Conservation genetics of an endangered grassland

Insect Conservation and Diversity (2016) doi: 10.1111/icad.12192

Conservation genetics of an endangered grassland butterfly (Oarisma poweshiek) reveals historically high gene flow despite recent and rapid range loss EMILY V. SAARINEN, 1 PATRICK F. REILLY 2 and JAMES D. AUSTIN 3 1

Division of Natural Sciences, New College of Florida, Sarasota, FL, USA, 2Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA and 3Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, USA

Abstract. 1. In poorly dispersing species gene flow can be facilitated when suitable habitat is widespread, allowing for increased dispersal between neighbouring locations. The Poweshiek skipperling [Oarisma poweshiek (Parker)], a federally endangered butterfly, has undergone a rapid, recent demographic decline following the loss of tallgrass prairie and fen habitats range wide. The loss of habitat, now restricted geographic range, and poor dispersal ability have left O. poweshiek at increased risk of extinction. 2. We studied the population genetics of six remaining populations of O. poweshiek in order to test the hypothesis that gene flow was historically high despite limited long-distance dispersal capability. Utilising nine microsatellite loci developed by PacBio sequencing, we tested for patterns of isolation by distance, low population genetic structure and alternative gene flow models. 3. Populations from southern Manitoba, Canada to the Lower Peninsula of Michigan, USA are only weakly genetically differentiated despite having low diversity. We found no support for isolation by distance, and Bayesian estimates of historical gene flow support our hypothesis that high levels of gene flow previously connected populations from Michigan to Wisconsin. 4. Prairie grasslands have been reduced tremendously over the past century, but the low mobility of O. poweshiek suggests that rapid loss of populations over the past decade cannot be simply explained by fragmentation of habitat. 5. As a species at high risk of extinction, understanding historical processes of gene flow will allow for informed management decisions with respect to head-starting individuals for population reintroductions and for conserving networks of habitat that will allow for high levels of gene flow. Key words. Conservation strategy, gene flow, generalist–specialist, head-start, Hesperiidae, Lepidoptera, microsatellite, PacBio, range decline, time lag.

Introduction The loss of the smaller species (Dunn, 2005; Cardoso et al., 2011) remains one of our greatest biodiversity challenges. Like other less vagile species, butterfly species with Correspondence: Emily V. Saarinen, Division of Natural Sciences, New College of Florida, 5800 Bay Shore Road, Sarasota, FL 34243-2109, USA. E-mail: [email protected].

Ó 2016 The Royal Entomological Society

low dispersal capabilities have suffered from reductions in the number and size of populations as a result of habitat degradation and loss (Summerville & Crist, 2001; Luoto et al., 2003; Thomas et al., 2004; Wenzel et al., 2006). This impact has been especially evident in grassland and prairie-dependent taxa whose habitat has been under intense pressure especially through conversion to agriculture. In the United States, only ~1% of the original tallgrass native prairie remains, the majority of the change 1

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Emily V. Saarinen, Patrick F. Reilly and James D. Austin

occurring between 1830 and 1900, with most habitat gone by the 1950s (Samson & Knopf, 1994; Swengel & Swengel, 2015). Within these grassland butterfly communities there exists a variety of species that are differently affected by habitat fragmentation based on ecological niche breadth and dispersal abilities (Louy et al., 2007). Along the ecological continuum, specialist species typically require specific larval host plants or habitat structures and tolerate narrow, relatively stable, environmental conditions, whereas generalists typically have a broader ecological niche breadth (Dennis et al., 2011). Dispersal behaviour has also been associated with niche breadth; specialists are relatively sedentary, whereas generalists are often better dispersers (Burke et al., 2011; Habel et al., 2013). This in turn may predict differential effects of anthropogenic habitat fragmentation on specialists and generalists, the former being more vulnerable to fragmentation due to their reduced dispersal abilities (Br€ uckmann et al., 2010). Multiple studies have considered dispersal abilities of lepidopterans and have shown that specialists possess higher inter-population genetic differentiation and lower diversity than do generalist species (Schmitt et al., 2005; Habel & Schmitt, 2009; Kadlec et al., 2010; Habel et al., 2013). The generalist–specialist paradigm is predicated upon the evolution of distinct niche breadth dimensions (Dennis et al., 2011), yet many species do not fit neatly into this dichotomy (Dapporto & Dennis, 2013). Habel et al. (2013, 2015) proposed that ecologically intermediate species may face unique conservation challenges. Intermediate species are less finely tuned to a particular niche than are specialists but may not be as adaptable as generalists. Intermediate species tend to have lower genetic diversity and abundance than habitat generalists, however they are more poorly adapted to small, ecologically narrow habitats than are specialist species (Habel & Schmitt, 2012). Intermediate species not only may disperse less than generalists but also have less strict habitat requirements than specialist species (Burke et al., 2011). Under these conditions, small populations of intermediate species may have increased extirpation probability with increased isolation (Thomas, 2000). For example, gene flow is important for maintaining additive genetic variance in small populations (Chakraborty & Nei, 1982; Lynch & Hill, 1986) which in turn is necessary for evolutionary potential (Willi et al., 2006). In populations suffering from reduced fitness as a result of inbreeding depression, dispersal limitations can also prevent genetic rescue (Frankham, 2015). The effects of large genetic loads in butterfly populations can be transient (Saccheri et al., 1996) assuming demographic factors do not lead to local extirpation first (Ellstrand & Elam, 1993). The negative consequences of being an intermediate niche species (i.e. lower dispersal potential, small effective population size and less specialised) have been proposed as the cause of swift population declines in formerly stable European butterfly species (Van Dyck et al., 2009; Kadlec et al., 2010). The Poweshiek skipperling [Oarisma poweshiek (Parker, 1870) (Lepidoptera: Hesperiidae)] is a grass-feeding

skipper native to the Midwestern United States and southern Manitoba. As recently as the 1990s, it was widespread and the most frequently and reliably encountered of the obligate-prairie skippers in Minnesota (Dana, 2008); it is now only found in two counties in the United States and in one preserve in Canada (Swengel et al., 2011; Swengel & Swengel, 2014, 2015; United States Fish & Wildlife Service, 2014). Oarisma poweshiek were previously very easy to find and many sites had hundreds of them flying at once (Dana, 2008). Compared to other butterflies, they have been considered relative ecological generalists due to their ability to feed on multiple grass genera and their once common occurrence in tallgrass prairies, wet prairie fens and other open habitats with appropriate flowers and larval grass species, though little is known about the early life stage requirements of this and other grass-feeding skippers (Bouseman et al., 2006; Jokela et al., 2016). Oarisma poweshiek, however, is described as a poor disperser with difficulty crossing physical barriers (e.g. country roads) or colonising new areas, even when suitable habitat is nearby (Burke et al., 2011). For example, mark–release–recapture studies of O. poweshiek have demonstrated that dispersal from core prairie habitat patches is extremely limited even within a few hundred metres of suitable habitat (R. Westwood, pers. comm.). This limited dispersal suggests that recolonisation and gene flow would be inhibited under the current distribution of tallgrass prairie habitat. In this study, we evaluated the population genetic status of remaining O. poweshiek populations. In light of its known generalist-feeding ecology and potentially limited dispersal ability, our prediction was that despite the very recent demographic decline O. poweshiek may still reflect its historical genetic signature of high population genetic diversity and historical patterns of high gene flow. Using a model-testing framework, we evaluated various gene flow models to test whether O. poweshiek had high historic gene flow. We discuss the conservation implications of our results because management decisions should be based on a firm understanding of a species’ population genetics.

Methods Sample collection and DNA extraction As part of the species management plan, regular surveys for adult Oarisma poweshiek were conducted during mid-June to mid-July 2012 in prairie and fen habitats in both occupied and potentially occupied habitats throughout its range. Surveyors collected tissue samples from extant populations in Manitoba (N = 1 population), Wisconsin (N = 1 population) and Michigan (N = 5 populations) (Cuthrell et al., 2015; United States Fish & Wildlife Service, 2014; Fig. 1). Surveyors were trained to non-lethally sample tissue by gently netting adults and removing a single leg from O. poweshiek after the census surveys

Ó 2016 The Royal Entomological Society, Insect Conservation and Diversity

Genetics of an endangered butterfly

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Fig. 1. Historical and extant range of Oarisma poweshiek. Large X marks represent locations of genetic sampling used in this study (MA, Tallgrass Prairie Preserve Manitoba; SC, Scuppernong Prairie Wisconsin; BR, Brandt Road Michigan; LL, Long Lake Michigan; BV, Big Valley Michigan; PL, Park Lyndon Michigan; GR, Grand River Michigan); black dots are confirmed historical locations (now extirpated), shading represents inferred historical habitat based on Bailey’s ecological sub-sections that overlap with at least one O. poweshiek record.

(following protocols for non-lethal tissue sampling of Hamm et al., 2009; Koscinski et al., 2011; Saarinen, 2016). We extracted DNA using a Qiagen DNeasy Extraction Kit (Valencia, California) and stored DNA at 80 °C until PCR experiments. For whole genome sequencing and marker development, we extracted DNA from the thorax of a voucher specimen (collected in southeastern Michigan, MI DNR permit #1981).

Microsatellite marker development and molecular analyses We quantified the genomic DNA using a Nanodrop and submitted 98 lL of 250 ng/lL to the University of Michigan DNA Sequencing Core (Ann Arbor, Michigan). Genomic DNA was sheared to 1 kb fragments with an

ultrasonicator and prepared for sequencing by annealing library adaptors in accordance with PacBio protocols (Pacific Biosciences, California). Three SMRT cells were used for the PacBio RSII sequencing run, each completed in circular consensus mode. The extra SMRT cells were run because the first one did not generate as many reads as expected. The second pair of cells ran quite well and generated additional reads. We trimmed results to remove sequences with low quality scores (Q < 20). The SMRTbell protocol used allows for redundant sequencing of the fragment to establish a consensus sequence, and we used the consensus file (.ccs) for all bioinformatics analyses. PacBio runs generated 42 129 560 bp across 62 481 reads (averaging 674 bp per read), split into six consensus FASTA files. We screened all 62 481 sequences for microsatellite repeats using msatcommander 0.8.1 (Faircloth, 2008).


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Following methods in Saarinen and Austin (2010), we designed tagged primers for 1059 loci. We then selected loci for polymorphism testing and further evaluation (using guidelines from Saarinen & Austin, 2010). We tested 89 loci (5 dinucleotides, 49 trinucleotides and 35 tetranucleotides) against seven O. poweshiek sample templates. We compared these microsatellite loci-containing reads against the NCBI GenBank database for nucleotide collection (nr/nt)optimised for similar sequences in the basic local alignment search tool (BLAST) algorithm (Altschul et al., 1990), blastn (see Abdelkrim et al., 2009) to verify that they were insect sequences and not associated with coding genes (i.e. that they were neutral and not under selection). We also visually confirmed that loci were indeed perfect repeats and that microsatellite repeats did not appear in the PacBio read from which the microsatellite marker and primers were designed. The remaining nine polymorphic loci (five trinucleotides and four tetranucleotides) were used to characterise the samples in all populations (Table 1). Reaction conditions and concentrations followed PCR conditions of Saarinen and Austin (2010), using the M13 primer labelling protocol and fluorescently labelled primer tags. PCR products were pooled together (following protocols in Saarinen & Austin, 2010) and PCR products were run on an ABI 3730 sequencer. Genotyping was performed with GeneMarker v2.6.3 and allele sizes were manually confirmed.

Genetic analyses We used Microchecker v.2.2.3 (van Oosterhout et al., 2004) to check for genotyping errors or problems in the

data set with a 95% CI and 1000 randomisations. We calculated linkage disequilibrium (LD) between loci and exact tests of Hardy–Weinberg equilibrium (HWE) in Arelquin v.3.5.2.2 (Excoffier & Lischer, 2010). Sequential Bonferroni corrections were used to control for multiple comparisons for both the LD and HWE data sets respectively (Rice, 1989). We performed the preceding analyses on the three largest populations: Brandt Road Michigan (BR), Long Lake Michigan (LL) and Scuppernong Wisconsin (SC) due to the limitations of these tests on small sample sizes. We evaluated allelic diversity, observed and expected heterozygosity and FIS (inbreeding coefficient) in every population, including those with smaller sample sizes, using GenAlEx 6.5 (Peakall & Smouse, 2012). Because it is useful for detecting recent losses in genetic diversity, we calculated allelic richness (AR) using a rarefaction method implemented in HP-RARE (Kalinowski, 2005) for all nine genes. To quantify genetic differentiation between samples we calculated pairwise FST (i.e. GST Nei, 1977) values as well G’ST and G”ST (Meirmans & Hedrick, 2011) using GenAlEx 6.5, each permuted 999 times. G’ST was used to account for the highly variable nature of microsatellites and corrects for bias by normalising FST. G”ST is useful when a small number of populations are sampled and also corrects for bias in G’ST. Following Jost (2008), we used allelic frequency to evaluate differentiation (DEST). We calculated analysis of molecular variance (AMOVA) to evaluate where genetic variance was greatest (between individuals or between populations). We applied a Mantel test to test for a positive relationship between genetic distance and geographic (Euclidean) matrices.

Table 1. Characteristics for nine polymorphic microsatellite loci as developed for Oarisma poweshiek by PacBio sequencing.

Locus name

Repeat motif

Primer sequences (50 –30 )

OapoTetra25

(ACAG)5

OapoTetra30

(ACAG)4

OapoTri 3

(AAT)7

OapoTri 7

(AAG)4

OapoTri 12

(AAT)5

OapoTri 14

(ACT)4

OapoTri 41

(AGG)5

OapoTetra 5

(AAAT)4

OapoTetra 7

(ACAG)5

F: CGACACCAAACACGATGGG R: GGGGCTTGAATTACCCTTACC F: TCCAAATCGGACCAGTGCC R: CAAAGGGACAGAGAAAGCGTC F: TGACATACAGCGCTCGAAAAG R: TGGAAAAGGATGGAGGAGGC F: TGGAGACGAATAGTCAGGCG R:GTTTAGCCATCGGTGTGCG F: ACCCGTGCTTAGTAGTGGAC R: TGCCCCAAACCTCAGGAAG F: ATTCATCGTTGCCTGGAGC R: ATACTGGACCCAGCACAGC F: GGAAACGGAGTCTAATTGAGAACG R: GCGTGTTAGCGAGGCTTTG F: AGGGTGTAGAAGTGTGGCG R: TTGCTATCCAGGCGAACCC F: ATTTCATCCAAATCGGACCAG R: GTTGACCCACGCAACACAG

Allelic size range (bp)

Total number of alleles found

269–293

2

226–250

3

240–267

5

212–330

8

400–414

3

257–273

4

233–280

3

255–268

4

234–268

4

Bold primers have a 50 -CACGACGTTGTAAAAGGAC-30 tag applied at 50 end. Allelic size range (in bp); total number of alleles found per locus after genotyping 128 individuals from populations across the species range.


Genetics of an endangered butterfly We used two complementary methods to evaluate genetic structuring. First, we ran principal coordinates analyses (PCoA) of genetic distances between individuals using GenAlEx 6.5 (Peakall & Smouse, 2012) for a visual representation of genetic distance. Next, we used Structure 2.3.4 (Pritchard et al., 2000) to evaluate how many discrete populations (K values) are supported in the data. We ran the admixture model with correlated allele frequencies for each value of K (for K = 1 to K = 18). Each value of K was run 20 times, using a parameter set of 500 000 burn-in and 750 000 MCMC replications after the burn-in. We used the same parameter set excluding the Manitoba samples (due to small sample size), running 20 iterations of each K for K = 1–6. We used the program Structure Harvester (Earl & vonHoldt, 2012) to calculate the average L(K) and standard deviation across the 20 runs for each K and then calculate the ln Pr(X|K) using the ad hoc delta K value (Evanno et al., 2005) to help infer which K was the most likely where different K values had similar likelihoods. We used the program CLUMPP (Jakobsson & Rosenberg, 2007) to combine, and DISTRUCT (Rosenberg, 2004) to visually represent the results of the Structure analyses and distribution of K population groups. We examined alternative gene flow models using the Bayesian coalescent framework implemented in Migrate 3.6.11 (Beerli & Felsenstein, 2001; Beerli, 2006). We evaluated the four largest samples from Michigan (BR, LL, GR, BV) and the one from Wisconsin (SC) to determine whether alternative migration models could provide insight into how O. poweshiek populations were structured by migration regionally (within Michigan) and between Michigan and Wisconsin. The Manitoba (MB) population sample size was too small to analyse. We used the ability of Migrate to utilise thermodynamic integration (Gelman & Meng, 1998) of the marginal likelihoods for comparison of nested and non-nested models (Beerli & Palczewski, 2010). We initially applied a full migration model where each butterfly sample represents a distinct population of potentially different sizes (h) that exchange migrants (M) at potentially different rates. We then examined (i) a model of panmixia across the entire system; (ii) panmixia restricted to the Michigan samples, with asymmetrical migration between it and Wisconsin; and (iii) a model with panmixia within MI and no gene flow between MI and WI. Initial exploratory runs were used to determine the required run length and priors to obtain unimodal posterior distributions for all parameters in the full model. Once this was established, each model was replicated using the h (mutation-scaled effective population size) and M (mutation-scaled migration rate) values as starting points, but otherwise retaining the same starting priors and conditions. We ran analyses under default conditions with the following exceptions: Brownian motion mutation model for microsatellite data; uniform h priors {min. = 0, mean = 5, max. = 10, delta = 1}; uniform M priors {min. = 0, mean 50, max. = 100, delta = 10}; increment

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between sampled genealogies {100}; samples per replicate {80 000}; initial discarded samples per replicate (burn-in) {100 000}. We ran 12 static heated chains {temperatures: 1, 1.10, 1.22, 1.38, 1.57, 1.83, 2.20, 2.75, 3.67, 5.50, 11, 1 000 000}. Model probabilities were calculated by subtracting the highest marginal likelihood from each of the competing models, taking the exponent of each value, summing and using this summed value as the denominator. Each exponentiated value was divided by the sum to estimate the probability of the individual model (P. Beerli, pers. comm.).

Results We discarded the loci that failed to amplify, only amplified a few templates, produced excessive stutter bands, we otherwise had difficulty scoring or were monomorphic. Microchecker identified no evidence of genotyping error due to stutter bands, nor large allele drop out, however individual loci showed evidence of null alleles in individual populations. Individual populations were in HWE at most loci evaluated (Table 2). In Michigan, BR deviated from HWE at three of nine loci (OapoTri3, OapoTri7 and OapoTri12), LL at locus OapoTri12 and SC at loci OapoTri3 and OapoTri12. We found two instances of linkage between loci after Bonferroni correction for multiple comparisons (a=0.05/36 = 0.0014). These significant results were not between the same two loci nor found across populations. In BR, linkage disequilibrium was detected between loci OapoTri41 and OapoTri7 (P = 0.00079) and in SC between loci OapoTri41 and OapoTri14 (P = 0.0005). Population-level statistics indicated low genetic diversity in each population with few alleles/locus and low values of observed heterozygosity (mean Ho=0.199 0.025). Allelic richness values (AR) were also low in all populations (mean 0.96 0.75) and there is little evidence for unique diversity in individual populations (i.e. very few private alleles). Populations also demonstrated moderately high levels of inbreeding (mean 0.257 0.095) (Table 2). All pairwise FST and G’ST values were significant and indicated low differentiation between populations (Table 3). Not all pairwise G”ST and DEST values were significant, but Jost’s D (using DEST as a derived measure of differentiation) had low variability (0.035–0.152) indicating limited divergence between populations (Table S1). Results of the principal coordinate analysis reflect limited genetic distance leading to geographic structure across all samples regardless of how refined (i.e. individual populations) or broad (i.e. all Michigan populations grouped together) the population category (Fig. 2). Results of AMOVA show that most of the genetic variance is among individuals (46%) and within individuals (44%) and among population variance is the lowest (10%) (Table 4). There is no correlation between genetic diversity and geographic distance when matrices were compared via a Mantel test (R2 = 0.0356), and we reject


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Table 2. Population-level statistics of Oarisma poweshiek summarised across microsatellite loci.

Population

Region/ subgroup

Brandt Road (BR) Long Lake (LL) Grand River (GR)

N

Na

AP

AR

Ho

He

MI USA/ northern MI USA/ northern MI USA/ southern

28

2.67

1

2.20

2.57

1

2.10

13

2.00

0

1.73

0.403 0.209 0.395 0.228 0.384 0.148

0.320

29

0.269 0.183 0.268 0.174 0.150 0.090

Park Lyndon (PL)

MI USA/ middle

5

1.56

0

1.54

0.480 0.230

0.418 0.142

0.277

Big Valley (BV) Scuppernong (SC) Tallgrass Prairie (MA)

MI USA/ northern Wisconsin USA Manitoba Canada

14

2.44

1

2.06

2.89

2

2.10

7

2.11

3

1.98

0.347 0.239 0.413 0.249 0.507 0.167

0.365

32

0.213 0.181 0.253 0.169 0.127 0.169

2.32 0.13

1.14 1.07

1.96 0.75

0.199 0.025

0.314 0.029

Mean SD

#loci in HWE/ polymorphic loci (monomorphic loci)

FIS

6/9 (0) 7/8 (OapoTri3) 6/6 (OapoTetra25, Tetra30, Tri41) 5/5 (OapoTetra30, Tri41, Tri7, Tri14) 7/8 (0) 5/7 (OapoTri41) 6/6 (OapoTetra25, Tetra30, Tri41)

0.309 0.591

0.377 0.727

0.257 0.095

Region/subgroup, pertains to the three locations within Michigan (MI); N, number of individuals successfully genotyped; Na, number of alleles/locus; AP, number of private alleles; AR average allelic richness over all loci; Ho and He, mean observed and expected heterozygosities SD; FIS, inbreeding coefficient; #loci in HWE, number of loci in HWE after Bonferroni correction (P < 0.005)/number of polymorphic loci across the population sampled in summer 2012 generation (monomorphic loci in parentheses).

Table 3. Pairwise FST (lower diagonal) and Hedrick’s G’st (upper diagonal) values between populations in 2012; BR, LL, GR, PL and BV are populations in Michigan; SC, is in Wisconsin, MA is in Manitoba, Canada. All values are significant at P < 0.02 or less. Population

BR

LL

GR

PL

BV

SC

MA

BR (N = 28) LL (N = 29) GR (N = 13) PL (N = 5) BV (N = 14) SC (N = 32) MA (N = 7)

– 0.070 0.168 0.135 0.096 0.150 0.080

0.111 – 0.127 0.191 0.037 0.195 0.057

0.235 0.179 – 0.240 0.163 0.100 0.109

0.058 0.118 0.201 – 0.226 0.223 0.128

0.161 0.065 0.261 0.095 – 0.161 0.048

0.163 0.250 0.128 0.061 0.228 – 0.123

0.122 0.117 0.013 0.067 0.099 0.031 –

BR, Brandt Road; BR, Big Valley; GR, Grand River; LL, Long Lake.

the hypothesis of isolation by distance. Taken together, results indicate that populations are genetically similar across the range and not geographically structured. The delta K score of the Structure results provide the highest support for K = 2 (DK = 108.28). Most of the structure was between Michigan samples and Wisconsin (Fig. 3). Exclusion of the Manitoba samples did not change these results. Manitoba samples clustered more closely with Michigan samples. Comparison of gene flow models revealed that constraining MI to be panmictic performed much better than the full migration, panmixia and no gene flow models (Table 5). The full migration model was the second best fit, and full panmixia had higher support than did the model assuming no historic gene flow between MI and

SC. Estimates of the historical effective number of immigrants (4hM) from MI (panmictic) to Wisconsin (SC) was 67.11 (95% CI: 56.05–77.74) and 58.32 (95% CI: 22.41– 92.99) from SC to MI. Migration rates within the full migration model ranged from as low as 2.29 (95% CI: 0.91–3.69; immigration into SC from LL) to 43.93 (95% CI: 31.03–56.53; BV into BR). Overall, immigration was high, and only three estimates had lower 95% CI that reached 0 (SC to LL, BV to GR and SC to GR), but these same estimates had large posterior distributions [4hM = 8.40 (95% CI: 0–16.20); 11.82 (95% CI: 0–23.18); 10.11 (95% CI: 0–21.02) respectively]. There was no evidence of asymmetry in significant gene flow that would be indicated by non-overlapping 95% confidence intervals.


Genetics of an endangered butterfly Michigan-Mid

Michigan-South

Wisconsin

Manitoba

Coord. 2

Michigan-North

Coord. 1

Fig. 2. Principal coordinates analysis using nine microsatellite markers based on a genetic distance covariance matrix with data standardisation. Each point represents a single individual.Coordinates 1 and 2 explain 13.89% and 11.20% of the variation respectively; Manitoba (MA); Wisconsin (SC); Michigan-North (BR, LL and BV); Michigan-Mid (PL) and Michigan-South (GR).

Discussion While we detected the presence of null alleles at six of nine loci used in this study, this is not unusual for butterflies (Meglecz & Solignac, 1998; Williams et al., 2003; Keyghobadi et al., 2005; Habel et al., 2013, 2015) and we did not observe any systematic issues with individual loci (i.e. null alleles would be expected in the same loci across locations). Microsatellites in Lepidoptera are notoriously difficult to isolate (Zhang, 2004), a point further underlined by the need to screen 89 loci before nine polymorphic markers were identified. Our efforts to genetically characterise remaining Oarisma poweshiek populations provide important insight into the recent history and dispersal ecology of the species. Bayesian clustering, genetic distances (PCoA) and comparison of gene flow models suggest that samples from Michigan represent a single genetic population, suggesting that at the scale of hundreds of kilometres, gene flow was historically very high. Gene flow between Michigan and Wisconsin was also high (Table 5), though panmixia was not supported at that geographic scale. The large scale of historic gene flow reflects the significance that grassland

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fragmentation can have on population persistence. Species with potentially limited dispersal capabilities like O. poweshiek require habitat connectivity in order for populations to remain viable over the long term (Thomas et al., 2001). The loss of tallgrass and other prairie habitat undoubtedly has played a major role in the rapid and recent extirpation of populations from across the range (Fig. 1). Under the fragmentation hypothesis, the low genetic diversity in O. poweshiek could be the result of recent isolation (decades), resulting in genetic bottlenecks from once effectively large populations. This would explain the significance of FST measures, which are sensitive to rapid population declines (Hedrick, 1999). Multiple authors and state-survey programmes have provided evidence that populations of O. poweshiek have recently and dramatically declined across its range (Dana, 2008; Swengel & Swengel, 2014, 2015; Delphey et al., 2016). In strongholds, recent surveys of O. poweshiek report very few individuals in Wisconsin (S. Borkin, A. Swengel, S. Swengel, pers. comm.), a population in Manitoba (R. Westwood, pers. comm.) and declining populations in Michigan (D. Cuthrell, pers. comm.), despite these areas historically reporting stable population numbers and census sizes (Delphey et al., 2016). The patterns of limited genetic structuring and high gene flow in this recently fragmented system reveal the time lag between habitat alteration and genetic response (Epps & Keyghobadi, 2015), and provide insights into the future of the species in the wild. Another component of understanding time lags is evaluating when, after habitat perturbation, a new genetic equilibrium will be reached. Populations of O. poweshiek are currently not at equilibrium, leading to concern that additional diversity may still be lost in the future. In their study of the alpine butterfly, Parnassius smintheus Doubleday (1847), Keyghobadi et al. (2005) demonstrate that genetic differentiation is best correlated with current forest landscape but that genetic diversity is better correlated with the landscape of 40 years before. Combining empirical study with simulation analyses, they found that several decades after habitat fragmentation, genetic diversity estimates within subpopulations overestimated diversity levels that would eventually be obtained by future populations at equilibrium (Keyghobadi et al., 2005). North American prairie habitats were mostly destroyed by 1950 (Samson & Knopf, 1994); genetic changes in O. poweshiek might not manifest until decades later. The low diversity we

Table 4. Hierarchical analysis of molecular variance (AMOVA) to determine the proportion of genetic variance partitioned among all populations, individuals within populations and within individuals. Tests were performed in GenAlEx 6.5 with 999 permutations. Source

Degrees of freedom

Sum of squares

Variance component

Variation %

P value

Among populations Among individuals within populations Within individuals Total

6 121 128 255

64.95 354.00 121.50 540.45

0.23 0.99 0.95 2.17

11% 46% 44% 100%

0.001 0.001 0.001


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Fig. 3. Results from Bayesian clustering using Structure. (a) Graph of mean –log likelihood (lnL) of clusters (K) 1–18 (black circle) with standard deviation (20 replicates) and corresponding delta K value reflecting rate of stepwise increase in –lnL. (b) Plots of results from K = 2 and K = 4 both having relatively high delta K values.

Table 5. Log marginal likelihoods (lML using thermodynamic integration between two different models (highest likelihood vs. alternative). M1

lML Model rank Difference from best model Model probability

2 254 564.53 2 108 363.53 ~0

M2

M3

3 224 493.52 3 1 078 292.52 ~0

M4

2 146 201.0 1 0 ~1.0

4 164 153.98 4 2 017 952.98 ~0

M1: full migration model between Michigan and Wisconsin samples (with independent population size and potentially different migration rates); M2: panmixia; M3: panmixia within Michigan, allowing for independent migration rates between MI and Wisconsin (SC); M4: no gene flow between Michigan and Wisconsin. Note that sample locations are not drawn to geographic scale nor arrangement. [Correction added on 31 August 2016, after first online publication: Model probability for M4 was previously incorrect at ~1.0 and has been corrected to ~0 in this current version.]

observe now, however, is not at equilibrium, heightening the concern that additional, potentially adaptive, diversity may still be lost in the future. While habitat fragmentation was an important component of the rapid decline of stronghold populations, there are additional reasons why this species may have declined so precipitously. Pathogen infection (Wolbachia) and field exposure to neonicotinoid pesticides (Delphey et al., 2016) are both potential causes of recent declines. Modified prairie grass species composition with the dominance of invasive grass species has been implicated as a possible reason for decline in other grass skipper species (Jokela et al., 2016). Early larval stages may require specific grass

growth forms and the absence of appropriate species and/ or forms may lead to or exacerbate population decline (Dana, 1989). On a positive note, there is little evidence that the invasive European skipper (Thymelicus lineola Ochsenheimer, 1808) competes with O. poweshiek for resources. Although T. lineola do appear to invade the range of O. poweshiek, there is little actual overlap between the two species; T. lineola preferring roadside ditches and old fields over tallgrass prairies (D. Cuthrell, R. Dana, R. Westwood, and R. Royer pers. comm. June 15, 2016). As a butterfly species with generalist feeding ecology but limited dispersal ability, O. poweshiek can be


Genetics of an endangered butterfly categorised as an intermediary on the generalist–specialist continuum and we speculate that this intermediary position has contributed to its decline. Habel and Schmitt (2012) propose that intermediary species are especially vulnerable and poorly adapted to rapid land-use change because habitat loss and fragmentation disrupt the gene flow and large population sizes necessary to maintain high levels of genetic diversity. As population connectivity is lost and populations become isolated (and smaller), some components of this genetic diversity are likely to have negative fitness consequences and may be viewed as contributing to the genetic load (described as the burden of genetic diversity by Habel & Schmitt, 2012). We observed several genetic diversity values that are very low (mean AR = 1.96 0.75, mean HO = 0.199 0.025), but comparable to levels found in several specialist butterfly species investigated using similar markers: Euphydryas aurinia (Rottemburg): AR = 5.823 and Ho = 0.355 (Sigaard et al., 2008) and two species of Phengaris Doherty (Maculinea): AR= 1.77–7.51 and Ho=0.070–0.705 (Rutkowski et al., 2009). Genetic diversity is in danger of being lost due to genetic drift in small populations (Templeton et al., 1990) and local extirpation events, but genetic diversity aside, we emphasise that population sizes are so low in O. poweshiek that stochastic factors (both environmental and demographic) may continue to have devastating impacts and cause extinction. This is especially true of species that are poor dispersers and already exist in fragmented landscapes, like O. poweshiek (Lande, 1993; Hanski & Ovaskainen, 2000; Saarinen, 2016). Targeted conservation actions, as have been proposed to recover specialist species suffering from similar situations, need to be considered for newly imperilled intermediary species (Habel & Schmitt, 2012).

Conservation implications Even given the reality of dramatic decline, the species still exists, meaning there is a chance for recovery (Kuussaari et al., 2009). Multiple conservation strategies have been suggested to help slow the decline of species extinction and the erosion of genetic diversity. Although in situ strategies are promoted to maintain populations in preserves and protected areas, we are becoming increasingly reliant on ex situ strategies in the direst cases of population declines (Russello & Amato, 2007). We want to underscore that the ex situ strategy of captive breeding is complicated and should not be considered as a practical solution in many instances (Daniels et al., 2016). Case in point, captive breeding efforts have proven difficult and unreliable as a means for demographic rescue in O. poweshiek (Delphey et al., 2016). Current IUCN recommendations call for head-starting this species as a way to increase population numbers while avoiding the difficulties and pitfalls of captive breeding (IUCN/SSC, 2014; Delphey et al., 2016). The head-starting strategy removes early stage immature organisms from the wild and raises

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them in captivity for release back into the wild at a later, less vulnerable, life stage (Smith & Sutherland, 2014; Daniels et al., 2016). The IUCN-proposed head-starting strategy aims to augment existing populations through intra-site reinforcement by decreasing larval mortality and increasing the density of reproductive adults (IUCN/SSC, 2014; Delphey et al., 2016). Indeed, head-starting has proven to be a successful strategy for other imperilled species, notably turtles and iguanas (Bell et al., 2005; Escobar et al., 2010), but has yet to be as widely implemented for endangered butterflies. In butterflies, the results of headstarting strategies are difficult to track due to inconsistent definitions and reporting but they have been attempted for Schaus’ swallowtail [Heraclides aristodemus ponceanus (Schaus)], Quino checkerspot [Euphydryas editha quino (Behr)] and Oregon silverspot [Speyeria zerene hippolyta (Edwards)] (J. Daniels, pers. comm.). Results from this project highlight the importance of all remaining populations as sources of individuals for future head-starting attempts and that sampling individuals from across the range is important for capturing the vestiges of remnant diversity for ex situ strategies. For example, even the small Manitoba population has several private alleles not found in other populations, suggesting that its survival may be important in maintaining the genetic diversity of the species as a whole. This population contains unique genetic diversity as well as existing at the species’ northernmost location, an important consideration under current warming climate conditions (Kerr et al., 2015). Conservation programs for O. poweshiek are now underway (Delphey et al., 2016), and understanding the importance of gene flow historically should guide both ex situ propagation and management of isolated tallgrass prairie and other reserves. We know that gene flow was historically high, despite this being a poor-dispersing butterfly. Therefore, habitat connectivity should play an important role in future conservation efforts. Given the current distances between habitat areas, it is unlikely that gene flow can be restored by the creation of reserves between extant populations. Knowledge of the small (~hundreds of metres) dispersal distance of O. poweshiek may, however, lead to important local improvements to reserves and lands in their immediate vicinities. Translocation and artificial gene flow may be necessary to maintain existing diversity levels and prevent further loss. Before translocating individuals between populations, we recommend testing to limit the possibility of pathogen transfer (Saarinen, 2016) and other potential negative outcomes (Lozier et al., 2015). Oarisma poweshiek is just one of many endangered prairie-dependent insects (Swengel et al., 2011) and may serve as an important umbrella species for others that are unrecognised. Unlike many other recently extirpated insect species (Dunn, 2005), this butterfly was not a narrow habitat specialist nor can its decline be pinned to the co-decline of another species (i.e. a singular species of host plant). The rapid, range-wide decline observed in O. poweshiek is consistent with the Habel and Schmitt


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(2012) hypothesis and demonstrates how quickly a once abundant, common species can be faced with extinction. Oarisma poweshiek would not have been on anyone’s most sensitive or most vulnerable list due to its extensive range and local abundance. Given massive and rapid declines, it is a species of great conservation concern. It is our hope that coupling knowledge of historical processes, like gene flow, with the implementation of new strategies, like head-starting, may help stabilise populations of endangered species and we encourage additional fundamental research into evaluating the efficacy of strategies like these.

Acknowledgements This work was supported by grants from the US Fish and Wildlife Service (F12AC00977) and The Nature Conservancy of Canada (to EVS). We thank Phil Delphey and the USFWS Twin Cities, MN office for the range map (Fig. 1). We thank Dr. Robert Lyons (University of Michigan) for generous assistance with the PacBio platform and Flossie Hall and Russell Bielman. Special thanks to members of the Poweshiek Skipperling Working Group including David Cuthrell (Michigan Natural Features Inventory), Su Borkin (Milwaukee Public Museum), Richard Westwood (University of Winnipeg), Erik Runquist and Cale Nordemeyer (Minnesota Zoo), Carey Hamel (TNC Canada), Ron Royer (Minot State Univ.), Jerry Selby, Robert Dana (MN DNR) and Ann and Scott Swengel for their long-term dedication to this species. We are grateful to Editor Laurence Packer and three anonymous reviewers for valuable comments on this manuscript. Supporting Information Additional Supporting Information may be found in the online version of this article under the DOI reference: doi: 10.1111/ icad.12192: Table S1. Pairwise G”ST (lower diagonal) and DES (upper diagonal) values between populations in 2012; BR, LL, GR, PL and BV are populations in Michigan; SC, is in Wisconsin, MA is in Manitoba, Canada. Values not significant at p