, 2009; Spradling et al., 2010; Pittman et al., 2011). One possible explanation is that the long life span of turtles buffers small populations against the genetic effects of habitat fragmentation and bottlenecks (Kuo and Janzen, 2004; Marsack and Swanson, 2009, Bennet et al., 2010). However, this result may also be affected by sampling bias. The commonly used program BOTTLENECK requires a minimum of 10 loci and 30 individuals per tested population to achieve reasonable statistical power in tests of heterozygote excess and in the qualitative mode-shift test (Piry et al., 1999). Unfortunately, the collection of large sample sizes of threatened turtles from multiple populations is logistically challenging, and the development of appropriate molecular markers is costly. Tests may therefore be performed with suboptimal sample sizes (see below). Furthermore, analyses of simulated data suggest that these tests may not accurately detect bottlenecks even when the test requirements are met (Peery et al., 2012). Thus, bottleneck tests may lead to incorrect conclusions about the loss of genetic diversity in threatened populations of turtles and other long-lived organisms. Native, severely reduced populations can serve to explore this problem. The spotted turtle, Clemmys guttata (Schneider, 1792), is globally endangered due to habitat loss and illegal collection (van Dijk, 2011). Across its range, this species occurs in small, isolated populations (van Dijk, 2011; COSEWIC, 2004) precluding a genetic rescue effect (Tallmon et 27
al., 2004). Low vagility (dispersal ability) may result in low levels of gene flow between isolated populations because this species has high site fidelity and individuals rarely travel > 2 km per year (Litzgus, 1996; Seburn, 2003; Ernst and Lovich, 2009). Most Canadian populations now contain < 150 individuals (COSEWIC, 2004). These small, isolated, and declining populations provide an excellent test case for commonly used genetic bottleneck tests. In this study I evaluate genetic diversity and population structure among Canadian populations of Cl. guttata. I use Cl. guttata to test the following conservation genetics hypotheses: 1)
That native, declining populations of a long-lived organism with overlapping generations
will show the genetic impacts of fragmentation and population decline predicted by conservation genetics theory (Frankham, 1996), namely, reduced genetic diversity in smaller populations relative to larger populations, and significantly reduced genetic diversity in populations relative to the metapopulation; 2)
That traditional bottleneck tests can be used effectively in studies of threatened
populations of long-lived organisms. This is tested using multiple populations of Cl. guttata along with a literature review of other species of turtles. I evaluate the data in the framework of recovery of this endangered species, and I test the efficacy of assignment tests for identifying the origin of Canadian Cl. guttata.
4.2 Methods 4.2.1
Sample collection and genotyping
I conducted capture-mark-recapture surveys for Cl. guttata at 13 sites across southern Ontario from April 2008 to October 2011. Sampled sites represented most of the known, extant populations of Cl. guttata in Canada (COSEWIC, 2004). Several sites were surveyed in collaboration with researchers working on existing long-term projects. Approximate locations of sampling sites are shown in Figure 4.1. Detailed location information is not provided to avoid an increase in illegal collection. Turtles were captured by hand, except for one opportunistic capture in a hoop trap. Each turtle was sexed, measured, photographed, and marked by shell notching (Cagle, 1939). I collected 28
0.05–0.10 mL of blood from mature individuals by caudal venipuncture and stored samples on FTA cards (Whatman, Inc., Clifton, NJ, USA). Muscle was sampled from freshly dead turtles encountered during surveys or dead on the road (DOR). Bone samples were taken from older carcasses or empty shells. Several additional samples were contributed by other researchers. I sampled 25–30 individuals/population where possible and > 10% of the estimated population at all other sampling sites. Published estimates of population size were used to estimate the proportion of the population sampled. Where published estimates were unavailable, I estimated population size (N) from mark-recapture data using the program MARK (White and Burnham, 1999), under a closed-capture model (data not shown). Capture probability was allowed to vary with time to account for differences in survey effort and field conditions among survey years. Genomic DNA was extracted from FTA cards and muscle following Davy et al. (2012). To extract DNA from old turtle shells, I removed a small piece of bone, ground it into a fine powder, and processed it using the QIAamp ® DNA Investigator Kit (Qiagen Inc., Valencia, CA). PCR was conducted for 11 microsatellite loci originally developed for the Bog Turtle (Glyptemys muhlenbergii; King and Julian, 2004). Amplification followed the methods of Schuelke (2000); using 4.0 µL of M13-labelled forward primer, 0.66 µL each of pigtailed reverse primer (Eurofins MWG Operon) and a 6-carboxyfluorescein dye (6-FAM; Eurofins MWG Operon), and 1.0 µL of DNA eluate (6-9 ng). PCR cycling parameters followed King and Julian (2004) with annealing temperatures optimized for each locus (Table 4.1). Fragment lengths were visualized using a 3730 DNA Analyzer (Applied Biosystems, Foster City, CA, USA) with size standard GS(500) Liz (Applied Biosystems). I scored genotypes with an RFU (relative fluorescence units) peak > 200 using GENEMARKER (SoftGenetics, State College, PA). Amplification was repeated for genotypes with a weak signal (< 200 RFU). Genotyping error was assessed by re-extracting and re-genotyping approximately three percent of the samples (8/256) at each locus. I used duplicate, independent samples from the same individual where possible (Pompanon et al., 2005). In three cases, duplicate extractions were taken from a single FTA card.
29
4.2.2
Population genetics analyses
Genotypes were checked for evidence of null alleles and long-allele drop-out using MICROCHECKER
v.2.2.3 (van Oosterhout et al., 2004). I used GENALEX v.6.0 (Peakall and Smouse,
2006) to quantify observed and expected heterozygosity (HO and HE) and probability of identity (PI) for each locus, for each site and globally. Because all sampled populations were small, I also calculated PISibs, the probability of identity taking into account the possibility that close relatives were sampled (Taberlet and Luikart, 1999; Waits et al., 2001). Linkage equilibrium and deviations from Hardy-Weinberg equilibrium (HWE) were tested in GENEPOP v.4.0.10 (Raymond and Rousset, 1995; Rousset, 2008). A sequential Bonferroni correction was applied to account for multiple pairwise comparisons (Rice, 1989). Each site was also tested for heterozygote deficit (indicating inbreeding) or heterozygote excess (which could indicate inbreeding avoidance or a recent bottleneck). Allelic richness (Ar) and private alleleic richness (PAr) were adjusted for unequal sample sizes by rarefaction in HP-RARE v.1.0 (Kalinowski, 2004; 2005). Effective population size (Ne) for each sampled site was estimated by approximate Bayesian computation in ONeSAMP (Tallmon et al., 2008) using prior lower and upper bounds of 4 and 200 on each estimate of Ne. I used Pearson’s correlation coefficient to test for significant correlations between N, Ne and genetic diversity (HO, Ar and PAr). Genetic diversity (HO, HE, Ar and PAr) were compared among sites and genetic populations using Friedman’s two-way analysis of variance by ranks. Correlations and Friedman`s test were conducted in SPSS v. 20.0 (SPSS Inc. Chicago, Illinois). I calculated absolute differentiation among sites, Dest (Jost, 2008), using SMOGD v.1.2.5 (Crawford, 2010). I also quantified differentiation using FST and assessed significance with 10,000 randomizations in FSTAT (Goudet, 1995). Both Dest and FST could have been biased by small sample sizes. Therefore sites BP1 and BP2, GH1 and GH2, and GB1 and GB2, which were each < 30 km apart, were combined for these calculations. Correlations between genetic distance (Dest) and Euclidean distance (Wright, 1943) were tested using IBDWS v.3.23 (Jensen et al., 2005). Significance of matrix correlations was assessed with a Mantel test (Mantel, 1967) with 30,000 randomizations. Geographic genetic structure was also visualized using principal coordinates analysis (PCoA) in GENALEX to ordinate genetic distance (Dest) among sampled sites. 30
No a priori assumptions were made about the amount of structure present because this is the first study of genetic structure in Cl. guttata. Instead, I used three programs that employ Bayesian inference to detect population structure in genetic data. First, I tested the relative probability of a series of models ranging from 1 to13 populations (K) using STRUCTURE v.2.3.4 (Pritchard et al., 2000), which used Bayesian inference to assign individuals to distinct clusters based on their genotypes by minimizing disequilibrium in each cluster. Each run involved 750,000 generations with a burn-in of 75,000 generations. The model assumed correlated allele frequencies (Falush et al., 2003) and historical admixture between populations (Pritchard et al., 2000). Eight runs were conducted at each value of K using the LOCPRIOR function to include sampling information (collection site of each individual) in the analysis. We compiled the output of the 104 runs with STRUCTURE HARVESTER v.0.6.92 (Earl and vonHoldt, 2012) and used two methods to estimate K, the most probable number of genetically distinct populations represented in the data. The increase in pr(X|K), the probability of the data given a particular value of K, typically plateaus at the most likely value of K (Pritchard et al., 2000). The ad hoc ∆K method (Evanno et al., 2005) implemented in STRUCTURE HARVESTER was also used to calculate the second-order rate of change in log likelihood between successive values of K, which typically peaks at the appropriate value of K. We used the Greedy and LargeKGreedy algorithms in CLUMPP v.1.1.2 (Jakobsson and Rosenberg, 2007) to permute and combine results from independent runs. Genetic clusters identified by STRUCTURE were visualized with DISTRUCT v.1.1 (Rosenberg, 2004). The second and third programs used to explore population structure were TESS v.2.3.1 (Chen et al., 2007) and GENELAND v.4.0.2 (Guillot et al., 2005), which explicitly considered spatial information. The TESS analysis assumed an admixture model (Durand et al., 2009) and considered increasing values of Kmax (the maximum number of populations in the dataset) from 2 to 9, with 10 runs at each Kmax. Each run included 50,000 sweeps with a burn-in of 10,000 sweeps and run data were assessed to ensure convergence. The most likely value of K was determined based on the value at which the decreasing deviance information criterion (DIC) values reached a point of inflection and the number of distinct clusters stabilized (Chen et al., 2007). In GENELAND, I assumed correlated allele frequencies between populations. I conducted
31
10 runs of 1,000,000 iterations each, exploring a range of K values from 1 to 15. A burn-in of 10% was applied post-processing. Analysis of molecular variance (AMOVA) was performed in ARLEQUIN v.3.1 (Excoffier et al., 2005). Partitioning of genetic variance was quantified within and among sampled sites and genetic populations identified with clustering analyses.
4.2.3
Development of genetic assignment tests for Canadian Cl. guttata
Assignment tests in GENECLASS v.2.0 (Piry et al., 2004) used the method of Rannala and Mountain (1997) with 100,000 simulated individuals. Assignment tests first considered each sampled site as an independent population, and were repeated using the genetic populations identified by STRUCTURE and TESS. I also used the posterior probability of assignment of each individual to the clusters identified by STRUCTURE (i.e. the individual q-matrix) to determine whether individuals could be accurately assigned to their site or populations of origin based on genetic data. Individuals were assigned to a cluster (site or population) when q > 0.5 (Latch et al., 2006).
4.2.4
Analyses of genetic bottlenecks
Genetic bottlenecks were inferred using BOTTLENECK, which tested for bottlenecks in the past 2Ne–4Ne generations (Piry et al., 1999). Five sites had sample sizes > 29, meeting the criteria recommended for this program (Table 4.2), and the remaining sites were also tested. I tested for heterozygote excess (Cornuet and Luikart, 1997) using 1,000 replicates under the two-phase model (TPM; Di Rienzo et al., 1994), with a variance of 12 among multiple steps. Parameters were set to 95% single-step mutations and 5% multiple-step mutations, and the Wilcoxon test was used to assess statistical significance of heterozygote excess as recommended for < 20 loci (Piry et al., 1999). The qualitative mode shift test (Luikart et al., 1997) was performed for all 13 sampled sites. Both tests were also applied to the populations identified by STRUCTURE and TESS.
4.3 Results I sampled 254 Cl. guttata from 13 sites (mean N = 19.5, s.d. = 10.6, range = 4–35; Table 4.2) representing approximately 10% of the total estimated Canadian population and most of the known Canadian range. I also sampled three DOR individuals that were not found near known 32
populations. Pairwise distances between sampled sites ranged from 3.2 to 670.0 km. (mean = 277.2; s.d. = 147.5). Eleven polymorphic loci amplified successfully (Table 4.1). Duplicated analyses of genotypes were identical in all cases, indicating negligible genotyping error. Genotypes with weak signal strength (> 200 RFU) received identical scores when the amplification was repeated and the signal increased. One sample from EO2 produced genotypes with three peaks. The triple peaks were replicated at all loci with four independent re-extractions and amplifications and the individual was subsequently excluded from the study. MICRO-CHECKER
identified homozygote excess across the entire dataset but did not detect
homozygote excess in any individual sites. Thus, homozygote excess in the entire dataset was likely a result of deviation from HWE due to population genetic structure rather than null alleles. Exact tests detected no deviations from HWE in the overall dataset. Four loci showed deviations from HWE in at least one population, but these were not significant after Bonferroni correction. GENEPOP
detected heterozygote excess in populations EO1 and EO2 (p < 0.05). Heterozygote
deficit detected in populations LH1, GH1 and GH2 was not significant after Bonferroni correction. Two pairs of loci (GmuD107 and GmuD21, and GmuD16 and GmuD79) showed evidence of linkage disequilibrium in the whole dataset, but this was not consistent among populations. Values of PI and PIsibs reached < 0.001 with inclusion of 4 and 8 loci, respectively. Number of alleles per locus ranged from 3 to 18, and HO ranged from 0.484 at GmuB08 to 0.891 at GmuD87 (mean HO = 0.689; Table 4.1). Within sites, HO ranged from 0.614 to 0.743 (mean HO = 0.672; Table 4.2). Sites LE1, GB, HC and EO2 had private alleles, and several alleles were restricted to only two or three sites. Allelic richness (Ar) ranged from 3.18 to 4.49; private allelic richness (PAr) ranged from 0.1 to 0.28 (Table 4.3). In STRUCTURE analyses ∆K showed an initial maximum at K = 2, with two increasingly smaller peaks at K = 5 and K = 8 indicating possible hierarchical population structure (Figure 4.2). At K = 2, southeastern and southwestern Ontario split (Figure 4.2). At K = 5, the following clusters were resolved: LH1 and LH2 (mean q = 0.80, s.d. = 0.14); BP1 and BP2 (mean q = 0.91, s.d. = 0.03); LE1, LE2, GH1, GH2, GB1, and GB2 (mean q = 0.77, s.d. = 0.10); HC (mean q = 0.97,
33
s.d. = 0.01); and EO1 and EO2 (mean q = 0.86, s.d. = 0.18). At K = 8, sites EO1 and GB (GB1 and GB2) became distinct, but no biologically relevant eighth cluster was apparent. The average change in DIC between Kmax values tested in TESS was 150.01. Runs reached a point of inflection at five clusters (mean DIC = 16460.830, s.d. = 129.039) and plateaued at Kmax = 6 (Figure 4.2). Although the DIC continued to decline past K = 5 no new clusters emerged in the individual q-matrices at K = 6 (Figure 4.2d,e). The TESS clusters corresponded to those identified by STRUCTURE for K = 5, except that Georgian Bay clustered with the Bruce Peninsula rather than with Lake Erie and the Golden Horseshoe. Georgian Bay was considered as a separate, sixth population for further population-level analyses. GENELAND
analysis gave the highest probability to K = 15 (log likelihood = -3611.943). Five
“ghost populations” were inferred and these were disregarded (Guillot et al., 2005). The analysis identified 10 clusters corresponding to sampled sites, with pairs of sites separated by < 30 km assigned to single clusters (BP1 and 2, GB1 and 2, GH1 and 2, and HC and the three nearby DOR samples). Thus, Bayesian analyses identified five genetic populations with Georgian Bay potentially forming a sixth. Ten subpopulations corresponding to the GENELAND clusters nested within these populations. Placement of the DOR samples varied with the different methods (Figure 4.2) and they were not included in further population-level analyses. Estimated census population size (mean = 110.8, median = 58, s.d. = 119, range = 12 – 423) was positively correlated with Ar (r = 0.563, p = 0.004), but not with either PAr (r = 0.435, p = 0.138) or HO (r = 0.358, p = 0.229). Estimated Ne (mean = 26.18, median = 33.48, s.d. = 13.00, range = 6.44 – 45.21) was also positively correlated with Ar (r = 0.705, p = 0.010) but not with PAr (r = 0.191, p = 0.552) or HO (r = 0.325, p = 0.302). Friedman’s test comparing heterozygosity and allelic richness among the K= 2 and K = 5 models and the subpopulations (Table 4.3) found that HE, Ar and PAr increased significantly with each level of structure (HE: χ2 = 21.294, d.f. = 2 p = 0.000, Ar: χ2 = 19.633, d.f. = 2, p = 0.000, PAr: χ2 = 22.240, d.f. = 2, p = 0.000). However, HO values of sites were not significantly different from larger populations (χ2 = 0.824, d.f. = 2, p = 0.662). Values of FIS were not significant after correction for multiple comparisons. All pairwise FST values were significant after correction for multiple comparisons, with the exception of LE2 and 34
GH. Pairwise Dest values (Table 4.4) were up to two times larger than FST values and ranged from 0.227 (LH1 vs. EO2) to near zero (0.014; LE2 vs. GH). Mantel tests detected a significant overall correlation between genetic and Euclidian distance (Z = 2687.4073, r = 0.3856, p = 0.018). AMOVA of the K = 5 model with Georgian Bay considered separately showed that variation within populations accounted for 91.79 % of the variation in the data (Table 4.5, AMOVA: ΦST = 0.082, p < 0.0001). Significant variation also occurred among subpopulations (ΦSC = 0.046, p < 0.0001) and between the populations (ΦCT = 0.038, p < 0.0001). PCoA showed each subpopulation occupying distinct coordinate space, with the first three axes accounting for 73.4% of total variation (Figure 4.3). Assignment tests in GENECLASS had a 66.3% success rate (167 individuals correctly assigned) when assigning individuals to their sampling site (Table 4.6). When the genetic populations identified by STRUCTURE and TESS were considered assignment accuracy increased to 77% and 78.6%, respectively. STRUCTURE correctly assigned all individuals to their cluster of geographic origin (q > 0.5) based on the K = 5 model. BOTTLENECK
did not detect heterozygote excess in any sites or populations. Only one site
showed evidence of a mode shift; this site had both a sample and census population size of four. In a literature review of 18 studies of tortoises and freshwater turtles that used bottleneck tests (Table 4.7), 14 studies did not use the recommended number of loci and 15 studies applied the test to samples of > 30 individuals. Only one study met both requirements (Kuo and Janzen, 2004).
4.4 Discussion Clemmys guttata shows significant genetic structure across its Canadian range that cannot be explained by either geographic proximity of sites or isolation by distance alone. Heterozygosity does not appear to vary with population size or effective population size in this species. Two commonly used bottleneck tests failed to detect recent population declines; these tests may have limited use in studies of threatened turtles and other long-lived organisms.
35
4.4.1
Biogeography and conservation genetics of Clemmys guttata
Patterns of genetic structure in Cl. guttata are reflected in other taxa across this landscape. Barriers to gene flow among the Great Lakes occur for the walleye, Sander vitreus (Stepien et al., 2009). Limited historic gene flow between central and southeastern Ontario exists for smallmouth bass, Micropterus dolomieu (Borden and Krebs, 2009), S. vitreus (Stepien et al., 2009), channel darters, Percina copelandi (Kidd et al. 2011), and snapping turtles, Chelydra serpentina (Chapter 4). The presence of a distinct population of Cl. guttata (site HC) in the Moira River watershed also occurs in P. copelandi (Kidd et al., 2011), suggesting that this area has a colonization history distinct from that of nearby watersheds. These broad-scale patterns apparently reflect historic processes and barriers to gene flow rather than influences of the current landscape. The division between southwestern and southeastern Ontario (Figure 4.2a) suggests two distinct routes for Cl. guttata colonizing Canada after the retreat of the Laurentide ice sheets. Two likely colonization routes are shown in Figure 4.4, and I hypothesize two independent Pleistocene glacial refugia. The southern Appalachian Mountains contain the first potential refugium, where a Pleistocene refugium has also been inferred for wood turtles (Glyptemys insculpta; Amato et al., 2008). This may have been the source of the current populations in southeastern Ontario, which could have entered Ontario either from the east, through present-day Quebec, or from the south by crossing the St. Lawrence River. To the southwest of our study area, a rich midHolocene fossil record from Indiana, Ohio and Michigan includes Cl. guttata and several other species of turtles, and suggests rapid post-glacial colonization of the Great Lakes region from nearby refugia (Holman, 1992). Thus, I infer a second refugium near present-day southern Indiana, to the south of the last glacial maximum. As Cl. guttata dispersed northward into Ontario, habitat succession and shifting water levels may have isolated peripheral populations that gradually diverged via genetic drift. As humans colonized Ontario, the relatively slow effects of genetic drift would have been compounded by anthropogenic habitat modification and further population declines due to a combination of habitat destruction and hunting pressures. Today, anthropogenic impacts maintain isolation of populations and subpopulations. Genetic differentiation of populations on the Bruce Peninsula is also documented in black bears (Ursus americanus; Pelletier et al., 2011) and Massasauga rattlesnakes (Sistrurus catenatus; 36
Gibbs et al., 1997). Repeated fires burning through the peninsula in the late 1800s may have caused bottlenecks that could potentially explain the differentiation of Bruce Peninsula populations. Long-term isolation may also explain the differentiation. Populations of S. catenatus on the Bruce Peninsula have apparently been isolated since before the arrival of Europeans to North America (Gibbs et al., 1997) and this scenario also seems plausible for Cl. guttata. Genetic structure of Cl. guttata in southwestern Ontario differs strikingly from that of the eastern foxsnake (Pantherophis gloydi), a marshland-prairie specialist that shows significant genetic structure along the north shore of Lake Erie (Row et al., 2010). Pantherophis gloydi shares the marshland habitat preferences of Cl. guttata but can also exploit a variety of other open habitats. Populations of P. gloydi on the north shore of Lake Erie have a broader distribution than Cl. guttata. Nevertheless, P. gloydi has distinct genetic units along a shoreline where Cl. guttata does not. Row et al. (2010) suggested that the pattern observed in P. gloydi is the result of reduced dispersal due to habitat conversion for agriculture and development. However, the distribution of populations of Cl. guttata in this area has been reduced more dramatically than the distribution of populations of P. gloydi. The difference in genetic population structure between the two species may be driven by differing generation times: approximately 5 years for P. gloydi versus > 25 years for Cl. guttata, which may reach > 100 years of age (COSEWIC, 2004; Litzgus, 2006; COSEWIC, 2008). This difference could allow populations of P. gloydi to express greater effects of genetic isolation than longer-lived Cl. guttata. Current migration among subpopulations of Cl. guttata is highly unlikely and the sites sampled here are demographically independent (COSEWIC 2004). Reduced gene flow among sites has resulted in genetic differentiation of subpopulations (Figure 4.2, Table 4.4). Occasional humanfacilitated translocations occur (S. Gillingwater, pers. comm.; F. Ross, pers. comm.) but clustering analyses (Figure 4.2) suggest that haphazardly translocated individuals have not impacted the genetic profile of any sampled subpopulations.
4.4.2
Management implications
Management units (MUs) are populations whose allele frequencies have diverged significantly (Moritz, 1994), and that lack significant, current genetic exchange with neighbouring populations (Palsbøll et al., 2006). The 10 subpopulations of Cl. guttata meet these criteria and, therefore, 37
represent 10 potential MUs. Effective recovery planning must account for the specific circumstances of each MU because population size, habitat type, habitat quality, and specific threats to persistence differ greatly among subpopulations. The larger populations identified through Bayesian inference also meet two of the first criteria for Designatable Units (DUs), the subspecific categorization recognized under Canadian law (Green, 2005). However, the final criterion for DUs is variation in risk of extinction among potential units. The risk of extirpation is high for all known subpopulations of Cl. guttata (Enneson and Litzgus, 2009). Thus, categorization of Canadian subpopulations of Cl. guttata as DUs may not be justifiable at this time. The population of Cl. guttata in Hastings County is distinct from all other sampled populations. It is particularly vulnerable to stochastic events because it is very small (N < 50), and no genetically similar Canadian populations can be paired with it to facilitate a genetic rescue (Tallmon et al., 2004). Although there is no evidence for the risk of extinction being higher for this population than for the others, the Hastings County population should be prioritized for protection because it represents a distinct genetic unit within Cl. guttata that is probably not represented elsewhere in Canada. Genetic assignment tests for Canadian Cl. guttata show a potential for repatriation of poached individuals to their genetic populations of origin. Unfortunately, fine-scale discriminations (i.e. between subpopulations) are not accurate enough to justify repatriations (< 70% accuracy for subpopulations). Profiling of populations in the United States will further strengthen our understanding of the genetic structure of Cl. guttata and may allow assignment of individuals back to their source populations with greater confidence. Increased sample sizes and the incorporation of additional markers might also increase the accuracy of identification. However, in some cases larger sampling is not feasible. I sampled more than 80% of the census population size at some sites, and these sample sizes are difficult to increase.
4.4.3
Long-lived organisms (turtles) and loss of diversity in fragmented populations
My results are consistent with the findings that small, isolated populations of long-lived organisms typically retain high heterozygosity (reviewed by Vargas-Ramirez et al., 2012). This 38
is encouraging for population recovery because it gives managers time to stabilize populations before loss of diversity becomes a concern (Kuo and Janzen, 2004; Marsack and Swanson, 2009). Furthermore, heterozygosity is often an important predictor of individual fitness (Frankham et al., 2002). However, comparison of heterozygosity among species sampled from different landscapes may be confounded by landscape effects. Allelic richness is correlated with population size in Cl. guttata and may provide a more useful measure than heterozygosity for comparing absolute genetic diversity among small and declining populations of long-lived organisms (see also Petit et al., 1995). Heterozygosity is not correlated with N or Ne in Cl. guttata, and even sites with critically reduced populations (N < 50) may retain HO comparable to larger populations. Unexpectedly high heterozygosity may indicate an undetected heterozygote advantage. Alternately, overlapping generations in turtle populations may slow the loss of heterozygosity as population size declines, in which case high heterozygosity would be expected in most turtle species. Several authors have made this suggestion, but a comparative approach that eliminates confounding landscape effects is required to test this prediction.
4.4.4
Bottleneck tests and long-lived organisms
The evaluation of bottleneck tests for detection of population declines in turtles raises two independent concerns: 1) the repeated, uncritical use of these tests in the literature with inadequate sample sizes, and 2) the ability of the tests to detect declines when the test requirements are met. As the literature review demonstrated, many studies of turtles and tortoises that use BOTTLENECK used small sample sizes and/or insufficient number of loci (Piry et al., 1999; Table 4.7). This study also has small sample sizes, which are common in studies of threatened taxa. It is not my intention to question the overall validity of previous studies but rather to highlight that a basic sampling problem may be leading to questionable conclusions regarding the true impacts of bottlenecks in declining populations of long-lived organisms such as turtles. The failure of the heterozygosity excess and mode shift tests to detect declines in populations of Cl. guttata — even when the requirements of the test were met — is consistent with the limitations of bottleneck tests already demonstrated in both natural and simulated populations 39
(Cristescu et al., 2010; Pittman et al., 2011; Peery et al., 2012). The consequences of bottlenecks can complicate species recovery (Frankham et al., 2002) even if the genetic signature of the bottleneck is not statistically detectable. The tests used here may fail to detect bottlenecks because insufficient time has passed since the bottleneck event (Mockford et al., 2005) or because long generation times may mask the genetic signature of bottlenecks (Marsack and Swanson, 2009; Bennett et al., 2010); or they may fail because of sampling issues or limitations inherent in the method (Peery et al., 2012). In all cases, incorrect conclusions about the genetic health of threatened populations may slow recovery efforts or remove focus from populations on the brink of extirpation or extinction. If bottleneck tests are used, they should be combined wherever possible with direct evidence, such as long-term mark-recapture studies. This combination will facilitate a more accurate evaluation of the demographic and genetic history of a population, and its consequences for conservation and recovery measures.
4.5 Acknowledgements This research was generously supported by the Government of Ontario (Species at Risk Stewardship Fund grant to CMD and RWM), Wildlife Preservation Canada (Canada Collection grant to CMD) and the National Science and Engineering Research Council of Ontario (NSERC Discovery Grant to RWM; Canada Graduate Scholarship to CMD). Thanks to S. Coombes and a large number of volunteers for assistance with field work. Site access and logistical support were provided by C. Brdar, M. Cairns, J. Cebek, S. Gillingwater, J. Litzgus, M. Rasmussen, D. Seburn, K. Yagi, A. Yagi , the Ausable Bayfield Conservation Authority, the Nature Conservancy of Canada, Ontario Parks, Ontario Nature, Ontario Hydro and Parks Canada. Genotyping costs were offset by the generous support of the Schad Foundation. Research methods were approved under animal use protocols ROM2008-11, 2009-02, 2009-21 and 201014) from the Animal Care Committee of the Royal Ontario Museum, under permits1045769, 1049600, 1062210, 1067079, SR-B-001-10 and AY-B-013-11 from the Ontario Ministry of Natural Resources and under research authorizations from Ontario Parks and Parks Canada. J. Litzgus, D. McLennan, R. Murphy and D. Seburn provided valuable comments on earlier versions of the manuscript.
40
4.6 References Alacs, E.A., Janzen, F.J., Scribner, K.T., 2007. Genetic issues in freshwater turtle and tortoise conservation. Chel. Res. Monogr. 4, 107–123. Amato, M.L., Brooks, R.J., Fu, J., 2008. A phylogeographic analysis of populations of the wood turtle (Glyptemys insculpta) throughout its range. Mol. Ecol. 17, 570–581. Banning-Anthonysamy, W.J., 2012. Spatial ecology, habitat use, genetic diversity, and reproductive success: measures of connectivity of a sympatric freshwater turtle assemblage in a fragmented landscape. PhD dissertation, University of Illinois at UrbanaChampaign. Bennett, A.M., Keevil, M., Litzgus, J.D., 2010. Spatial ecology and population genetics of northern map turtles (Graptemys geographica) in fragmented and continuous habitats in Canada. Chel. Conserv. Biol. 9, 185–195. Borden, W.C., Krebs, R.A., 2009. Phylogeography and postglacial dispersal of smallmouth bass (Micropterus dolomieu) into the Great Lakes. Can. J. Fish. Aquat. Sci. 66, 2142–2156. Cagle, F., 1939. A system of marking turtles for future identification. Copeia 1939, 170–173. Chen, C., Durand, E., Forbes, F., François, O., 2007. Bayesian clustering algorithms ascertaining spatial population structure: a new computer program and a comparison study. Mol. Ecol. Notes 7, 747–756. Congdon, J.D., Dunham, A.E., van Loben Sels, R.C., 1993. Delayed sexual maturity and demographics of Blanding’s turtles (Emydoidea blandingii): Implications for conservation and management of long-lived organisms. Conserv. Biol. 7, 826–833. Congdon, J.D., Dunham A.E., van Loben Sels R.C., 1994. Demographics of common snapping turtles (Chelydra serpentina): Implications for conservation and management of longlived organisms. Am. Zool. 34, 397–408. Cornuet J.M., Luikart G., 1997. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144, 2001–2014. COSEWIC, 2004. COSEWIC assessment and update status report on the spotted turtle Clemmys guttata in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. vi + 27 pp. (www.sararegistry.gc.ca/status/status_e.cfm). COSEWIC. 2008. COSEWIC assessment and update status report on the eastern foxsnake Elaphe gloydi, Carolinian population and Great Lakes/St. Lawrence population, in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. vii + 45 pp. (www.sararegistry.gc.ca/status/status_e.cfm). Crawford, N.G., 2010. SMOGD: software for the measurement of genetic diversity. Mol. Ecol. Resour. 10, 556–557. Cristescu, R., Sherwin, W.B., Handasyde, K., Cahill, V., Cooper, D.W., 2010. Detecting bottlenecks using BOTTLENECK 1.2.02 in wild populations: the importance of the microsatellite structure. Conserv. Genet. 11, 1043–1049.
41
Cunningham, J., Baard, E.H.W., Harley, E.H., O'Ryan C., 2002. Investigation of genetic diversity in fragmented geometric tortoise (Psammobates geometricus) populations. Conserv. Genet. 3, 215–223. Davy, C.M., Edwards, T., Lathrop, A., Bratton, M., Hagan, M., Henen, B., Nagy, K., Stone, J., Hillard, L.S., Murphy, R.W., 2011. Polyandry and multiple paternities in the threatened Agassiz’s desert tortoise, Gopherus agassizii: conservation implications. Conserv. Genet. 12, 1313–1322. Davy, C.M., Leifso, A.E., Conflitti, I.M., Murphy, R.W., 2012. Characterization of 10 novel microsatellite loci and cross-amplification of two loci in the snapping turtle (Chelydra serpentina). Conserv. Genet. Resour. 4, 695–698. Di Rienzo, A., Peterson, A.C., Garza, J.C., Valdes, A.M., Slatkin, M., Freimer N.B., 1994. Mutational processes of simple-sequence repeat loci in human populations. P. Natl. Acad. Sci. USA. 91, 3166–3170. Durand, E., Jay, F., Gaggiotti, O.E., François O., 2009. Spatial inference of admixture proportions and secondary contact zones. Mol. Biol. Evol. 26, 1963–1973. Earl, D.A., vonHoldt, B.M., 2012. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361. Echelle, A.A., Hackler, J.C., Lack, J.B., Ballard, S.R., Roman, J., Fox, S.F., Leslie D.M., Van Den Bussche, R.A., 2010. Conservation genetics of the alligator snapping turtle: cytonuclear evidence of range-wide bottleneck effects and unusually pronounced geographic structure. Conserv. Genet. 11, 1375–1387. Edwards, T., Schwalbe, C.R., Swann D.E., Goldberg, C.S., 2004. Implications of anthropogenic landscape change on inter-population movements of the desert tortoise (Gopherus agassizii). Conserv. Genet. 5, 485–499. Enneson, J.J., Litzgus, J.D., 2009. Stochastic and spatially explicit population viability analyses for an endangered freshwater turtle, Clemmys guttata. Can. J. Zool. 87, 1241–1254. Ernst, C.H., Lovich, J.L., 2009. Turtles of the United States and Canada, 2nd ed, Johns Hopkins University Press, Baltimore. Escalona, T., Engstrom, T.N., Hernandez, O.E., Bock, B.C., Vogt R.C., Valenzuela, N., 2009. Population genetics of the endangered South American freshwater turtle, Podocnemis unifilis, inferred from microsatellite DNA data. Conserv. Genet. 10, 1683–1696. Evanno, G., Regnaut, S., Goudet J., 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620. Ewing, S.R., Nager, R.G., Nicoll, M.A.C., Aumjaud, A., Jones, C.G., Keller, L.F., 2008. Inbreeding and loss of genetic variation in a reintroduced population of Mauritius kestrel. Conserv. Biol. 22, 395–404. Excoffier, L., Laval, G., Schneider, S., 2005. ARLEQUIN ver. 3.0: An integrated software package for population genetics data analysis. Evol. Bioinform. Online 1, 47–50.
42
Falush, D., Stevens, M., Pritchard, J.K., 2003. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164, 1567–1587. Frankham, R., 1996. Relationship of genetic variation to population size in wildlife. Conserv. Biol. 10, 1500–1508. Frankham, R., Ballou, J.D., Briscoe, D.A., 2002. Introduction to Conservation Genetics. Cambridge University Press, Cambridge, U.K. Fritz, U., Alcalde, L., Vargas-Ramírez, M., Goode, E.V., Fabius-Turoblin, D.U., Praschag P., 2012. Northern genetic richness and southern purity, but just one species in the Chelonoidis chilensis complex. Zool. Scr. 41, 220–232. Gibbs, H.L, Prior, K.A., Weatherhead, P.J., Johnson, G., 1997. Genetic structure of populations of the threatened eastern Massasauga rattlesnake, Sistrurus c. catenatus: evidence from microsatellite DNA markers. Mol. Ecol. 6, 1123–1132. Goudet, J., 1995. FSTAT (Version 1.2): A computer program to calculate F-statistics. J. Hered. 86, 485–486. Green, D.M., 2005. Designatable units for status assessment of endangered species. Conserv. Biol. 19, 1813–1820. Guillot, G., Mortier, F., Estoup, A., 2005. GENELAND: a program for landscape genetics. Mol. Ecol. Notes 5, 712–715. Guillot, G., Leblois, R., Coulon A., Frantz, A.C., 2009. Statistical methods in spatial genetics. Mol. Ecol. 18, 4734–4756. Hauswaldt, J.S., Glenn, T.C., 2005. Population genetics of the diamondback terrapin (Malaclemys terrapin). Mol. Ecol. 14, 723–732. Holman, J.A., 1992. Late Quaternary herpetofauna of the central Great Lakes region, U.S.A.: zoogeographical and paleoecological implications. Quaternary Sci. Rev. 11, 345–351. Jakobsson, M., Rosenberg, N.A., 2007. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23, 1801–1806. Jensen, J.L., Bohonak, A.J., Kelley, S.T., 2005. Isolation by distance, web service. BMC Genetics 6, 13. v.3.23 http://ibdws.sdsu.edu/ Jost, L., 2008. GST and its relatives do not measure differentiation. Mol. Ecol. 17, 4015–4026. Kalinowski, S.T., 2004. Counting alleles with rarefaction: private alleles and hierarchical sampling designs. Conserv. Genet. 5, 539–543. Kalinowski, S.T., 2005. HP-Rare: A computer program for performing rarefaction on measures of allelic diversity. Mol. Ecol. Notes 5, 187–189. Kidd, A., Reid S., Wilson, C., 2011. Local and regional population genetic structure of the threatened channel darter in Ontario and Quebec. Poster presentation at American Fisheries Society 41st Annual Meeting, Seattle, Washington, Sept. 4–8, 2011.
43
King, T.L., Julian, S.E., 2004. Conservation of microsatellite DNA flanking sequences across 13 emydid genera assayed with novel bog turtle (Glyptemys muhlenbergii) loci. Conserv. Genet. 5, 719–725. Kuo, C.H., Janzen, F.J., 2004. Genetic effects of a persistent bottleneck on a natural population of ornate box turtles (Terrapene ornata). Conserv. Genet. 5, 425–437. Latch, E.K., Dharmarajan, G., Glaubitz, J.C., Rhodes, O.E., 2006. Relative performance of Bayesian clustering software for inferring population substructure and individual assignment at low levels of population differentiation. Conserv. Genet. 7, 295–302. Litzgus, J.D., 1996. Life history and demography of a northern population of spotted turtles, Clemmys guttata. MSc Thesis, University of Guelph, Ontario. 145 pp. Litzgus, J.D., 2006. Sex differences in longevity in the spotted turtle (Clemmys guttata). Copeia 2006, 281–288. Luikart, G., Allendorf, F.W., Cornuet, J.M., Sherwin, W.B., 1997. Distortion of allele frequency distributions provides a test for recent population bottlenecks. J. Hered. 89, 238–247. Manel, S., Schwartz, M.K., Luikart, G., Taberlet, P., 2003. Landscape genetics: combining landscape ecology and population genetics. Trends Ecol. Evol. 18, 189–197. Mantel, N., 1967. The detection of disease clustering and a generalized regression approach. Cancer Res. 27, 209–220. Marsack, K., Swanson, B.J., 2009. A genetic analysis of the impact of generation time and roadbased habitat fragmentation on eastern box turtles (Terrapene c. carolina). Copeia 2009, 647–652. Mockford, S.W., McEachern, L., Herman, T.B., Snyder, M., Wright, J.M., 2005. Population genetic structure of a disjunct population of Blanding’s turtle (Emydoidea blandingii) in Nova Scotia, Canada. Biol. Conserv. 123, 373–380. Moritz, C., 1994. Defining ‘evolutionarily significant units’ for conservation. Trends Ecol. Evol. 9, 373–375. Murphy, R.W., Berry, K.H., Edwards, T., McLuckie, A.M., 2007. A genetic assessment of the recovery units for the Mojave population of the desert tortoise, Gopherus agassizii. Chelonian Conserv. Biol. 6, 229–251. Paetkau, D., Slade, R., Burden M., Estoup, A., 2004. Direct, real-time estimation of migration rate using assignment methods: a simulation-based exploration of accuracy and power. Mol. Ecol. 13, 55–65. Palsbøll, P.J., Bérubé M., Allendorf, F.W., 2006. Identification of management units using population genetic data. Trends Ecol. Evol. 22, 11–16. Peakall, R., Smouse, P.E., 2006. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295. Pearse, D.E., Arndt, A.D., Valenzuela, N., Miller, B.A., Cantarelli, V., Sites J.W. Jr., 2006. Estimating population structure under nonequilibrium conditions in a conservation context: continent-wide population genetics of the giant Amazon river turtle, Podocnemis expansa (Chelonia; Podocnemididae). Mol. Ecol. 15, 985–1006. 44
Peery, M.Z., Kirby, R., Reid, B.N., Stoelting, R., Doucet-Bëer, E., Robinson, S., VásquezCarrillo, C., Pauli, J.N., Palsbøll, P.J., 2012. Reliability of genetic bottleneck tests for detecting recent population declines. Mol. Ecol. 21, 3403–3418. Pelletier, A., Obbard, M.E., White, B.N., Doyle, C., Kyle, C.J. 2011. Small-scale genetic structure of American black bears illustrates potential postglacial recolonization routes. J. Mammal. 92,629-644. Perez, M., Leblois, R., Livoreil, B., Bour, R., Lambourdiere, J., Samadi, S., Boisselier, M., 2011. Effects of landscape features and demographic history on the genetic structure of Testudo marginata populations in the southern Peloponnese and Sardinia. Biol. J. Linn. Soc. 105, 591–606. Petit RJ, El Mousadi A, Pons O (1995) Identifying populations for conservation on the basis of genetic markers. Conserv. Biol. 12, 844–855. Piry, S., Luikart, G., Cornuet, J.M., 1999. BOTTLENECK: A program for detecting recent effective population size reductions from allele frequency data. J. Hered. 90, 502–503. Piry, S., Alapetite, A., Cornuet, J.M., Paetkau, D., Baudouin, L., Estoup, A., 2004. GENECLASS2: a software for genetic assignment and first-generation migrant detection. J. Hered. 95, 536–539. Pittman, S. E., King, T., Faurby, S., Dorcas, M.E., 2011. Genetic and demographic status of an isolated bog turtle (Glyptemys muhlenbergii) population: implications for the conservation of small populations of long-lived animals. Conserv. Genet. 12, 1589–1601. Pompanon, F., Bonin, A., Bellemain, E., Taberlet, P., 2005. Genotyping errors: causes, consequences and solutions. 2005. Nat. Rev. Genet. 6, 847–846. Pritchard, J.K., Stephens, M., Donnelly, P.J., 2000. Inference of population structure using multilocus genotype data. Genetics 155, 945–959. Rannala, B., Mountain J. L., 1997. Detecting immigration by using multilocus genotypes. P. Natl. Acad. Sci. USA. 94, 9197–9221. Raymond, M., Rousset, F., 1995. GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J. Hered. 86, 248–249. Rice, W.R., 1989. Analyzing tables of statistical tests. Evolution 43, 223–225. Richter, S.C., Jackson, J.A., Hinderliter, M., Epperson, D., Theodorakis, C.W., Adams, S.M., 2011. Conservation genetics of the largest cluster of federally threatened gopher tortoise (Gopherus polyphemus) colonies with implications for species management. Herpetologica 67, 406–419. Rosenberg, N.A., 2004. DISTRUCT: a program for the graphical display of population structure. Mol. Ecol. Notes 4, 137–138. Rousset, F., 2008. GENEPOP`007: a complete reimplementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Res. 8, 103–106. Row, J.R., Blouin-Demers, G., Lougheed, S.C., 2010. Habitat distribution influences dispersal and fine-scale genetic population structure of eastern foxsnakes (Mintonius gloydi) across a fragmented landscape. Mol. Ecol. 19, 5157–5171. 45
Rubin, C.S., Warner, R.E., Bouzat, J.L., Paige, K.N., 2001. Population genetic structure of Blanding’s turtles (Emydoidea blandingii) in an urban landscape. Biol. Conserv. 99, 323– 330. Schuelke, M., 2000. An economic method for the fluorescent labeling of PCR fragments. Nat. Biotechnol. 18, 233–234. Schwartz, T.S., Karl, S.A., 2005. Population and conservation genetics of the gopher tortoise (Gopherus polyphemus). Conserv. Genet. 6, 917 – 928. Seburn, D.C., 2003. Population structure, growth, and age estimation of spotted turtles, Clemmys guttata, near their northern limit: an 18-year follow-up. Can. Field Nat. 117, 436–439. Spradling, T.A., Tamplin, J.W., Dow, S.S., Meyer, K.J., 2010. Conservation genetics of a peripherally isolated population of the wood turtle (Glyptemys insculpta) in Iowa. Conserv. Genet. 5, 1667–1677. Stepien, C.A., Murphy, D.J., Lohner, R.N., Sepulveda, O.J., Haponski, A.E., 2009. Signatures of vicariance, postglacial dispersal and spawning philopatry: population genetics of the walleye Sander vitreus. Mol. Ecol. 18, 3411–3428. Taberlet, P., Luikart G., 1999. Non-invasive genetic sampling and individual identification. Biol. J. Linn. Soc. 68, 41–55. Tallmon, D.A., Luikart G., Waples, R.S., 2004. The alluring simplicity and complex reality of genetic rescue. Trends Ecol. Evol. 19, 489–496. Tallmon, D.A., Koyuk, A., Luikart, G.H., Beaumont, M.A., 2008. ONeSAMP: a program to estimate effective population size using approximate Bayesian computation. Mol. Ecol. Res. 8, 299-301. Tessier, N., Paquette, S., Lapointe, F.J., 2005. Conservation genetics of the wood turtle (Glyptemys insculpta) in Quebec, Canada. Can. J. Zool. 83, 765–772. van Dijk, P.P., 2011. Clemmys guttata. In: IUCN 2012. IUCN Red List of Threatened Species. Version 2012.1. . Downloaded on 21 August 2012 van Oosterhout, C., Hutchinson, W.F., Wills, D.P.M., Shipley, P., 2004. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538. Vargas-Ramirez M., Stuckas, H., Castaňo-Mora, O.V., Fritz., U., 2012. Extremely low genetic diversity and weak population differentiation in the endangered Colombian river turtle Podocnemis lewyana (Testudines: Podocnemididae) Conserv. Genet. 13, 65–77. Velo-Antón, G., Becker C.G., Cordero-Rivera, A., 2011. Turtle carapace anomalies: the roles of genetic diversity and environment. PLoS ONE, 6:e18714. Waits, L.P., Luikart, G., Taberlet, P., 2001. Estimating the probability of identity among genotypes in natural populations: cautions and guidelines. Mol. Ecol. 10, 249–256. White, G.C., Burnham, K. P., 1999. Program MARK: Survival estimation from populations of marked animals. Bird Study 46 (supplement), 120–138. Wright, S., 1943. Isolation by distance. Genetics 28, 114–138. 46
Table 4.1. Summary statistics for 11 microsatellite loci originally developed for the Glyptemys muhlenbergii (King and Julian 2004) and amplified in 256 Clemmys guttata from southern Ontario. Temp. = annealing temperature (°C) used in PCR amplification. “*” indicates an initial touchdown of 1°C/cycle from 10°C above the annealing temperature, followed by a constant annealing temperature for the remaining cycles; N = number of individuals amplified at each locus; k = number of alleles; Ne = number of effective alleles; HO = observed heterozygosity; HE = expected heterozygosity; PI = probability of identity, PIsibs = Probability of identity for siblings at a locus.
Locus GmuA19 GmuB08 GmuD16 GmuD21 GmuD55 GmuD79 GmuD87 GmuD88 GmuD107 GmuD114 GmuD121
Temp ( °C) 61.5 58* 58 58* 58* 61.5 61.5 61.5 54 60 56
N 245 250 250 242 245 252 238 243 240 249 254
k 3 3 11 9 10 7 18 12 15 4 10
Ne 2.955 1.993 5.710 4.225 4.498 4.892 13.309 2.761 8.641 2.241 3.936
HO 0.624 0.484 0.768 0.769 0.702 0.758 0.891 0.547 0.858 0.518 0.657
HE 0.662 0.498 0.825 0.763 0.778 0.796 0.925 0.638 0.884 0.554 0.746
PI 0.189 0.372 0.052 0.08 0.073 0.071 0.01 0.158 0.024 0.279 0.1
PIsibs 0.466 0.594 0.35 0.388 0.379 0.37 0.29 0.47 0.313 0.543 0.402
47
Table 4.2. Number of alleles (private alleles in parentheses), observed and expected heterozygosities (HO and HE), and estimated frequency of a null allele (for each locus across all populations), for 256 Clemmys guttata from southern Ontario, by locus and populations (see Figure 1 for definition of site acronyms). LH1
LH2
BP1
BP2
LE1
LE2
GH1
GH2
GB1
GB2
HC
EO1
EO2*
DOR1
DOR2
GmuB08 Estimated null allele frequency = 0.00
Number of alleles HO HE N
2 0.414 0.428 29
2 0.433 0.495 30
2 0.500 0.375 4
2 0.556 0.489 27
2 0.533 0.491 30
3 0.455 0.541 11
2 0.480 0.461 25
2 0.333 0.500 6
2 0.333 0.278 6
3 0.647 0.493 17
2 0.438 0.342 16
2 0.571 0.459 14
2 0.471 0.457 34
– – – 0
1 – – 1
GmuD16 Estimated null allele frequency = 0.00
Number of alleles HO HE N
7 0.828 0.815 29
8 0.800 0.782 30
5 1.000 0.688 4
6 0.593 0.763 27
8 0.700 0.812 30
8 0.917 0.813 12
8 0.792 0.821 24
4 0.667 0.653 6
4 0.667 0.625 6
9 0.941 0.794 17
6 0.688 0.693 16
5 0.643 0.617 14
5 (1) 0.824 0.734 34
– – – 0
2 – – 1
GmuD55 Estimated null allele frequency = 0.134
Number of alleles HO HE N
5 0.414 0.542 29
8 0.600 0.639 30
3 0.750 0.531 4
7 0.741 0.768 27
9 0.833 0.764 30
6 0.583 0.476 12
7 0.591* 0.697 22
3 0.400 0.540 5
4 0.750 0.688 4
6 0.882 0.739 17
6 0.688 0.668 16
7 0.929 0.778 14
7 0.853 0.804 34
– – – 0
2 – – 1
GmuD79 Estimated null allele frequency = 0.00
Number of alleles HO HE N
5 0.759 0.748 29
6 0.800 0.784 30
4 0.750 0.719 4
7 0.815 0.720 27
6 0.871 0.793 31
5 0.727 0.669 11
7 0.750 0.700 24
4 0.429 0.367 7
3 0.667 0.611 6
5 0.765 0.619 17
4 0.750 0.686 16
4 0.929 0.640 14
5 0.647 0.554 34
– – – 0
1 – – 2
GmuD88 Estimated null allele frequency = 0.173
Number of alleles HO HE N
6 0.370* 0.517 27
6 0.517 0.650 29
3 0.500 0.531 4
5 0.577 0.649 26
7 (1) 0.667 0.655 30
4 0.545 0.442 11
7 0.720 0.694 25
2 0.000 0.278 6
4 (1) 0.500 0.514 6
3 0.400 0.464 15
3 0.188* 0.365 16
4 0.615 0.559 13
6 (1) 0.765 0.707 34
– – – 0
2 – – 1
GmuD107 Estimated null allele frequency = 0.00
Number of alleles HO HE N
8 0.815* 0.776 27
11 0.833 0.842 30
5 1.000 0.781 4
9 0.913 0.854 23
11 0.867 0.862 30
8 0.917 0.795 12
12 0.818 0.857 22
5 0.571 0.673 7
6 1.000 0.806 6
6 0.882 0.777 17
5 (1) 0.800 0.716 15
5 0.786 0.615 14
9 0.935 0.830 31
2 – – 1
2 – – 1
GmuD114 Estimated null allele frequency = 0.00
Number of alleles HO HE N
3 0.379 0.372 29
4 0.533 0.534 30
3 0.500 0.406 4
3 0.444 0.529 27
3 0.500 0.545 30
3 0.750 0.538 12
2 0.478 0.496 23
2 0.333 0.444 6
2 0.500 0.375 6
3 0.529 0.562 17
2 0.375 0.469 16
2 0.643 0.497 14
4 0.706 0.643 34
– – – 0
1 – – 1
GmuD121 Estimated null allele frequency = 0.078
Number of alleles HO HE
7 0.724 0.781
6 0.933 0.796
2 0.250 0.469
4 0.519 0.578
6 0.613 0.743
7 0.545 0.727
7 0.615 0.753
3 0.667 0.611
5 0.833 0.694
5 0.588 0.683
3 0.750 0.639
3 0.643 0.582
5 0.588 0.500
2 (1) – –
2 – –
48
N
29
30
4
27
31
11
26
6
6
17
16
14
34
1
2
GmuD21 Estimated null allele frequency = 0.00
Number of alleles HO HE N
5 0.852 0.752 27
7 0.852 0.799 27
1 0.000 0.000 4
6 0.538 0.498 26
7 0.800 0.773 30
5 0.900 0.735 10
6 0.769 0.791 26
4 0.714 0.602 7
4 0.833 0.597 6
5 (1) 0.600 0.484 15
5 0.688 0.678 16
5 0.917 0.708 12
6 0.912 0.787 34
1 – – 1
2 – – 1
GmuD87 Estimated null allele frequency = 0.00
Number of alleles HO HE N
12 0.852 0.861 27
15 0.900 0.866 30
4 0.750 0.563 4
9 0.846 0.770 26
15 0.933 0.844 30
12 0.900* 0.890 10
13 0.875 0.882 24
7 0.833 0.708 6
6 1.000 0.778 6
11 0.938 0.873 16
4 0.643 0.630 14
7 1.000 0.830 10
13 0.971 0.891 34
– – – 0
2 – – 1
GmuA19 Estimated null allele frequency = 0.00
Number of alleles HO HE N
3 0.786 0.663 28
3 0.700 0.626 30
3 0.750 0.531 4
3 0.593 0.656 27
3 0.567 0.638 30
3 0.364 0.665 11
3 0.480 0.655 25
3 0.667 0.625 6
3 0.500 0.611 6
3 0.667 0.624 15
3 0.867 0.660 15
3 0.500 0.630 14
3 0.606 0.496 33
– – –
2 – – 0
1
* One individual from this site was excluded because of repeated triplicate peaks in its pherograms. Data from this individual are excluded from this table.
49
Table 4.3. Genetic diversity (heterozygosity, allelic richness and private allelic richness) of sampled regions, genetic populations and sites for 253 Clemmys guttata genotyped at 11 microsatellite loci. Allelic and private allelic richness are rarefacted to account for variation in sample sizes (Kalinowski 2004). Pop1–5 = genetic clusters supported by both STRUCTURE and TESS analyses. Georgian Bay is considered independently. HO = observed heterozygosity averaged across all loci; HE = expected heterozygosity averaged across all loci. See Figure 1 and text for definitions of site acronyms. Region Genetic Allelic Private allelic population HO HE richness richness Sampling Site 0.679 0.728 6.75 1.72 Southwestern Ontario 0.680 0.724 5.52 0.23 Pop1 0.654 0.659 4.01 0.18 LH1 0.718 0.71 4.44 0.25 LH2 0.687 0.703 4.64 0.16 Pop2 0.614 0.509 3.18 0.19 BP1 0.649 0.661 3.92 0.13 BP2 0.644 0.662 6.88 0.57 Pop3 0.717 0.72 4.49 0.24 LE1 0.691 0.663 4.37 0.35 LE2 0.67 0.71 4.48 0.28 GH1 0.51 0.546 3.35 0.1 GH2 0.708 0.651 5.31 0.35 Georgian Bay 0.689 0.598 3.81 0.24 GB1 0.713 0.647 3.98 0.35 GB2 0.718 0.707 6.11 1.08 Southeastern Ontario 0.625 0.595 3.34 0.18 Pop4 0.625 0.595 3.34 0.18 HC 0.749 0.694 6.11 0.61 Pop5 0.743 0.629 3.53 0.16 EO1 0.742 0.673 4.1 0.35 EO2 50
Table 4.4. Pairwise values FST (below the diagonal) and Dest (Jost 2008, above diagonal) for 13 putative subpopulations of Clemmys guttata sampled across southern ON (N = 253; see Figure 1 for definition of site acronyms). Sites in the Golden Horseshoe, Georgian Bay and the Bruce Peninsula are analyzed together. FST values in italics are not significant (p > 0.05). LH1
LE1 LE2 GB GH HC EO1 EO2
BP 0.039
LH1 LH2 BP
LH2
0.031 0.091 0.061 0.067 0.071 0.051 0.134 0.112 0.107
0.069 0.041 0.042 0.049 0.045 0.095 0.078 0.078
LE1
LE2
GB
GH
HC
EO1
EO2
0.16
0.104
0.1
0.095
0.072
0.161
0.171
0.229
0.137
0.067 0.102
0.04 0.052
0.068 0.104
0.065 0.088
0.111 0.15
0.142 0.084
0.164 0.161
0.045
0.062
0.021
0.118
0.133
0.142
0.077
0.013 0.06
0.144 0.136
0.094 0.12
0.172 0.098
0.123
0.089
0.116
0.157
0.226 0.102
0.058 0.047 0.065 0.057 0.11 0.082 0.096
0.032 0.044 0.022 0.097 0.067 0.078
0.052 0.009 0.124 0.079 0.082
0.043 0.12 0.082 0.078
0.104 0.055 0.073
0.138 0.135
0.089
Table 4.5. Hierarchical analysis of molecular variance (AMOVA; Excoffier et al. 1992) conducted in ARLEQUIN. Each source of variation was significant (p < 0.0001). Tested populations were those identified by both STRUCTURE and TESS analyses with GB treated as a separate, sixth population. Subpopulations refer to sampling sites except GB, GH and BP which are treated as single subpopulations. Source of variation
Sum of squares
Among populations
102.237
Variance components (σ2) 0.158
Among subpopulations within populations
59.966
0.180
4.384
Within subpopulations
1637.154
3.778
91.787
Total
1799.357
4.116
% variation 3.829
51
Table 4.6. Assignment of individuals in GENECLASS analysis based on sampling sites; 66.3% of individuals were assigned correctly. Shaded areas indicate clustering of sites in genetic populations supported by both STRUCTURE and TESS. Sampling sites correspond to Figure 1.
Sample source:
Assigned to: LH1 LH2 BP1 BP2 LE1 LE2 GH1 GH2 GB1 GB2 HC EO1 EO2 LH1 LH2 BP1 BP2 LE1 LE2 GH1 GH2 GB1 GB2 HC EO1 EO2
20 1
6 26 2
1 2 1
1
2 1 3 1 2 3
2 18
1
3 1
8 26 2 5 4 1 2 4 1 1
2
3 1
1
2 6 15 2 1 1 1 2
2 9 11 9
2 30
52
Table 4.7. Summary of bottleneck tests in published studies of population genetics of tortoises and freshwater turtles. Loci = the number of loci used in bottleneck tests (in some cases this was lower than the total number amplified). Individuals = the maximumminimum and mean ( ) number of individuals genotyped per tested population. When only populations above a certain size limit were used, only these populations were included in the summary. Where values for loci and individuals are in bold this indicates that the minimum sampling recommendations for tests in BOTTLENECK were met.
Source Cunningham et al. (2002) Edwards et al. (2004) Kuo and Janzen (2004) Mockford et al. (2005) Schwartz and Karl (2005) Hauswaldt and Glen (2005) Pearse et al. (2006) Murphy et al. (2007) Escalona et al. (2009) Marsack and Swanson (2009) Echelle et al. (2010)
Species Psammobates geometricus Gopherus morafkai Terrapene ornata Emydoidea blandingii Gopherus polyphemus Malaclemys terrapin Podocnemis expansa Gopherus agassizii Podocnemis unifilis Terrapene carolina carolina Macrochelys temmincki
Loci Individuals 8 25–28; = 26.3
Mode shift detected? No
Significant heterozygosity excess detected? No
7
9–38;
--
No
Significantly decreased M-ratio detected? 3 of 3 tests significant. No
11
73–74;
= 73.5
No
No
No
5
27–43;
= 36.7
No
No
--
9
11–26;
= 19.1
--
5 of 14 tests significant
--
6
12–56;
= 24.8
--
No
No
9
16–37; = 26.6
--
5 of 11 tests significant
11
18–83;
= 41.9
No
2 of 15 tests significant
11 of 11 tests significant No
5
14–55;
= 28.4
No
No
8
40–70;
= 54.3
No
2 of 3 tests significant
7
N ≥ 10 per tested population
Y, 4 of 12 tests significant
No
= 18.8
Yes, 10 of 11 significant No
Yes, 10 of 12 significant 53
Spradling et al. 2010 Pittman et al. 2011 Richter et al. 2011 Velo-Anton et al. 2011 Vargas-Ramirez et al. 2012
Glyptemys insculpta Glyptemys muhlenbergii Gopherus polyphemus Emys orbicularis
9
51 and 80
No
No
No
18
8.9–34.7;
--
2 of 6 tests significant
--
9
11–40,
= 18.8
--
No
--
7
23–36;
= 29.7
No
3 of 9 tests significant
2 of 3 tests significant §
7 of 7 tests significant
5 of 9 tests significant 7 of 7 tests significant
Podocnemis lewyana
10
4–49;
Perez et al. 2012
Testudo marginata
11
18
No
21
--
Significant under IAM but not under TPM (Piry et al. 1999) No
Fritz et al. 2012
Chelonoidis chilensis
10
= 15.5*
= 21
Yes
--
* Pittman et al. (2011) present mean sample size per locus § Tests of seven sites were not significant; when sites were grouped into three populations, two of these demonstrated a significant mode shift.
54
Figure 4.1. Approximate location of sampled sites. LE = Lake Erie; LH = Lake Huron; BP = Bruce Peninsula; GB = Georgian Bay; GH = Golden Horseshoe; HC = Hastings County; DOR = dead on road; EO = Eastern Ontario.
55
Figure 4.2. A) Population structure inferred by STRUCTURE and
TESS
for increasing values of K.
Colours indicating populations in the K = 5 model (marked with an asterisk) match colours used in Figure 4.4. B) Estimated ln probability of the data (L(K)) for
STRUCTURE
analyses at
increasing values of K with 8 independent runs at each. C) ∆K (Evanno et al. 2005) calculated from (B). D) TESS results: decreasing deviance information criterion (DIC) with increasing Kmax. 56
Figure 4.3. Principal Coordinates Analysis plot based on Dest (Table 4.4) for populations (a, b) and based on genetic distance for individuals (c). 57
Figure 4.4. Genetic population structure identified by STRUCTURE and TESS with K = 5 (Figure 2). Georgian Bay was assigned to different populations by the two analyses. Hypothesized dispersal routes for Clemmys guttata colonizing Canada after glacial retreat are indicated by the large grey arrows.
58
Chapter 5 Unexpected patterns of genetic diversity in two sympatric species of turtle. Formatted for Molecular Ecology.
5
Abstract
Studies of conservation genetics in natural populations often assumed that the genetic diversity of wild populations can be predicted by the population size, behavior and ecology of the study species. I used approximate Bayesian computation to estimate effective population size (Ne) and a suite of spatial genetics analyses to compare genetic diversity in two sympatric species of freshwater turtles, Chelydra serpentina and Clemmys guttata, sampled across southern Ontario, Canada. The results did not support the hypothesis that higher vagility, fecundity and population size predicted higher genetic diversity. Bayesian clustering analyses revealed significant population structure in both species across the study area. Despite substantial differences in contemporary population sizes, estimates of Ne were unexpectedly comparable between species. The different in the Ne:Nc ratios in these two species may result from behavioural differences (specifically mating and nesting behaviour) that serve to increase reproductive variance in the snapping turtle, depressing Ne relative to Nc, while decreasing reproductive variance in the spotted turtle with the opposite effect. Unexpectedly high Ne in Cl. guttata may have improved the outlook for recovery of this endangered species. In contrast, depressed Ne estimates in Ch. serpentina suggested that this “common” species may be particularly vulnerable to the genetic impacts of population declines and overharvesting. These results illustrated the dangers of making assumptions about the genetic health of populations of “common” and “rare” species.
59
Keywords: microsatellite, effective population size, heterozygosity, STRUCTURE, TESS, landscape genetics
5.1 Introduction A central tenet of conservation biology involves understanding the drivers that affect genetic variation within and among populations because genetic variation predicts the evolutionary potential of a species (Fisher 1930; Frankham 1995a). For example, genetic drift, the stochastic shift in allelic frequencies over time due to random sampling of alleles between generations, tends to affect smaller populations more strongly than larger ones. Consequently, smaller populations are considered especially vulnerable to the negative effects of genetic drift, specifically loss of heterozygosity, loss of allelic richness and potential inbreeding depression (Frankham 1995a; Frankham et al. 2002). The theoretical relationship between population size (N) and the rate of genetic drift is clear in the equation 1/(2Ne), where Ne is the effective population size, an estimate of the relative number of breeding individuals per generation. The equation estimates the stochastic per-generation loss of heterozygosity and the probability that an allele will be lost from one generation to the next due to random sampling. In wild populations, Ne typically averages 10% of the census population size (Frankham 1995b). Thus, the effect of genetic drift inversely correlates with population size. This predicts that small, fragmented populations will lose genetic diversity more quickly than large, connected ones (Nei et al. 1975; Frankham et al. 2002; Ewing et al. 2008). Meta-analyses show a positive correlation between population size and genetic diversity in many taxa, which is consistent with this prediction (Soulé 1976; Frankham 1996; Leimu et al. 2006). Other correlates of genetic diversity include dispersal ability, fecundity and range size (Frankham 1996; Mitton 1997). Recent studies of the population genetics of turtles reveal high heterozygosity across most species (summarized in Vargas-Ramirez et al. 2012), even in bottlenecked populations in which reduced diversity is expected (e.g. Kuo & Janzen 2004; Marsack & Swanson 2009; Chapter 3). The extreme longevity of turtles (over 100 years in some cases) is usually invoked to explain this phenomenon (Kuo & Janzen 2004; Alacs et al. 2007). The only empirical test of this hypothesis 60
to date compared a long-lived species of turtle to a short-lived species of snake sampled from different landscapes; generation time did not significantly impact on diversity (Howes et al. 2009). In this study I compare genetic diversity and population structure in two sympatric species of freshwater turtle across a single landscape to understand whether drivers of genetic diversity are consistent among long-lived organisms. I test the hypothesis that snapping turtles (Chelydra serpentina) will exhibit higher genetic diversity than spotted turtles (Clemmys guttata) based on the following five, widely accepted predictions (Frankham 1996; Mitton 1997): (1) genetic variation will be higher in a species with larger population size than a species with smaller population size, considering both census and effective population sizes; (2) genetic variation will be higher and populations will show less structure in a species with larger ranges and higher dispersal ability than in a species with smaller ranges and lower dispersal ability; (3) genetic variation will be higher in a widespread species than in a restricted species; and (4) genetic variation will be higher in a species with high fecundity than in a species with low fecundity.
5.2 Methods 5.2.1
Study species
The widespread snapping turtle (Chelydra serpentina) occurs across the eastern United States and south-eastern Canada, and has been introduced to locations on the west coast of North America. Fecundity is high with a mean clutch size of 35.2 (Ernst & Lovich 2009). Chelydra serpentina is considered abundant across its range, but recent data suggest population declines due to over-harvesting (van Dijk 2011). Dispersal ability is relatively high, and radio-tracking studies and observational studies have regularly recorded movements of 2 to 5 km (Ernst & Lovich 2009; J. Paterson, pers. comm.). Estimated generation time in Ontario is 31 years and longevity may exceed 100 years in the wild (Galbraith & Brooks 1987a; Galbraith et al. 1989; R. Brooks, unpublished data, in COSEWIC 2008). The endangered spotted turtle (Clemmys guttata) is distributed along the east coast of the United States from Florida northwards to Maine and westwards through southern Ontario and into Illinois. Fecundity is low with a mean clutch size of 3.5 (Ernst & Lovich 2009). Populations of Cl. guttata are typically small and are geographically isolated across its range (Ernst & Lovich 61
2009). Low dispersal ability compounds the isolation of populations: individuals may not move more than 500m in a year, and rarely undertake movements > 2 km (Litzgus 1996; Ernst & Lovich 2009; Banning-Anthonysamy 2012). Many populations of Cl. guttata are in decline due to habitat loss and illegal collection and several local extirpations have been recorded (COSEWIC 2004; Ernst & Lovich 2009). Most Ontarian populations have < 200 individuals, and several populations have a census size < 50 (COSEWIC 2004; C. Davy, unpublished data). Generation time is > 25 years (COSEWIC 2004) and longevity is high, potentially up to 110 years (Litzgus 2006).
5.2.2
Data collection and analyses – Chelydra serpentina
Figure 5.1 shows approximate locations of collection sites for Ch. serpentina. Specific location details were withheld to avoid exposing populations to increased harvesting pressure. Mature Ch. serpentina were captured by hand, in dip-nets and in hoop traps baited with sardines. Blood sampling and DNA extraction followed Davy et al. (2012). Further blood samples stored in heparin were also obtained from turtles rehabilitated at the Kawartha Turtle Trauma Centre (Peterborough, Ontario). I isolated genomic DNA from heparinized blood with a phenol-chloroform extraction (Sambrook et al. 1989) and cleaned the DNA using EtOH precipitation. I genotyped samples at 11 species-specific polymorphic microsatellite loci following Davy et al. (2012). Genotypes with an RFU (relative fluorescence units) peak > 200 were scored and PCR was repeated for genotypes with a weaker signal. Seven samples (4%) were extracted twice and genotyped twice at each locus to assess genotyping error. Duplicate, independently taken blood samples (Pompanon et al. 2005) were available from only one individual. Other duplicate extractions were taken from single samples.
5.2.2.1
Analysis
Genotypes were checked for errors and for evidence of stuttering, long allele dropout and null alleles using MICRO-CHECKER v.2.2.3 (vanOosterhout et al. 2004). I used the method of Brookfield (1996) to estimate frequencies of null alleles.
62
Chelydra serpentina is still relatively widespread in Ontario (http://www.ontarionature.org/protect/species/reptiles_and_amphibians/map_snapping_turtleSO. html). Therefore, I considered panmixia to be likely and did not assign individuals to “populations” a priori. Instead, genetically continuous populations were defined based on analyses of the data using STRUCTURE V.2.3.4 (Pritchard et al. 2000) and TESS V.2.3.1 (Chen et al. 2007), following the parameter settings outlined in Chapter 3. Eight models were tested using STRUCTURE
ranging from panmixia (K = 1) to a highly structured population (K = 8). TESS was
run for a range of Kmax values from two to nine, with 10 independent runs at each Kmax. STRUCTURE
and TESS output were compiled following Chapter Four.
I used GENALEX (Peakall & Smouse 2006) to calculate the number of alleles at each locus, the mean observed and expected heterozygosity (HO and HE) of each sampled site, and heterozygosity of genetic clusters identified by STRUCTURE and TESS. Deviations from HardyWeinberg Equilibrium (HWE) were assessed using GENEPOP v.4.0.10 (Raymond & Rousset 1995; Rousset 2008). Deviation from HWE was tested in sampling areas with N > 18, and for populations identified by STRUCTURE and TESS. Each test was run with 1,000 iterations. A sequential Bonferroni correction was applied to multiple pairwise comparisons (Rice 1989). ARLEQUIN (Excoffier et al. 2005) was used to calculate FIS for each population and pairwise FST values for population pair, and I assessed significance with 10,000 randomizations. I also estimated absolute pairwise differentiation (Dest, Jost 2008) using SMOGD (Crawford 2010). Allelic richness and private allelic richness were rarefacted using HP-RARE (Kalinowski 2004; 2005) to account for variation in sample size. Genetic distances (Dest) were ordinated among populations and among individuals using principal coordinates analysis (PCoA) in GENALEX. Correlations between genetic distance (Dest) and Euclidean distance between sample sites (isolation by distance; Wright 1943) were tested for significance using IBDWS V.3.23 (Jensen et al. 2005). Effective population size was estimated for each site and each inferred population using approximate Bayesian computation, implemented in ONeSAMP (Tallmon et al. 2008). Estimates of Ne for each site were given prior lower and upper bounds of 4 and 400. Estimates of Ne for each inferred genetic population were constrained between 4 and 1,500. 63
5.2.3
Data collection and analyses – Clemmys guttata
Data collection and analysis for Cl. guttata paralleled those for Ch. serpentina and were described in detail in Chapter 3.
5.2.4
Interspecific comparisons
Genetic diversity (HO, HE and Ar) and Ne were compared twice between species using SPSS v.20.0 (SPSS Inc. Chicago, Illinois). Diversity and Ne between species were compared between the two species at five paired sampling sites using a paired Wilcoxon signed ranks test. Where possible, samples were collected from both species at one location. Nearby sites were paired for comparisons when exact overlap was not possible due to differences in distribution of the two species (Figure 5.1, inset). Secondly, normality of the data was confirmed using a Shapiro-Wilks test and an independent samples t-test was used to compare mean diversity and Ne between species across all sampled sites.
5.2.5
Data Accessibility
Microsatellite genotypic data for both species and all raw data used in analyses were archived at the Royal Ontario Museum. Specific locations of sample collection have been withheld at the request of the Ontario Ministry of Natural Resources (OMNR); these data were archived with the OMNR Natural Heritage Information Centre (http://nhic.mnr.gov.on.ca/).
5.3 Results I genotyped 167 Ch. serpentina for 11 microsatellite loci (Table 5.1). No evidence of genotyping error due to stuttering or long-allele drop-out was found. Evidence for null alleles was detected only at locus MteD111 (Hackler et al. 2006; Table 5.2). Consequently, this locus was excluded from all further analyses. The PI and PIsibs reached values < 0.01 with inclusion of two and six loci, respectively. Mean pairwise distance between sampling sites was 255 km (s.d. 127.89; range = 41 – 544). Isolation by distance was significant among sampled sites (Z = 173.435, r = 0.267, p = 0.038).
64
5.3.1
Bayesian clustering analyses
Results from TESS showed minimal decrease in DIC values from K = 2 to K = 13 (mean ∆ DIC = 17.9). The q-matrix stabilized at K = 2 (mean DIC = 7815.94, s.d. = 0.89; Figures 5.2 and 5.3) dividing south-western Ontario from all other sampling areas. Population A included sites LE1 and LH1 (mean q = 0.97, s.d. =0.05). Population B included all other sites (mean q = 0.92, s.d. = 0.08). STRUCTURE analysis indicated that K = 2 and K = 4 were the models that best explained the data (Figures 5.2 and 5.3). At K = 2, STRUCTURE also resolved Population A (mean q = 0.92, s.d. = 0.03) and Population B (0.92, s.d. = 0.04). Estimated Ne of Population A was 119.63; Ne of Population B was 195.38 individuals. At K = 4, STRUCTURE divided each population into two subpopulations (Figure 5.3). Site LE1 (subpopulation 1; mean q = 0.88, s.d. = 0.02; Ne = 29.71) separated from LH1 (subpopulation 2; mean q = 0.83, s.d. = 0.02; Ne = 43.24). Sites LH2, BP, GB and N (subpopulation 3; mean q = 0.72, s.d. = 0.12; Ne = 32.63) separated from Alg., Kaw., LO, EO2 and two samples from EO1 (subpopulation 4; mean q = 0.82, s.d. = 0.09; Ne = 62.82). A cline occurred between subpopulations 3 and 4, and to the east of subpopulation 4. The cline was represented by extensive admixture in all GH samples, 17 EO1 samples and the single sample from EO3. All admixed individuals were genetically intermediate between subpopulations 3 and 4. An independent STRUCTURE analysis of population B using identical methods indicated no further sub-structure beyond subpopulations 3 and 4 (data not shown). Thus, the dataset consisted of two distinct populations, each containing two subpopulations. This configuration was used for all further tests, with the admixed sites GH and EO1 considered separately. Estimated Ne of GH and EO1 was 6.46 and 22.33, respectively, but the GH estimate is not robust due to low sample size (N = 6). Population structure was higher in Cl. guttata than in Ch. serpentina across approximately the same spatial scale (Figure 5.3).
5.3.2
Population differentiation
Sampling site EO1 exhibited heterozygote deficit (p = 0.002). No other deviations from HWE or linkage equilibrium occurred. Allelic richness ranged from 3.000 to 3.440 and HO ranged from 65
0.480 to 0.650 (Table 5.2). Inbreeding was not significant within subpopulations; FIS ranged from -0.020 to 0.010 (all 95% confidence intervals overlapped zero). Low but significant levels of differentiation were detected between populations A and B and among all four subpopulations (Table 5.3). Locus Cs18 was fixed for a single allele in subpopulation 3 (N = 22). Similarly, locus 22 was fixed for a single allele in the samples from GH (N = 6). Variation within subpopulations accounted for 95.34 % of the variation in the dataset (Table 5.4, AMOVA: ΦST = 0.047, p = 0.000). Significant variation was also detected among subpopulations within the two populations (ΦSC = 0.028, p = 0.000) and between the populations (ΦCT = 0.019, p = 0.014). The first two principal coordinates of the PCoA accounted for 95.42% of variation among subpopulations. The first component (PCo1) divided LE1 and LH1 from all other sites (Figure 5.4a), while PCo2 divided LH1 from the other distinct subpopulations (Figure 5.4b). At the level of individuals, several samples from different populations overlapped in principal coordinate space but a low level of structure was apparent (Figure 5.4c).
5.3.3
Interspecific comparison
There was no difference in Ne (Z = 0.944; p = 0.345), Ar (Z = -0.944, p = 0.345) or PAr (Z = 1.483, p = 0.138) between Cl. guttata and Ch. serpentina at the paired sites, but Clemmys guttata had significantly higher HO (Z = -2.023, p = 0.043) and HE (Z = -2.023, p = 0.043) than Ch. serpentina (Figure 5.5). Values of Ne, HO and HE across all sampled sites met assumptions of normality. Unpaired tests of values from all sites also showed that Ne was not different between species (t = 1.265, d.f. = 17, p = 0.223) and that HO and HE were significantly higher in Cl. guttata than Ch. serpentina (HO: t = 3.937, df = 17, p = 0.001; HE: t = 3.791, df = 17, p = 0.001).
5.4 Discussion A comparative approach to population genetics can identify or rule out potential drivers of diversity across a landscape. For example, Howes et al. (2009) show that genetic variation is comparable in populations of the long-lived Blanding’s turtle (Emydoidea blandingii) and the 66
black rat snake (Pantherophis obsoleta), which has a much shorter generation time. However, these two species were sampled across two distinct landscapes introducing potential confounding landscape effects. In this study, I compared two equally long-lived species across a single landscape to test the hypothesis that a common, relatively vagile, abundant species with high fecundity (the snapping turtle: Chelydra serpentina) should exhibit higher genetic diversity than an endangered, less vagile, rare species with low fecundity (the spotted turtle: Clemmys guttata). As predicted, population structure across the study landscape was higher in Cl. guttata than Ch. serpentina. On the other hand, heterozygosity was higher in Cl. guttata than in Ch. seprentina. Quite unexpectedly, estimates of Ne were comparable between the two species, which is surprising given the well-documented disparity in abundance and therefore population size between them. Heterozygosity is related to Ne. The unexpected patterns of heterozygosity might, therefore, be best explained by considering factors other than longevity that can affect the Ne:N ratio (summarized by Charlesworth 2009). In the following paragraphs I discuss two possible explanations for the study results: 1) differential variance in reproductive success resulting from different nest success, mating systems and mate choice behaviour, and 2) stochastic effects during post-glacial colonization events. High variance in reproductive success among individual males, females, or both sexes causes Ne to decrease (Hedrick 2000; Karl 2008; Galbraith 2008; Charlesworth 2009). Reproductive success has not been robustly quantified in any population of freshwater turtle. Nevertheless, behavioural observations may provide evidence for differential variance between Ch. serpentina and Cl. guttata. Male Ch. serpentina at some sites may defend territories that provide access to females and fight other males who enter their territory (Galbraith et al. 1987b). Male Ch. serpentina are also thought to coerce the smaller females to mate (Berry & Shine 1980) but this assumption remains to be verified. These strategies may maximize the success of dominant individual males. However, they may also increase average variance in male reproductive success by effectively removing less dominant males from the breeding pool. In contrast, Cl. guttata aggregate to breed after emerging from hibernation. Although males chase and occasionally bite females they are pursuing, territorial behaviour has not been observed and aggregations may contain several individuals of both sexes (Ernst & Lovich 2009, Liu et al. in review). Therefore, breeding aggregations of Cl. guttata may serve to increase the frequency of 67
mate encounters and multiple mating, and to decrease the overall variance in reproductive success of both males and females. Mating systems and patterns of paternity also affect Ne (Sugg and Chesser 1994; Karl 2008). Turtles exhibit promiscuous mating systems (polygynandry) and often exhibit multiple paternity (Galbraith et al. 1993; Uller & Olsson 2008; Davy et al. 2011). Multiple mating increases Ne relative to monogamy, but multiple paternities in single clutches reduce Ne unless the paternal contributions are equal (Zbinden et al. 2007; Karl 2008). In multiply-sired small clutches such as those of Cl. guttata variance in paternal contribution is likely lower than in large clutches (> 20 eggs) such as those laid by Ch. serpentina. Some mate choice behaviours can increase heterozygosity, and can similarly impact Ne:N ratios by affecting average variance in reproductive success. For example, inbreeding avoidance is well-documented in a number of species (Pusey 1987; Johnson et al. 2010; Dunn et al. 2012; Varian-Ramos & Webster 2012). Inbreeding avoidance serves to minimize the relatedness of an offspring’s parents, typically maximizing heterozygosity of offspring and maintaining Ne above the levels expected with inbreeding. Heterozygosity sometimes correlates with factors such as survivorship, immunity, or reproductive success (Frankham et al. 2002). Thus, inbreeding avoidance may also maximize offspring fitness (Foerster et al. 2003; Fossøy et al. 2008; but see Balloux et al. 2004). Little is known about mate choice in either Cl. guttata or Ch. serpentina. However, my data suggest mate choice in Cl. guttata is not random because it is unlikely that multiple populations containing < 50 individuals can randomly maintain high heterozygosity. Thus, it is possible that aspects of the mating system in Cl. guttata such as possible inbreeding avoidance might buffer genetic diversity, at least for awhile. This buffering effect, when present, is a boon for conservation strategies because it might “buy us more time” in the race to rescue an endangered species. On the other hand, if increased reproductive variance in Ch. serpentina may be depressing Ne within populations, then the opposite is true and populations may be affected more strongly by declines than is currently recognized. Further study incorporating direct observation, genetic profiling of adults and paternity testing of hatchlings in one or more populations can serve to test for potential inbreeding avoidance in Cl. guttata. A parallel study of 68
Ch. serpentina or other sympatric species would allow direct comparison of the average relatedness of mating pairs among species. Differences in nesting behaviour may also affect reproductive success. Chelydra serpentina produces on average 10 times as many eggs per clutch as Cl. guttata, but predation of Ch. serpentina nests exceeds 90% at some sites, as I have observed at sites LE1 and LH1. This increases variance in reproductive success among individuals and family groups, both of which reduce Ne relative to N (Karl 2008; Charlesworth 2009). It is possible that the less obvious nests of Cl. guttata have higher average survivorship despite lower fecundity, thus reducing variance in reproductive success in Cl. guttata relative to Ch. serpentina. Direct evidence would be required to test this hypothesis; in particular, data from multiple sites that account for inter-site variation in nest success. Stochastic or unknown historic factors can also cause unexpected patterns of diversity among populations. For example, Cl. guttata likely colonized Ontario from two or more independent refugia after the retreat of the Laurentide Glacier (Chapter 3). If Ch. serpentina colonizing Ontario came from a single refugium, but Cl. guttata came from multiple refugia, then the higher genetic diversity in populations of Cl. guttata may reflect this different history. Fossil evidence, however, suggests this is not the case because Ch. serpentina appears to be one of the first species to re-enter Ontario after the end of the last ice age, and fossil Ch. serpentina are known from a greater number of Holocene locations than Cl. guttata (Holman 1992; Holman & Andrews 1994). Thus, Ch. serpentina appears to have also spent glacial Pleistocene periods in multiple refugia. Additionally, the pattern of higher genetic diversity in Cl. guttata is consistent across multiple sampling sites and populations. Stochastic effects would be more likely to affect a single population than to cause a consistent trend across > 500 km. Therefore, I consider factors involved in differential reproductive success to be the more likely explanation for the unexpectedly high levels of heterozygosity in Cl. guttata. This is the first study to detect genetic population structure in Ch. serpentina. A range-wide study of Ch. serpentina mitochondrial DNA (Phillips et al. 1996) failed to detect population structure and extremely low mtDNA variation was reported across the southwestern portion of the range (Walker et al. 1998). Similarly, no structure was found based on microsatellite 69
genotypes across a small geographic scale in Illinois (60 km; Banning-Anthonysamy 2012). The discovery of genetic structure across several hundred kilometres in Ontario was thus unexpected, but no previous studies of Ch. serpentina have employed microsatellite markers across a broad geographic scale. Galbraith (2008) predicted that northern populations of Ch. serpentina should have lower genetic diversity and structure than southern populations. This prediction was based on geographic variance in clutch size of Ch. serpentina, which is correlated with latitude and predicts higher reproductive variance in northern females. Founder effects during post-glacial colonization would also predict lower diversity in northern populations relative to southern populations (Galbraith, 2008). My results show that variation in genetic diversity in Ch. serpentina is sufficient that microsatellite genotyping of samples collected across a broad geographic scale can be used to test these predictions.
5.4.1
Summary
Biologists often incorporate information about factors such as longevity, vagility, fecundity and population size into explanations of why levels of genetic diversity vary among species (e.g. Frankham 1996, Howes et al. 2009, Pittman et al. 2011). This study indicates that behavioural differences that impact reproductive success may have a greater impact on genetic diversity than these traditionally considered factors, and should be explicitly considered in our analyses (see also Gregory et al. 2012). Because of the complicated interactions among all of these factors, it should not be assumed a priori that small populations of endangered species will be genetically depauperate compared to abundant species with large populations. Similarly, it cannot be assumed a priori that genetic diversity in widespread and relatively abundant species will necessarily be high. In this study, genetic diversity in the common Ch. serpentina at the northern edge of its range was unexpectedly low, possibly due to a combination of reduced reproductive variance based on the mating system coupled with high levels of nest predation. Overharvesting across the range of Ch. serpentina is likely causing significant population declines, although the data necessary for robust evaluations of population size or trends are not available (van Dijk, 2011). In a world in which conservation decisions are often predicated upon estimates of population size and species rarity, species such as Ch. serpentina are rarely prioritized for conservation measures. This study raises the possibility that anthropogenic change might have a 70
more dramatic effect than predicted on some widespread species because they have low genetic diversity despite being abundant. In such species we are “losing time” and are not even aware of it. I thus recommend that future population genetic studies be coupled with further studies on behaviour and ecology of the study species to build a more robust framework on which to base conservation decisions.
5.5 Acknowledgments This research was generously supported a Canada Collection grant to CMD from Wildlife Preservation Canada and by the National Science and Engineering Research Council of Ontario (NSERC Discovery Grant to RWM; Canada Graduate Scholarship to CMD). Thanks to S. Coombes and a large number of volunteers for assistance with field work. Site access and logistical support were provided by M. Cairns, J. Urquhart, the Ausable Bayfield Conservation Authority, Ontario Parks, Ontario Nature and Parks Canada. Genotyping costs were offset by the generous support of the Schad Foundation. Research methods were approved under animal use protocols ROM2008-11, 2009-02, 2009-21 and 2010-14) from the Animal Care Committee of the Royal Ontario Museum, under permits1045769, 1049600, 1062210, 1067079, SR-B-001-10 and AY-B-013-11 from the Ontario Ministry of Natural Resources and under research authorizations from Ontario Parks and Parks Canada. D. McLennan, R. Murphy, J. Miller and L. Einarson provided valuable comments on earlier versions of the manuscript.
5.6 References Alacs EA, Janzen FJ, Scribner KT (2007) Genetic issues in freshwater turtle and tortoise conservation. Chelonian Research Monographs, 4, 107–123. Balloux F, Amos W, Coulson T (2004) Does heterozygosity estimate inbreeding in real populations? Molecular Ecology, 13, 3021–3031. Banning-Anthonysamy WJ (2012) Spatial ecology, habitat use, genetic diversity, and reproductive success: measures of connectivity of a sympatric freshwater turtle assemblage in a fragmented landscape. PhD dissertation, University of Illinois at UrbanaChampaign. Berry JF, Shine R (1980) Sexual size dimorphism and sexual selection in turtles (Order: Testudines). Oecologia, 44, 185–191. Brookfield JFY (1996) A simple new method for estimating null allele frequency from heterozygote deficiency. Molecular Ecology, 5, 453–455.
71
Charlesworth B (2009) Effective population size and patterns of molecular evolution and variation. Nature Reviews Genetics, 10, 195–205. Chen C, Durand E, Forbes F, François O (2007) Bayesian clustering algorithms ascertaining spatial population structure: a new computer program and a comparison study. Molecular Ecology Notes, 7, 747–756. COSEWIC (2004) COSEWIC assessment and update status report on the spotted turtle Clemmys guttata in Canada. Committee on the status of endangered wildlife in Canada. Ottawa. vi + 27 pp. (www.sararegistry.gc.ca/status/status_e.cfm). COSEWIC (2008) COSEWIC assessment and status report on the Snapping Turtle Chelydra serpentina in Canada. Committee on the status of endangered wildlife in Canada. Ottawa. vii + 47 pp. (www.sararegistry.gc.ca/status/status_e.cfm). Crawford NG (2010) SMOGD: software for the measurement of genetic diversity. Molecular Ecology Resources, 10, 556-557. Davy CM, Edwards T, Lathrop A, Bratton M, Hagan M, Nagy K, Stone J, Hillard LS, Murphy RW (2011) Polyandry and multiple paternities in the threatened Agassiz’s desert tortoise, Gopherus agassizii: conservation implications. Conservation Genetics, 12, 1313–1322. Davy CM, Leifso AE, Conflitti IM, Murphy RW (2012) Characterization of 10 novel microsatellite loci and cross-amplification of two loci in the snapping turtle (Chelydra serpentina). Conservation Genetics Resources, 4,695–698. Dunn SJ, Clancey E, Waits LP, Byers JA (2012) Genetic evidence of inbreeding avoidance in pronghorn. Journal of Zoology, 288, 119–126. Ernst CH, Lovich JL (2009) Turtles of the United States and Canada, 2nd ed, Johns Hopkins University Press, Baltimore, Maryland. Evanno G, Regnaut S, Goudet J, (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology 14, 2611–2620. Ewing SR, Nager RG, Nicoll MAC, Aumjaud A, Jones CG, Keller LF (2008) Inbreeding and loss of genetic variation in a reintroduced population of Mauritius Kestrel. Conservation Biology, 22, 395–404. Excoffier L, Laval G, Schneider S (2005) Arlequin ver. 3.0: An integrated software package for population genetics data analysis. Evolutionary Bioinformatics Online, 1, 47–50. Fisher RA (1930) The genetical theory of natural selection. Clarendon Press Foerster K, Delhey K, Johnsen A, Lifjeld JT, Kempenaers B (2003) Females increase offspring heterozygosity and fitness through extra-pair matings. Nature, 425, 714–717. Fossøy F, Johnsen A, Lifjeld JT (2008) Multiple genetic benefits of female promiscuity in a socially monogamous passerine. Evolution, 62, 145–156. Frankham R (1995a) Conservation genetics. Annual Review of Genetics, 29, 305–327. Frankham R (1995b) Effective population size/adult population size ratios in wildlife: a review Genetical Research, 66, 95–107.
72
Frankham R (1996) Relationship of genetic variation to population size in wildlife. Conservation Biology, 10, 1500–1508. Frankham R, Ballou JD, Briscoe DA (2002) Introduction to conservation genetics. Cambridge University Press, Cambridge, U.K. Galbraith DA, Brooks RJ (1987a) Survivorship of adult females in a northern population of common snapping turtles, Chelydra serpentina. Canadian Journal of Zoology, 65, 1581– 1586. Galbraith DA, Chandler MW, Brooks RJ (1987b) The fine structure of home ranges of male Chelydra serpentina: are snapping turtles territorial? Canadian Journal of Zoology, 65: 2623–2629. Galbraith DA, Brooks RJ, Obbard ME (1989) The influence of growth rate on age and body size at maturity in female snapping turtles Chelydra serpentina. Copeia, 1989, 896–904. Galbraith DA, White BN, Brooks RJ, Boag PT (1993) Multiple paternity in clutches of snappng turtles (Chelydra serpentina) detected using DNA fingerprints. Canadian Journal of Zoology, 71, 318–324. Galbraith DA (2008) Population biology and population genetics. In: The Biology of the Snapping Turtle (eds Steyermark AC, Finkler MS and Brooks RJ), pp 168–180. Johns Hopkins University Press, Baltimore, Maryland. Gregory AJ, Kaler RSA, Prebyl TJ, Sandercock BK, Wisely SM (2012) Influence of translocation strategy and mating system on the genetic structure of a newly established population of island ptarmigan. Conservation Genetics, 13, 465–474. Hackler JC, van den Bussche RAV, Leslie DM (2006) Characterization of microsatellite DNA markers for the alligator snapping turtle, Macrochelys temminckii. Molecular Ecology Notes, 7, 474–476. Hedrick PW (2000) Genetics of Populations, 2nd edn, pp. 244–255, Jones & Bartlett Publishers, Sudburg, MA. Holman JA (1992) Late Quaternary herpetofauna of the central Great Lakes region, U.S.A.: zoogeographical and paleoecological implications. Quaternary Science Review, 11, 345– 351. Holman JA, Andrews KD (1994) North American Quaternary cold-tolerant turtles: distributional adaptations and constraints. Boreas, 23, 44–52. Howes BJ, Brown JW, Gibbs HL, Herman TB, Mockford SW, Prior KA, Weatherhead PJ (2009) Directional gene flow patterns in disjunct populations of the black ratsnake (Pantheropis obsoletus) and the Blanding’s turtle (Emydoidea blandingii). Conservation Genetics, 10, 407–417. Jensen JL, Bohonak AJ, Kelley ST (2005) Isolation by distance, web service. BMC Genetics, 6, 13. v.3.23 http://ibdws.sdsu.edu/ Johnson AM, Chappell G, Price AC, Rodd HF, Olendorf R, Hughes KA (2010) Inbreeding Depression and Inbreeding Avoidance in a Natural Population of Guppies (Poecilia reticulata). Ethology, 116, 448–457. 73
Jost L (2008) GST and its relatives do not measure differentiation. Molecular Ecology, 17, 4015– 4026. Kalinowski ST (2004) Counting alleles with rarefaction: private alleles and hierarchical sampling designs. Conservation Genetics, 5, 539–543. Kalinowski ST (2005) HP-Rare: A computer program for performing rarefaction on measures of allelic diversity. Molecular Ecology Notes, 5, 187–189. Karl SA (2008) The effect of multiple paternity on the genetically effective size of a population Molecular Ecology, 17, 3973–3977. Kuo CH, Janzen FJ (2004) Genetic effects of a persistent bottleneck on a natural population of ornate box turtles (Terrapene ornata). Conservation Genetics, 5, 425–437. Leimu R, Mutikainen R, Koricheva J, Fischer M (2006) How general are positive relationships between plant population size, fitness and genetic variation? Journal of Ecology, 94, 942–952. Litzgus JD (1996) Life history and demography of a northern population of spotted turtles, Clemmys guttata. MSc Thesis, University of Guelph, Ontario. 145 pp. Litzgus JD (2006) Sex differences in longevity in the spotted turtle (Clemmys guttata). Copeia, 2006, 281-288. Liu Y, Davy CM, Shi HT, Murphy RW (In review) Sex in the half-shell: a review of the history, signal-function, and evolution of courtship behavior in freshwater turtles. In review, Chelonian Conservation and Biology. Marsack K, Swanson BJ (2009) A Genetic Analysis of the Impact of Generation Time and RoadBased Habitat Fragmentation on Eastern Box Turtles (Terrapene c. carolina). Copeia, 4, 647–652. Mitton JB (1997) Selection in natural populations. 240 pp. Oxford University Press: Oxford. Nei M, Maruyama T, Chakraborty R (1975) Bottleneck effect and genetic variability in populations. Evolution, 29, 1–10. Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes, 6, 288–295. Phillips CA, Dimmick WW, Carr JL (1996) Conservation genetics of the common snapping turtle (Chelydra serpentina). Conservation Biology, 10, 397–405. Pittman SE, King T, Faurby S, Dorcas ME (2011) Genetic and demographic status of an isolated bog turtle (Glyptemys muhlenbergii) population: implications for the conservation of small populations of long-lived animals. Conservation Genetics, 12, 1589–1601. Pompanon F, Bonin A, Bellemain E, Taberlet P (2005) Genotyping errors: causes, consequences and solutions. Nature Reviews Genetics, 6, 847–846. Pritchard JK, Stephens M, Donnelly PJ (2000) Inference of population structure using multilocus genotype data. Genetics, 155, 945–95 Pusey AE (1987) Sex-biased dispersal and inbreeding avoidance in birds and mammals. Trends in Ecology and Evolution, 2, 295–299. 74
Raymond M, Rousset F (1995) GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. Journal of Heredity, 86, 248–249. Rice WR (1989) Analyzing tables of statistical tests. Evolution, 43, 223–225. Rousset F (2008) Genepop’007: a complete reimplementation of the Genepop software for Windows and Linux. Molecular Ecology Resources, 8, 103–106. Sambrook J, Fritsch EF, Maniatis T (1989) Molecular cloning—a laboratory manual. 2nd edn. Cold Spring Harbor Laboratory Press, New York. Soulé ME (1976) Allozyme variation, its determinants in space and time. In: Molecular evolution (ed Ayala FJ), pp. 60–88. Sinauer Associates, Sunderland, Massacheusetts. Sugg DW, Chesser RK (1994) Effective population size with multiple paternity. Genetics, 137, 1147–1155. Tallmon DA, Koyuk A, Luikart GH, Beaumont MA (2008) ONeSAMP: a program to estimate effective population size using approximate Bayesian computation. Molecular Ecology Resources, 8, 299–301. Uller T, Olsson M (2008) Multiple paternity in reptiles: patterns and processes. Molecular Ecology, 17, 2566–80. van Dijk PP (2011) Chelydra serpentina. In: IUCN 2012. IUCN Red List of Threatened Species. Version 2012.1. . Downloaded on 24 September 2012. van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Molecular Ecology Notes, 4, 535–538. Vargas-Ramirez M, Stuckas H, Castaňo-Mora OV, Fritz U (2012) Extremely low genetic diversity and weak population differentiation in the endangered Colombian river turtle Podocnemis lewyana (Testudines: Podocnemididae) Conservation genetics, 13, 65–77. Varian-Ramos CW, Webster MS (2012) Extrapair copulations reduce inbreeding for female redbacked fairy-wrens, Malurus melanocephalus. Animal Behaviour, 83, 857–864. Walker D, Moler PE, Buhlmann KA, Avise JC (1998) Phylogeographic uniformity in mitochondrial DNA of the snapping turtle (Chelydra serpentina). Animal Conservation, 1, 55–60. Wright S (1943) Isolation by distance. Genetics, 28, 114–138. Zbinden JA, Largiader CR, Leippert F, Margaritoulis D, Arlettaz R (2007) High frequency of multiple paternity in the largest rookery of Mediterranean loggerhead sea turtles. Molecular Ecology, 16, 3703–3711.
75
Table 5.1. Summary statistics for 11 microsatellite loci (Hackler et al. 2007; Davy et al. 2012) amplified in 167 Chelydra serpentina from southern Ontario. N = number of individuals successfully amplified at each locus; k = number of alleles; Ne = number of effective alleles; HO = observed heterozygosity; HE = expected heterozygosity; PI = probability of identity; PIsibs = Probability of identity for siblings at a locus. Locus MteD111 showed evidence of potential null alleles and was excluded from all multi-locus analyses. Locus
N
k
Ne
HO
HE
PI
PIsibs
Cs08
165
11
7.167
0.861
0.860
0.035
0.328
Cs12
159
12
5.226
0.818
0.809
0.054
0.359
Cs16
164
4
3.260
0.622
0.693
0.152
0.441
Cs17
165
5
2.998
0.630
0.666
0.159
0.457
Cs18
163
3
1.173
0.141
0.147
0.737
0.861
Cs19
167
5
2.127
0.503
0.530
0.263
0.551
Cs22
159
4
1.533
0.333
0.348
0.444
0.687
Cs24
163
3
2.298
0.521
0.565
0.262
0.533
Cs25
161
3
2.290
0.578
0.563
0.288
0.540
MteD9
152
6
4.162
0.737
0.760
0.094
0.318
MteD111
151
14
4.324
0.561
0.701
0.027
0.394
76
Table 5.2. Genetic diversity in 167 Chelydra serpentina sampled across southern Ontario based on 10 microsatellite loci. Populations (Pop) and subpopulations (SP) were identified with Bayesian clustering analyses (see text for details). GH = Golden Horseshoe, EO1 = Eastern Ontario 1. Number of alleles (private alleles in parentheses); HO and HE: observed and expected heterozygosities; N: sample size per tested unit. Estimated frequency of a null allele was calculated for each locus across all populations. Summary statistics are presented for locus MteD111, but this locus was excluded from calculations of mean heterozygosity and allelic richness. POPA
SP 1
SP 2
PopB
SP 3
SP 4
GH
EO1
Cs08 Estimated null allele frequency: 0.00
Number of alleles HO HE N
10 0.855 0.845 55
8 0.852 0.821 27
9 0.857 0.844 28
11 0.864 0.853 110
9 0.826 0.813 23
11 0.881 0.852 59
6 0.875 0.797 8
8 0.850 0.806 20
Cs12 Estimated null allele frequency: 0.00
Number of alleles HO HE N
12 0.852 0.809 54
10 0.815 0.765 27
11 0.889 0.837 27
9 0.800 0.799 105
8 0.773 0.769 22
9 0.821 0.800 56
6 0.714 0.694 7
7 0.800 0.798 20
Cs16 Estimated null allele frequency: 0.00
Number of alleles HO HE N
4 0.600 0.593 55
4 0.519 0.559 27
3 0.679 0.610 28
4 0.633 0.716 109
4 0.625 0.598 24
4 0.655 0.745 58
4 0.143 0.704 7
4 0.750 0.656 20
Cs17 Estimated null allele frequency: 0.00
Number of alleles HO HE
5 0.673 0.743
5 0.704 0.709
4 0.643 0.744
4 0.609 0.605
3 0.458 0.518
4 0.644 0.632
4 0.429 0.367
4 0.750 0.648
N
55
27
28
110
24
59
7
20
Number of alleles HO HE N
2 0.164 0.150 55
2 0.296 0.252 27
2 0.036 0.035 28
3 0.130 0.146 108
1 0.000 0.000 22
2 0.138 0.128 58
3 0.125 0.227 8
2 0.250 0.289 20
Cs18 Estimated null allele frequency: 0.00
77
Cs19 Estimated null allele frequency: 0.00
Number of alleles HO HE N
4 0.236 0.218 55
4 0.222 0.205 27
4 0.250 0.228 28
5 0.634 0.624 112
4 0.708 0.669 24
4 0.600 0.604 60
3 0.625 0.625 8
4 0.650 0.544 20
Cs22 Estimated null allele frequency: 0.00
Number of alleles HO HE N
4 0.309 0.276 55
4 0.259 0.237 27
3 0.357 0.309 28
4 0.346 0.383 104
4 0.545 0.598 22
4 0.232 0.258 56
1 0.000 0.000 6
4 0.550 0.480 20
Cs24 Estimated null allele frequency: 0.00
Number of alleles HO HE N
3 0.545 0.616 55
3 0.481 0.612 27
3 0.607 0.605 28
3 0.509 0.532 108
3 0.435 0.455 23
3 0.552 0.542 58
3 0.429 0.357 7
3 0.500 0.584 20
Cs25 Estimated null allele frequency: 0.00
Number of alleles HO HE N
3 0.545 0.521 55
2 0.481 0.431 27
3 0.607 0.536 28
3 0.594 0.581 106
3 0.682 0.590 22
3 0.561 0.561 57
3 0.714 0.500 7
3 0.550 0.584 20
MteD9 Estimated null allele frequency: 0.00
Number of alleles HO HE N
6 0.691 0.740 55
6 0.778 0.735 27
5 0.607 0.581 28
6 0.763 0.767 97
6 0.714 0.654 21
6 0.750 0.771 52
5 0.750 0.750 4
6 0.850 0.798 20
MteD111* Estimated null allele frequency: 0.3017
Number of alleles HO HE N
11 (1) 0.566 0.846 53
8 0.615 0.787 27
7 (1) 0.519 0.807 23
13 (3) 0.592 0.881 98
9 0.609 0.822 23
12 (2) 0.580 0.865 50
5 0.667 0.667 6
9 0.579 0.783 19
Mean HO Mean HE Allelic richness Private allelic richness
0.547 0.551 4.63 0.63
0.541 0.533 3.86 0.18
0.553 0.533 3.61 0.27
0.588 0.601 4.92 0.91
0.577 0.566 3.63 0.35
0.588 0.601 3.91 0.22
0.480 0.502 4.16 0.38
0.650 0.619 3.96 0.28
* MteD111 was excluded from all analyses, including mean heterozygosity and allelic richness presented in this table, due to possible presence of null alleles. 78
Table 5.3. Population differentiation (Dest above the diagonal, FST below) for four subpopulations and two admixed groups of Chelydra serpentina identified by STRUCTURE analysis (Figure 3). FST values in bold are significant (p < 0.05). Subpopulations (SP) are described in the text. GH = Golden Horseshoe. EO = EO1 and EO3. SP 1
SP 2
SP 3
SP 4
GH
EO
Subpopulation 1 0
0.013
0.043
0.016
0.025
0.032
Subpopulation 2 0.024
0
0.029
0.023
0.005
0.002
Subpopulation 3 0.097
0.064
0
0.006
0.006
0.006
Subpopulation 4 0.045
0.034
0.036
0
0.001
0.000
GH
0.035
0.042
0.010
-0.004
0
0.000
EO
0.042
0.030
0.014
0.005
0.001
0
Table 5.4. Hierarchical partitioning of molecular variance in Chelydra serpentina from southern Ontario with AMOVA (Excoffier et al. 1992). All sources of variation were significant (p < 0.02). Source of variation
Sum of squares
Variance components (σ2)
% variation
Among populations
16.75
0.058
1.92
Among subpopulations within populations
26.91
0.083
2.73
Within subpopulations
916.55
2.890
95.34
Total
960.22
3.031
79
Figure 5.1. Sampling sites for 167 Chelydra serpentina (blue squares) sampled in this study and 256 Clemmys guttata (yellow squares) sampled in Chapter 3. Bi-coloured squares indicate sites where both species were sampled. Insert shows pairs of sampling areas used for comparisons of genetic diversity between species. LE1 = Lake Erie 1; LE2 = Lake Erie 2; LH1 = Lake Huron 1; LH 2 = Lake Huron 2; BP = Bruce Peninsula; GB = Georgian Bay; GH = Golden Horseshoe; N = North of Golden Horseshoe; KAW = Kawartha Lakes area; ALG = Algonquin Provincial Park; HC = Hastings County; LO = north-east shore of Lake Ontario; EO = Eastern Ontario. Base map modified from http://www.aquarius.geomar.de/omc/make_map.html and used under the GNU Free Documentation license.
80
Figure 5.2. Results of Bayesian clustering analyses of Ch. serpentina for increasing values of K, the number of genetically distinct populations represented in the sample following analyses described in Methods. Structure results for K = 1 – 8 : A) Log likelihood (L(K)) of the data (mean ± standard deviation); B) ∆K following Evanno et al. (2005). TESS results for Kmax = 2– 14; C) Deviance information criterion (mean ± standard deviation) following analyses described in Methods.
81
Figure 5.3. Results of Bayesian clustering analyses of Ch. serpentina for a range of models with increasing values of K inferred using STRUCTURE and TESS. Models shown here are those that best fit the data based on criteria described in Methods. Population structure in Cl. guttata across the same landscape is shown for comparison (from Chapter 3). Colours used for subpopulations in the K = 4 model are consistent with colours used in Figure 4. 82
83
Figure 5.4. Principle component analysis of genetic distance for 167 Ch. serpentina based on 10 microsatellite loci. A and B: PCoA of populations based on Dest ; C: PCoA based on genetic distance among individuals labelled by sampling site.
Figure 5.5. Heterozygosity and effective population sizes of Ch. serpentina and Cl. guttata compared across five pairs of sites (Figure 1, inset). HO: observed heterozygosity. HE: expected heterozygosity. Ne: effective population size. Error bars show standard deviation of HO and HE and 95% confidence intervals of Ne estimates.
84
Chapter 6 Conservation genetics of Blanding’s turtle (Emys blandingii) in Ontario, Canada. Formatted for Conservation Genetics.
6
Abstract
Blanding’s turtle, Emys blandingii, is a globally endangered species with a range centred on the Great Lakes. Several disjunct populations occur along the East Coast of North America. Previous studies suggest that gene flow may be uninterrupted in the Great Lakes portion of the range. However, E. blandingii is restricted to relatively small populations across its range and, therefore, panmixia across large geographic distances is unlikely. Here, Bayesian analyses of population structure among samples collected across southern Ontario (N = 97) rejected a null hypothesis of panmixia. These data were used to identify potential management units. Ontario contains four distinct genetic clusters of E. blandingii and these should be considered as independent management units. Preliminary evidence suggests that further structure may be present in some poorly sampled areas, and these deserve further consideration. Genetic diversity at sampled sites is comparable to that reported for other freshwater turtles. Comparison between this study and previous work confirms reduced genetic diversity in disjunct eastern populations compared to populations centred on the Great Lakes. Genetic diversity in E. blandingii is not correlated with latitude, which may indicate post-glacial dispersal of this species from multiple Pleistocene glacial refugia. Keywords: population structure, STRUCTURE, TESS, GENECLASS, heterozygosity
85
6.1 Introduction Blanding’s turtle, Emys (=Emydoidea) blandingii is a moderately sized freshwater species found in the northeastern United States and southern Canada. The main portion of its range is centred on the Great Lakes region. Disjunct populations occur in New York, Massachusetts, and Nova Scotia (Figure 1; Ernst and Lovich 2009). Mean age of maturity in a well-studied Michigan population is 17.5 years, generation time is approximately 37 years, and longevity exceeds 75 years (Congdon and van Loben Sels 1991; Congdon et al. 1993; Brecke and Moriarty 1989). One consequence of this life history is that populations are sensitive to any increase in the mortality rate of reproductive adults (Congdon et al. 1993). Even a small increase in adult mortality can cause significant population declines. A number of factors including road mortality, illegal collection, and habitat degradation are currently causing such declines. Therefore, E. blandingii was recently up-listed from Least Concern to Endangered by the International Union for Conservation of Nature (IUCN; van Dijk and Rhodin 2011). Using random amplified polymorphic DNA (RAPD) markers and microsatellites, Mockford et al. (1999; 2005; 2007) and Rubin et al. (2001) quantified genetic variation in E. blandingii across the species’ range. Band-sharing analyses of RAPD data showed that the disjunct Nova Scotian population differed genetically from central populations (Mockford et al. 1999; Rubin et al. 2001). Within Nova Scotia, analyses of microsatellite data based on FST values suggested significant differentiation among three subpopulations despite separation by < 30 km. However, very little population structure was detected in the main portion of the range based on samples from Minnesota, Wisconsin, Illinois, Michigan, and Ontario (Mockford et al. 2005). Based on these data, Mockford et al. (2007) proposed that E. blandingii comprised three Evolutionarily Significant Units (ESU): 1) the Nova Scotian population; 2) isolated populations in Massachusetts and New York; and 3) populations extending from the Great Lakes. The ESU concept does not apply to the legal conservation of turtles in either the USA or Canada, where protection is based on the concepts of distinctive population segments (Pennock and Dimmick 2002) and designatable units (DUs; Green 2005), respectively. In Canada, the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) recognizes E. blandingii in Nova Scotia and the populations around the Great Lakes as two DUs.
86
Recent studies highlight concerns with analyses based on FST. For example, Jost (2008) demonstrated that FST and related measures of diversity do not necessarily measure actual population differentiation. He proposed an alternative, more accurate measure (Dest). Jost (2008) also pointed out that statistical significance of FST was primarily a factor of sample size and may be biologically meaningless. Howes et al. (2009) summarized further concerns with analyses of population structure based on FST, including that assumptions of these analyses often were not met in natural populations (Whitlock and McCaughley 1999) and that FST does not reflect contemporary gene flow (Paetkau et al. 2004). FST and related measurements can provide information about historical migration rates, but they are not appropriate measures of population differentiation or structure (Jost 2009). Bayesian methods for detection of population structure and connectivity (e.g. Pritchard et al. 2000; Chen et al. 2007) do not rely on the assumptions of FST-based analyses. Howes et al. (2009) applied Bayesian methods to the data of Mockford et al. (2005) to study population connectivity in the three subpopulations of E. blandingii in Nova Scotia. The results demonstrated moderate historic and current gene flow among all three subpopulations, and clustered the two nearest subpopulations together indicating that they were genetically continuous. Bayesian analysis of three E. blandingii populations separated by 10 km overland (Power 1989). Thus, population structure in this species is more likely to occur on a relatively large geographic scale (> 100 km). Ontario has a large portion of the core range of E. blandingii, but previous studies included only 11 samples from one site in southeastern Ontario, St. Lawrence Islands National Park (Mockford et al. 2007). Presence-absence data show that the distribution of E. blandingii in Ontario is not continuous (Ontario Nature Herpetofaunal Atlas, http://www.ontarionature.org/protect/species/reptiles_and_amphibians/map_blandings_turtle.ht ml). Gaps in occurrence records may reflect historic or current barriers to gene flow and suggest that populations may not be panmictic across the province. Nevertheless, COSEWIC currently
87
considers the “Great Lakes/St. Lawrence population,” comprised of all E. blandingii in Ontario and Quebec, as a single unit for management and recovery purposes (COSEWIC 2005). Here, I use three Bayesian analyses and a principal coordinates analysis to investigate population structure in E. blandingii across > 500 km in southern Ontario. I investigate the level of population structure and genetic diversity present at sampled sites to test the hypothesis that populations of E. blandingii around the Great Lakes show little differentiation and consist of a single genetic unit. Further, I compare genetic variation (heterozygosity) among populations in Ontario and the populations studied by Mockford et al. (2007) to test two hypotheses: a) that variation will be lower in disjunct eastern populations than in populations around the Great Lakes, as suggested by Mockford et al. (2005); and b) that variation will decrease with proximity to the northern limit of the species’ range.
6.2 Methods I collected DNA from E. blandingii across southern Ontario between 2008 and 2011, and additional samples were contributed by other researchers and government biologists (Figure 6.1). Turtles were captured by hand or in hoop traps baited with sardines at sites LE, GH, EO, ALG., LHsouth, and LHnorth. Blood was taken by caudal venipuncture with a sterile syringe and blotted onto FTA cards (Whatman Inc., Clifton, NJ) for storage. All individuals were released at their initial capture site. Blood was extracted from FTA cards following Smith and Burgoyne (2004). At site PSD, muscle samples were collected from road-killed individuals. At KAW, blood samples were taken from turtles injured on local highways and rehabilitated at the Kawartha Turtle Trauma Centre (Peterborough, Ontario); these blood samples were stored in heparin before analysis. Extraction of DNA from muscle and heparinized blood followed the phenol-chloroform procedure of Sambrook et al. (1999) and extracted DNA was cleaned with EtOH precipitation. Four additional blood samples were collected from captive E. blandingii at Scales Nature Park (Orillia, Ontario) that were from Ontario, but whose exact locations of origin were unknown. Samples were amplified at four microsatellite loci developed for E. blandingii (Eb09, Eb11, Eb17 and Eb19; Osentoski et al. 2002). These loci were used by Mockford et al. (2007) and, therefore, allowed for some direct comparison of diversity between the two studies. In addition, I 88
amplified 13 loci from Glyptemys muhlenbergii that cross-amplified in E. blandingii (GmuB08, GmuD16, GmuD21, GmuD28, GmuD55, GmuD70, GmuD87, GmuD88, GmuD89, GmuD90, GmuD93, GmuD107 and GmuD121; King and Julian 2004). Amplification and allele scoring followed Chapter 3, using the locus-specific annealing temperatures listed in Table 6.1. Genotyping error was assessed by including positive controls with each PCR reaction and reamplifying approximately 6% of the samples. Evidence for null alleles and long allele drop-out was assessed with MICRO-CHECKER (vanOosterhout et al. 2004) using 1,000 iterations. Frequency of null alleles was calculated with the method of Brookfield (1996). I calculated the number of alleles per locus, observed heterozygosity (HO) and expected heterozygosity (HE) in GENALEX (Peakall and Smouse 2006). Allelic richness was rarefacted to correct for unequal sample sizes in HP-RARE (Kalinowski 2004; 2005). Linkage disequilibrium and deviations from Hardy-Weinberg equilibrium (HWE) were tested in GENEPOP v.4.0.1 (Raymond and Rousset 1995; Rousset 2008). Significance levels were corrected for multiple comparisons following Rice (1989). I assessed genetic differentiation among sample sites by calculating absolute differentiation (Dest, Jost 2008) of all sites with N ≥ 15 in SMOGD (Crawford, 2010). For purposes of comparison with previous studies and for calculations of historic migration rates (Nm) I also used FSTAT (Goudet, 1995) to calculate pairwise FST and assessed significance with 10,000 randomizations. Isolation by distance (IBD, a significant correlation between geographic and genetic distance, Wright 1943) was assessed using IBDWS (Jensen et al. 2005) with an input matrix of Dest and pairwise distances (km) between sites. Within-population heterozygosity (HO) from Ontario sites was compared to HO values reported in Mockford et al. (2007) using an independent-samples t-test in SPSS v.20.0 (IBM-SPSS, Chicago, IL) after testing normality of the data. Comparisons were made for each locus sampled in both studies and across all sampled loci. Comparisons were made between sampled sites in Ontario and the “western” sites from Mockford et al (2007; all sampled sites west of the Appalachian Mountains). I also compared sites east of the Appalachian Mountains to western populations, combining study sites from Ontario with western sites from Mockford et al. (2007).
89
Pearson’s correlation coefficient was used to test for significant relationships between latitude and HO among sites surrounding the Great Lakes. Population structure was assessed by Bayesian inference (BI) in STRUCTURE V.2.3.4 (Pritchard et al. 2000) and TESS V.2.3.1 (Chen et al. 2007) following the run parameters outlined in Chapter 3. STRUCTURE considered
possible K values (number of genetically distinct populations) from one
to six with 10 independent runs at each value of K. TESS considered possible Kmax values (maximum possible number of populations represented by the data) from two to eight, with 10 independent runs at each Kmax. Assignment tests were conducted in GENECLASS V.2.0 (Piry et al. 2004) using the Bayesian method of Rannala and Mountain (1997), with 100,000 iterations and a Type I error level of 0.05. This duplicates the analyses conducted by Howes et al. (2009), allowing a reasonable level of comparison between studies. Assignment tests considered only sampling areas with six or more samples. Individual samples from other sites and samples of unknown origin were then assessed by the program as “unknown”, and assigned to the most similar sampling area. Population structure was also visualized with principal coordinates analysis (PCoA) in GENALEX, based on Dest for sampled sites and on Nei’s unbiased genetic distance for individuals.
6.3 Results Loci Eb09, Eb11, GmuD70, GmuD89, and GmuD90 either did not amplify, or could not be scored consistently despite multiple adjustments of PCR conditions. Thus, 12 loci were used for analyses. In total, 116 samples were collected but several yielded degraded DNA and were successfully amplified at only five or six loci. These samples were excluded and a total of 97 individuals (91 individuals from known locations) were genotyped at > 10 loci and included in the final analysis. All duplicated genotypes were identical. MICRO-CHECKER found evidence for potential null alleles at three loci (Eb19, GmuD93 and GmuD107). However, when the four largest samples were tested independently, potential null alleles were not consistent among sites; only EO and GH showed evidence for nulls, and only at locus Eb19.
90
Deviations from HWE were detected at locus Eb19 in PSD, GH, and EO, but not in KAW or LE. Evidence for LD was detected across the entire dataset between two pairs of loci: GmuD55– GmuD107 and GmuD28–GmuD107. However, LD was not detected when testing sampled areas independently and, therefore, I accepted the null hypothesis of linkage equilibrium. PI and PIsibs decreased to < 0.01 with the inclusion of three and six loci, respectively. The 12 loci exhibited 3– 16 alleles (mean 8.917, s.d. = 4.187), and HO ranged from 0.253 at locus GmuD21 to 0.845 at locus GmuD28 (Table 6.1). Summary statistics for all sampling sites are shown in Table 6.2. Pairwise values of Dest ranged from 0.010 to 0.156 (mean = 0.083, s.d. = 0.044, Table 6.3). Values of pairwise FST ranged from 0.039 to 0.099 (mean 0.072, s.d. = 0.021). Nm among sites averaged 2.432, and Nm between each pair of sites ranged from 0.095 (GH–PSD) to 3.380 (PSD–EO; Table 6.3). No evidence suggested significant isolation by distance among the four sites with N > 12 (Z = 194.900, r = 0.233, p = 0.301). In the PCoA of sampling sites, the first principal coordinates axis accounted for 60.19% of total variation. This axis separated sites LE and GH from PSD, Kaw, and EO (Figure 6.2). When the PCoA was conducted at the individual level individuals clustered by site but with overlap indicating that differentiation in this dataset may have occurred along a gradient rather than along sharply defined boundaries. GENECLASS assigned individuals from LE, GH, PSD, KAW, and EO to their area of origin with 69% accuracy (Table 6.4). Samples from KAW were assigned to PSD (N=5) or GH (N=1). When KAW was removed from assignment tests, overall accuracy increased to 79%. The two samples from the north shore of Lake Huron were assigned to PSD. The two samples from Algonquin Park and the sample from the south shore of Lake Huron were not assigned to any sampled clusters (p < 0.01). The deviance information criterion (DIC) in the TESS analysis decreased gradually from Kmax = 2 with no clear point of inflection (mean ∆DIC = 54.2, Figure 6.3A). Individual q-matrices stabilized at Kmax = 2; no clearly defined new clusters appeared at higher values of Kmax, although potential admixture from a third population became apparent in site EO at Kmax = 3. The first resolved population included LE, GH, and LHsouth (mean q = 0.969, s.d. = 0.094). The 91
second population included all other samples (mean q = 0.709, s.d. = 0.377). One sample from PSD was assigned with approximately equal probability to both populations (0.493 vs. 0.507). STRUCTURE
resolved the same two populations as TESS at K = 2 (Figure 6.3B). At K = 3, LE and
LHsouth separated from population GH with evidence of admixture remaining between the two clusters. At K = 4, EO separated from a final population consisting of PSD, KAW, LHnorth, ALG, and Hastings County. Heterozygosity data were normally distributed and Levene’s test indicated equal variances (F = 0.09, p = 0.927). Data from the two loci used both in this study and in Mockford et al. (2007; Eb17 and Eb19) were combined for comparison. Observed heterozygosity in the Great Lakes portion of the species’ range was significantly higher at locus Eb17 (t = -3.621, d.f. = 15, p = 0.003) but not at locus Eb19 (t = -1.823, d.f. = 15, p = 0.088). When mean heterozygosity across all loci in both studies was compared, HO was significantly higher in western populations (t = 3.749, d.f. = 15, p = 0.002) than in the disjunct eastern populations. No difference in heterozygosity occurred between the western populations sampled by Mockford et al. (2007) and the populations sampled in this study (t = -0.413, df = 10, p = 0.688). Latitude and HO were not correlated (Pearson’s correlation = 0.056, N = 11, p = 0.869). Site EO had substantially higher heterozygosity than the site from Ontario sampled by Mockford et al. (2007) (0.636 compared to 0.48) despite their proximity (< 40 km apart).
6.4 Discussion Three independent Bayesian analyses and a principle components analysis reveal consistent population structure in E. blandingii in southern Ontario. Sampling localities in this study include two genetic populations and four subpopulations, refuting the hypothesis of panmixia in E. blandingii in Ontario. Assignment tests identify individuals to their subpopulation of origin with relatively high accuracy considering the small sample sizes available for this study. Intensive urban development and expanding road networks make current migration between these four subpopulations unlikely. Bayesian assignment of individual samples to the larger dataset suggests that the population on the north shore of Lake Huron at the northern extreme of the species’ range may be continuous 92
with the population in PSD. Assignment of individuals from KAW to PSD reflects genetic continuity between these two areas (Table 6.4; Figure 6.3). However, assignment tests cannot determine a likely origin for the sample from the south shore of Lake Huron or the two samples from Algonquin Park. Thus, these areas may be genetically distinct, especially because E. blandingii in both locations are apparently isolated from other nearby populations (Ontario Nature Reptile and Amphibian Atlas). Based on my results, I propose four tentative management units (MUs) for E. blandingii in Ontario: Lake Erie, Golden Horseshoe, Georgian Bay-Parry Sound District and Eastern Ontario. These four areas are unlikely to qualify as DUs under Canadian law (Green 2005) because I am not aware of any evidence to suggest that they are subject to significantly different risks of extinction. However, they appear to be both demographically and genetically independent of one another and this should be considered when planning for population management and recovery. Future studies of geographically disjunct areas of occurrence such as Algonquin Park and the south shore of Lake Huron may identify further MUs or clarify relationships between these sites and the proposed MUs. Currently under-sampled areas should not be considered part of the four tentative management units until genetic data are available to confirm this categorization. Genetic diversity (HO) is significantly lower in the disjunct eastern populations than in populations around the Great Lakes. This pattern was first shown by Mockford et al. (2007) and is not altered by the inclusion of additional populations from Ontario. Diversity in E. blandingii does not vary with latitude. A negative correlation between genetic diversity and latitude is expected in turtles in North America (Galbraith 2008) because colonization following the last ice age proceeded from south to north making founder effects more likely in northern populations, although this has not been tested in other species of turtle in Ontario. However, E. blandingii has a compressed latitudinal range and likely underwent east-west migrations as well as north-south migrations after the last ice age. Fossil evidence places E. blandingii in southern Indiana 15–14 ka BP, and fossils are also known from Indiana and Michigan 6–4 ka BP (Holman 1992). Although some populations might have used Pleistocene refugia in the southern Atlantic plain (Bleakney 1958), it is probable that other populations persisted near the Great Lakes throughout the Wisconsonian ice age, rapidly recolonizing the Great Lakes area as the ice sheets retreated (Holman 1992). This hypothesis places a major refugium for E. blandingii south of the centre of 93
the Great Lakes portion of the current range. Gradual expansion from this refugium to the sites considered in this study is consistent with the similar levels of genetic diversity reported from a range of central populations. Lower values of Nm for sites in Ontario compared to those in Nova Scotia (Mockford et al. 2005) are probably due in part to geographic distance. The Nova Scotian sites compared by Mockford et al. (2005) were 15 – 25 km apart, with Nm = 1.76 – 5.8, and they estimated Nm = 0.54 – 0.74 between Nova Scotia and a Michigan population approximately 1510 km in distance. Ontario populations sampled here were 151 – 516 km apart and estimated Nm values were intermediate between the two extremes reported by Mockford et al. (2005). Several sites have private alleles at one or more loci, indicating possible effects of genetic drift. However, no alleles are fixed and heterozygosity is comparable to that reported for other populations of turtles (Vargas-Ramirez et al. 2012). Heterozygosity in continental chelonian species ranges from 0.33 (Podocnemis lewyana) to 0.76 (Astrochelys radiata and Malaclemys terrapin), and the mean heterozygosity of sampled populations of E. blandingii in Ontario (0.64) is within this range. If population sizes can be stabilized (or kept stable), there is no reason to believe loss of genetic diversity is cause for immediate concern at these sites. Thus, recovery plans need not consider genetic management measures at this time. Instead, effort should be made to mitigate high adult mortality and low recruitment (Congdon et al. 2008). Increasing or at the very least maintaining population size is the most effective way to prevent loss of genetic diversity in threatened populations (Frankham et al. 2002). Although active genetic management appears to be unnecessary, the genetic structure demonstrated here should be considered when planning measures that will increase or modify habitat connectivity. For example, anthropogenic features that fragment habitat (e.g. highways, urban development) also reduce gene flow among population fragments. Where possible, the effect of this fragmentation can be mitigated using tools such as wildlife underpasses, or corridors of suitable habitat. Alternatively, actions such as translocations that involve moving individuals across the landscape should include explicit consideration of genetic structure and social interactions of turtles (Chapters 4 and 6). Mixing of genetic populations can have serious consequences for fitness if locally adapted genes or co-adapted gene complexes are disrupted 94
(outbreeding depression, Templeton 1986). For example, Sletvold et al. (2012) demonstrated a 47% fitness reduction when individuals from two populations of a nectariferous orchid (Gymnadenia conopsea) located 1.6 km apart were crossed. The situation was reversed in a study concerning the translocation of Bighorn sheep (Miller et al. 2012); more outbred individuals (i.e. individuals with more introduced alleles) lived longer and had higher reproductive success than individuals who were not affected by the genetic rescue. Increased fitness in outbreeding Bighorn sheep supports the efficacy of facilitated rescue effects on declining populations (Hogg et al. 2006; Miller et al. 2012). There is no evidence to suggest that mixing of populations of E. blandingii is likely to cause outbreeding depression, but minimal genetic data exist for this species and the possibility has not been investigated. Interestingly, results from both STRUCTURE and TESS suggest possible past translocations of individuals between populations (Fig.2; individuals with an approximately 50% probability of membership to two populations may be first-generation offspring of migrants who mated with residents). Collection of individual turtles by members of the public occurs regularly and these individuals are often released elsewhere than their collection site (F. Ross, pers. comm.; S. Gillingwater, pers. comm.; C. Davy, unpublished data). There are no data on the frequency of these casual translocations, but attempts to maintain existing genetic structure of populations are unlikely to succeed without public education. Such efforts should explain not only the laws that prohibit collection of turtles in Ontario, but also the impact that collection and translocation can have on wild turtle populations, as well as clarifying the low chance of survival for their former pets after release. This study addresses the first two areas for research in genetics of turtles recommended by Alacs et al (2007) : 1) “… identification of genetic discontinuities at landscape and species levels to delineate management units, and 2) Predicting effects of landscape-level changes and concomitant changes in population demography and movement patterns on apportionment of genetic diversity within and among populations.” I achieve the delineation of management units based on existing genetic discontinuities. Application of Bayesian methods to identify and profile populations across the central range of E. blandingii will likely reveal further population structure at appropriate spatial scales. Future studies should more clearly delineate boundaries among populations and significant barriers to gene flow including those hypothesized by 95
Mockford et al. (2007). For example, the Appalachian Mountains may have played a role in the isolation of the disjunct eastern populations. However, FST values suggest that individuals from New York were most similar to individuals in St. Lawrence Islands National Park (Ontario). Perhaps these populations are historically connected via the Delaware water gap or a similar landscape feature. Alternatively, perhaps Bayesian analyses will reveal a completely different pattern of structure than was previously suggested.
6.5 Acknowledgments Sample collection was accomplished with the assistance of Sue Carstairs, Brennan Caverhill, Suzanne Coombes, Joe Crowley, Jacqueline Litzgus, James Paterson, James Baxter-Gilbert, Jim Trottier, Julia Riley, Jeremy Rouse, David Seburn, John Urquhart and Amelia Whitear. Jeff Hathaway and Jenny Pierce allowed me to sample E. blandingii at Scales Nature Park. Pedro Bernardo assisted with the laboratory analyses. Field collection was funded in part by a Canada Collection grant from Wildlife Preservation Canada to CD. Laboratory analyses were funded by a Species at Risk Research Fund for Ontario grant from the Government of Ontario; I thank Bob Johnson, Julia Philips and Robert Murphy for collaborating on this grant. Comments from Robert Murphy and Deborah McLennan improved an earlier version of this manuscript.
6.6 References Alacs EA, Janzen FJ, Scribner KT (2007) Genetic issues in freshwater turtle and tortoise conservation. Chelon Res Monogr 4:107-123 Banning-Anthonysamy WJ (2012) Spatial ecology, habitat use, genetic diversity, and reproductive success: measures of connectivity of a sympatric freshwater turtle assemblage in a fragmented landscape. Dissertation, University of Illinois at UrbanaChampaign Barton NH, Slatkin M (1986) A quasi-equilibrium theory of the distribution of rare alleles in a subdivided population. Heredity 56:409-416 Bleakney JS (1958) A zoogeographic study of the amphibians and reptiles of eastern Canada. Natl Mus Can Bull 155:1-119 Brecke BJ, Moriarty JJ (1989) Emydoidea blandingii (Blanding’s turtle). Longevity. Herpetol Rev 20:53 Brookfield JFY (1996) A simple new method for estimating null allele frequency from heterozygote deficiency. Mol Ecol 5:453-455
96
Chen C, Durand E, Forbes F, François O (2007) Bayesian clustering algorithms ascertaining spatial population structure: a new computer program and a comparison study. Mol Ecol Notes 7:747-756 Congdon JD, van Loben Sels RC (1991) Growth and body size in the Blanding’s turtles (Emydoidea blandingii): relationships to reproduction. Can J Zool 69:239-245 Congdon JD, Dunham AE, van Loben Sels RC (1993) Delayed sexual maturity and demographics of Blanding’s Turtles (Emydoidea blandingii): implications for conservation and management of long-lived organisms. Conserv Biol 7:826-833 Congdon JD, Graham TE, Herman TB, Lang JW, Pappas MJ, Brecke BJ (2008) Emydoidea blandingii (Holbrook 1838) – Blanding’s turtle. In: Rhodin AGJ, Pritchard PCH, van Dijk PP, Saumure RA, Buhlmann KA, Iverson JB (eds.). Conservation biology of freshwater turtles and tortoises: a compilation project of the IUCN/SSC Tortoise and Freshwater Turtle Specialist Group. Chelon Res Monogr 5:015.1-015.12. doi:10.3854/crm.5.015.blandingii.v1.2008, http://www.iucn-tftsg.org/cbftt/ COSEWIC (2005) COSEWIC assessment and update status report on the Blanding's Turtle Emydoidea blandingii in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. viii + 40 pp. (www.sararegistry.gc.ca/status/status_e.cfm) Crawford NG (2010) SMOGD: software for the measurement of genetic diversity. Mol Ecol Res 10:556-557 Ernst CH, Lovich JL (2009) Turtles of the United States and Canada, 2nd ed, Johns Hopkins University Press, Baltimore, Maryland Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611-2620 Frankham R, Ballou JD, Briscoe DA (2002) Introduction to conservation genetics. Cambridge University Press, Cambridge Galbraith DA (2008) Population biology and population genetics. In: Steyermark AC, Finkler MS, Brooks RJ (eds) The biology of the snapping turtle. Johns Hopkins University Press, Baltimore, Maryland pp 168-180 Goudet J (1995) FSTAT (Version 1.2): A computer program to calculate F-statistics. J Hered 86:485-486 Green DM (2005) Designatable units for status assessment of endangered species. Conserv Biol 19:1813-1820 Hogg JT, Forbes SH, Steele BM, Luikart G (2006) Genetic rescue of an insular population of large mammals. Proc R Soc Lond B Biol Sci 273:1491-1499. Holman JA (1992) Late Quaternary herpetofauna of the central Great Lakes region, U.S.A.: zoogeographical and paleoecological implications. Quaternary Sci Rev 11:345-351 Howes BJ, Brown JW, Gibbs HL, Herman TB, Mockford SW, Prior KA, Weatherhead PJ (2009) Directional gene flow patterns in disjunct populations of the black ratsnake (Pantherophis obsoletus) and the Blanding’s turtle (Emydoidea blandingii). Conserv Genet 10:407-417 97
Jensen JL, Bohonak AJ, Kelley ST (2005) Isolation by distance, web service. BMC Genet 6:13. v.3.23 http://ibdws.sdsu.edu/ Jost L (2008) GST and its relatives do not measure differentiation. Mol Ecol 17:4015-4026 Jost L (2009) D vs. GST: Response to Heller and Siegismund (2009) and Ryman and Leimar. Mol Ecol 18:2088-2091 Kalinowski ST (2004) Counting alleles with rarefaction: private alleles and hierarchical sampling designs. Conserv Genet 5:539-543 Kalinowski ST (2005) HP-Rare: A computer program for performing rarefaction on measures of allelic diversity. Mol Ecol Notes 5:187-189 King TL, Julian SE (2004) Conservation of microsatellite DNA flanking sequences across 13 Emydid genera assayed with novel bog turtle (Glyptemys muhlenbergii) loci. Conserv Genet 5:719-725 Miller JM, Poissant J, Hogg JT, Coltman DW (2012) Genomic consequences of genetic rescue in an insular population of bighorn sheep (Ovis canadensis). Mol Ecol 21:1583-1596 Mockford SW, Snyder M, Herman TB (1999) A preliminary examination of genetic variation in a peripheral population of Blanding’s turtle, Emydoidea blandingii. Mol Ecol 8:323-327 Mockford SW, McEachern L, Herman TB, Snyder M, Wright JM (2005) Population genetic structure of a disjunct population of Blanding’s turtle (Emydoidea blandingii) in Nova Scotia, Canada. Biol Conserv 123:373-380 Mockford SW, Herman TB, Snyder M, Wright JM (2007) Conservation genetics of Blanding’s turtle and its application in the identification of evolutionarily significant units. Conserv Genet 8:209-219 Osentoski MF, Mockford S, Wright JM Snyder M, Herman TB, Hughes CR (2002) Isolation and characterization of microsatellite loci from the Blanding’s turtle, Emydoidea blandingii. Mol Ecol Notes 2:147-149 Paetkau D, Slade R, Burden M, Estoup A (2004) Direct, real-time estimation of migration rate using assignment methods: a simulation-based exploration of accuracy and power. Mol Ecol 13:55-65 Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol 6:288-295 Pennock DS and Dimmick WW (1997) Critique of the Evolutionarily Significant Unit as a Definition for “Distinct Population Segments” under the U.S. Endangered Species Act. Conservation Biology 11:611-619 Piry S, Alapetite A, Cornuet JM, Paetkau D, Baudouin L, Estoup A (2004) GeneClass2: a software for genetic assignment and first-generation migrant detection. J Hered 95:536539 Power TD (1989) Seasonal movements and nesting ecology of a relict population of Blanding’s turtles (Emydoidea blandingii) in Nova Scotia. M.Sc. Thesis, Acadia University, Wolfville, Nova Scotia. 98
Pritchard JK, Stephens M, Donnelly PJ (2000) Inference of population structure using multilocus genotype data. Genetics 155:945-959 Rannala B, Mountain JL (1997) Detecting immigration by using multilocus genotypes. P Natl Acad Sci USA 94:9197-9221 Raymond M, Rousset F (1995) GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J Hered 86:248-249 Rice WR (1989) Analyzing tables of statistical tests. Evolution 43:223-225 Rousset F (2008) Genepop’007: a complete reimplementation of the Genepop software for Windows and Linux. Mol Ecol Res 8:103-106 Rubin CS, Warner RE, Bouzat JL, Paige KN (2001) Population genetic structure of Blanding’s turtles (Emydoidea blandingii) in an urban landscape. Biol Conserv 99:323-330 Sambrook J, Fritsch EF, Maniatis T (1989) Molecular cloning—a laboratory manual, 2nd edn. Cold Spring Harbor Laboratory Press, New York, NY Sletvold N, Grindeland JM, Zu P, Ågren J (2012) Strong inbreeding depression and local outbreeding depression in the rewarding orchid Gymnadenia conopsea. Conserv Genet 13:1305-1315. Smith LM, Burgoyne LA (2004) Collecting, archiving and processing DNA from wildlife samples using FTA® databasing paper. BMC Ecol 4:4: http://www.biomedcentral.com/1472-6785/4/4 Templeton AR (1986) Coadaptation and outbreeding depression. In: Soulé M (ed) Conservation biology: the science of scarcity and diversity. Sinauer, Sunderland, Massachusetts, pp 105-116 van Dijk PP, Rhodin AGJ (2011) Emydoidea blandingii. In: IUCN 2011. IUCN Red List of Threatened Species. Version 2011.2. . Downloaded on 15 December 2011 van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes 4:535-538 Vargas-Ramirez M, Stuckas H, Castaňo-Mora OV, Fritz U (2012) Extremely low genetic diversity and weak population differentiation in the endangered Colombian river turtle Podocnemis lewyana (Testudines: Podocnemididae) Conserv Genet 13:65-77 Whitlock MC, McCaughley DE (1999) Indirect measures of gene flow and migration: FST ≠ 1/(4Nm + 1). Heredity 82:117–125 Wright S (1943) Isolation by distance. Genetics 28:114-138
99
100
Table 6.1. Genetic diversity at 12 microsatellite loci for 97 Emys blandingii from southern Ontario. Temp. (optimal annealing temperature (°C) determined from temperature gradients of initial PCR reactions) sample size (N), allelic richness (k), observed and expected heterozygosity (HO, HE) and two measures of probability of identify (PI, PISibs) are shown for each locus. Total values show mean ± standard error for N, k, Ne, HO and HE, and PI/PIsibs values with all loci included. Temp.
N
k
HO
HE
PI
PISibs
GmuB08
58
96
7
0.406
0.403
0.378
0.643
GmuD16
56
95
13
0.811
0.818
0.055
0.357
GmuD21
58
95
3
0.253
0.230
0.617
0.789
GmuD28
61
97
16
0.845
0.862
0.034
0.328
GmuD55
56
96
13
0.792
0.816
0.056
0.356
GmuD87
54
88
11
0.659
0.723
0.123
0.419
GmuD88
58
96
11
0.792
0.848
0.040
0.336
GmuD93
58
95
4
0.421
0.552
0.294
0.548
GmuD107
58
96
11
0.771
0.854
0.038
0.332
GmuD121
58
94
8
0.766
0.725
0.103
0.413
Eb17
58
95
6
0.705
0.742
0.109
0.406
Eb19
58
92
4
0.478
0.704
0.140
0.433
94.583 ± 0.701
8.917 ± 1.209
0.642 ± 0.057
0.690 ± 0.057
0.000
0.000
Total
100
101
Table 6.2. Number of alleles (number of private alleles in parentheses) and observed and expected heterozygosities (HO and HE) for 97 Emys blandingii sampled across southern Ontario and genotyped at 12 microsatellite loci. Loci Gmu– from King and Julian (2004). Loci Eb– from Osentoski et al. (2002). Acronyms for sampling areas are defined in Figure 1. Estimated frequency of a null allele is based on analysis of the entire data set following Brookfield (1996). No loci showed consistent evidence for null alleles when sampling areas were analyzed independently. HO = observed heterozygosity; HE = expected heterozygosity; Ar = allelic richness; PAr = private allelic richness.
ALG
EO
LHnorth
PSD
GH
KAW
LE
LHsouth
GmuB08 Estimated null allele frequency = 0.00
Number of alleles HO HE N
2 0.500 0.375 2
4 (1) 0.591 0.583 22
3 (1) 0.500 0.625 2
5 (1) 0.522 0.436 23
4 0.214 0.199 14
3 0.333 0.292 6
2 0.150 0.139 20
1 – – 1
GmuD16 Estimated null allele frequency = 0.00
Number of alleles HO HE N
3 1.000 0.625 2
8 0.905 0.796 21
2 0.500 0.375 2
8 0.826 0.751 23
9 (1) 0.800 0.767 15
7 0.667 0.819 6
7 0.800 0.743 20
1 – – 1
GmuD21 Estimated null allele frequency = 0.00
Number of alleles HO HE N Number of alleles HO HE N
1 0.000 0.000 2 2 0.000 0.500 2
2 0.091 0.087 22 9 (1) 0.818 0.789 22
2 1.000 0.500 2 3 1.000 0.625 2
2 0.174 0.159 23 11 0.870 0.843 23
2 0.286 0.245 14 8 0.733 0.791 15
3 (1) 0.800 0.580 5 5 1.000 0.722 6
2 0.350 0.289 20 10 0.900 0.851 20
2 – – 1 2 – – 1
Number of alleles HO HE N
4 1.000 0.750 2
9 0.773 0.784 22
2 0.500 0.375 2
8 0.773 0.826 22
5 0.800 0.664 15
8 (2) 1.000 0.819 6
6 0.850 0.711 20
1 – –
GmuD28 Estimated null allele frequency = 0.00
GmuD55 Estimated null allele frequency = 0.000
1 101
102
GmuD87 Estimated null allele frequency = 0.000
Number of alleles HO HE N
3 0.500 0.625 2
5 (1) 0.636 0.727 22
2 1.000 0.500 2
8 (3) 0.696 0.641 23
6 0.818 0.736 11
3 0.250 0.656 4
5 (1) 0.579 0.677 19
2 – – 1
GmuD88 Estimated null allele frequency = 0.00
Number of alleles HO HE N
3 1.000 0.625 2
8 (1) 0.818 0.790 22
4 1.000 0.750 2
9 0.913 0.855 23
7 0.500 0.640 14
6 0.500 0.778 6
8 0.800 0.800 20
2 – – 1
GmuD93 Estimated null allele frequency = 0.148
Number of alleles HO HE N
2 0.500 0.375 2
4 (1) 0.455 0.567 22
2 0.500 0.375 2
2 0.522 0.491 23
2 0.286 0.408 14
2 0.667 0.500 6
3 0.400 0.531 20
1 – – 1
GmuD107 Estimated null allele frequency = 0.073
Number of alleles HO HE N
2 1.000 0.500 2
8 0.773 0.721 22
4 1.000 0.750 2
9 0.783 0.823 23
6 0.643 0.694 14
6 0.833 0.694 6
7 0.750 0.659 20
2 – – 1
GmuD121 Estimated null allele frequency = 0.000
Number of alleles HO HE N
3 0.500 0.625 2
7 0.818 0.751 22
3 1.000 0.625 2
6 0.762 0.718 21
5 0.867 0.598 15
6 1.000 0.800 5
5 0.600 0.484 20
1 – – 1
Eb17 Estimated null allele frequency = 0.000
Number of alleles HO HE N
2 0.500 0.375 2
4 0.591 0.699 22
2 0.500 0.375 2
5 0.636 0.636 22
5 (1) 0.733 0.709 15
3 0.600 0.460 5
5 0.950 0.696 20
1 – – 1
Eb19 Estimated null allele frequency = 0.234
Number of alleles HO HE N
2 0.500 0.375 2
4 0.364 0.673 22
2 0.500 0.375 2
4 0.591 0.577 22
4 0.250 0.642 12
3 0.600 0.660 5
3 0.550 0.594 20
2 – – 1
0.583 0.479 – –
0.636 0.664 5.09 0.62
0.75 0.521 – –
0.672 0.646 5.25 0.39
0.578 0.591 4.8 0.53
0.688 0.648 – –
0.64 0.598 4.64 0.33
– – – –
Mean HO Mean HE Ar PAr
102
103
Table 6.3. Genetic differentiation of Emys blandingii among sites in Ontario with N ≥ 15. All FST values are significant (p < 0.05). All FST values were significant (p < 0.05). Average historical number of migrants per generation (Nm) was calculated following Barton and Slatkin (1986). Approximate distance (km)
Dest
FST
Nm
Lake Erie–Golden Horseshoe
151
0.057
0.066
1.350
Lake Erie–Parry Sound District
310
0.062
0.062
1.442
Lake Erie–Eastern Ontario
516
0.100
0.089
1.029
Golden Horseshoe–Parry Sound District
266
0.156
0.100
0.952
Golden Horseshoe–Eastern Ontario
367
0.143
0.099
1.013
Parry Sound District–Eastern Ontario
337
0.064
0.040
3.380
Table 6.4. GENECLASS results for Bayesian assignment tests. Values represent the proportion of individuals from each sampled population assigned to each population. Values in bold indicate the proportion of individuals from each sampled population assigned correctly to their source population. Grey shaded areas indicate the two larger genetic clusters identified by TESS and STRUCTURE.
Sampled population
Assigned population LE
GH
PSD
KAW EO
LE
0.8
0
0.2
0
0
GH
0.07
0.67
0.26
0
0
PSD
0
0
0.74
0.04
0.22
KAW
0
0.17
0.83
0
0
EO
0
0
0.27
0
0.73 103
104
Figure 6.1. Approximate location of collection areas for Emys blandingii sampled across southern Ontario. Top right inset indicates species range in North America (shown in red). Sampling was focused on sites indicated with grey squares: LE = Lake Erie; GH = Golden Horseshoe; PSD = Parry Sound District; KAW = Kawartha Lakes; EO = Eastern Ontario. Sample sizes are included in each site marker. Grey triangles indicate extra samples included opportunistically (each triangle represents an individual turtle): LHsouth = south shore of Lake Huron; LHnorth = north shore of Lake Huron; ALG = Algonquin Provincial Park. Variation in sample sizes results from differential sampling effort; differences in sample sizes are not reflective of variation in actual population sizes. Base map modified from http://www.aquarius.geomar.de/omc/make_map.html and used under the GNU Free Documentation license; range map modified from COSEWIC (2005).
104
105
Figure 6.2. Principal coordinates analysis of sampling areas (A, B) and individuals (C) for 91 Emys blandingii sampled from across southern Ontario based on 12 microsatellite loci.
105
106
106
107
Figure 6.3 (previous page) Population structure inferred by Bayesian inference for 91 Emys blandingii collected across southern Ontario. A) TESS results showing decreasing deviance information criterion (DIC) with increasing values of Kmax. B) STRUCTURE results, mean estimated ln probability of the data (L(K)) for increasing values of K, and ∆K, the second order rate of change of L(K) following Evanno et al. (2005). Site abbreviations are explained in Figure 6.1.
107
108
Chapter 7 Genotypes and ghosts: comparative landscape genetics reveals incongruent barriers to gene flow amongst three species of freshwater turtle Formatted for Conservation Genetics.
7
Abstract
The genetic connectivity of populations is determined by levels of gene flow across the landscape, which is affected strongly by landscape features. Understanding population connectivity is a priority for conservation because maintenance of additive genetic diversity within populations affects their probability of persistence. Comparative approaches to landscape genetics can help to prioritize areas for applied conservation approaches that increase connectivity of multiple species across the landscape, such as wildlife corridors. Here, I compared population structure in three sympatric species of turtle with varying dispersal ability. I used Bayesian clustering analyses, Monmonier’s algorithm, and estimates of gene flow based on data from microsatellite markers to identify areas of genetic connectivity and barriers to gene flow that were shared among species. Monmonier’s algorithm revealed significant but discordant barriers to gene flow in all three species, and boundaries between populations inferred with Bayesian clustering analyses were also incongruent among species. Dispersal ability based on previously published radio-telemetry studies did not predict either estimated gene flow or the number of significant barriers to gene flow. Apart from a possible common barrier to gene flow near the base of the Bruce Peninsula, genetic structure in the three species differed strongly, precluding generalization of biogeographic patterns among species. The discrepancy between the genetic results and previous ecological studies suggested that we may need to re-evaluate our
108
109
understanding of these relatively well-studied species, and highlighted potential areas for future research. Keywords: Clemmys guttata, Chelydra serpentina, Emys blandingii, Ontario, BARRIER, dispersal
7.1 Introduction The genetic connectivity of populations affects their long-term probability of extinction and is, therefore, a priority for conservation (Frankham and Ralls 1998; Frankham et al. 2002). Recently developed methods allow the inference of genetic population structure, rates of gene flow among populations and spatial patterns of gene flow based on genetic data (Wilson and Rannala 2003; Chen et al. 2007; Guillot et al. 2009). It can take many generations for the effects of a changing landscape to be genetically detectable (Landguth et al. 2010, Blair et al. 2012). Therefore, genetic structure in long-lived organisms may indicate the effects of past, but not current, landscapes. A comparative approach can be used both to test hypotheses about the historic distribution and structure of populations and maximize the effectiveness of applied conservation measures by identifying common patterns of genetic population structure among species. Genetic connectivity is measured in terms of gene flow among populations, and differs from demographic connectivity, which determines the impact of immigrants on a population’s growth rate and size but does not necessarily affect its genetic profile (Lowe and Allendorf 2010). In large populations, allopatric speciation may result from the loss of connectivity followed by genetic divergence over time. However, in small, threatened populations, genetic connectivity may be vital to persistence. Genetic drift gradually erodes genetic diversity in small, isolated populations and reduces their long-term adaptive potential (Frankham et al. 2002). Without connectivity to neighboring populations there is no possibility of a rescue effect (augmentation of the gene pool by reproductively successful immigrants; Thrall et al. 1998; Tallmon et al. 2004). Thus, understanding genetic structure of threatened populations is essential for their effective conservation and recovery.
109
110
Anthropogenic barriers to gene flow often have significant demographic and genetic effects on wildlife, but the genetic impacts may be difficult to detect in long-lived organisms (Bennett et al. 2009; Bennett et al. 2010). Thus, studies testing the genetic impact of anthropogenic barriers (dams, highways, urban, and agricultural development) on long-lived species often fail to detect an effect (Kuo and Janzen 2004; Marsack and Swanson 2009; Pittman et al. 2011). This does not necessarily indicate that the tested barriers are permeable because the genetic signatures of barriers develop over generations. It may take 10 - 200 generations for a new barrier to modify the genetic profile of affected populations sufficiently for detection, and up to 15 generations for the removal of a barrier to be detectable (Landguth et al. 2010, Blair et al. 2012). As a result, tests of genetic connectivity in long-lived organisms are especially unlikely to detect effects of relatively recent anthropogenic landscape modifications. This applies even if the demographic impact of the modification is devastating. For example, the endangered spotted turtle (Clemmys guttata) has a generation time > 25 years (COSEWIC 2004). Extant populations of Cl. guttata are extremely isolated from one another and the isolation is maintained by current habitat modifications that make gene flow among them impossible (COSEWIC 2004). However, genetic structure among populations of Cl. guttata in Ontario most likely reflects the signature of a landscape inhabited > 500 - 5,000 years ago. Population genetic structure may therefore indicate historical landscape effects, while population persistence is affected by the current landscape structure. The field of landscape genetics involves measurements of genetic connectivity of populations across landscapes and investigations into how landscape features affect gene flow (Manel et al. 2003, Epps et al. 2007). Genetic and spatial data can be integrated in Bayesian inference of population structure (Chen et al. 2007; Guillot et al. 2005) to define the geographic limits of genetic populations. More complex analyses integrate resistance layers to explicitly test the effects of different landscape features and habitat types on gene flow among populations. Resistance layers describe the relative ease of dispersal of a study organism or the relative rate of gene flow through different habitat types (O’Brien et al. 2006; Wang et al. 2008). They allow explicit tests of hypotheses related to landscape structure when integrated into least cost path models (Adriaensen et al. 2003) or when considered using circuit theory (McRae 2006). Unfortunately, assigning costs to resistance layers requires data such as dispersal distances, habitat selection and relative survivorship of individuals in different habitats that are not 110
111
available for most wild species. As a result, parameterization of resistance layers often relies on “expert opinion” or direct evidence from telemetry studies with small sample sizes (Spear et al. 2010). When data are insufficient to assign accurate values to resistance layers, population-level analyses provide a simpler but more robust alternative. For example, Manni et al. (2004) use Delaunay triangulation to connect populations in a single geometric network, and apply Monmonier’s maximum difference algorithm (Monmonier 1973) to identify boundaries between neighboring populations where the change in genetic distance is significant. This method provides less fine-scale information about gene flow across the landscape than least cost path models or circuit theory but relies on fewer assumptions. It is also well-suited to clustered sampling designs. Genetic and demographic connectivity of populations can be increased using conservation tools ranging in scale from small wildlife underpasses beneath large highways to translocations or large wildlife corridors. The financial cost of these mitigation measures is significant. Therefore, the most economical mitigation measures will target multiple species. Comparative approaches to landscape genetics (DiLeo et al. 2010; Goldberg & Waits 2010; Cyr and Angers 2011) can identify areas of historic connectivity for multiple species. Such areas could be prioritized for mitigation measures. Comparative studies can also identify pairs of populations that have been isolated for many generations, and assign a lower priority for mitigation to the area separating them compared to areas of historic connectivity. Interpretation of genetic population structure in the context of direct evidence from field research provides a more holistic view of a species’ behavior and may highlight knowledge gaps in both types of research. The objective of this study is to test congruence of detectable barriers to gene flow in three species of sympatric freshwater turtles that have differing dispersal abilities. I test the hypothesis that species with higher vagility experience fewer barriers to gene flow, and I use Bayesian analyses from Chapters 3, 4 and 5 and analyses based on Monmonier’s algorithm to identify common genetic boundaries and barriers to gene flow among species.
111
112
7.2 Methods 7.2.1
Study species and relative dispersal ability
I compared genetic population structure in the spotted turtle (Cl. guttata), the Blanding’s turtle (Emys (=Emydoidea) blandingii) and the snapping turtle (Chelydra serpentina). These species have similar generation times, but direct evidence from radio-telemetry studies demonstrates that their vagility differs substantially. Clemmys guttata typically move less than 500 m in a year and rarely move farther than 2 km (Litzgus 1996; Rasmussen and Litzgus 2010; Ernst and Lovich 2009; Banning-Anthonysamy 2012). Chelydra serpentina may undertake movements of >10 km between wetlands or to find a suitable nesting site, although overland movements are typically shorter (summarized in Ernst and Lovich 2009; Obbard and Brooks 1980; J. Paterson pers. comm.). Emys blandingii may also migrate several kilometres to nest and have been recorded migrating > 10km overland (COSEWIC 2005; Power 1989). This direct evidence was used as a proxy for vagility and I categorized the dispersal ability of Cl. guttata, Ch. serpentina and E. blandingii as being low, moderate or high, respectively (Table 7.1).
7.2.2
Bayesian delineation of population boundaries
Microsatellite data were compiled from three previous studies, using 11 loci for Cl. guttata (Chapter 3, N = 253), 10 for Ch. serpentina (Chapter 4, N = 167) and 12 for E. blandingii (Chapter 5, N = 91). Sampling sites are shown in Figure 7.1. Population differentiation was calculated using Dest (Jost 2008) and Nei’s absolute differentiation (DST, Nei, 1973) in SMOGD (Crawford 2010) and MSANALYZER (Dieringer and Schlötterer 2003). Populations were defined based on Bayesian inference in the programs STRUCTURE (Pritchard et al. 2000) and TESS (Chen et al. 2007), as described in Chapters 3–5. Genetically distinct clusters from each species were used as independent units (“genetic populations”) for barrier estimation (Figure 7.1, inset). Effective population sizes of E. blandingii populations were estimated in ONeSAMP (Tallmon et al. 2008) and compared to estimates for Cl. guttata and Ch. serpentina (Chapter 4) using a one-way ANOVA in SPSS v.20.0 (SPSS Inc., Chicago, Illinois).
112
113
7.2.3
Barrier estimation with Monmonier’s algorithm
Barriers to gene flow were also estimated for each data set using Monmonier’s maximum distance algorithm and Delaunay triangulation implemented in BARRIER V.2.2 (Manni et al. 2004). BARRIER used pairwise matrices of genetic and geographic distance among sampling sites to infer barriers to gene flow relative to site locations. These analyses were based on measures of pairwise genetic distance (Nei’s D, Dest and FST) among populations. Because measures of population differentiation are sensitive to small sample sizes (Kalinowski 2005), only populations with N > 12 were included in these analyses. The analysis was run first with Dest matrices (Jost 2008) because Dest provided the most accurate available measure of population differentiation. Bootstrap replicates were required to test significance of barriers, but these could not be calculated for Dest (N. Crawford, pers. comm.). Therefore, the analysis was re-run with Nei’s absolute difference (DST, Nei 1973) to verify congruence between barriers based on the two measures. Finally, 5,000 bootstrap replicates of DST were used to determine the significance of each inferred barrier. Bootstrap support > 0.90 was considered significant.
7.2.4
Estimation of migration among populations
The average historical number of migrants per generation (Nm) was calculated for each pair of genetically differentiated clusters within each species following the private alleles method of Barton and Slatkin (1986). This value is a historical average of the number of individuals exchanged among populations per generation and it does not represent contemporary gene flow. Rather, it provides a basis for comparison of historic, genetic population connectivity among species. Estimates of Nm and pairwise distances between sites were log-transformed to achieve a normal distribution. Pearson`s correlation coefficient was used to test the relationship between geographic distance and Nm. Estimates of Nm were also compared directly among the four areas where sufficient samples were available from all three species: LE1, GH, GB/PSD and EO1. For Ch. serpentina, subpopulation 3 was used for GB/PSD comparisons (Figure 7.1, inset; Fig. 2). These data remained non-normal after transformation. Therefore, I tested for differences in Nm among species with Friedman’s test for related samples in SPSS v.20.0 (SPSS Inc., Chicago, Illinois). 113
114
The number of first-generation migrants in each population was estimated in GENECLASS V. 2.0 (Piry et
al. 2004) using the Bayesian method of Rannala and Mountain (1997), and re-
sampling 100,000 times with the Markov Chain Monte Carlo method of Paetkau (2004). GENECLASS estimated
the likelihood of each individual’s genotype originating in the population
where it was sampled (L = L_home, the likelihood of sampling an individual`s genotype in a population based on the genetic profile of that population). The estimate L= L_home was appropriate in this case because it does not assume that all existing populations were sampled (Piry et al. 2004). I considered also applying the Bayesian method of Wilson and Rannala (2003) to estimate contemporary gene flow. However, Faubert et al. (2007) showed that this method performed poorly when FST < 0.05 and several tested population pairs met this criterion (Chapter 3; 4; 5).
7.3 Results 7.3.1
Bayesian delineation of population boundaries
Comparison of population structure inferred previously with STRUCTURE and TESS (Chapters 3, 4, 5) revealed a substantial lack of geographic congruence in inferred boundaries among species (Figure 7.2). For example, three sampled sites along the shore of Lake Huron (LH1, LH2 and BP) were clustered differently in Cl. guttata (LH1 and LH2 vs. BP) than in Ch. serpentina (LH1 vs. LH2 and BP). Samples from GH formed a potentially distinct subpopulation in E. blandingii, while GH grouped with samples from the northwest shore of Lake Erie in Cl. guttata, and grouped with Georgian Bay and the Bruce Peninsula in Ch. serpentina. A general east-west split occurred in all three species but its location was inconsistent. In Cl. guttata, STRUCTURE resolved HC, EO1 and EO2 into a single eastern cluster at K = 2 and grouped all other samples together. In Ch. serpentina, all samples from LH2 eastwards, including GH, cluster together at K = 2. In E. blandingii, LE and GH separate from all other samples at K = 2. Effective population sizes estimated in ONeSAMP did not differ significantly among species (ANOVA: F = 0.165, d.f = 2, p = 0.850). Average estimated Ne and ranges of the estimates for each species are listed in Table 7.2. 114
115
7.3.2
Barrier estimation with Monmonier’s algorithm
Monmonier’s algorithm resolved significant barriers for each species (Figures 7.3, 7.4) and indicated further barriers that approached significance. Barriers showed some potential overlap near the base of the Bruce Peninsula but were otherwise spatially dissimilar. Monmonier’s algorithm detected fewer significant boundaries in each dataset than Bayesian clustering.
7.3.3
Estimation of migration among populations
GENECLASS
identified four potential first-generation migrants (p < 0.01) in samples of Cl.
guttata. Two of these (one each from LH2 and EO2) were assigned most strongly to their source population, indicating either that they were migrants from unknown, unsampled populations or that they were not in fact migrants. One Cl. guttata from LE1 was implicated as a potential migrant from GB1, and an individual from EO1 was implicated as a potential migrant from EO2. Estimates of Nm between sites ranged from 0.33 to 3.03, with an overall Nm of 1.74 among all sampled sites (Table 7.3a). No first generation migrants were detected among sampled Ch. serpentina populations (α = 0.01). The value of Nm between populations A and B was 3.78; average Nm among all sampled sites was 2.62. Pairwise Nm among subpopulations ranged from 4.585 to 1.016 (Table 7.3b). GENECLASS
detected 27 E. blandingii as possible first-generation migrants (p < 0.01); of these,
13 were assigned most strongly to their population of origin. The 14 others included three potential migrants in PSD (two from LE, two from KAW); three in GH (one from LE, two from PSD), four in KAW (three from PSD, one from GH), one in LE (from PSD), and two in EO (one from PSD, one from KAW). Estimates of Nm ranged from 0.952 to 3.380 (Table 7.3c). Geographic distance was not correlated with Nm within any species or over all species (Pearson`s correlation coefficient, r = -0.114, N = 66, p = 0.363). Estimates of Nm did not differ among species at the four sites tested with Friedman’s test (N = 6, d.f. = 2, χ2 = 4.000, p = 0.135, Figure 7.5) or when comparing the means of all pairwise Nm among species (ANOVA: F = 1.874, d.f. = 2, p = 0.162).
115
116
7.4 Discussion Genetic structure and barriers to gene flow in three sympatric species of turtle sampled across > 500 km show remarkably little congruence. This variation represents the most important finding of this study and demonstrates that genetic population structure from one species cannot predict structure in similar species in the same landscape. Furthermore, variance in patterns of gene flow and diversity cannot be explained simply by variation in the dispersal ability of these three species. These results are inconsistent with predictions based on previous studies of these species’ spatial ecology and behavior, and overall have important implications for the conservation of genetic diversity in communities of threatened species.
7.4.1
Comparative landscape genetics of freshwater turtles
A general east-west break occurs in Cl. guttata, Ch. serpentina and E. blandingii across southern Ontario, with further sub-structuring of populations within each eastern and western cluster (Figure 7.2a; Chapters 7.3, 7.4, 7.5). However, the location of this break is discordant among the three species. In Cl. guttata, significant barriers isolate the populations from Hastings County and the Bruce Peninsula from their nearest neighbors. The Hastings County samples are differentiated from all other sampled Cl. guttata (Chapter 3). Given similar patterns of differentiation recorded in channel darters from the same watershed (Kidd et al. 2011), it would be informative to sample Ch. serpentina and E. blandingii from this area as well. Unfortunately, samples of Ch. serpentina and E. blandingii from Hastings County were not available for this study. The barrier isolating Cl. guttata in the Bruce Peninsula is not reflected in Ch. serpentina because samples from LH2 occur in the same genetic population as BP, GB and N. However, individual-based analyses in STRUCTURE and TESS show
significant differentiation between Ch. serpentina from LH1 and
LH2, sites between which Cl. guttata are genetically continuous (Figure 7.2). Thus, dispersal along the Lake Huron shoreline is disrupted in both Cl. guttata and Ch. serpentina, but in different places. These patterns likely reflect differing colonization routes following the end of the last ice age, 6–4 ka BP, because the lag time needed to detect effects of genetic barriers may be as long as 200 generations (Landguth et al. 2010; > 5,000 years for these species). The current landscape may be maintaining this genetic structure or the removal of a previous barrier may 116
117
have occurred but may not yet be detectable. Given that Cl. guttata currently occurs in only a few disjunct populations in Ontario (the majority of them sampled here), it is more likely that the remaining extant populations of Cl. guttata will remain isolated. The western barrier to gene flow inferred for E. blandingii extends towards the base of the Bruce Peninsula and coincides roughly with the shift in allele frequencies in Ch. serpentina between sites LH1 and LH2. Resolution of this barrier’s location is especially limited because only one sample of E. blandingii was obtained from site LH1 in four years of extensive mark-recapture surveys. Emys blandingii was common at LH1 in the mid-1990s (J. Skevington, pers. comm.) but this population has apparently declined severely over the last 10 years (C. Davy, unpublished data). North of LH1, E. blandingii becomes rare. Only four records of the species exist from the Bruce Peninsula (Ontario Nature Reptile and Amphibian Atlas; J. Paterson and J. Urquhart, pers. comm.). No robust populations are known from the Bruce Peninsula despite the presence of suitable habitat (J. Crowley, pers. comm.) and these four reports may represent released animals or rare long-distance migrants. Therefore, there may be a small E. blandingii population on the Bruce Peninsula but the lack of large populations north of LH1 indicates a real gap in distribution rather than a sampling bias. This gap is consistent with the placement of the inferred barrier to gene flow in E. blandingii south of LH2. Overall, the landscape of south-western Ontario was apparently more permeable to Cl. guttata than to E. blandingii or Ch. serpentina, while the opposite pattern occurs in eastern Ontario (from Parry Sound district eastwards). South-western Ontario is characterized by sand and claysoil substrates, while a large portion of central and eastern Ontario (including PSD, Alg, KAW, EO2 and EO3) is located on the Canadian Shield. The observed pattern suggests potential variation in landscape permeability among these species, and this pattern deserves further consideration. Estimates of Nm < 10 among all populations indicate that none of the sampled populations are in drift connectivity, the genetic connectivity required to maintain approximately equal allele frequencies among populations (Lowe and Allendorf 2010). Lack of drift connectivity across the study area is also indicated by Bayesian clustering analyses that indicate K > 1 for all three species. Maintenance of inbreeding connectivity, the genetic connectivity required to prevent 117
118
inbreeding depression, requires a minimum of one migrant per generation (Mills and Allendorf 1996). Each species in this study has Nm < 1.0 between one or more pairs of sites. However, some of the tested sites are hundreds of kilometers apart, and they do not represent all extant populations of these species in southern Ontario. The occurrence of Nm > 1.0 in all species between two or more sites demonstrates that freshwater turtles were historically able to maintain low levels of gene flow across large landscapes despite evidence for significant barriers to gene flow. Estimates of Nm based on private alleles (Barton and Slatkin 1986) represent an average number of immigrants exchanged between populations per generation. This estimate is a historic average and cannot reflect the effects of severe habitat destruction in southern Ontario in the past 200 years. As a result, it is surprising that apparent dispersal ability does not appear to predict Nm or the number of barriers to gene flow estimated by Monmonier’s algorithm. The average Nm among populations did not differ among species. Monmonier’s algorithm estimated two barriers for Emys blandingii, a single barrier for Ch. serpentina surrounding the population at the Golden Horseshoe, and only three barriers for Cl. guttata., which was less than expected based on this species’ apparently low dispersal tendency. Clustering in TESS and STRUCTURE identified a greater number of genetic clusters than were inferred based on boundary estimation in BARRIER (consistent with the findings of Blair et al. 2012). However, the clusters estimated by TESS and STRUCTURE show similar incongruence among species to the barriers estimated using Monmonier’s algorithm. All three analyses support a hypothesis of greater historic landscape permeability in south-western Ontario for Cl. guttata, and in central and eastern Ontario for E. blandingii and Ch. serpentina, as noted above. The long generation times of turtles may result in sufficient movement per generation to maintain migration rates between distant populations, even in species with low vagility. However, perhaps our understanding of vagility, which influences demographic connectivity of populations, is not a good predictor of actual gene flow across the landscape, which influences genetic connectivity (Lowe and Allendorf 2010). The genetic results are somewhat counterintuitive when considered in the context of the relative vagility of the species. For example, although populations of Cl. guttata are not in drift connectivity, sufficient gene flow exists (or existed before significant landscape modification occurred) to prevent significant loss 118
119
of alleles from populations. The relatively low vagility of Cl. guttata has been documented in numerous studies (e.g. Litzgus 1996; 2004; Seburn 2003; Kaye et al. 2006; Rasmussen and Litzgus 2010; Yagi and Litzgus 2012) and predicts relatively low gene flow among populations. Yet although significant structure exists within Cl. guttata in Ontario, no populations are fixed for alleles at any loci and estimates of Nm are comparable to those for other species. One possible explanation is that multiple unknown populations exist or existed recently across the landscape. While there are probably some unknown populations of Cl. guttata in Ontario, I consider this explanation highly unlikely given the amount of survey effort expended by professional biologists and amateur naturalists across the province. As discussed in Chapter 4, non-random mating could also maintain genetic diversity in small populations of Cl. guttata, and this possibility should be explored further. On the other hand, Ch. serpentina is relatively widespread across southern Ontario, and telemetry studies regularly record movements of many kilometers within a single year (e.g. Obbard and Brooks 1980; Paterson et al. 2012). High gene flow among populations seems especially likely because females will migrate long distances to nest, which should serve to disperse their genetic material across large distances. In spite of this apparently high vagility, Ch. serpentina individuals in subpopulation 3 (SP3; sites LH2, BP, GB and N) are fixed for an allele at locus Cs18, while individuals from GH are fixed for a single allele at locus Cs22. The Euclidean distance between sites N and GH is less than 50 kilometers, but the genetic evidence demonstrates that these sites have been isolated for several generations. A combination of direct evidence (radio telemetry) and further genetic sampling targeted along boundaries between identified populations could shed light on the mechanisms that maintain genetic differentiation on small spatial scales in a species with apparently high dispersal ability.
7.4.2
Long-lived organisms and landscape genetics
Landscape genetics strives to understand the effect of landscape features on the genetic structure of populations (Manel et al. 2003; Holderegger and Wagner 2008). This is an important and appealing objective, especially in the context of current, rapid anthropogenic landscape modification (e.g. Amos et al. 2012). However, evidence for the effects of specific, recent landscape modifications on long-lived freshwater turtles is either equivocal or lacking (e.g. Kuo and Janzen 2004; Marsack and Swanson 2010; Bennett et al. 2010; Pittman et al. 2011; Banning119
120
Anthonysamy 2012). Based on their long generation times, simulation studies predict this result (Landguth et al. 2010), a point that many of these studies independently acknowledge. Simulation studies (Balkenhol et al. 2009; Landguth et al. 2010; Blair et al. 2012) show that it is inadvisable to investigate the impact of a specific, recent landscape modification (or modifications) on long-lived organisms by testing for differences in allele frequencies around the potential “barrier” because a genetic signature can take many generations to develop. This renders hypotheses about the effects of new barriers to gene flow impossible to test based solely on genetic data. At the very least, such studies should simultaneously investigate genetic structure elsewhere in the landscape to provide a context in which the data can be more accurately interpreted. Ideally, direct evidence of changes in demographic connectivity should also be obtained (for example, from radio-tracking or capture-mark-recapture studies; Lowe and Allendorf 2010; Segelbacher et al. 2010). Whichever approach is taken, researchers must always interpret their data with the understanding that genetic signatures in populations of long-lived organisms generally reflect the ghost of historic landscapes. When genetic connectivity of long-lived organisms is a question of interest I recommend an approach similar to the one taken here. Geographically representative and intensive sampling of populations across a wide geographic range (relative to the dispersal ability of the species) will avoid sampling within a panmictic area and obtaining uninformative results. Identification of broad-scale population structure and barriers to gene flow (if possible, using more sophisticated methods to detect barriers than those used here) will provide the context necessary to study the long-term effects of potential anthropogenic barriers to gene flow. However, genetic methods will not detect effects of recent landscape modifications in long-lived organisms or species with low levels of dispersal (Landguth et al. 2010; Cyr and Angers 2011) and should probably not be used to do so. The Introduction of this Chapter provides a brief discussion of resistance layers and the challenges inherent in their parameterization (Spear et al. 2010; Braunisch et al. 2010). Landscape resistance describes the relative ease with which a species can move through different parts of the landscape. Resistance is ideally quantified using direct, empirical measures of the relative cost of movement through different habitat types across the landscape but may also be 120
121
based on “expert opinion” (Segelbacher et al. 2010). This study illustrates the potential risks of assigning costs to resistance layers based on expert opinion (Segelbacher et al. 2010) because an “expert opinion” of landscape resistance based on vagility of Cl. guttata, Ch. serpentina and E. blandingii would be inconsistent with the genetic data. To my knowledge, resistance layers have never been quantified for any species of turtle. Parameterization of resistance layers for turtles will require specific data that could be collected alongside ongoing field studies. Such analyses could be extremely informative, but the challenge is the accurate parameterization of resistance layers. In less robust organisms such as amphibians, possible correlates of landscape resistance include the relative risk of dehydration in different habitat types (see Mazerolle and Desrochers 2005; Stevens et al. 2006). No obvious corollary exists for turtles, but possible measurements could include the relative probability that different species will cross roads and highways of various sizes or move between patches of suitable habitat separated by agricultural, urbanized, forested, and other less suitable habitat types. Variation among populations and habitat types is a further challenge because spatial ecology and habitat preferences may vary across the range of a species, between sexes and among individuals (Litzgus et al. 2004; Edge et al. 2010; Rasmussen and Litzgus 2010; Paterson et al. 2012 ). Temporal variation in spatial ecology and dispersal behavior may also occur as a landscape changes over time (Yagi and Litzgus 2012). Nevertheless, finding a way to parameterize resistance layers for turtles and combining these with in-depth sampling will provide a more detailed understanding of demographic and genetic connectivity in natural and modified landscapes.
7.4.3
Conservation implications
Maintenance of genetic population structure includes increasing gene flow among historically connected, recently isolated sites, and avoiding increased gene flow among historically isolated sites (Frankham et al. 2002). Comparative population genetics of multiple species allows identification of shared areas of high gene flow among populations and facilitates the prioritization of areas for mitigation measures. These could include wildlife corridors, highway underpasses and restoration of riparian zones that might decrease landscape resistance for the three species. Unfortunately, I detected no substantial overlap in areas of gene flow between 121
122
sampling sites, which makes it more difficult to suggest mitigation measures that will impact all three species equally. On the other hand, some overlap occurred in the approximate locations of barriers: all three species appear to have experienced historic barriers to gene flow in the area south of the Bruce Peninsula. Artificially increasing connectivity between historically isolated populations can alter existing genetic structure and decrease overall genetic diversity by genetically homogenizing the metapopulation (Frankham et al. 2002). Facilitated breeding among historically isolated populations may also lead to outbreeding depression that can cause reduced fitness of offspring from differentiated populations (Templeton 1986). Outbreeding depression is unlikely among recently diverged populations (Frankham et al. 2011), but it only takes a few generations to alter existing genetic structure of a population. Thus, an area where several species experience a barrier to dispersal and gene flow would be a poor choice for large-scale measures to increase connectivity. Similar disparity in population structure among species occurs in two sympatric snakes in southwestern Ontario (DiLeo et al. 2010). Discordant patterns of gene flow and population structure in the eastern garter snake (Thamnophis sirtalis) and the eastern foxsnake (Mintonius gloydi) may result from differing effects of habitat fragmentation causing drastically different landscape permeability in the two species. Discordant patterns may result from differing effective population size (Ne), because populations with smaller Ne are affected more strongly by genetic drift and diverge more quickly as a result (DiLeo et al. 2010). However, my results show overall discordance among species without comparable differences in Ne. It is unlikely that any single factor can explain the lack of correspondence observed here. Thus, the most important finding of this study is the overall disparity of genetic population structure among species. Analysis of genetic population structure is time-consuming and costly, and the ability to generalize genetic population structure from a studied species to other, similar species would be very useful. However, my results demonstrate that population structure of one species cannot predict structure in another. The three species of turtle sampled here have different microhabitat preferences but have similar current distributions and are sympatric in many locations. They share similar post-glacial colonization histories and life-history strategies 122
123
(Holman 1992; Chapter 4). Yet populations of these three species are structured very differently across the landscape, with patterns of genetic structure that are inconsistent with our understanding of their spatial ecology based on direct evidence from field studies. The identification of historic and persistent barriers to gene flow can highlight gaps in our knowledge of well-studied species; in this case, genetic methods indicate that current assumptions about the relative dispersal abilities of three species of turtle may be inaccurate. Directions for further research include the combination of targeted genetic sampling along boundaries between populations with field studies of spatial ecology. Integrating genetic methods with field studies will allow testing of further hypotheses about fine-scale patterns of gene flow across the landscape in these three species and result in a more holistic understanding of their biology. Finally, consideration must be given to the low effective population sizes of turtles in Ontario. All but two sampled populations have Ne < 50, the often quoted theoretical lower limit required to avoid the short-term deleterious effects of inbreeding (Franklin 1980). The traditional estimate for effective population size required to maintain genetic diversity in the long-term is 500 (Franklin 1980). In wild populations the minimum effective population size actually required for long-term persistence varies substantially among species and is likely to be significantly larger than the “50:500 rule” suggests (Traill et al. 2007; Traill et al. 2010). Thus, effective population sizes for turtles in Ontario – including Ch. serpentina, which until recently was called the “common” snapping turtle – are probably too low to avoid the genetic impacts of population decline over the coming generations. Demographic impacts may prove more harmful to populations than a gradual increase in inbreeding or loss of allelic richness, but these data provide further evidence that rapid action is required to conserve these long-lived but highly threatened species.
7.5 References Adriaensen F, Chardron JP, DeBlust G, Swinnend E, Villalbad S, Gulinckd H, Matthysen E (2003) The application of ‘least-cost’ modelling as a functional landscape model. Landscape Urban Plan 64:233–247 Amos JN, Bennett AF, MacNally R, Newell G, Pavlova A, Radford JQ, Thomson JR, White M, Sunnucks P (2012) Predicting landscape-genetic consequences of habitat loss, fragmentation and mobility for multiple species of woodland birds. PLoS ONE 7: e30888 123
124
Balkenhol N, Waits LP, Dezzani RJ (2009) Statistical approaches in landscape genetics: an evaluation of methods for linking landscape and genetic data. Ecography 32:818–830 Banning-Anthonysamy WJ (2012) Spatial ecology, habitat use, genetic diversity, and reproductive success: measures of connectivity of a sympatric freshwater turtle assemblage in a fragmented landscape. PhD dissertation, University of Illinois at UrbanaChampaign. Barton NH, Slatkin M (1986) A quasi-equilibrium theory of the distribution of rare alleles in a subdivided population. Heredity 56:409–416 Bennett AM, M Keevil, JD Litzgus (2009) Demographic differences among populations of Northern Map Turtles (Graptemys geographica) in intact and fragmented sites. Can J Zool 87:1147–1157 Bennett AM, Keevil M, Litzgus JD (2010) Spatial ecology and population genetics of northern map turtles (Graptemys geographica) in fragmented and continuous habitats in Canada. Chel Conserv Biol 9:185–195 Blair C, Weigel DE, Balazik M, Keeley ATH, Walker FM, Landguth E, Cushman S, Murphy M, Waits L, Balkenhol N (2012) A simulation-based evaluation of methods for inferring linear barriers to gene flow. Mol Ecol Res 12:822–833 Braunisch V, Segelbacher G, Hirzel AH (2010) Modelling functional landscape connectivity from genetic population structure: a new spatially explicit approach. Mol Ecol 19:36643678 Chen C, Durand E, Forbes F, François O (2007) Bayesian clustering algorithms ascertaining spatial population structure: a new computer program and a comparison study. Mol Ecol Notes 7:747–756 Congdon JD, Graham TE, Herman TB, Lang JW, Pappas MJ, Brecke BJ (2008) Emydoidea blandingii (Holbrook 1838) – Blanding’s turtle. In: Rhodin AGJ, Pritchard PCH, van Dijk PP, Saumure RA, Buhlmann KA, Iverson JB (eds.). Conservation biology of freshwater turtles and tortoises: a compilation project of the IUCN/SSC Tortoise and Freshwater Turtle Specialist Group. Chelon Res Monogr 5:015.1-015.12. doi:10.3854/crm.5.015.blandingii.v1.2008, http://www.iucn-tftsg.org/cbftt/ COSEWIC (2004) COSEWIC assessment and update status report on the spotted turtle Clemmys guttata in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. vi + 27 pp. (www.sararegistry.gc.ca/status/status_e.cfm). COSEWIC (2005) COSEWIC assessment and update status report on the Blanding's Turtle Emydoidea blandingii in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. viii + 40 pp. (www.sararegistry.gc.ca/status/status_e.cfm) COSEWIC (2008) COSEWIC assessment and status report on the Snapping Turtle Chelydra serpentina in Canada. Committee on the status of endangered wildlife in Canada. Ottawa. vii + 47 pp. (www.sararegistry.gc.ca/status/status_e.cfm). Crawford NG (2010) SMOGD: software for the measurement of genetic diversity. Mol Ecol Resour 10:556–557 124
125
Cyr F, Angers B (2011) Historical process lead to false genetic signal of current connectivity among populations. Genetica 139:1417–28 DiLeo MF, Row JR, Lougheed SC (2010) Discordant patterns of population structure for two codistributed snake species across a fragmented Ontario landscape. Divers Distrib 16:571– 581 Dieringer D, Schlötterer C (2003) Microsatellite analyser (MSA): a platform independent analysis tool for large microsatellite data sets. Mol Ecol Notes 3:167–169 Edge CB, Steinberg BD, Brooks RJ, Litzgus JD (2010) Habitat selection by Blanding’s Turtles (Emydoidea blandingii) in a relatively pristine landscape. Écoscience 17:90–99 Epps CW, Wehausen JD, Bleich VC, Torres SG, Brashares JS (2007) Optimizing dispersal and corridor models using landscape genetics J Appl Ecol 44:714–724 Ernst CH, Lovich JL (2009) Turtles of the United States and Canada, 2nd ed, Johns Hopkins University Press, Baltimore, Maryland Faubert P, Waples R, Gaggiotti O (2007) Evaluating the performance of a multilocus Bayesian method for the estimation of migration rates. Mol Ecol 16:1149–1166 Frankham R, Ralls K (1998) Inbreeding leads to extinction. Nature 392:441–442 Frankham R, Ballou JD, Briscoe DA (2002) Introduction to conservation genetics. Cambridge University Press, Cambridge Frankham R, Ballou JD, Eldridge MDB, Lacy RC, Ralls K, Dudash MR, Fenster CB (2011) Predicting the probability of outbreeding depression. Conserv Biol 25:465–475 Franklin R (1980) Evolutionary change in small populations. In: Soulé ME, Wilcox BA (eds.), Conservation Biology: An Evolutionary Ecological Perspective. Sinauer Associates, Sunderland, Massachusetts, pp135–140 Galbraith DA, Chandler MW, Brooks RJ (1987) The fine structure of home ranges of male Chelydra serpentina: are snapping turtles territorial? Can J Zool 65:2623–2629 Galbraith DA, Brooks RJ, Obbard ME (1989) The influence of growth rate on age and body size at maturity in female snapping turtles Chelydra serpentina. Copeia 1989:896–904 Goldberg CS, Waits LP (2010) Comparative landscape genetics of two pond-breeding amphibian species in a highly modified agricultural landscape. Mol Ecol 19:3650–3663 Guillot G, Mortier F, Estoup A (2005) GENELAND: a program for landscape genetics. Mol Ecol Notes 5:712–715 Guillot G, Leblois R, Coulon A, Frantz AC (2009) Statistical methods in spatial genetics. Mol Ecol 18:4734–4756 Holderegger R, Wagner HH (2008) Landscape genetics. BioScience 58:199–207 Holman JA (1992) Late Quaternary herpetofauna of the central Great Lakes region, U.S.A.: zoogeographical and paleoecological implications. Quaternary Sci Rev 11:345–351 Jost L (2008) GST and its relatives do not measure differentiation. Mol Ecol 17:4015–4026
125
126
Kalinowski ST (2005) Do polymorphic loci require large sample sizes to estimate genetic distances? Heredity 94:33–36 Kaye DR, Walsh KM, Rulison EL and Ross CC (2006) Spotted turtle use of a culvert under relocated Route 44 in Carver, Massachusetts. In: Irwin CL, Garrett P, McDermott KP (eds) Proceedings of the 2005 International Conference on Ecology and Transportation. Center for Transportation and the Environment, North Carolina State University, Raleigh, NC, pp 426-432 Kidd A, Reid S, Wilson C (2011) Local and regional population genetic structure of the threatened channel darter in Ontario and Quebec. Poster presentation at American Fisheries Society 41st Annual Meeting, Seattle, Washington, Sept. 4–8, 2011. Kuo CH, Janzen FJ (2004) Genetic effects of a persistent bottleneck on a natural population of ornate box turtles (Terrapene ornata). Conserv Genet 5:425–437 Landguth EL, Cushman SA, Schwartz MK, McKelvey KS, Murphy M, Luikart G (2010) Quantifying the lag time to detect barriers in landscape genetics. Mol Ecol 19:4179–4191 Litzgus JD (1996) Life history and demography of a northern population of spotted turtles, Clemmys guttata. MSc Thesis, University of Guelph, Ontario. 145 pp. Litzgus JD, Mousseau TA, Lannoo MJ (2004) Home range and seasonal activity of southern spotted turtles (Clemmys guttata): implications for management. Copeia 2004:804–817 Litzgus JD (2006) Sex differences in longevity in the spotted turtle (Clemmys guttata). Copeia 2006:281–288 Lowe WH, Allendorf FW (2010) What can genetics tell us about population connectivity? Mol Ecol 19:3038–3051 Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18:189–197 Manni F, Guérard E, Heyer E (2004) Geographic patterns of (genetic, morphologic, linguistic) variation: how barriers can be detected by “Monmonier’s algorithm”. Hum Biol 76:173– 190 Marsack K, Swanson BJ (2009) A genetic analysis of the impact of generation time and roadbased habitat fragmentation on eastern box turtles (Terrapene c. carolina). Copeia 2009:647–652 Mazerolle MJ, Desrochers A (2005) Landscape resistance to frog movements. Can J Zool 83:455–464 McRae BH (2006) Isolation by resistance. Evolution 60:1551–1561 Mills LS, Allendorf FW (1996) The one-migrant-per-generation rule in conservation and management. Conserv Biol 10:1509–1518 Monmonier M (1973) Maximum-difference barriers: An alternative numerical regionalization method. Geogr Anal 3:245–261 Nei M (1973) Analysis of gene diversity in subdivided populations. Proc Nat Acad Sci USA 70:3321–3323 126
127
O’Brien D, Manseau M, Fall A, Fortin MJ (2006) Testing the importance of spatial configuration of winter habitat for woodland caribou: an application of graph theory. Biol Conserv 130:70–83 Obbard ME, Brooks RJ (1980) Nesting migrations of the Snapping Turtle (Chelydra serpentina) Herpetologica 36:158–162 Paetkau D, Slade R, Burden M, Estoup A (2004) Direct, real-time estimation of migration rate using assignment methods: a simulation-based exploration of accuracy and power. Mol Ecol 13:55–65 Paterson JE, Steinberg B, Litzgus JD (2012) Generally specialized or especially general? Habitat selection by snapping turtles (Chelydra serpentina) in central Ontario. Can J Zool 90:139–149 Piry S, Alapetite A, Cornuet JM, Paetkau D, Baudouin L, Estoup A (2004) GeneClass2: A software for genetic assignment and first-generation migrant detection. J Hered 95:536– 539 Pittman SE, King T, Faurby S, Dorcas ME (2011) Genetic and demographic status of an isolated bog turtle (Glyptemys muhlenbergii) population: implications for the conservation of small populations of long-lived animals. Conserv Genet 12:1589–1601 Power TD (1989) Seasonal movements and nesting ecology of a relict population of Blanding’s turtles (Emydoidea blandingii) in Nova Scotia. M.Sc. Thesis, Acadia University, Wolfville, Nova Scotia Pritchard JK, Stephens M, Donnelly PJ (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959 Rannala B, Mountain JL (1997) Detecting immigration by using multilocus genotypes. Proc Natl Acad Sci USA USA 94:9197–9221 Rasmussen ML, Litzgus JD (2010) Habitat selection and movement patterns of spotted turtles (Clemmys guttata): effects of spatial and temporal scales of analyses. Copeia 2010:86–96 Seburn, D.C., 2003. Population structure, growth, and age estimation of spotted turtles, Clemmys guttata, near their northern limit: an 18-year follow-up. Can Field Nat 117:436–439 Segelbacher G, Cushman SA, Epperson BK, Fortin MJ, Francois O, Hardy OJ, Holderegger R, Taberlet P, Waits LP, Manel S (2010) Applications of landscape genetics in conservation biology: concepts and challenges. Conserv Genet 11:375–385. Spear SF, Balkenhol N, Fortin MJ, McRae B, Scribner K (2010) Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysis. Mol Ecol 19:3576–3591 Stevens VM, Verkenne C, Vandewoestijne S, Wesselingh RA, Baguette M (2006) Gene flow and functional connectivity in the natterjack toad. Mol Ecol 15:2333–2344 Tallmon DA, Luikart G, Waples RS (2004) The alluring simplicity and complex reality of genetic rescue. Trends Ecol Evol 19:489–496
127
128
Tallmon DA, Koyuk A, Luikart GH, Beaumont MA (2008) ONeSAMP: a program to estimate effective population size using approximate Bayesian computation. Mol Ecol Resour 8:299–301 Templeton AR (1986) Coadaptation and outbreeding depression. In: Soulé ME (ed) Conservation biology: the science of scarcity and diversity. Sinauer, Sunderland, Massachusetts pp 105–116 Thrall PH, Richards CM, McCauley DE, Antonovics J (1998) Metapopulation collapse: the consequences of limited gene-flow in spatially structured populations. In: Bascompte J, Sole RV (eds.) Modeling Spatiotemporal Dynamics in Ecology, Springer Verlag, Berlin, pp 83–104 Traill LW, Bradshaw CJA, Brook BW (2007) Minimum viable population size: A meta-analysis of 30 years of published estimates. Biol Conserv 139:159–166 Traill, LW, Brook BW, Frankham R, Bradshaw CJA (2010) Pragmatic population viability targets in a rapidly changing world. Biol Conserv 143:28–34 van Dijk PP (2011) Clemmys guttata. In: IUCN 2012. IUCN Red List of Threatened Species. Version 2012.2. . Downloaded on 23 October 2012. van Dijk PP (2012) Chelydra serpentina. In: IUCN 2012. IUCN Red List of Threatened Species. Version 2012.2. . Downloaded on 23 October 2012. van Dijk PP, Rhodin AGJ (2011) Emydoidea blandingii. In: IUCN 2012. IUCN Red List of Threatened Species. Version 2012.2. . Downloaded on 23 October 2012. Wang YH, Yang KC, Bridgman CL, Lin LK (2008) Habitat suitability modeling to correlate gene flow with landscape connectivity. Landscape Ecol 23:989–1000 Wilson GA, Rannala B (2003) Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163:1177–1191 Yagi KT, Litzgus JD (2012) The effects of flooding on the spatial ecology of spotted turtles (Clemmys guttata) in a partially mined peatland. Copeia 2012:179–190
128
Table 7.1. Life history, distribution and behavioral traits of Clemmys guttata, Chelydra serpentina and Emys blandingii. Global conservation status is determined by the International Union for Conservation of Nature (IUCN); Canadian conservation status is determined by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC).
Estimated longevity
Clemmys guttata > 110 years
Chelydra serpentina > 100 years
Emys blandingii > 75 years
Source Litzgus 2006; R. Brooks, unpublished data, in COSEWIC 2008, Congdon 2008.
Estimated generation time
> 25 years
~ 31 years
> 40 years
COSEWIC 2004, 2005, 2008; Galbraith and Brooks 1987; Galbraith et al. 1989
Average clutch size
3.5
35.2
10.7
Ernst and Lovich 2009
Vagility (dispersal ability) based on telemetry data
low
moderate
high
See Methods
Estimated area of occupancy in Canada
12 as sampling units (inset, bottom right, based on STRUCTURE results). ALG = Algonquin Provincial Park; BP = Bruce Peninsula; EO = Eastern Ontario; GB = Georgian Bay; GH = Golden Horseshoe; HC = Hastings County; KAW = Kawartha Lakes; LE = Lake Erie; LH = Lake Huron; LO = Lake Ontario; N = area north of GH and south of GB; PSD = Parry Sound District. SP = subpopulation. Base map modified from http://www.aquarius.geomar.de/omc/make_map.html and used under the GNU Free Documentation license. 132
133
Figure 7.2 (previous page). Genetic population structure inferred in A) TESS, and B) STRUCTURE for Clemmys guttata (yellow/brown), Chelydra serpentina (blue), and Emys blandingii (red). SP = subpopulation. See Figure 7.1 for explanation of site abbreviations. Inferred clusters are plotted on maps to the right of each set of results. Division of Cl. guttata samples under a K = 2 model (implemented in STRUCTURE;
see Chapter 3) is shown by a dashed black line on the bar plot and the map for comparison.
134
Figure 7.3. Barriers to gene flow identified with Monmonier’s algorithm. Colored numbers indicate sampling sites; thin green lines indicate boundaries between populations based on Delaunay triangulation. A) Clemmys guttata (yellow; N = 253); B) Chelydra serpentina (blue; N = 167); and C) Emys blandingii (red; N = 91). Estimates are based on 5,000 bootstrap replicates of genetic distance matrices (Nei’s absolute distance). The thickness of each line and the numbers in black text indicate the strength of bootstrap support. D) Barriers and sampling sites for the three species overlaid on top of one another; barriers with bootstrap support > 0.90 are marked with a dashed line.
135
Figure 7.4. Significant barriers (bootstrap support > 0.90) inferred using Monmonier’s algorithm for Clemmys guttata (yellow dashed lines), Chelydra serpentina (blue dashed lines) and Emys blandingii (red dashed lines).
136
Figure 7.5. Average number of migrants per generation (Nm) for Clemmys guttata, Chelydra serpentina and E. blandingii estimated following Barton and Slatkin (1986) among four sites at which all three species were sampled.
137
Chapter 8 Summary and Conclusions
8 8.1 Summary In this thesis I developed molecular markers for conservation genetics studies of turtles and used a suite of analyses to investigate genetic population structure in three freshwater turtle species. My study species included the spotted turtle, Clemmys guttata, and the Blanding’s turtle, Emys blandingii. Both species are listed as endangered by the International Union for Conservation of Nature (IUCN, van Dijk 2011; van Dijk and Rhodin 2011). I also studied the snapping turtle, Chelydra serpentina, and the spiny softshell, Apalone spinifera. These species are listed as Least Concern globally, but some populations show evidence of decline due to overharvesting (van Dijk 2012). Sufficient genetic variation exists in the novel microsatellite loci developed for Ch. serpentina and A. spinifera to study patterns of genetic variation across landscapes and delimit management units (Chapters 2, 3, 5; Alacs et al. 2007). Bayesian clustering analyses, principal coordinates analysis and Monmonier’s algorithm reveal significant population structure in Cl. guttata, Ch. serpentina and E. blandingii (Chapters 4 – 7). In the introduction, I stated that conservation genetics studies should result in conclusions or recommendations that maintain a species’ genetic diversity. Maintenance of genetic diversity in Cl. guttata, Ch. serpentina and E. blandingii in Ontario will require explicit consideration of population structure when developing management strategies. Significant shifts in allele frequencies among populations of all three species demonstrate that they have been isolated for many generations. Preliminary data from A. spinifera (Chapter 3) also suggests that significant genetic variation likely occurs among populations (Chapter 3) and this should be investigated further. These results suggest that any attempts to artificially mix populations (for example, through translocations) should increase connectivity only between closely related sites to avoid genetic homogenization of the Ontario meta-populations. Conversely, efforts to maintain genetic connectivity among related sites through habitat corridors, wildlife underpasses, translocations and other applied conservation tools could mitigate the gradual genetic impacts of population declines. Unfortunately, dissimilarity in patterns of gene flow among species requires each 138
species to be assessed independently for such measures – no “one size fits all” solutions appear to be possible (Chapter 7). It is encouraging that there is no evidence of inbreeding in turtles in Ontario (Chapters 3, 4 and 5). Fragmentation of turtle populations in Ontario and increased mortality of adults due to vehicle-related mortality, hunting (legal and illegal), boat propeller strikes and persecution are well-documented (e.g. COSEWIC 2004; 2005; 2008; OMSTARRT 2012). The genetic impacts of these effects are not yet detectable, but these impacts are probably inevitable over the next generations as populations continue to decline. Mitigation measures such as those suggested above could help to prevent decline in genetic variation. Although most populations retain relatively high genetic diversity, effective population sizes (Ne) are low (90% of a population. The unexpectedly low connectivity of some snapping turtle populations (Chapters 4 and 6) raises interesting questions about the relationship between dispersal and gene flow. Different, fixed alleles in Ch. serpentina in the Golden Horseshoe and a nearby subpopulation indicate 140
reproductive isolation of nearby sites although the distance between them is small relative to movements recorded in telemetry studies. Because female Ch. serpentina may travel long distances to nest, high gene flow is expected across the landscape (Galbraith 2008). However, Chapters 4 and 6 indicate that genetic structure may occur on relatively small geographic scales. Targeted sampling along population boundaries combined with radio telemetry may help to clarify the relative roles of dispersal and gene flow in shaping populations. The microsatellite markers developed for Ch. serpentina may also facilitate studies of its tropical congeners Ch. rossignoni and Ch. acutirostris. These species were only recently separated from Ch. serpentina and, therefore, their biology is poorly understood (Phillips et al. 1996). Comparative studies of these three closely related species provide an interesting opportunity to investigate reptile adaptations to temperate and tropical climates. Comparative studies of several long-lived turtles allowed me to remove the confounding effects of longevity from inter-species comparisons. A similar comparative approach of turtles with different life spans would also be informative. Most turtle species are long-lived, but the chicken turtle (Deirochelys reticularia) may mature in only two to five years (Buhlmann et al. 2009). It would be interesting to see how genetic structure and the Ne:Nc ratio in this relatively shortlived species compare to that of other turtles. The Order Testudines has survived on Earth since the Triassic (Spotila 2004; http://www.bbc.co.uk/news/science-environment-19872821). Today, many species of turtle are threatened and the pressures on populations of turtles continue to increase (Turtle Conservation Coalition 2011). Given these pressures, I would like to finish by expressing my sincere hope that turtles will persist well into the future. Turtles provide excellent model organisms for studies of the implications of long-lived life history strategies. Some species provide important ecosystem services, and turtles are central to many human cultures (e.g. Moll and Jansen 1995; Campbell 2003; COSEWIC 2008; Griffiths et al. 2011). I hope that we will have the opportunity to gain a better understanding of their endlessly fascinating biology, and that we can continue to share Ontario and the rest of the planet with these beautiful and complex creatures.
8.3 References Alacs EA, Janzen FJ, Scribner KT (2007) Genetic issues in freshwater turtle and tortoise conservation. Chel Res Monogr 4:107–123 141
Buhlmann KA, Congdon JD, Gibbons JW, Greene JL, Mathis A (2009) Ecology of chicken turtles (Deirochelys reticularia) in a seasonal wetland ecosystem: exploiting resource and refuge environments. Herpetologica 65:39–53 Campbell L (2003) Contemporary culture, use, and conservation of sea turtles. In: Lutz PA, Musick JA, Wyneken J (eds.) The biology of sea turtles Vol. 2. Boca Raton, Florida: CRC Press, pp 307–338 Charlesworth B (2009) Effective population size and patterns of molecular evolution and variation. Nat Rev Genet 10:195–205 Congdon, JD, AE Dunham, van Loben Sels RC (1993) Delayed sexual maturity and demographics of Blanding’s turtles (Emydoidea blandingii): implications for conservation and management for long-lived organisms. Conserv Biol 7:826–833 Congdon, JD, Dunham AE, van Loben Sels RC (1994) Demographics of common snapping turtles (Chelydra serpentina): implications for conservation and management of longlived organisms. Amer Zool 34:397-408 COSEWIC (2004) COSEWIC assessment and update status report on the spotted turtle Clemmys guttata in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. vi + 27 pp. (www.sararegistry.gc.ca/status/status_e.cfm). COSEWIC (2005) COSEWIC assessment and update status report on the Blanding's Turtle Emydoidea blandingii in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. viii + 40 pp. (www.sararegistry.gc.ca/status/status_e.cfm) COSEWIC (2008) COSEWIC assessment and status report on the Snapping Turtle Chelydra serpentina in Canada. Committee on the status of endangered wildlife in Canada. Ottawa. vii + 47 pp. (www.sararegistry.gc.ca/status/status_e.cfm). Cunnington DC, Brooks RJ (1996) Bet-hedging theory and eigen elasticity: a comparison of the life histories of loggerhead sea turtles (Caretta caretta) and snapping turtles (Chelydra serpentina). Can J Zool 74:291–296 Enneson JJ, Litzgus JD (2008) Using long-term data and a stage-classified matrix to assess conservation strategies for an endangered turtle (Clemmys guttata). Biol Conserv 141:1560–1568 Frankham R (1996) Relationship of genetic variation to population size in wildlife. Conserv Biol 10:1500–1508 Galbraith DA (2008) Population biology and population genetics. In: Steyermark AC, Finkler MS, Brooks RJ (eds) The biology of the snapping turtle. Johns Hopkins University Press, Baltimore, Maryland pp 168–180 Griffiths CJ, Hansen DM, Jones CG, Zuë N, Harris S (2011) Resurrecting extinct interactions with extant substitutes. Curr Biol 21:1–4 Heppell SS (1998) Application of life-history theory and population model analysis to turtle conservation. Copeia 1998:367–375 Jamieson IG, Allendorf FW (2012) How does the 50/500 rule apply to MVPs? Trends Ecol Evol 27:578–584 142
Kuo CH, Janzen FJ (2004) Genetic effects of a persistent bottleneck on a natural population of ornate box turtles (Terrapene ornata). Conserv Genet 5:425–437 Moll D, Jansen KP (1995) Evidence for a role in seed dispersal by two tropical herbivorous turtles. Biotropica 27:121–127 OMSTARRT (Ontario multi-species turtles at risk recovery team) (2012) Rationale for Ending the Harvest of Snapping Turtles (Chelydra serpentina) in Ontario. 42 pp. Pittman SE, King T, Faurby S, Dorcas ME (2011) Genetic and demographic status of an isolated bog turtle (Glyptemys muhlenbergii) population: implications for the conservation of small populations of long-lived animals. Conserv Genet 12:1589–1601 Phillips CA, Dimmick WW, Carr JL (1996) Conservation genetics of the common snapping turtle (Chelydra serpentina). Conserv Biol 10:397–405 Seburn DC (2007) Recovery Strategy for Species at Risk Turtles in Ontario (draft). Ontario Multi-Species Turtles at Risk Recovery Team. Spotila J (2004) Sea Turtles: A complete guide to their biology, behavior, and conservation. Baltimore, Maryland: The Johns Hopkins University Press and Oakwood Arts Turtle Conservation Coalition. (2011) Turtles in Trouble: The World’s 25+ Most Endangered Tortoises and Freshwater Turtles—2011. Rhodin AGJ, Walde AD, Horne BD, van Dijk PP, Blanck T, Hudson R (eds.): IUCN/SSC Tortoise and Freshwater Turtle Specialist Group, Turtle Conservation Fund, Turtle Survival Alliance, Turtle Conservancy, Chelonian Research Foundation, Conservation International, Wildlife Conservation Society, and San Diego Zoo Global, 54 pp. Lunenburg, MA van Dijk PP (2011) Clemmys guttata. In: IUCN 2012. IUCN Red List of Threatened Species. Version 2012.2. . Downloaded on 23 October 2012. van Dijk PP (2012) Chelydra serpentina. In: IUCN 2012. IUCN Red List of Threatened Species. Version 2012.2. . Downloaded on 23 October 2012. van Dijk PP, Rhodin AGJ (2011) Emydoidea blandingii. In: IUCN 2012. IUCN Red List of Threatened Species. Version 2012.2. . Downloaded on 23 October 2012. Vargas-Ramirez M, Stuckas H, Castaňo-Mora OV, Fritz U (2012) Extremely low genetic diversity and weak population differentiation in the endangered Colombian river turtle Podocnemis lewyana (Testudines: Podocnemididae). Conserv Genet 13:65–77
143
Copyright Acknowledgements
Chapters 2 and 3 are published in Conservation Genetics Resources and are reproduced here with permission of the co-authors.
144
