First and foremost, I want to thank my supervisors Dianne Gleeson and Elise .... altitudinal and biodiversity gradient, indicate that both river morphology and ...... six Experimental Spawning Tanks with low (1 fish / 50L) and high (5 fish / 50 L) fish ...... included in the extraction protocol to test for cross-contamination occurring ...
Monitoring freshwater fish communities with environmental DNA (eDNA) metabarcoding.
Jonas Bylemans
This thesis is submitted in fulfilment of the requirements for the degree of Doctor of Philosophy
July 2018 Institute for Applied Ecology University of Canberra Australia
Certificate of authorship Except where clearly acknowledged in footnotes, quotations and the bibliography, I certify that I am the sole author of the thesis submitted today entitled: Monitoring freshwater fish communities with environmental DNA (eDNA) metabarcoding I further certify that to the best of my knowledge the thesis contains no material previously published or written by another person except where due reference is made in the text of the thesis. The material in the thesis has not been the basis of an award of any other degree or diploma except where due reference is made in the text of the thesis. The thesis complies with University requirements for a thesis as set out in the Examination of Higher Degree by Research Theses Policy. Refer to http://www.canberra.edu.au/currentstudents/current-research-students/hdr-policy-and-procedures
______________________________________
12/12/2017
Candidate’s Signature
Date
________________________________________
12/12/2017
Primary Supervisor’s Signature
Date
i
ii
Animal ethics statement
All experimental work presented in this thesis was approved by the University of Canberra Animal Ethic Committee under the permit number CEAE 14-02. Furthermore, scientific licences were obtained from the ACT Government to import and hold the animals used in the experiment studies under the licence numbers LT2014741 and IF201418.
iii
iv
Acknowledgements First and foremost, I want to thank my supervisors Dianne Gleeson and Elise Furlan for giving me this PhD opportunity and for their assistance throughout this project. I am very grateful for the time and effort both of you have invested to help develop this research project; assist with laboratory work and provide detailed comments on proposals, grant applications, manuscripts and thesis chapters. Furthermore, I want to thank the remaining members of my supervisory panel Christopher Hardy and Stephen Sarre. Chris, thanks for sharing your technical expertise with me over course of this PhD and for providing valuable comments to publications and thesis chapters. Stephen, I want to thank you for your role in leading the WGL’s lab and your many constructive comments on my presentations in the lab meetings. I also want to acknowledge Richard Duncan and Mark Lintermans who have had a significant contributed to this thesis. Thank you Richard for your assistance with the statistical analyses and the many discussions we had. Mark, thank you for sharing your expertise in fish ecology and your help in developing and refining the research questions addressed in this thesis. I also want to thank everyone that has contributed to this work. Firstly, I want to thank the IAE community and the WGL members for the support provided and the organisation of various social events. In particular, I want to thank the IAE admin team for helping me navigate through the many administrative documents. Thanks to Sam Venables and Llara Weaver for always make sure the lab runs smoothly. Further thanks go out to Wendy Ruscoe for always taking good care of my fish and to Peter Unmack for provide expert advice on fish husbandry. Thanks to Ben Broadhurst and Rhian Clear for their various efforts to collect fish for my experimental work. Thank you Dean Gilligan and Martin Asmus for providing Macquarie perch for the experimental studies and thank you Prudence McGuffie for your contributions to the Macquarie perch field surveys. Thanks to Matthew Beitzel for provided expert advice and to Luke Pearce and Trevor Daly for their contribution toward the field work conducted in Blakney Creek. Additionally, I want to thank Bernd Gruber and Janine Deakin for their role as HDR coordinators and Tony Buckmaster for his role as education leader within the IA CRC and for organizing the various training camps. Finally, all the field work would not have been possible without the help of many volunteers. Thanks Jack, Matt, Margi, Andrew and Teresa for your companionship and your meticulous note taking skills.
v
Without the financial contributions of various organisations this research would not have been possible. I want to thank the Invasive Animals CRC for being the main funder of this project. Furthermore, I want to thank the NSW Fisheries Committee, the Australian Wildlife Society and the Holsworth Wildlife Research Endowment for providing additional support. I also want to thank all the past and present HDR students which have made this a memorable PhD journey. Thank you Matt, Cat and Elodie for all your assistance in the lab. Thanks to all my office mates for providing a great atmosphere. In particular, I want to thank Margi, Al, Sal, Adrian, Will, Rod, Teresa, Yasmin and Andrew for all the good times we had making videos, making and tasting our own homebrew. Special thanks go out to Teresa who, as my partner, has always been supportive of me. Teresa you are simply the best, better than all the rest! Ten slotte wil ik ook mijn family en vrienden in België bedanken. Ik ben mijn ouders eeuwig dankbaar voor de opvoeding en de kansen die zij mij gegeven hebben. Bedankt aan mijn kameraden in Olmen die altijd klaar stonden om van elke avond op cafe een feestje te maken. Verder wil ik ook iedereen bedanken die ervoor gezorgd heeft dat mijn periode in Leuven memorable is geweest. Alejandro, Loren, Driss, Elke, Stuart, Maarten, Rik, Gert, Giel-Jan, Sofie, Pieter, Niels, Dieter, Gerard, Bert, Peter, Catherine, Mieke, Jennifer, Edith, Vincent: bedankt voor al de momenten die we samen gedeeld hebben.
vi
Abstract Efficient monitoring and management of species biodiversity requires sensitive and reliable detection tools, especially when management focusses on species that are present at low densities. Recent technological advances now allow biodiversity to be monitored through the DNA that organisms leave behind in the environment (eDNA). These eDNA-based tools are less susceptible to biases and can significantly improve the data from monitoring surveys, which in turn leads to a better management of natural resources. The aim of this thesis is to examine the potential of different DNA fragments for eDNA-based monitoring and evaluate the potential of eDNA metabarcoding as a monitoring tool for freshwater fish communities within the Murray-Darling Basin (MDB, Australia). In the current literature, it is generally accepted that short mitochondrial DNA fragment are more abundant in environmental samples and are less prone to environmental degradation. Consequently, mitochondrial DNA fragments shorter than 200 base-pairs are commonly used for eDNA-based monitoring. The research presented in chapter 2 and 3 aims to provide a deeper understanding of the availability of different DNA fragments for eDNA-based monitoring of aquatic biodiversity. First, experimental and field-based studies were used to confirm that nuclear eDNA fragments can be highly abundant in the water column during the reproductive period. Second, an experimental study was used to evaluate the relative abundance and degradation rates of eDNA fragments of different size and origin (i.e. mitochondrial or nuclear). Longer mitochondrial and nuclear eDNA fragments were found to be less abundant than short mitochondrial fragments (ca. 100 base-pairs). However, eDNA fragment length did not have an effect on the degradation rates, which provides greater insight into mechanisms underlying eDNA degradation. Overall, the results show that non-standard eDNA fragments could be valuable to further advance and diversify eDNA-based monitoring applications.
vii
In order to ensure that eDNA-based monitoring will be implemented in standard surveys, a thorough understanding of the optimal laboratory and sampling protocols is needed. In chapter 4, a novel workflow was used to perform a thorough evaluation of potential metabarcoding primers on an eco-region scale. Additionally, artificial community samples and field samples were used to validate the performance of a novel primer pair which is shown to be less susceptible to amplification biases and provide a higher taxonomic resolution compared to previously published primers. The effect of different sampling strategies was evaluated in chapter 5. The results of field surveys, conducted along an altitudinal and biodiversity gradient, indicate that both river morphology and species richness affect the optimal sampling strategy. While eDNA metabarcoding protocols are likely to undergo further optimization, the combined findings provide a solid basis for the future implementation of eDNA metabarcoding surveys. The potential of eDNA-based monitoring to improve the management of invasive species is widely recognized. The ability to detect species at low densities makes it particularly powerful tool to determine the outer distribution limits of invasive species which can be used to determine appropriate management strategies. In chapters 6 and 7, eDNA-based monitoring was used in an intermittent river system to assess the distribution limits of an invasive fish and to evaluate the performance of both targeted eDNA monitoring and eDNA metabarcoding. The results show that targeted eDNA-based monitoring can provide highly detailed distribution data which can subsequently be used to implement on-ground management actions (i.e. construction of an exclusion barrier). Environmental DNA metabarcoding surveys were found to be less sensitive than a targeted monitoring approach but are more suitable to monitor species interactions and are able to reveal hidden distribution patterns. Although the current data highlight the potential advances and limitation of both eDNA monitoring approaches, further comparative studies are needed to determine systemand species-specific effects.
viii
Table of contents Certificate of authorship .............................................................................................................. i Animal ethics statement ............................................................................................................ iii Acknowledgements .................................................................................................................... v Abstract .................................................................................................................................... vii List of publications .................................................................................................................. xiii List of figures ........................................................................................................................... xv List of tables .......................................................................................................................... xxiii CHAPTER 1.
General introduction ................................................................................... 25
The impacts and management of IAS .................................................................................. 26 Monitoring fish biodiversity................................................................................................. 27 Conventional monitoring methods ................................................................................... 27 Targeted eDNA-based monitoring ................................................................................... 29 Monitoring species communities through eDNA metabarcoding .................................... 30 Scope of this research ........................................................................................................... 37 CHAPTER 2.
An environmental DNA (eDNA) based method for monitoring spawning
activity: a case study, using the endangered Macquarie perch (Macquaria australasica). ..... 41 Introduction .......................................................................................................................... 41 Material and methods ........................................................................................................... 44 Primer design and testing ................................................................................................. 44 Experimental protocol ...................................................................................................... 45 Field survey ...................................................................................................................... 46 Sample processing and analyses ...................................................................................... 48 Results .................................................................................................................................. 50 Primer design and testing ................................................................................................. 50 Experimental results ......................................................................................................... 51 Field survey ...................................................................................................................... 54
ix
Discussion ............................................................................................................................ 56 Conclusion ........................................................................................................................ 58 CHAPTER 3.
Does size matter? An experimental evaluation of the relative abundance
and decay rates of aquatic eDNA fragments of different size and origin. ............................... 59 Introduction .......................................................................................................................... 59 Material and methods ........................................................................................................... 60 Primer design and testing ................................................................................................. 60 Experimental protocol ...................................................................................................... 63 Sample processing and analyses ...................................................................................... 64 Statistical analyses............................................................................................................ 65 Results .................................................................................................................................. 67 Primer design and testing ................................................................................................. 67 Analyses of the mitochondrial eDNA fragments ............................................................. 67 Analyses of the nuclear eDNA fragments ........................................................................ 72 Comparison of eDNA decay models ................................................................................ 73 Discussion ............................................................................................................................ 76 Conclusion ........................................................................................................................ 79 CHAPTER 4.
The evaluation of eDNA metabarcoding primers at an ecoregion scale: a
case-study for the Murray-Darling Basin (Australia). ............................................................. 81 Introduction .......................................................................................................................... 81 Material and methods ........................................................................................................... 82 Workflow for in silico primer evaluation ......................................................................... 82 Primer validation .............................................................................................................. 86 Results .................................................................................................................................. 91 In silico primer evaluation................................................................................................ 91 Primer validation .............................................................................................................. 96 Discussion .......................................................................................................................... 102 Conclusion ...................................................................................................................... 104 x
CHAPTER 5.
Monitoring riverine fish communities through eDNA metabarcoding:
determining optimal sampling strategies along an altitudinal and biodiversity gradient. ...... 105 Introduction ........................................................................................................................ 105 Material and methods ......................................................................................................... 107 Sampling sites ................................................................................................................ 107 Expert opinion survey (EOS) ......................................................................................... 108 Electrofishing survey (EFS) ........................................................................................... 108 eDNA metabarcoding survey (MBS) ............................................................................. 109 Data analyses .................................................................................................................. 111 Results ................................................................................................................................ 113 Evaluation of the expert opinion survey ........................................................................ 113 Performance evaluation of eDNA metabarcoding ......................................................... 113 Effect of sampling replication for eDNA metabarcoding surveys ................................. 115 Discussion .......................................................................................................................... 121 Conclusion ...................................................................................................................... 123 CHAPTER 6.
Improving the containment of a freshwater invader using environmental
DNA (eDNA) based monitoring. ........................................................................................... 127 Introduction ........................................................................................................................ 127 Material and methods ......................................................................................................... 129 Conventional monitoring................................................................................................ 130 eDNA-based monitoring ................................................................................................ 130 Results ................................................................................................................................ 133 Conventional monitoring................................................................................................ 133 eDNA-based monitoring ................................................................................................ 133 Discussion .......................................................................................................................... 135 Conclusion ...................................................................................................................... 137 CHAPTER 7.
The performance of targeted eDNA monitoring and eDNA metabarcoding
for monitoring fish distributions. ........................................................................................... 139 xi
Introduction ........................................................................................................................ 139 Material and methods ......................................................................................................... 141 eDNA sampling and sample processing......................................................................... 141 Targeted eDNA monitoring ........................................................................................... 142 eDNA metabarcoding ..................................................................................................... 143 Data analyses .................................................................................................................. 145 Results ................................................................................................................................ 148 Targeted eDNA monitoring ........................................................................................... 148 eDNA metabarcoding ..................................................................................................... 149 Targeted eDNA monitoring vs. eDNA metabarcoding .................................................. 153 The impact of invasive fish species................................................................................ 154 Discussion .......................................................................................................................... 157 Conclusion ...................................................................................................................... 159 CHAPTER 8.
General discussion .................................................................................... 161
Selecting optimal DNA barcodes for eDNA-based monitoring..................................... 161 Evaluation of metabarcoding primers and sampling strategies ...................................... 162 Improving IAS management through eDNA-based monitoring .................................... 163 The future of molecular monitoring methods .................................................................... 164 References .............................................................................................................................. 167 Appendix 1. ............................................................................................................................ 193 Appendix 2. ............................................................................................................................ 195 Appendix 3. ............................................................................................................................ 201 Appendix 4. ............................................................................................................................ 211 Appendix 5. ............................................................................................................................ 213 Appendix 6. ............................................................................................................................ 217
xii
List of publications
Chapter 2 Bylemans, J., Furlan, E.M., Hardy, C.M., McGuffie, P., Lintermans, M., Gleeson, D.M. (2017) An environmental DNA-based method for monitoring spawning activity: a case study, using the endangered Macquarie perch (Macquaria australasica). Methods in Ecology and Evolution, 8, 646-655
Chapter 6 Bylemans, J., Furlan, E.M., Pearce, L., Daly, T., Gleeson, D.M. (2016) Improving the containment of a freshwater invader using environmental DNA (eDNA) based monitoring. Biological Invasions, 18, 3081-3089
xiii
xiv
List of figures Figure 1.1. Schematic overview of the different steps for environmental DNA (eDNA) based monitoring of single species: (1.A.) Development of species-specific primers. (1.B.) Validation of the species-specific primers. (2.A.) Collection of environmental samples. (2.B.) DNA extractions (3) Real-Time PCR amplification to determine either the presence/absence of the species of interest or the eDNA concentrations for the species of interest. ................... 31 Figure 1.2. Schematic overview of the different steps for environmental DNA (eDNA) metabarcoding studies: (1.A) Collection of environmental samples. (1.B) Evaluation of potential metabarcoding primers. (2) DNA extractions. (3) Quantitative Real-Time PCR for the screening of primers (i.e. determine optimal PCR conditions) and samples (i.e. evaluate the influence of PCR inhibitors through DNA dilution series). (4). Amplification of DNA barcodes and addition of Multiplex Identification (MID) tags, sequencing primers and sequencing adaptors. (5) High-Throughput Sequencing (HTS) of the pooled libraries. (6) Bioinformatics analyses to filter out low quality sequencing reads and assign barcodes to individual samples and species. ............................................................................................... 33 Figure 2.1. Schematic overview of the experimental set-up. Each tank configuration consisted of 7 tanks containing 50 L of UV-sterilized tap water. The first tank configuration was set-up in October 2014 and consisted of a single Negative Control Tank (NCT) (no fish present) and six Experimental Spawning Tanks with low (1 fish / 50L) and high (5 fish / 50 L) fish densities (EST-LD and EST-HD respectively). Tank configuration 2 was set-up in December 2014 and contained a single NCT and six Experimental Control Tanks with low (ECT-LD) and high (ECT-HD) fish densities. ........................................................................................... 47 Figure 2.2. Map of the sampling locations within the Upper Murrumbidgee River (UMR) (NSW, Australia). Sample locations are numbered from downstream (UMR01) to upstream (UMR06). ................................................................................................................................. 48
xv
Figure 2.3. The log10 transformed Macquarie perch environmental DNA (eDNA) concentrations over time for low and high density treatments. The top and middle graphs give a comparison between the Experimental Control Tanks (ECT) and Experimental Spawning Tanks (EST) for mitochondrial (mt-) and nuclear (nu-) eDNA respectively. The bottom graphs give a comparison between mt- and nu-eDNA concentrations within the EST. Tanks were supplemented with 10 mL of water (ECT) or a mixture of milt and water (EST) after 336 hours (grey dashed line). Grey shading represents ± 1 SD from the mean. ............................. 52 Figure 2.4. The ratios between Macquarie perch nuclear and mitochondrial environmental DNA (eDNA) concentrations over time for the low and high density treatments. Solid lines represent the Experimental Spawning Tanks (EST) while dashed lines represent the Experimental Control Tanks (ECT). Ten millilitres of water (ECT) or a mixture of milt and water (EST) were added after 336 hours (dashed grey line). ................................................... 53 Figure 2.5. The ratios between Macquarie perch nuclear and mitochondrial environmental DNA (eDNA) concentrations for all field sites sampled before (October 8, 2015) and during (October 24 and 27, 2015) the presumed spawning period. Black arrow points indicate the sampling dates and sites at which eggs were collected using drift nets. .................................. 55 Figure 3.1. Effects of varying density and fragment length on the environmental DNA (eDNA) equilibrium concentration. Differences in log-transformed concentration are expressed with respect to a reference class (high density (HD) treatment and COI (096bp) fragment), which is set to zero. Solid points show the mean and the lines represent the 95% confidence intervals for the mean difference in the log transformed concentration relative to the reference class. ................................................................................................................... 68 Figure 3.2. The estimated mean equilibrium concentrations (solid black line) for each density treatment and all environmental DNA (eDNA) fragments plotted against the actual data for the equilibrium eDNA concentrations (grey points) (i.e. 24h ≤ Sampling time ≤ 336h). Plots are labelled with the density treatments (i.e. Low Density (LD) - 1 fish / 60 L, Medium Density (MD) – 1 fish / 30L and High Density (HD) – 1 fish / 10L) and the eDNA fragments given as the genetic target region (i.e. mitochondrial cytochrome c oxidase subunit I (COI) gene and nuclear internal transcribed spacer (ITS) region) with the fragment size in between brackets expressed as the total number of base pairs (bp). ...................................................... 69
xvi
Figure 3.3. Effects of varying density and fragment length on the decay rate as estimated from the first-order decay model. Differences in decay rate are shown with respect to a reference class (high density (HD) treatment and COI (096bp) fragment), which is set to zero. Solid points show the mean and the lines represent the 95% confidence intervals for the mean difference in decay rate relative to the reference class. ............................................................ 70 Figure 3.4. The best fit of the first-order exponential decay model (solid black line) for each density treatment and all environmental DNA (eDNA) fragments plotted against the actual data obtained after fish removal (grey points) (i.e. Sampling time ≥ 342 h). Plots are labelled with the density treatments (i.e. Low Density (LD) - 1 fish / 60 L, Medium Density (MD) – 1 fish / 30L and High Density (HD) – 1 fish / 10L) and the eDNA fragments given as the genetic target region (i.e. mitochondrial cytochrome c oxidase subunit I (COI) gene and nuclear internal transcribed spacer (ITS) region) with the fragment size in between brackets expressed as the total number of bas pairs (bp). ...................................................................... 71 Figure 3.5. The effect of varying density and fragment length on the estimated logarithm of the Weibull parameter (log(β)) after fitting the Weibull decay model. Solid points show the mean and error bars represent the 95% confidence interval. The different density treatments are shown on x-axis (i.e. Low Density (LD) - 1 fish / 60 L, Medium Density (MD) – 1fish / 30L and High Density (HD) – 1fish / 10L) while the different panels show the results of the different fragments. Labels of the different fragments show the genetic target region (i.e. mitochondrial cytochrome c oxidase subunit I (COI) gene and nuclear internal transcribed spacer (ITS) region) with the fragment size in between brackets expressed as the total number of base pairs (bp). ..................................................................................................................... 73 Figure 3.6. The best fit of the Weibull decay model (solid black line) for each density treatment and all fragments plotted against the actual data obtained after fish removal (grey points) (i.e. Sampling time ≥ 342 h). Plots are labelled with the density treatments (i.e. Low Density (LD) - 1 fish / 60 L, Medium Density (MD) – 1 fish / 30L and High Density (HD) – 1 fish / 10L) and the fragments given as the genetic target region (i.e. mitochondrial cytochrome c oxidase subunit I (COI) gene and nuclear internal transcribed spacer (ITS) region) with the fragment size in between brackets expressed as the total number of base pairs (bp). ............. 75
xvii
Figure 4.1. Summary statistics for each primer pair obtained during the initial screening of primer performance using the R package PrimerTree. Threshold values, for each summary statistic, are shown using a dashed red line. (A) The taxonomic resolution power of the barcodes is expressed as the average number of base-pair (bp) differences between species per 100 bases. (B) The specificity of the primer pairs is shown as the percentage of unique sequences belonging to Actinopterygii species. (C) The taxonomic coverage for each primer pair was evaluated as the number of Actinopterygii orders for which sequences were amplified in silico. .................................................................................................................... 94 Figure 4.2. Estimates of amplification success for all vertebrate classes (horizontal panels) and primer pairs (vertical panels) using the R package PrimerMiner. Amplification thresholds values used to estimate amplification success ranged from 10 to 300 (i.e. light grey to black) with a stepwise increase of 10. Higher threshold values allow for more primer-template mismatches thus leading to a higher amplification success. Amplification success was evaluated using sequence records from Actinopterygii (ACTI), Chondrichthyes (CHON), Amphibia (AMPH), Reptilia (REPT), Aves (AVES) and Mammalia (MAMM). ................... 95 Figure 4.3. The number of sequence records removed during the bio-informatics filtering process for the artificial community (AC), Blakney Creek (BC) and Murrumbidgee River (MR) samples and the different primer pairs. The different grey scales represent the number of sequence reads removed when: (A) trimming the sequencing reads, (B) assigning sequencing reads to their respective samples, (C) removing short and low abundant sequence reads, (D) removing sequences with PCR and sequencing errors and (E) assigning taxonomic information to the sequence reads (i.e. unassigned reads and non-Actinopterygii reads). The sequence records assigned to Actinopterygii species for each sample are shown in black (F). .................................................................................................................................................. 97 Figure 4.4. The estimated regression slopes for each primer pair obtained from fitting a linear mixed-effect model to the proportion read abundance data, obtained from the artificial community sample, as a function of the PrimerMiner penalty scores. Solid points show the mean and the error bars represent the 95% confidence interval around the mean. .................. 98 Figure 4.5. The species accumulation curves for the different primer pairs, the two sampling sites (i.e. Blakney Creek (BC) and Murrumbidgee River (MR)) and the different levels of sequencing depth (i.e. 10,000; 30,000 and 60,000 reads per sample). ..................................... 99
xviii
Figure 4.6. The results of the overall dissimilarity between the fish community data obtained using the different primer pairs (A) and a heat map showing the average contribution of each species to the overall dissimilarity (B). Community dissimilarity was evaluated for both the Blakney Creek (BC) and Murrumbidgee River (MR) sampling sites using presence/absence community data (light grey bars in plot A and upper panels in plot B) and proportional abundance data (dark grey bars in plot A and lower panels in plot B). ................................. 101 Figure 5.1. Map of all sampling sites within the main channel of the Murrumbidgee River (MR). Sampling sites are numbered from upstream (MR01) to downstream (MR05). ......... 107 Figure 5.2. The detection probabilities obtained from both the expert opinion survey and the standard electrofishing surveys for each species at the three most downstream sampling locations within the Murrumbidgee River (i.e. MR03, MR04 and MR05). .......................... 114 Figure 5.3. The species richness for each sampling site within the Murrumbidgee River obtained from the standard electrofishing surveys, the expert opinion survey and the eDNA metabarcoding survey. ........................................................................................................... 115 Figure 5.4. The sequence reads removed during the bio-informatics filtering process for the different samples obtained from the sampling sites within the Murrumbidgee River. .......... 116 Figure 5.5. Overall dissimilarity between the fish community data obtained from the samples collected from the left bank (LB), mid river (MR) and right bank (RB) for the different sampling sites (A). The heat map shows the average contribution of each species to the overall community dissimilarity (B). ..................................................................................... 118 Figure 5.6. Species accumulation curves (SAC) (A-B) and rank abundance curves (RAC) (C) for the four most downstream sampling sites in the Murrumbidgee River (Australia). SAC were constructed for two different sampling strategies (A) (i.e. using all available samples and only samples collected from the river-banks) and different sequencing depths (C). ............. 120 Figure 6.1. Map of all sampling sites within Blakney Creek (BC) and Urumwalla Creek (UC). Shading indicates the sampling methods employed at the different sampling sites. ............. 129
xix
Figure 7.1. Map of all sampling sites within Blakney Creek (BC) and Urumwalla Creek (UC) (NSW, Australia) which were samples during spring 2015 and spring 2016. Additionally, for eight sites, samples collected during autumn 2015 were available (indicated by an asterisk). ................................................................................................................................................ 142 Figure 7.2. The estimated concentrations of redfin perch (Perca fluviatilis) eDNA for four sites within the Blakney Creek catchment sampled during autumn 2015 and spring 2015. Mean concentration are shown as solid points while the thick and thin lines represent the 50% and 95% credible intervals, respectively. ............................................................................... 149 Figure 7.3. The overall probability of detection (sensitivity) of the targeted eDNA survey used in the current study as a function of the mean redfin perch (Perca fluviatilis) eDNA concentrations (solid curve). The probability of detection was calculated assuming a random dispersion of the eDNA molecules at the sampling sites. ...................................................... 150 Figure 7.4. The results of the overall community dissimilarity between the autumn and spring sample collections (A) and a heat map showing the average contribution of each species to the overall dissimilarity (B). The community dissimilarity was evaluated for eight sampling sites within the Blakney Creek catchment using presence/absence data (light grey bars in plot A and upper panel in plot B) and proportional read abundance data (dark grey bars in plot A and lower panel in plot B). The y-axis of the heat map contains the labels of the sampling sites with the total number of available samples for the different seasons in between brackets (autumn/spring). Labels within the heat map show the number of samples testing positive (upper panel) and the average proportion of sequence reads (lower panel) for the different species during the autumn/spring sampling seasons. ............................................................. 151 Figure 7.5. Species accumulation curves (SAC) for eights sampling sites within the Blakney Creek catchment. The different coloured curves show the SAC for the samples collected during the two different sampling seasons (black lines: autumn 2015; grey lines: spring 2015) while the different line types represent the data derived from amplicon libraries constructed with different amounts of template eDNA (solid lines: 4 µL per PCR replicate; dashed lines: 8 µL per PCR replicate). ........................................................................................................... 152
xx
Figure 7.6. The probability of detection as a function of the mean redfin perch (Perca fluviatilis) eDNA concentrations for both the targeted eDNA survey (black line) and the eDNA metabarcoding survey (grey line). The probability of detection was estimated considering only sample replicates and solid points show the proportions of samples showing a positive detection for redfin perch eDNA. .......................................................................... 153 Figure 7.7. The non-metric multidimensional scaling plot of the presence/absence fish community data per sampling site (solid points) for the spring 2015 and spring 2016 sampling season (R2 = 0.999, Stress = 0.034). ...................................................................................... 154 Figure 7.8. The estimated regression slopes for the relationship between the logit-transformed proportional read abundances for the native species and the number of species detected (A) and the proportional read abundances of both invasive species (B) (i.e. common carp (Cyprinus carpio) and redfin perch (Perca fluviatilis)). The linear regression model for the relationship between the logit-transformed abundance data for southern pygmy perch (Nannoperca australis) and both invasive species was fitted to data (C). The 95% confidence intervals around the mean estimated regression slopes are shown as a line (A and B) and the dashed lines show the 95% confidence interval around the best fitting model (C). .............. 156
xxi
xxii
List of tables Table 2.1. Non-exhaustive list of categories for monitoring methods that can be used to monitor reproductive activity in aquatic vertebrates relying on external fertilization. Definitions for the different categories were modified from Lefort et al. (2015). ................... 42 Table 2.2. Details of the Macquarie perch (Macquaria australasica) specific primers used in the quantitative Real-Time PCR analyses. Primer pairs were designed to amplify fragments of the mitochondrial 12S gene and the nuclear ITS1 region. ....................................................... 50 Table 2.3. The number of Macquarie perch eggs collected with drift nets for each sampling site and sampling date. ............................................................................................................. 54 Table 3.1. Details of goldfish (Carassius auratus) specific primer pairs for short (ca. 100 bp), medium (ca. 300 bp) and long (ca. 500 bp) fragments of the mitochondrial cytochrome c oxidase subunit I (COI) gene and nuclear internal transcribed spacer (ITS) region. ............... 62 Table 3.2. Akaike Information Criterion values for the first-order exponential decay model (AICEXP) and the Weibull decay model (AICWEI) for each fragment by density treatment combination. Density treatments consisted of a single fish within a 10L (High density), 30 L (Medium density) and 60 L (Low density) tank. ..................................................................... 74 Table 4.1. List of species for which amplicons of the entire 12S gene region were used to construct an artificial community. For each species and each primer pair the PrimerMiner penalty score is given which is a measure of the level of primer template mismatches (i.e. value of zero indicates no primer template mismatches and higher values suggest reduced amplification efficiency due to primer template mismatches). The Mifish-U and Teleo primer pairs have previously been validated (Miya et al. 2015; Valentini et al. 2016). The AcMDB07 primer pair was designed in the current study. ......................................................................... 87 Table 4.2. Primer pairs used during the in silico analyses. ...................................................... 92 Table 4.3. The taxonomic resolution for all barcodes amplified by the different primer pairs. Results are presented as the percentage of sequences correctly identified to the genus and species level using a threshold of barcode similarity of 2 base-pair (bp) and 5 bp for each primer pair. ............................................................................................................................... 96
xxiii
Table 5.1. Details of the available electrofishing data for each sampling site. Electrofishing surveys were conducted by the ACT Government between 2008 and 2017 using the Sustainable Rivers Audit protocol (i.e. 12 electrofishing shots per site). .............................. 108 Table 5.2. Summary of the data obtained from the expert opinion survey (EOS), the electrofishing survey (EFS) and the eDNA metabarcoding survey (MBS) for the five sampling sites within the Murrumbidgee River (Australia). Data are shown as the total number of detections out of the total available sampling units (i.e. number of experts consulted, number of electrofishing shots and number of water samples for the EOS, EFS and MBS, respectively). ................................................................................................................ 117 Table 6.1. Primer and probe details designed to amplify a short fragment of the 12S mitochondrial gene region of redfin perch (Perca fluviatilis) and a short fragment of the 16S mitochondrial gene region in multiple fish species (Furlan & Gleeson 2016b,a). ................. 132 Table 6.2. Results of the conventional and eDNA surveys for all sites within Blakney Creek (BC) and Urumwalla Creek (UC). Results of the conventional survey show the number of redfin perch (RP) caught per site. eDNA survey results are shown as the total number of PCR replicates performed per site and the number of valid and positive PCR replicates per site. In addition, the mean and the standard deviation (SD) of the Ct-values obtained from the positive PCR replicates are given........................................................................................... 134
xxiv
CHAPTER 1.
General introduction
The Earth’s biodiversity is rapidly changing as a consequence of anthropogenic disturbances (Barnosky et al. 2011; Pereira et al. 2012). The freshwater environment in particular is one of the most endangered ecosystems in the world (Geist 2011). This endangered status results from the high number of species that are confined to this ecosystem (10% of all animal species and one third of all vertebrate species), the scarceness of the environment (freshwater covers only 0.8% of the Earth’s surface) and the severe competition from humans for clean fresh water (building of dams, discharging waste water, overexploitation, etc.) (Revenga et al. 2005; Dudgeon et al. 2006; Strayer & Dudgeon 2010; Geist 2011). After habitat destruction and degradation, competition and predation by Invasive Alien Species (IAS) is considered to be the most important driver for species endangerment and extinction (Wilcove et al. 1998; Hulme 2009). Effective management of external stressors such as IAS is needed to prevent and/or control the rapid loss of biodiversity. The design and implementation of effective management actions is, however, often complicated by incomplete and/or biased biological data (Campbell et al. 2009). While detailed data can be obtained by undertaking extensive monitoring surveys, budget and time requirements for most monitoring methods are important constrains for environmental managers (Campbell et al. 2009; Harvey et al. 2009). Even if budget and time constrains are not limiting survey efforts, the data collected during surveys are often biased as a result of monitoring methods used and problems associated with morphological species identification (Bickford et al. 2007; Ko et al. 2013). Recently, the use of DNA extracted from environmental samples, environmental DNA (eDNA), has been proposed as a novel method for monitoring aquatic biodiversity (Ficetola et al. 2008). As aquatic species continuously secrete trace amount of DNA into the water column, the presence of species can be inferred by extracting and analysing eDNA fragments from water samples (Ficetola et al. 2008). This approach has the potential to improve the detection sensitivity and reduce biases compared to conventional monitoring methods (Jerde et al. 2011; Thomsen et al. 2012b). Even so, eDNA analyses also suffer from biases and a thorough understanding of the advantages and limitations is essential to further promote the use of eDNA-based monitoring.
25
The impacts and management of IAS IAS are generally defined as species that are non-native and have a negative impact on ecosystems, habitats or species (Pejchar & Mooney 2009). It is important to note that under this definition, non-native species that do not have any adverse effects are not considered IAS. The negative impacts of IAS are very diverse and only a short overview will be given here as more comprehensive reviews are available in the scientific literature (Pimentel et al. 2000; Lovell et al. 2005; Xu et al. 2006; Pejchar & Mooney 2009). One of the most studied effects of IAS is the loss of native biodiversity. A significant amount of threatened and endangered species worldwide are considered to be at risk of extinction due to negative interactions with IAS. However it is often difficult to disentangle the true effects of IAS from other environmental disturbances that might have facilitated the introduction or spread of invasive species (MacDougall & Turkington 2005; Hermoso et al. 2011). Overall, the different effects of IAS can be subdivided into direct and indirect impacts. Impacts are considered direct when IAS physically interact with native species, either through predation, competition for space and/or hybridization (Cucherousset & Olden 2011). Indirect impacts include habitat modifications, alterations of trophic cascades and/or competition for resources (Cucherousset & Olden 2011). Invasive species can also have a significant effect on the human well-being through the alteration of ecosystem services, the spread of diseases and parasites and the decrease of agricultural yields (Pimentel et al. 2000; Pejchar & Mooney 2009; Anderson et al. 2014). All of the above mentioned effects can have a substantial economic impact, including increased investments in healthcare, pest control, and management of biodiversity and ecosystems. The cost of IAS in China, the US and Australia are currently estimated at USD 14.45 billion, USD 137 billion and over USD 670 million per year respectively (Pimentel et al. 2000; McLeod & Norris 2004; Xu et al. 2006). Typically the invasion process consist of three steps: (1) the introduction into a new environment, (2) the establishment of a viable population and (3) the spread throughout the new environment (Jeschke & Strayer 2005). Management actions to control IAS can target one or multiple stages of this invasion process. As the prevention of new introductions is most effective, many studies have focussed on performing risk evaluations for species that are not yet present and their potential introduction pathways (Padilla & Williams 2004; Hulme 2009; Britton et al. 2011a; Vilizzi & Copp 2013). Alternative management strategies include controlling the spread of and/or the eradication of IAS. Reducing the spread of IAS can be
26
achieved by the installation of physical barriers and/or the removal of environmental disturbances that facilitate the spread of IAS (With 2002). Eradication efforts have used a multitude of methods including; physical removal, poisoning, genetic manipulation (release of sterile or daughterless males), biological control and immuno-contraception (Davidson 2002; Shanmuganathan et al. 2010; Thuesen et al. 2011; Thresher et al. 2013). Although multiple tools are currently available to control and/or eradicate IAS, such management actions will only be effective when species are spatially constrained and/or newly introduced (Genovesi 2005; Britton et al. 2011a). Highly sensitive monitoring methods are thus needed to map the distribution of invasive species and to identify low density populations to prioritize management efforts (Myers et al. 2000; Genovesi 2005; Gozlan et al. 2010). Monitoring fish biodiversity Freshwater fish biodiversity has both a high recreational and ecological value. Obtaining accurate monitoring data, however, is often difficult due to biases associated with the survey methods (Gunzburger 2007; Harvey et al. 2009; Britton et al. 2011b). Biases in monitoring data can significantly influence the performance of ecological studies and environmental management actions (Campbell et al. 2009; Lintermans 2013c). This can be particularly problematic for invasive and endangered native fish, which often occur at low densities (Harvey et al. 2009; Britton et al. 2011b). Recently developed eDNA-based monitoring tools have the potential to improve ecological surveys and environmental management actions through an improved detection of species present in low abundance (Ficetola et al. 2008; Lodge et al. 2012; Jerde et al. 2013). Conventional monitoring methods Conventional monitoring tools do not require high technical expertise and rely on morphological classification of the observed species. Monitoring methods which fit this description can be either passive or active (Growns et al. 1996; NSW Fisheries and the CRC for Freshwater Ecology 1997; Pidgeon 2004). Passive monitoring methods are defined as those that rely on the movement of the fish in order to trap them (Pidgeon 2004) (eg. fyke nets, gill nets and G-minnow traps). These methods are effective across a wide range of habitats except for densely vegetated localities. An additional advantage of gill nets and G-minnow traps is their commercial availability and ease of use. Fyke nets on the other hand are not widely available, are bulky and labour 27
intensive to set up. Compared to gill nets, trapping methods can be left in place unsupervised and for prolonged periods (with the exception of habitats where endangered air breathing species are present) since the chance of damage and mortality is much lower (Pidgeon 2004). In addition to the gear specific advantages and disadvantages, there are more universal limitations to passive monitoring methods. The definition of passive monitoring methods implies that they are inherently biased towards more active fish species. Furthermore, the mesh size used in the netting and trapping gear will increase the selectivity of the method employed (NSW Fisheries and the CRC for Freshwater Ecology 1997; Porreca et al. 2013). The most straightforward active monitoring methods rely on visual observations or the poisoning and sampling of the whole fish community within a locality. Visual observations, although useful for initial screening studies, are highly subjective and cannot be used in turbid water bodies (Hankin & Reeves 1988; Wildman & Neumann 2003). Poisoning the local fish community and collecting all individuals for species identification is the most direct method currently available (Robertson & Smith-vaniz 2008). Although highly efficient and more objective, the poisons that are used (e.g. rotenone) can have negative effects on non-target species and cannot be used in systems where rare and/or threatened species are thought to be present (Billman et al. 2011). Due to these limitations, visual surveys and poisoning are rarely used in monitoring surveys (Pidgeon 2004). More commonly used active sampling methods are netting methods (e.g. seine and trawl netting) and electrofishing. Although easy to use and relatively inexpensive, current netting methods can only be used in limited areas due to their ineffectiveness in habitats containing vegetation and/or a highly variable bottom structures (Pidgeon 2004). Additionally, the strong size and species biases arising from active netting methods have led to a preferential use of electrofishing in monitoring surveys (Pidgeon 2004; Davies et al. 2012). While the initial costs of electrofishing gear can be high, the preference for this method is due to an overall high capture efficiency (i.e. number of species and individuals detected) (Growns et al. 1996; Porreca et al. 2013). Electrofishing can also reduce sampling time, size and species selectivity compared to other methodologies (Growns et al. 1996; NSW Fisheries and the CRC for Freshwater Ecology 1997; Specziár et al. 2012). However, the method is not free from biases and current monitoring efforts by governmental agencies are believed to be insufficient to obtain reliable estimates of species richness (Ebner et al. 2008).
28
No single conventional survey method will provide highly representative data on the total fish community present within a water body (Ruetz et al. 2007). Data obtained with conventional methods are inherently biased due to density biases, selectivity biases and taxonomic identification biases (Growns et al. 1996; Paukert 2004; Harvey et al. 2009; Ko et al. 2013). While the use of multiple methods can reduce the biases, this will increase labour and cost requirements and does not resolve taxonomic identification biases (NSW Fisheries and the CRC for Freshwater Ecology 1997; Kennard et al. 2006). Targeted eDNA-based monitoring In recent decades the use of genetic monitoring techniques has increased in popularity (Pauls et al. 2014). More specifically, there has been an increasing interest in the use of DNA extracted from environmental samples to monitor macro-organisms (Bellemain et al. 2005; Waits & Paetkau 2005; Sarre et al. 2013). One of the first studies recognizing the potential to use DNA extracted from water samples to monitor aquatic macro-organisms was conducted by Ficetola et al. (2008). Since this initial study, the scientific literature around eDNA-based monitoring of aquatic species has increased substantially and has been the subject of extensive reviews (Rees et al. 2014b, 2015, Goldberg et al. 2014, 2016; Barnes & Turner 2016). Early studies have primarily focussed on the use of eDNA for the detection of single species (Figure 1.1.) (Goldberg et al. 2011; Jerde et al. 2011; Olson et al. 2012). In addition to presence/absence detections, quantitative Real-Time PCR can be used to assess eDNA concentration in the water column which can be indicative of the species abundance (Figure 1.1.) (Takahara et al. 2012; Lacoursière-Roussel et al. 2015; Yamamoto et al. 2016; Jo et al. 2017). The ability to monitor aquatic organisms through the trace DNA fragments they leave behind in the water column is particularly well suited to monitor rare, endangered and invasive species as eDNA-based monitoring can significantly reduce density, selectivity and taxonomic identification biases (Darling & Mahon 2011; Jerde et al. 2011; Thomsen et al. 2012a; Piaggio et al. 2014; Sigsgaard et al. 2015). Density dependency in eDNA-based monitoring is lower for two reasons. First, aquatic species continuously secrete DNA into the surrounding water through faeces, urine and/or epidermal cells (Ficetola et al. 2008; Valentini et al. 2009b). The temporary persistence of eDNA within the water body ensures that DNA fragments are more abundant in the water column than the individual species (Dejean et al. 2011). Second, eDNA is believed to be suspended within the water column and in well mixed water bodies this will ensure that eDNA is more homogeneously distributed than the
29
organisms themselves (Jerde et al. 2011). Selectivity and taxonomic identification biases are often less problematic for eDNA-surveys as highly species-specific primers can be used to target the DNA of species of interest (Wilcox et al. 2013; Goldberg et al. 2016). While these early studies have shown the potential advantages of eDNA-based species monitoring, several challenges remain and need to be addressed to ensure the uptake of eDNA surveys by environmental managers (Goldberg et al. 2014; Roussel et al. 2015). As many of the issues are not exclusive to species-specific eDNA monitoring surveys, these will be addressed in detail in the ‘Monitoring species communities through eDNA metabarcoding’ section below. Monitoring species communities through eDNA metabarcoding More recently, the combination of environmental DNA with High-Throughput Sequencing (HTS) technologies has led to a new scientific discipline called eDNA metabarcoding (Taberlet et al. 2012; Cristescu 2014). The amplification of DNA barcodes from complex DNA mixtures coupled with massive parallel sequencing of multiple DNA fragments (i.e. HTS) allow for whole community analyses from environmental samples (Figure 1.2.) (Creer et al. 2010; Chariton et al. 2010; Glenn 2011; Shokralla et al. 2012; Taberlet et al. 2012). Environmental DNA metabarcoding is rapidly transforming our ability to monitor species assemblages (Creer et al. 2016; Deiner et al. 2017a). However, practical (e.g. sampling), technical (e.g. target sequence) and computational (e.g. reference databases and bioinformatic tools) limitations remain and further work is needed to ensure the uptake of eDNA metabarcoding into standard monitoring surveys.
30
1.A. • • •
SAMPLE COLLECTION
• • • •
CONSTRUCT DATABASE ALIGN SEQUENCES DESIGN TARGET-SPECIFIC PRIMERS
2.B.
PRIMER VALIDATION
Δ RN
1.B.
2.A.
PRIMER DEVELOPMENT
WATER SAMPLES SOIL SAMPLES AIR SAMPLES FAECAL SAMPLES
DNA EXTRACTIONS
▪ TARGET DNA ▪ NON-TARGET DNA
•
NEG. CONTROLS
CYCLE
3.
REAL-TIME PCR AMPLIFICATION PRESENCE/ABSENCE DATA
CYCLE
• •
TARGET DNA
POSITIVE PCR NEGATIVE PCR
CYCLE
CYCLE
≈ SPECIES PRESENCE ≈ SPECIES ABSENCE
STANDARD CURVE
Δ RN
Δ RN
ABSENCE
Δ RN
Δ RN
PRESENCE
QUANTITATIVE DATA
• eDNA CONC. ≈
CYCLE
SPECIES ABUNDANCE
Figure 1.1. Schematic overview of the different steps for environmental DNA (eDNA) based monitoring of single species: (1.A.) Development of species-specific primers. (1.B.) Validation of the species-specific primers. (2.A.) Collection of environmental samples. (2.B.) DNA extractions (3) Real-Time PCR amplification to determine either the presence/absence of the species of interest or the eDNA concentrations for the species of interest.
31
Practical challenges Multiple studies have already shown that eDNA metabarcoding is able to provide a snapshot of the species diversity present within a water body (Jerde et al. 2011; Thomsen et al. 2012b; Dejean et al. 2012; Valentini et al. 2016). However, current methods are far from perfect and sampling design can be an important source of biases. Environmental DNA analyses are not completely density independent (Takahara et al. 2013; Wilcox et al. 2014). The abundance of eDNA fragments in the water is a function of both eDNA production and decay rates and this will subsequently influence the probability of species detections (Thomsen et al. 2012b; Pilliod et al. 2013; Furlan et al. 2016). The amount of eDNA in a water body has been shown to be positively influenced by the species density (Takahara et al. 2012; Thomsen et al. 2012b; Pilliod et al. 2013); but eDNA production rates will also be influenced by environmental variables (e.g. temperature, oxygen, etc.), the physiological state of the individuals (e.g. reproduction, metabolism, etc.) and the specific species under investigation (Maruyama et al. 2014; Turner et al. 2014a; Spear et al. 2014; Erickson et al. 2016; Sassoubre et al. 2016). Additionally, degradation rates for eDNA fragments are influenced by environmental conditions such as temperature, pH, UV-radiation and microbial activity (Strickler et al. 2014; Barnes & Turner 2016; Eichmiller et al. 2016). The distribution and transport of eDNA in the aquatic system will also influence the outcomes of eDNA metabarcoding surveys (Barnes & Turner 2016). In the lentic systems of temperate climate zones, the distribution of eDNA is likely to change seasonally due to sequential stratification and mixing of the water body (Gorham & Boyce 1989; Matsui et al. 2001). While the transport of eDNA in perennial lotic systems is primarily influenced by the direction of the water flow (Deiner & Altermatt 2014; Jane et al. 2015; Shogren et al. 2017), seasonal variation in eDNA transport can be expected in intermittent river systems. Additionally, species-specific difference in the distribution of eDNA fragments in the water are like to occur due to different habitat preferences (e.g. benthic vs. pelagic species) and seasonal migrations (Yamamoto et al. 2016; Minamoto et al. 2017; Stoeckle et al. 2017). Overall, the abundance and the distribution of eDNA in the water column will influence the sampling effort needed. Detailed studies are thus required to determine optimal sampling strategies for eDNA-based monitoring of both single-species and whole communities.
32
• • • •
2.
1.B.
SAMPLE COLLECTION
• • •
WATER SAMPLES SOIL SAMPLES AIR SAMPLES FAECAL SAMPLES
3.
DNA EXTRACTION
•
PRIMER EVALUATION TAXONOMIC RESOLUTION TAXONOMIC COVERAGE PRIMER BIASES
Q-PCR SCREENING
Δ RN
1.A.
NEG. CONTROLS
CYCLE
4.
PCR AMPLIFICATION ONE-STEP PCR
TARGET DNA
5.
& LIBRARY PREPARATION TWO-STEP PCR
PCR AND LIGATION
MID-TAG
PCR PRIMER
6.
HT SEQUENCING
SEQUENCING PRIMER/ADAPTOR
BIO-INFORMATICS
• TRIMMING / QUALITY FILTERING • SAMPLE ASSIGNMENT • READ DEREPLICATION • REMOVE PCR AND SEQ ERRORS • TAXONOMIC ASSIGNMENTS
Figure 1.2. Schematic overview of the different steps for environmental DNA (eDNA) metabarcoding studies: (1.A) Collection of environmental samples. (1.B) Evaluation of potential metabarcoding primers. (2) DNA extractions. (3) Quantitative Real-Time PCR for the screening of primers (i.e. determine optimal PCR conditions) and samples (i.e. evaluate the influence of PCR inhibitors through DNA dilution series). (4). Amplification of DNA barcodes and addition of Multiplex Identification (MID) tags, sequencing primers and sequencing adaptors. (5) High-Throughput Sequencing (HTS) of the pooled libraries. (6) Bioinformatics analyses to filter out low quality sequencing reads and assign barcodes to individual samples and species.
33
Technical challenges As eDNA in the water column is potentially highly degraded, studies generally target short but highly informative DNA regions of the mitochondrial genome (Meusnier et al. 2008; Dejean et al. 2012). For eDNA metabarcoding purposes in particular, choosing the most appropriate DNA fragment(s) can be a daunting task. Ideally, primers should: (1) Amplify a short mitochondrial fragment (i.e. typically less than 150-200 base pairs (bp)) as longer and nuclear DNA fragments are believed to be less abundant (Valentini et al. 2009b). (2) Amplify a region with sufficient taxonomic resolution to accurately identify species based on the genetic variation in the barcoding region (Ficetola et al. 2010). (3) Bind to highly conserved gene regions to ensure a high taxonomic coverage (i.e. the ability to amplify a specific regions across all taxonomic groups of interest) (Ficetola et al. 2010). (4) Amplify the region of interest with equal efficiency for all species of interest in order to minimize primer biases (i.e. the preferential amplification of eDNA for some species). Primers used for eDNA metabarcoding surveys can thus strongly influence the quality of the data (Pinol et al. 2015; Clarke et al. 2017). Mitochondrial DNA (mtDNA) fragments are preferred to nuclear DNA (nuDNA) for samples in which DNA is believed to be highly degraded (Holland & Parsons 1999; Willerslev et al. 2003; Waits & Paetkau 2005; Shokralla et al. 2014). The current preference for mtDNA fragments is because they are believed to be more abundant in environmental samples and they are less susceptible to environmental degradation (Deagle et al. 2006; Foran 2006). However, the use of a single mtDNA sequence can result in an overestimation of the true species biodiversity due to incomplete lineage sorting (ILS) or the co-amplification of nuclear mitochondrial pseudo genes (NUMTS) (Rubinoff et al. 2006; Song et al. 2008; Taberlet et al. 2012). Additionally, the long persistence of mitochondrial eDNA (mt-eDNA) fragments relative to nuclear eDNA (nu-eDNA) could increase false positive detections (i.e. a positive detection although a viable target organism is absent). Consequently, the use of nu-eDNA barcodes in metabarcoding analyses could improve the taxonomic assignments and may results in a stronger indication of recent species presence (Porazinska et al. 2009; Raupach et al. 2010; Darling & Mahon 2011; Zhan et al. 2014). Nuclear gene regions of particular value for eDNA metabarcoding are the genes within the ribosomal RNA (rRNA) operon as they occur in high copy numbers per cell which could partially compensate for the higher degradation rate (Foran 2006).
34
Targeting specific DNA barcodes typically involves a PCR amplification step (Figure 1.2) which can be an important source of biases (Clarke et al. 2014; Poretsky et al. 2014). Ideally, universal primers are used to ensure that the barcode sequence can be amplified across a broad range of taxa with equal efficiency. Universal primers are not perfect and base-pair mismatches between the primer and the target sequences are common. The preferential hybridization between primers and templates without bp mismatches may thus result in a skewed amplification efficiency (Bellemain et al. 2010; Thomsen et al. 2012a; Minamoto et al. 2012; Elbrecht & Leese 2015; Pinol et al. 2015). Furthermore, the relative abundances of eDNA fragments within a sample also affect the outcomes of eDNA metabarcoding studies (Vestheim & Jarman 2008; Shehzad et al. 2012). Abundant eDNA fragments will compete with rare fragments for polymerase enzymes during amplification and may result in the failed amplification and detection of rare fragments (Boessenkool et al. 2012). Methodological considerations can reduce the impact of PCR biases. Possible solutions include the use of multiple group specific primers or using a single universal primer in combination with PCR blocking primers (Shehzad et al. 2012; Bronnenhuber & Wilson 2013; De Barba et al. 2014). In summary, the preferred barcode sequence and the primers used for the amplification of this barcode will depend on the aim of the study (Kuczynski et al. 2012). Furthermore, biodiversity differences between different systems will need to be considered to determine the most optimal eDNA metabarcoding strategy (Hajibabaei et al. 2011; Takahara et al. 2012; Thomsen et al. 2012a; Minamoto et al. 2012; Collins et al. 2013). Computational challenges Bio-informatics packages can be used to resolve the technical challenges mentioned above and greatly facilitate the processing of the large amount of data obtained from HTS (Figure 1.2). Although multiple bio-informatics packages are available for eDNA metabarcoding studies, custom analytical pipelines are generally needed to address the aim of the study.
35
Bio-informatics packages can be used during the initial steps of the metabarcoding pipeline to develop and evaluate appropriate metabarcoding primers (Figure 1.2). Both primer and barcode selection need to be considered to ensure that the targeted sequences have sufficient resolution capacity (barcode selection) and taxonomic coverage (primer selection). Multiple packages are currently available to help with primer selection (Ficetola et al. 2010; Boyer et al. 2012; Cannon et al. 2016; Elbrecht & Leese 2016). However, a thorough evaluation of metabarcoding primers will require the use of multiple software packages (i.e. each individual package has its strengths and weaknesses) and empirical testing remains essential. Sequencing of barcodes originating from multiple independent samples in a single run (i.e. sample multiplexing) increases the cost-efficiency of eDNA metabarcoding studies. In order to assign the obtained barcoding sequences back to the individual samples, unique Multiplex Identification (MID) tags are added to the amplified barcodes prior to HTS (Figure 1.2) (Coissac et al. 2012). While standard multiplexing kits are available for most sequencing platforms, the number of unique MID-tags is generally limited in these commercial kits. To increase multiplexing capacity a number of bio-informatics packages have been developed to design custom MID-tags which can increase the number of independent samples that can be combined on a single HTS run (Frank 2009; Faircloth & Glenn 2012; Boyer et al. 2016). As HTS platforms will generate millions of sequences for each run, extensive computational power is needed for eDNA metabarcoding analyses (Goodwin et al. 2016). The first stage in the post-processing of eDNA metabarcoding data is to filter out low quality sequences and assigning the obtained reads to their respective samples (Coissac et al. 2012; Boyer et al. 2016). To remove PCR amplification errors, sequencing errors and chimeric sequences further quality filtering of the data is needed. Finally, the genetic variation in the barcoding region will be used to assign taxonomic information to the obtained reads. For robust taxonomic assignment, high quality reference databases are needed that contain sequences from verified species. While publicly available and semi-curated sequence databases (i.e. National Centre for Biotechnology Information database (NCBI), Barcode of Life Database (BOLD), European Molecular Biology Laboratory database (EMBL)) can be used, custom developed reference databases will allow for more accurate taxonomic identifications (Valentini et al. 2009b; Hajibabaei et al. 2011). Specific data processing packages have now been developed for eDNA metabarcoding analyses and can be used to set-up automated bio-informatics pipelines (Caporaso et al. 2010; Boyer et al. 2016).
36
While different bio-informatics packages are available, further improvements could reduce the costs of experimental testing and could help advance eDNA metabarcoding studies. In particular, a single software package allowing for a thorough evaluation of multiple metabarcoding primers and PCR blocking primers will be highly valuable. Furthermore, the use of multiple barcoding regions could mitigate the impact PCR amplification biases and would allow for more robust taxonomic assignments (Olds et al. 2016). While custom analytical pipelines can be used to analyse data from multiple barcoding regions, the development of appropriate bio-informatics packages for such analyses would help promote eDNA metabarcoding as a robust tool for standard biodiversity surveys. Scope of this research The relatively long isolation of the Australian continent (approximately 50-60 million years) has resulted in a unique biota (Short et al. 2002). After European colonization this unique species diversity has been under pressure due to changing land management practices and the introduction of IAS by early settlers (Ritchie et al. 2013). Particularly, the relatively depauperate and highly endemic Australian freshwater fish biodiversity has been strongly affected. Current estimates suggest that 40% of the native freshwater fish species are in need of conservation. Given the relatively high number of introduced fish species (43 of the 93 recorded invasive vertebrate species), it is not unsurprising that the majority of the rare, threatened or endangered fish species are negatively affected by alien fish species (Koehn & MacKenzie 2004; Lintermans 2007, 2013b). Consequently, effective conservation requires a detailed understanding of the current distribution of rare, endangered and threatened fish species and the threats they face such as the increasing spread of IAS. In order to obtain such detailed information extensive monitoring surveys are needed. However, biases in detection sensitivity, along with time and budget limitations, often makes the acquisition of such detailed information with conventional monitoring tools unfeasible (NSW Fisheries and the CRC for Freshwater Ecology 1997; Kennard et al. 2006; Harvey et al. 2009). The aim of this thesis is to evaluate the feasibility of eDNA metabarcoding as a monitoring tools for freshwater fish communities within the Murray-Darling Basin (MDB) (Australia). Three main research objectives will be addressed:
37
1. Selecting optimal DNA barcodes for eDNA-based monitoring Variation in production and degradation rates for different eDNA barcodes will be evaluated in chapter 2 and chapter 3. Specifically, the relative abundance of mt- and nu-eDNA fragments is evaluated in chapter 2 to determine if nu-DNA barcodes can be used to further diversify the applications of eDNA monitoring. The relative abundance and degradation rates of eDNA fragments of different size and origin (mt- versus nu-eDNA) is evaluated in chapter 3 using an experimental study. The results from both studies will contribute to a better understanding of the state of eDNA fragments within the water column which can ultimately be used formulate guideline for optimal selection of eDNA barcodes. 2. Evaluation of metabarcoding primers and sampling strategies The performance of eDNA metabarcoding primers and eDNA sampling strategies will be assessed in chapter 4 and chapter 5. In chapter 4, a thorough evaluation of different eDNA metabarcoding primer will be conducted and the results will be used to select the most appropriate primer pair for further studies. A field study will be used in chapter 5 to determine the impact of different sampling strategies on eDNA metabarcoding surveys in riverine systems. Based on the findings from both studies recommendations can be formulated for future studies with regards to primer selection and eDNA sampling protocols. 3. Improving IAS management through eDNA-based monitoring In the final two chapters, eDNA will be used to monitor the spread of an invasive fish species and its impact on the native fish community. First, targeted eDNA monitoring will be used to determine the extent of the invasion front (chapter 6). A comprehensive monitoring survey will then be conducted using both targeted eDNA monitoring and eDNA metabarcoding to evaluate the performance of both methods (chapter 7).
38
DECLARATION OF CO-AUTHORED PUBLICATION CHAPTER For use in theses which include publications. This declaration must be completed for each coauthored publication and to be placed at the start of the thesis chapter in which the publication appears. Declaration for Thesis Chapter 2 Declaration by candidate In the case of Chapter 2, the nature and extent of my contribution to the work was the following:
Nature of contribution
Extent of contribution
I designed the study, conduct both experimental and field work, performed the laboratory work, analysed the data and led the writing of the manuscript.
70 %
The following co-authors contributed to the work.
Name
Nature of contribution
Contributor is also a student at UC Y/N
Elise M. Furlan
Study design, assistance with laboratory work and manuscript writing.
N
Christopher M. Hardy
Study design, assistance with laboratory work and manuscript writing.
N
Prudence McGuffie
Study design, assistance with field work and manuscript writing.
Y
Mark Lintermans
Study design and manuscript writing.
Y
Dianne M. Gleeson
Study design, assistance with field work and manuscript writing.
N
Candidate’s Signature
12/12/2017
39
Declaration by co-authors The undersigned hereby certify that: (1) the above declaration correctly reflects the nature and extent of the candidate’s contribution to this work, and the nature of the contribution of each of the co-authors. (2) they meet the criteria for authorship in that they have participated in the conception, execution, or interpretation, of at least that part of the publication in their field of expertise; (3) they take public responsibility for their part of the publication, except for the responsible author who accepts overall responsibility for the publication; (4) there are no other authors of the publication according to these criteria; (5) potential conflicts of interest have been disclosed to (a) granting bodies, (b) the editor or publisher of journals or other publications, and (c) the head of the responsible academic unit; and (6) the original data are stored at the following location(s) and will be held for at least five years from the date indicated below: Location(s)
University of Canberra, Canberra, Australia
Signature 1 12/12/2017 Signature 2 12/12/2017 Signature 3 12/12/2017 Signature 4 12/12/2017 Signature 5 12/12/2017
40
CHAPTER 2.
An environmental DNA (eDNA) based method for
monitoring spawning activity: a case study, using the endangered Macquarie perch (Macquaria australasica). Introduction Monitoring reproduction in aquatic organisms is important for the conservation and management of species and/or populations (Koenig et al. 2000; Merz & Setka 2004; King et al. 2010; Di Franco et al. 2012; Kearns et al. 2012). Current survey methods suffer from biases, do not provide direct evidence for reproduction or are unable to distinguish between reproductive failure and high mortality rates of early life-history stages. DNA-based methods provide promising opportunities to overcome these challenges through the monitoring of environmental DNA (eDNA) signals that are correlated with reproductive activity in aquatic organisms. Many aquatic organisms reproduce sexually through a process called spawning, i.e. the mass release of reproductive cells (oocytes and spermatozoa) into the water column, allowing external fertilization (Harrison et al. 1984; Beebee 1996; Coward et al. 2002). Determining the timing and location of spawning events is important to: increase our understanding of the species’ biology (Harrison et al. 1984; Rose 1993; Grant et al. 2009); evaluate the reproductive output of populations (Levitan et al. 2014); determine population establishment for both invasive and translocated native species (Pearce 2013); and design and evaluate management actions (Koenig et al. 2000; King et al. 2010; Kearns et al. 2012). For aquatic vertebrates relying on external fertilizations (e.g. most fishes and frogs) monitoring reproductive activity can be achieved by destructive, injurious or non-invasive methods (Table 2.1.) (Lefort et al. 2015). The extra mortality rate imposed by destructive sampling methods makes them undesirable for monitoring reproduction in rare and threatened species (Tsukamoto 2006; Wei et al. 2009; Engstedt et al. 2014). On the other hand, injurious methods (i.e. use of acoustic telemetry) are often unable to deliver direct evidence of spawning and non-invasive methods are sensitive to observer biases and taxonomic misidentification (Caswell et al. 2004; Miller et al. 2012; Koster et al. 2013; Ko et al. 2013; Diana et al. 2015). Overall, all currently available survey techniques are biased and combining multiple methods to reduce the effects of biases (which is a common practice) increases cost and time requirements. Hence, an efficient non-invasive sampling method that
41
can accurately determine the timing and location of spawning events would be a valuable tool for the management of aquatic biodiversity. Table 2.1. Non-exhaustive list of categories for monitoring methods that can be used to monitor reproductive activity in aquatic vertebrates relying on external fertilization. Definitions for the different categories were modified from Lefort et al. (2015). Categories Destructive
Injurious Non-invasive
Definition
Examples
Monitoring methods that require sacrificing all or a subset of all organisms (adults, juveniles, larvae or fertilized eggs) collected. Monitoring methods that require direct contact and may cause physical injury or wounds. Monitoring methods that do not affect the physical integrity of the organism, but may affect fitness or behaviour.
Gonad maturation Otolith micro-chemistry Acoustic telemetry Visual surveys Acoustic surveys
Environmental DNA (eDNA) based species detection is a relatively new technique which is particularly useful for detecting aquatic species at low densities (Ficetola et al. 2008; Thomsen et al. 2012a). The sensitivity of this technology can be used to improve presence/absence data for cryptic species (Thomsen et al. 2012b; Dejean et al. 2012; Sigsgaard et al. 2015) and accurately delineate the distribution of species (Jerde et al. 2011; Laramie et al. 2014). Furthermore, it has been suggested that the absolute abundance of mitochondrial (mt-) eDNA fragments can be used to determine the reproductive status of populations (Spear et al. 2014). Due to the correlation between mt-eDNA abundance and species biomass, an increase in mt-eDNA concentrations during the spawning season might be due to the formation of spawning aggregations (Takahara et al. 2012; Lacoursière-Roussel et al. 2015; Yamamoto et al. 2016). A different approach is thus needed to successfully determine spawning activity from eDNA abundances. As highlighted earlier, spawning is often characterized by the mass release of oocytes and spermatozoa into the water column. While oocytes are relatively large and are often attached to bottom substrate or aquatic vegetation; spermatozoa are small, mobile, more abundant and generally distributed more homogeneously within the waterbody (Cosson et al. 2008). As such, spermatozoa are likely to be a major source of eDNA during spawning. Spermatozoa are genetically very different to somatic cells as they contain highly condensed and protected nuclear DNA while the number of mitochondrial genomes is relatively low. It is therefore reasonable to hypothesize that the amount of nuclear (nu-) eDNA fragments will increase relative to mt-eDNA fragments and
42
changes in the ratios between the concentrations of both fragments will be indicative of recent reproductive activity (Coward et al. 2002; Islam & Akhter 2012). Macquarie perch (Macquaria australasica) is a medium-bodied freshwater fish endemic to Australia and is currently listed as nationally endangered with only a handful self-sustaining populations remaining (Ingram et al. 2000; Lintermans 2007). The abundance and distribution of this species has declined as a result of anthropogenic disturbances and negative interactions with invasive species (Ingram et al. 2000; Koehn & MacKenzie 2004; Broadhurst et al. 2009). While multiple recovery actions have been undertaken to ensure the future survival of this species, evaluating their effectiveness is often difficult due to the lack of long-term monitoring surveys and biases associated with individual monitoring methods (Lintermans 2013a,c, 2016). In recent years, efforts have been undertaken to increase the understanding of Macquarie perch spawning biology (Tonkin et al. 2010, 2015; Broadhurst et al. 2012; Koster et al. 2013). However, obtaining detailed information about the timing and location of spawning currently requires destructive or injurious sampling methods (Tonkin et al. 2010, 2015). Spawning in Macquarie perch generally occurs when water temperatures reach 1418°C and they remain reproductively active for up to two months (Ingram et al. 2000; Tonkin et al. 2010, 2015). Mature adults undertake spawning migrations and form aggregations at the tail end of pools (Tonkin et al. 2010; Koster et al. 2013). Spawning occurs both day and night although fish are thought to be more active in the late afternoon and early morning (Tonkin et al. 2010; Kearns et al. 2012). Gametes are generally released in fast flowing areas of the riffles and the relatively large demersal adhesive eggs will flow downstream and get lodged into the gravel bed until they hatch (Lintermans 2007; Tonkin et al. 2010; Kearns et al. 2012). Here a novel eDNA-based method for monitoring reproductive activity in aquatic organisms is presented. Using Macquarie perch as a target species in laboratory and field based studies we show that the relative abundance of mt- and nu-eDNA can be indicative of recent reproductive activity. The presented methodology will be broadly applicable and has the potential to increase the knowledge of the reproductive biology of wide variety of species, which could ultimately lead to improved management strategies.
43
Material and methods Primer design and testing Primers amplifying fragments of ca. 150 bp of Macquarie perch mitochondrial 12S gene and the nuclear Internal Transcribed Spacer (ITS) region were designed using Geneious v. 7.1.7 (Kearse et al. 2012). Target regions were selected on the basis that they are present in multiple copies within a cell and they contain sufficient genetic variability between species to develope species-specific primers (Long & Dawid 1980; Foran 2006; Hardy et al. 2011). Primers targeting a ca. 150 bp fragment of the Macquarie perch 12S gene were developed by constructing a CLUSTALW alignment of previously published 12S sequences from all fish within the Murray Darling Basin (MDB) (Hardy et al. 2011). Species-specific primers were designed and tested in silico for undesirable primer interactions using the standard settings for SYBR® Green quantitative Real-Time PCR (qPCR) analyses using Beacon DesignerTM Free Edition software (PREMIER Biosoft, Palo Alto, USA). Primer specificity was evaluated in silico using the NCBI nucleotide database and the Primer-BLAST tool with default settings (Ye et al. 2012). Genomic DNA of Macquarie perch and closely related co-occurring species (i.e. Maccullochella macquariensis, M. peelii, M. ambigua, Gadopsis bispinosus, G. marmoratus, Nannoperca australis and Perca fluviatilis) was used to confirm the specificity of the selected primers in vitro. Three qPCR replicates were performed per species using a dilution series of genomic DNA (i.e. 0.2, 0.02, 0.002 ng/µL). Individual PCR reactions contained 10 µL of SYBR® Select Master Mix (Applied Biosystems, Foster City, USA), 1.2 µL of each primer (5 µM), 2 µL of UltraPureTM BSA (4 µg/µL) (Invitrogen, Carlsbad, USA), 2 µL of DNA extract and DEPC-treated water (Invitrogen, Carlsbad, USA) in a total volume of 20 µL (Barnes et al. 2014). All qPCR analyses were performed using a 96-well plate and the Viia7 Real-Time PCR System (Applied Biosystems, Foster City, USA). Cycling conditions consisted of an initial activation step of 2 min at 95°C, 55 2-step cycles of 15 sec at 95°C and 1 min at 60°C, and a melting curve step with a continuous increase of 0.05°C/sec from 60°C to 95°C. To confirm the absence of non-specific amplification, PCR amplicons of all positive amplifications were purified using the MinElute PCR Purification Kit (Qiagen, Hilden, Germany) and Sanger sequenced at the ARCF Biomolecular Resource Facility (Australian National University) using an Applied Biosystems 3730xl DNA Analyzer (Applied Biosystems, Foster City, USA) following the manufacturer’s protocol.
44
The complete Internal Transcribed Spacer (ITS) region (ITS1, 5.8S and ITS2) was PCR amplified and sequenced for Macquarie perch (3) and closely related co-occurring species (i.e. M. macquariensis (2), G. bispinosus (2), N. australis (2) and P. fluviatilis (1)). Tissue samples and DNA extracts were available from previous studies and additional tissue samples were obtained for Macquarie perch (Narrandera Fisheries Centre, Narrandera, Australia) and P. fluviatilis (Cotter River, ACT, Australia) (Hardy et al. 2011; MacDonald et al. 2014). The quality of all available DNA extracts was assessed by gel-electrophoresis using a 2% Molecular Grade Agarose gel (BioLine, London, UK) with a run time of 60 min at 90 V. DNA was visualized using SYBR® Safe Gel Stain (Invitrogen, Carlsbad, USA) and the Gel DocTM XR+ system (Bio-Rad Laboratories, Hercules, USA). When the genomic DNA showed signs of degradation, DNA was re-extracted from the tissue samples using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany). Genomic DNA concentrations were determined for all extracts using the NanoDrop® ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, USA). Amplification of the entire ITS region was performed using universal primers All18SF (5’-TGGTGCATGGCCGTTCTTAGT-3’) and FITS4 (5’GTCTCGTCTGATCTGAGGTC-3’) (Hardy et al. 2010). PCR reaction consisted of a total volume of 25 µL containing 12.5 µL MyTaqTM HS Red Mix (BioLine, London, UK), 0.25 µL of each primer (10 µM), ca. 80 ng DNA and DEPC-treated water. Cycling conditions consisted of an initial activation step of 2 min at 94°C; 35 3-step cycles of 1 min at 95°C, 1 min at 51°C and 1 min at 72°C, and a final extension for 10 min at 72°C. Successful amplification was confirmed by gel-electrophoresis and PCR products were purified using the MinElute PCR Purification Kit. Sanger sequencing was performed using the above mentioned primers and the internal sequencing primer FITS2 (5’- GCACGAGCCGAGTGATCCAC-3’). Contigs were assembled de novo or by mapping reads to a consensus sequence of the same species. The quality of the contigs was evaluated manually and all consensus sequences were aligned using the MUSCLE alignment tool with 20 iterations. Macquarie perch specific primers were designed and the performance of the primers was evaluated in silico and in vitro as described previously. Experimental protocol Prior to the experimental set-up, all equipment was soaked for approximately 20 min in a 10% bleach solution and thoroughly rinsed with UV-sterilized tap water to destroy any potential contaminating DNA. Experimental fish, 18 one-year old Macquarie perch, were sourced from the Narrandera Fisheries Centre (NFC) and used in two tank configurations. Due to the 45
limited number of individuals, configurations were set-up at different times (October 2014 and December 2014) (Figure 2.1.). Each set-up consisted of six experimental tanks (three replicates per treatment) and a single Negative Control Tank (NCT), which contained no fish to evaluate potential cross-contamination. The first set-up, containing low (1 fish / 50 L) and high (5 fish / 50 L) density Experimental Spawning Tanks (EST), was used to simulate spawning for solitary (i.e. the release of gametes by a single male and female) and broadcast (i.e. the release of gametes by multiple males and females) spawning species respectively. The second configurations consisted of low and high density Experimental Control Tanks (ECT) to evaluate the potential impact of the sampling strategy on eDNA concentrations. Water samples (50 mL) were collected prior to stocking tanks with experimental animals to confirm the absence of Macquarie perch eDNA. After introducing experimental animals to the tanks, five samples were collected over a 14 day period for each tank (Figure 2.1). After collecting samples at day 14 (336 h), EST were supplemented with a 10 mL mixture of Macquarie perch milt (fish seminal fluid obtained from NFC) and UV-sterilized tap water to replicate a single spawning event. Appropriate milt volumes for low and high density treatments were calculated based on the milt production of single ripe male (10 mL / 700g body weight) (Asmus M., pers. comm.) and the mean body weight of the experimental animals (17.2 ± 7.5 g). An equivalent volume of UV-sterilized tap water was added to all the control tanks (NCT and ECT). Water samples were collected from all tanks for an additional 8 days (Figure 2.1.). After each sampling event, 50 mL of UV-sterilized tap water was added to each tank to keep water volumes constant. Field survey In order to confirm the applicability of the method in the field, a small-scale field survey was conducted targeting known spawning grounds for Macquarie perch within the Upper Murrumbidgee River (UMR) (NSW, Australia). Over a period of three years (2012-2015), acoustic telemetry methods were used to track the spawning movements of adult Macquarie perch in the UMR (P. McGuffie, unpublished data). Six potential spawning riffles, located within a remote section of the UMR, were identified based on this previous research and monitored using egg collections and eDNA sampling during the spring of 2015 (Figure 2.2.). Over three sampling events (October 20, 23 and 26), four double-winged fine-meshed (0.5 mm) drift nets were set to capture eggs and confirm spawning at each location. At the top and bottom of each riffle two individual nets were set overnight at a minimum depth of 0.5 m.
46
Tank configuration 1 Sampling (h): 0 1X
Sampling (h): 24, 48, 96, 192, 336
Sampling (h): 337, 360, 384, 432, 528
NCT + 10mL H2O
3X
EST - LD + 10mL milt & H2O
3X
EST - HD + 10mL milt & H2O
Tank configuration 2 Sampling (h): 0 1X
Sampling (h): 24, 48, 96, 192, 336
Sampling (h): 337, 360, 384, 432, 528
NCT + 10mL H2O
3X
ECT - LD + 10mL H2O
3X
ECT - HD + 10mL H2O
Figure 2.1. Schematic overview of the experimental set-up. Each tank configuration consisted of 7 tanks containing 50 L of UV-sterilized tap water. The first tank configuration was set-up in October 2014 and consisted of a single Negative Control Tank (NCT) (no fish present) and six Experimental Spawning Tanks with low (1 fish / 50L) and high (5 fish / 50 L) fish densities (EST-LD and EST-HD respectively). Tank configuration 2 was set-up in December 2014 and contained a single NCT and six Experimental Control Tanks with low (ECT-LD) and high (ECT-HD) fish densities.
Nets were retrieved the following days (October 21, 24 and 27) and eggs were collected, transported to a field laboratory and counted. Because of the remote sampling locations, the collection of water samples for eDNA analyses (four 2 L samples from pools downstream of the spawning riffles) was limited to day-time hours. Sampling bottles were sterilized by soaking them for approximately 20 min in a 10% bleach solution and thoroughly rinsing with UV-sterilized tap water. Samples were collected before the presumed spawning period (October 8) to obtain baseline information on the relative concentrations of nu- and mteDNA. Given that Macquarie perch are reproductively active for one-two months (Tonkin et al. 2010, 2015), eDNA sampling was continued after the first records of eggs in the drift nets and was stopped after spawning fish aggregations were observed on the riffles and egg counts showed a clear sign of recent spawning activity (October 24 and 27). A Blank Field Control
47
(BFC) was included at each sampling site and consisted of a 2 L sampling bottle filled with UV-sterilized tap water that was opened on site, closed and submerged in the water. After collection, all samples were stored on ice and transported to the University of Canberra for further processing.
Figure 2.2. Map of the sampling locations within the Upper Murrumbidgee River (UMR) (NSW, Australia). Sample locations are numbered from downstream (UMR01) to upstream (UMR06).
Sample processing and analyses Environmental DNA of all collected samples was captured by filtering water samples immediately (experimental samples) or within 24 h (field samples) through a 1.2 μm glass fibre filter (MicroScience, Taren Point, Australia). Before and between filtering samples, all equipment was soaked for 10 min in a 10 % bleach solution and thoroughly rinsed with UVsterilized tap water. After sterilization of the filtering equipment, a Negative equipment control (NEC) was obtained by filtering 500 mL of UV-sterilized water before loading experimental or field samples. All filters were placed in a 5 mL tube using sterilized forceps and stored at -20 °C. 48
Environmental DNA extractions, using the PowerWater DNA Extraction Kit (MoBio Laboratories, Carlsbad, USA), and further analyses were conducted in the trace DNA laboratory at the University of Canberra (Australia). Concentrations of nu- and mt-eDNA in all samples were determined by performing three qPCR replicates per sample for each target fragment using a SYBR® Green chemistry as described on page 44. For the nu-eDNA analyses of the field samples primer concentrations were halved to reduce the occurrence of non-specific amplification. Low levels of non-target amplification were especially problematic when concentrations of target eDNA were low (i.e. samples collected on October 8). Consequently, six qPCR replicates were run for the nu-eDNA analyses for these samples in order to retain sufficient valid qPCR replicates. For each plate a five point standard curve with three qPCR replicates for each DNA concentration (5 x 106 – 5 x 102) was used to infer absolute eDNA abundance. Standard curves consisted of PCR amplicons of the target fragments which were PCR amplified in a 25 µL reaction volume containing 12.5 µL MyTaqTM HS Red Mix, 1 µL of each primer (5 µM), ca. 80 ng Macquarie perch DNA and DEPC-treated water. Cycling conditions consisted of an initial activation step of 2 min at 94°C; 35 3-step cycles of 1 min at 95°C, 1 min 15 sec at 60°C and 1 min 30 sec at 72°C, and a final extension for 1 min at 72°C. The presence of a single amplicon of the desired size was confirmed through gel electrophoresis and amplicons were purified using the MinElute PCR Purification Kit. Amplicon concentrations were determined using spectrophotometry (NanoDrop ND-1000) and converted to amplicon copy numbers based on the average molecular weight of the amplicons calculated using OligoCalc (Kibbe 2007). The average molecular weight of the 12S and ITS amplicons was determined using published sequences (NCBI accession number HQ615499.1 and HQ615500) and all de novo generated sequences, respectively. Quantitative Real-Time PCR reactions were considered positive if a clear exponential amplification curve was observed and the melting curve did not deviate from those observed in the standard curve samples (Appendix 1.A.). Additionally, positive PCR replicates obtained from negative control samples (NCT and BFC) and a random subset (≥ 10%) of experimental and field samples were purified (MinElute PCR Purification Kit) and Sanger sequenced. All obtained sequence reads matched the Macquarie perch target sequence.
49
From the eDNA copies per reaction we calculated the number of eDNA copies per litre of water collected. A correction for the dilution effect was incorporated for all experimental samples and qPCR replicates showing no amplification were assigned a concentration of zero eDNA copies per litre. The obtained eDNA concentrations were subsequently log transformed using equation 2.1. [𝑒𝐷𝑁𝐴] = log10 ((𝑒𝐷𝑁𝐴 𝑐𝑜𝑝𝑖𝑒𝑠⁄𝐿) + 1)
2.1.
The relative abundance of nu- and mt-eDNA fragments was determined on a per sample basis by calculating the ratios of [nu-eDNA] to [mt-eDNA] across all independent qPCR replicates. All graphs used to visualize changes in the log transformed eDNA concentrations and the ratios of [nu-eDNA] to [mt-eDNA] were constructed using the packages ggplot2, gridExtra and cowplot in R v.3.1.3 (R Development Core Team 2010). Results Primer design and testing After in silico and in vitro testing of the potential primer pairs, the best performing primer combinations were selected for further analyses. Final primer combinations amplified a 148 bp and 157 bp fragment of the mitochondrial 12S gene and the nuclear ITS1 region of Macquarie perch, respectively (Table 2.2.). Both primer pairs were considered highly specific as no amplification was observed in closely related co-occurring species. Table 2.2. Details of the Macquarie perch (Macquaria australasica) specific primers used in the quantitative Real-Time PCR analyses. Primer pairs were designed to amplify fragments of the mitochondrial 12S gene and the nuclear ITS1 region. Primer MP-12S-183F23 MP-12S-330R25 MP-ITS1-444F21 MP-ITS1-600R19
Sequence (5’-3’) CAGCTTACCCTGTGAAGGACTAA CCTTCAGGATGTACGTTTCAGTATA TAGTTCAATTGCCGTCGTGCA CGACGAGGGAGAGAGAGAC
50
Amplicon length 148 bp 157 bp
Experimental results The ECTs showed a rapid increase in nu- and mt-eDNA within the first 24 h after stocking the tanks (Figure 2.3.). After this initial build-up phase, eDNA concentrations reached a plateau at which equilibrium between eDNA production and degradation was achieved. The observed equilibrium concentrations were generally higher in the high-density treatments and this trend was observed for both nu- and mt-eDNA. When evaluating the relative abundance of nu- and mt-eDNA, the results indicate that both target fragments are equally abundant since the calculated ratios do not deviate strongly from one (Figure 2.4.). Although a small increase in nu-eDNA concentrations can be observed in the low density ECT at 384 h (Figure 2.3.), a closer inspection of the raw data revealed that this was due to an increase in a single tank. As the ECT were set-up a month after the EST and immature experimental animals were used, this increase in nu-eDNA is unlikely to be caused by the presence of spermatozoa. A more likely explanation for this pattern is the higher natural variation in eDNA concentrations when species densities are low or increased stress levels experienced by individual fish due to the increased temporal sampling between 336 and 384 h. Within the EST the nu- and mt-eDNA concentrations generally follow the same trend as in the ECT for the first part of the experiment. After supplementing the tanks with Macquarie perch milt, however, the abundance of both target fragments increases (Figure 2.3.). Samples collected 1 h after milt supplementation had the highest concentrations of both target fragments. While there is no obvious difference in these peak concentrations between density treatments, there is a clear difference in concentrations between target fragments. In both density treatments, milt supplementation resulted in an approximately 100-fold increase in nueDNA relative to mt-eDNA. This relative difference in eDNA concentrations remains detectable in the high and low density treatments for approximately 40 to 60 h respectively (Figure 2.4.). Additionally, the results indicate that the degradation profiles for eDNA originating from spermatozoa are dependent on the fish density and the target fragment. A comparison between density treatments reveals that spermatozoa eDNA, both nu- and mteDNA, degrades faster in the high density tanks. When comparing the nu- and mt-eDNA degradation curves after milt supplementation for both density treatments, the results indicate that the mt-eDNA follows a typical exponential degradation curve while the nu-eDNA degradation curve follows an inverse logistic function (Figure 2.3.). This trend can also be observed in Figure 2.4. where the calculated ratios show a peak 24 h after milt supplementation. 51
Figure 2.3. The log10 transformed Macquarie perch environmental DNA (eDNA) concentrations over time for low and high density treatments. The top and middle graphs give a comparison between the Experimental Control Tanks (ECT) and Experimental Spawning Tanks (EST) for mitochondrial (mt-) and nuclear (nu-) eDNA respectively. The bottom graphs give a comparison between mt- and nu-eDNA concentrations within the EST. Tanks were supplemented with 10 mL of water (ECT) or a mixture of milt and water (EST) after 336 hours (grey dashed line). Grey shading represents ± 1 SD from the mean.
52
Figure 2.4. The ratios between Macquarie perch nuclear and mitochondrial environmental DNA (eDNA) concentrations over time for the low and high density treatments. Solid lines represent the Experimental Spawning Tanks (EST) while dashed lines represent the Experimental Control Tanks (ECT). Ten millilitres of water (ECT) or a mixture of milt and water (EST) were added after 336 hours (dashed grey line).
All NEC and the samples from the NCT associated with the ECT showed no signs of contamination. In contrast, contamination was observed in the NCT which was run simultaneously with the EST. Analyses of all the samples collected from this tank showed low levels of nu-eDNA shortly before and after EST were supplemented with milt while no mt-eDNA was detected. The most likely source of contamination is thus the handling of Macquarie perch milt prior to collecting the 336 h samples which has caused the transfer of few spermatozoa into the associated NCT. Given that only low levels of nu-eDNA were detected in the NCT (i.e. average log transformed nu-eDNA concentration across all positive samples is 2.94 ± 2.32) and no increase in eDNA concentrations was observed in the EST samples collected at 336 h, the observed contamination levels are unlikely to affect the general trends observed. 53
Field survey Through the conventional monitoring methods we were able to confirm the absence of spawning migration during the first day of eDNA sampling (October 8, 2015). In addition, egg collections indicated that Macquarie perch were reproductively active at two spawning riffles (UMR04 and UMR06) during October 21st, 24th and 27th (Table 2.3.). The eDNA analyses clearly show that outside of the reproductive period (October 8, 2015) the ratios between nu- and mt-eDNA concentrations do not deviate from one (Figure 2.5.). In contrast to the conventional monitoring, eDNA monitoring did not show evidence of spawning activity during October 24th but all samples collected during October 27th showed an increase in ratios between nu- and mt-eDNA (Figure 2.5.). Table 2.3. The number of Macquarie perch eggs collected with drift nets for each sampling site and sampling date. Sampling site
Number of eggs collected st
UMR01 UMR02 UMR03 UMR04 UMR05 UMR06
October 21
October 24th
October 27th
0 0 0 1 0 4,564
0 0 0 40 0 485
0 0 0 399 0 1,585
The eDNA analyses performed on both BFC’s and NEC’s yielded one positive amplification for the 12S gene fragment in the BFC associated with the samples collected from UMR04 on October 27th. Given that all other controls tested negative for Macquarie perch DNA and high concentrations were obtained from a single qPCR replicate (1201 DNA copies/reaction), it is highly likely that contamination occurred during the PCR set-up. This was further supported by performed an additional six qPCR replicates for both target fragments for this BFC which did not produce a positive amplification. As such, contamination due to improper sample handling or DNA extractions can be excluded and the results obtained from all associated samples were not omitted from the analyses.
54
Figure 2.5. The ratios between Macquarie perch nuclear and mitochondrial environmental DNA (eDNA) concentrations for all field sites sampled before (October 8, 2015) and during (October 24 and 27, 2015) the presumed spawning period. Black arrow points indicate the sampling dates and sites at which eggs were collected using drift nets.
55
Discussion The results presented here show that changes in the relative abundance of nuclear and mitochondrial eDNA can be used to monitor spawning activity of Macquarie perch. Although this study focused on a single species, the methods are likely transferrable to other aquatic species relying on external fertilization (e.g. teleost fish and frog species). While the relatively high numbers of mitochondria found in frog spermatozoa compared to teleost fish might reduce the strength of the eDNA signal (Jamieson 1991; Lee & Jamieson 1992), this may be partially compensated by higher copy numbers of the nuclear ribosomal operon in frog species (Long & Dawid 1980). Besides the direct applications of this method, the results obtained from our study also contradict the popular belief that mitochondrial DNA fragments are more abundant in water samples (Olson et al. 2012). This is consistent with recently published research which has shown that amplicon targets (nuclear vs mitochondrial) do not have a significant effect on eDNA detection rates and the use of nu-eDNA targets might actually increase the sensitivity of eDNA studies (Minamoto et al. 2016b; Piggott 2016). These findings and the fact that nuclear DNA fragments are thought to degrade faster in environmental samples than mitochondrial DNA (Foran 2006), offers new opportunities to improve the reliability of eDNA-based species detections by targeting both eDNA fragments using a multiplex PCR. Although the results obtained from the drift nets and eDNA sampling are inherently biased due to differences in sampling times (i.e. night- and day-time collections respectively), a comparison between the data collected from both monitoring methods highlight some of their limitations. Firstly, drift nets were able to collect Macquarie perch eggs during October 24th while eDNA monitoring did not show any sign of recent spawning activity. This observation could indicate that our eDNA method is less sensitive than the use of drift nets to detect low levels of spawning activity. Alternatively, the pattern could be explained if previously deposited eggs were dislodged from the gravel beds and washed into the drift nets. As such, relying on egg counts for the detection of recent spawning activity might suffer from false positive errors. Comparing eDNA and conventional monitoring results when both showed signs of reproductive activity (October 27, 2015) reveals that sites showing the highest ratios between nu- and mt-eDNA do not correspond to the spawning areas as defined by conventional monitoring (Figure 2.5.). This difference could be explained when taking into consideration water flow and the method used to infer spawning activity. Owing to the limited transport distance of the demersal and slightly adhesive eggs of Macquarie perch, drift nets 56
can determine spawning locations very accurately. However, their labour intensive nature makes them unsuitable to precisely determine temporal variation in spawning activity. In contrast, the eDNA-based methodology relies on the presence of highly mobile spermatozoa in the water samples to infer spawning. Consequently, the downstream transport of spermatozoa will affect our ability to determine the exact spawning locations. However, eDNA sampling can be automated (Greenfield et al. 2008) and thus temporal and spatial sampling efforts can be increased to obtain more detailed information on the exact spawning time and location. While the current study shows that eDNA-based monitoring can be utilized as a non-invasive method for monitoring reproduction, additional studies comparing conventional methods with our eDNA-based approach are needed to assess the advantages and disadvantages of this method. Future comparative studies in lotic systems will be valuable to better understand the impact of water flow and will benefit from temporal sampling strategies in which sampling time/period is consistent between methods. While the transport of eDNA originating from spermatozoa is likely to be more limited in lentic environments, additional work is needed to determine dispersal rates of spermatozoa in these systems. The application of this method to other species will be beneficial to understand the influence of reproductive behaviour (e.g. solitary vs broadcast spawning) on the efficiency of conventional methods and eDNA-based monitoring of reproductive activity. From a management context, determining the magnitude of spawning events is important to monitor changes in the reproductive output of populations. After milt supplementations, we found that both nuclear and mitochondria eDNA concentrations were higher in the high-density tanks compared to the low-density tanks (i.e. a 3- and 5-fold increase respectively). Although this indicates that eDNA concentrations might be related to the magnitude of spawning events, more rigorous studies are required to provide conclusive evidence. Finally, the uptake of the presented method will depend strongly on cost and time requirements as these are often limiting factors in monitoring surveys. Surveys using eDNA are generally considered less time-intensive than conventional monitoring and this is particularly true when conventional methods are very time consuming (e.g. collecting and counting eggs) (Jerde et al. 2011). Although the methods used in this study are likely to be relatively costly, a multiplex PCR could evaluate the relative abundance of nu- and mt-eDNA fragments within a single reaction and will reduce costs significantly. Finally, it is important to note that contamination was observed in some negative control samples (NCT and BFC). As the presence of contaminating DNA can significantly affect the validity of future studies, future research should take into consideration recently published guidelines for eDNA studies (Goldberg et al. 2016). 57
Conclusion The presented eDNA methodology for detecting reproductive activity in aquatic organisms has the potential to increase our knowledge of the reproductive biology of elusive species and help evaluate management actions aimed at increasing the reproductive output of endangered populations. Environmental DNA monitoring provides several advantages in that it is a noninvasive method, highly species-specific and can be automated, allowing it to be applied across large temporal and spatial scales. Furthermore, given that this method relies on the detection of the direct products of spawning activity, it could help evaluate whether population declines are caused by spawning failure or high mortality rates of early life history stages. While the presented method could have broad applications, future comparative studies are required to better understand the sensitivity and the cost- and time-requirements of this method, which will ultimately determine its potential for species management and conservation.
58
CHAPTER 3.
Does size matter? An experimental evaluation of the
relative abundance and decay rates of aquatic eDNA fragments of different size and origin. Introduction Environmental DNA (eDNA) extracted from environmental samples is increasingly being used to monitor biodiversity (Thomsen et al. 2012b; Rees et al. 2014b; Goldberg et al. 2014). A key issue with using eDNA for biodiversity monitoring is that DNA in the environment can decay rapidly. Over time, longer DNA fragments will break into smaller fragments causing the latter to be relatively more common in environmental samples (Deagle et al. 2006; Foran 2006; Zhu 2006; Pietramellara et al. 2009; Woodruff et al. 2015). Mitochondrial DNA fragments are also likely to be more abundant in environmental samples than nuclear DNA fragments because mitochondrial DNA is present in higher copy numbers per cell and is believed to be less susceptible to environmental decay. Several studies have shown that recovery rates from environmental samples are lower for long relative to short fragments, and for nuclear relative to mitochondrial DNA fragments (Alonso et al. 2004; Deagle et al. 2006; Foran 2006; Allentoft et al. 2012; Sawyer et al. 2012). Consequently, most studies use short mitochondrial DNA barcodes (i.e. typically between 50 and 200 base pairs (bp)) to identify species from ancient and highly degraded samples (e.g. ice cores, permafrost, fossil bones, faeces, etc.) (Willerslev et al. 2003; Deagle et al. 2006; Allentoft et al. 2012; Andersen et al. 2012; Epp et al. 2012; Sarre et al. 2013; De Barba et al. 2014). In recent years, the use of eDNA from water samples to monitor aquatic biodiversity has increased in popularity (Ficetola et al. 2008; Thomsen et al. 2012a; Piaggio et al. 2014). While the optimization of sampling, capturing and extraction protocols has received considerable attention, the relative abundance and decay rates of DNA fragments of different size and origin (i.e. mitochondrial versus nuclear) has not been formally evaluated (Renshaw et al. 2014; Takahara et al. 2014; Minamoto et al. 2016a; Spens et al. 2016). Within the water column the relative abundance of different DNA fragments depends on both their rate of production and the rate of decay. Although the actual physiological source of aquatic eDNA remains unclear, eDNA production rates are known to be influenced by the species’ biomass, ontogeny and metabolism (Maruyama et al. 2014; Pilliod et al. 2014; Klymus et al. 2014; Barnes & Turner 2016). Decay rates of aquatic eDNA on the other hand are known to be
59
influenced by temperature, pH, UV-radiation and microbial activity (Matsui et al. 2001; Pilliod et al. 2014; Barnes et al. 2014; Strickler et al. 2014; Tsuji et al. 2017). Although rapid decay rates have been observed for DNA in sediment and faecal samples, recent evidence suggests that aquatic environments preserve eDNA relatively well (Dell’Anno & Corinaldesi 2004; Deagle et al. 2006; Pietramellara et al. 2009). For example, aquatic eDNA has been found to mainly occur within whole organelles and/or cells, suggesting they may be partly protected from environmental factors that could promote strand breaks (Turner et al. 2014a; Wilcox et al. 2015). Furthermore, DNA fragments thought to decay rapidly (i.e. long mitochondrial fragments and nuclear fragments) have been successfully extracted and amplified from water samples (chapter 2) (Piggott 2016; Deiner et al. 2017b; Jo et al. 2017). Given that the ability to use non-standard fragments may advance eDNA-based monitoring of aquatic biodiversity, through more flexible primer design and increased taxonomic resolution of barcodes for whole community analyses (i.e. eDNA metabarcoding), a thorough evaluation of the relative abundance and decay rates of different eDNA fragments is needed. This study aims to quantify the relative abundance and decay rates of mitochondrial eDNA fragments of different size in a freshwater environment. In addition, the feasibility of using nuclear fragments for eDNA studies will be evaluated. An experiment, comprising replicate tanks stocked with three different densities of common goldfish (Carassius auratus), was used to test the prediction that longer mitochondrial fragments and nuclear fragments degrade faster than shorter mitochondrial fragments and mitochondrial fragments of a similar size, respectively. Material and methods Primer design and testing We designed primers to amplify different sized fragments of the mitochondrial cytochrome c oxidase subunit I (COI) gene and the nuclear internal transcribed spacer (ITS) region of common goldfish. Target regions were chosen due to high genetic variability between species and the presence of multiple copies within a cell (Long & Dawid 1980; Hebert et al. 2003; Foran 2006). DNA fragment sizes were chosen based on current size recommendations for eDNA studies (i.e. length of 50 to 200 bp) and the limitations of currently preferred HighThroughput Sequencing (HTS) platforms (i.e. 550 to 570 bp using 2 x 300 bp paired-end sequencing on the Illumina MiSeq and allowing for a 50 to 30 bp overlap for sequence
60
assembly) (Rees et al. 2014b; Goodwin et al. 2016). For each region, primers were designed to amplify a short (ca. 100 bp), medium (ca. 300 bp) and long (ca. 500 bp) DNA fragment. Goldfish specific primers amplifying different sized fragments of the mitochondrial COI gene were designed using all available COI sequences of C. auratus voucher specimens and closely related species which co-occur in the Murray-Darling Basin (MDB) (i.e. C. carassius, Cyprinus carpio, Rutilus rutilus and Tinca tinca) (NCBI accessed on January 20th 2016). All sequences were imported into Geneious v. 7.1.7, unique sequences were extracted and a CLUSTALW alignment was construct and used for primer development (Kearse et al. 2012). The complete Internal Transcribed Spacer (ITS) region (ITS1, 5.8S and ITS2) was PCR amplified and sequenced for goldfish (2) and closely related co-occurring species (i.e. C. carpio (2), G. holbrooki (1), R. rutilus (1) and T. tinca (1)). Amplification and sequencing of the ITS region for G. holbrooki followed the protocol described on page 45. For all other species slight modifications were made to the protocol. For C. carpio and R. rutilus primers 18S FORWARD (5’-TGCCATTTGTACACACCGCCCG-3’) and 28S REVERSEb (5’TTAAGTTGAGCGGGTTGTCTC-3’) were used for PCR amplification (Johansen et al. 2006). A newly designed primer, CA-ITS-For1 (5’-GAACGAGACTCCGGCTTGTTA-3’), was used in combination with the 28S REVERSEb primer to amplify the ITS region for C. auratus. Reaction conditions and thermal cycling profiles were largely consistent with the protocol described on page 45 exception that an annealing temperature of 55 °C was used. Clean-up of PCR amplicons and subsequent Sanger sequencing was as described previously (page 45). Contigs were assembled de novo before manually checking the quality of the assemblies. Using all consensus sequences a
MUSCLE
alignment (20 iterations) was
constructed and used for primer design. The performance and specificity of all primer pairs was tested in silico and in vitro following the protocol described on page 44. Genomic DNA extracts used to verify the specificity of the mitochondrial primers in vitro were derived from C. auratus tissue samples, closely related species co-occurring within the MDB (C. carpio, G. holbrooki, M. anguillicaudatus, R. rutilus and T. tinca) and species for which the in silico analyses indicated that non-target amplification was possible (Macquaria ambigua, Gadopsis bispinosus, Gadopsis marmoratus and Hypseleotris klunzingeri). Nuclear primers were validated in vitro using genomic DNA extracts from C. auratus and the closely related species co-occurring. Quantitative Real-Time PCR (qPCR) reactions followed the protocol described on page 44 for all primers except
61
those amplifying a ca. 500 bp fragment of the ITS region. As consistent amplification of this fragment in the C. auratus samples required a three-step cycling stage (i.e. 55 cycles of 15 sec at 95°C, 15 sec at 60°C and 1 min 30 sec at 72°C), this modified cycling profile was used for all subsequent analyses of the long ITS fragment. All primer pairs designed to specifically amplify short, medium and long fragments of the COI and ITS regions of goldfish are given in Table 3.1. Hereafter, different DNA fragments will be referred to by the eDNA fragment ID’s given in Table 3.1. consisting of the target region (COI or ITS) followed by the total length of the amplicon in between brackets (i.e. COI (096bp), COI (285bp) and COI (515bp) for the short, medium and long mitochondrial fragments respectively and ITS (095bp), ITS (280bp) and ITS (476bp) for the short, medium and long nuclear fragments respectively). Table 3.1. Details of goldfish (Carassius auratus) specific primer pairs for short (ca. 100 bp), medium (ca. 300 bp) and long (ca. 500 bp) fragments of the mitochondrial cytochrome c oxidase subunit I (COI) gene and nuclear internal transcribed spacer (ITS) region. Fragment ID* COI (096bp) COI (285bp) COI (515bp) ITS (095bp) ITS (280bp) ITS (476bp)
Sequence (5’-3’)
Primer ID CA-COI-224F22 CA-COI-319R24 CA-COI-290F25 CA-COI-574R24 CA-COI-060F21 CA-COI-574R24 CA-ITS-678F19 CA-ITS-772R19 CA-ITS-731F22 CA-ITS-1010R18 CA-ITS-535F20 CA-ITS-1010R18
GATAATCGGAGCCCCAGACATG CCAGAGGAAGCTAGTAGTAACAGG ATCATTCCTGTTACTACTAGCTTCC AGAACAGGTAGTGATAGGAGAAGG ACCGCTTTAAGCCTCCTCATC AGAACAGGTAGTGATAGGAGAAGG GGACCGTGGGCTCAAAGTC CCTTTAGCCGCAGACAAGG GACCCCCTTTTCATTCCCATAC GCGACCCCTACGAAGCTC GCGAGAGAGACAGTCGGAAC GCGACCCCTACGAAGCTC
* Fragment ID code consists of the target region (COI or ITS) followed by the fragment length given in between brackets and expressed as the total number of base pairs (bp).
62
Experimental protocol Prior to setting up the experiment, contaminant DNA was removed from all experimental and sampling equipment using a 10% bleach solution and thorough rinsing with UV-sterilized tap water. Goldfish were purchased from a local pet shop and used to set-up three density treatments with three replicate tanks per treatment. A single goldfish (mean mass = 5.7 g, range = 3.3-9.5 g) was stocked in 10 L, 30 L and 60 L tanks filled with UV-sterilized tap water to simulate high density (HD), medium density (MD) and low density (LD) treatments, respectively. A negative control tank (NCT), consisting of a 60 L tank without fish, was included to test for cross-contamination. Just prior to stocking the tanks, a 50 mL water sample was collected from each tank to ensure that they were initially free from goldfish DNA, with the time of this sample set to zero hours (0 h). Further 50 mL water samples were collected at 2, 6, 24, 72, 168 and 336 h after stocking. Immediately after collecting the 336 h sample, all fish were removed from the tanks and further water samples were collected at 338, 342, 360, 408, 504, 672 and 1008 h. Water volumes within tanks were kept constant by adding 50 mL of UV-sterilized tap water after each sample collection. After removing fish from the experimental tanks, fin clips were collected to test for individual primer-template mismatches. While the C. auratus ITS sequences used for primer development were obtained directly from the experimental animals, we used COI sequence records from voucher specimens for the development of the mitochondrial primers. Only sequences from voucher specimens were used to exclude sequence records from specimens with a questionable taxonomic identification. However, these sequence records may not capture the complete COI haplotype diversity for C. auratus. To evaluate the compatibility between the mitochondrial primers and the experimental animals genomic DNA was extracted from the fin clips using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) and the standard COI barcoding region was PCR amplified using the LC01490 and HC02198 primers (Folmer et al. 1994). PCR reaction consisted of a total volume of 25 µL containing 12.5 µL MyTaqTM HS Red Mix (BioLine, London, UK), 0.25 µL of each primer (10 µM), 2 µL of DNA and DEPC-treated water. The thermal cycling profile consisted of an initial activation step of 1 min at 94°C; 35 cycles of 1 min at 94°C, 1 min at 40°C and 1 min 30 sec at 72°C; and a final extension of 7 min at 72°C. Amplicon clean-up and Sanger sequencing followed the protocol described on page 44. After assembling the obtained sequence reads the possible impact of primer-template mismatches was evaluated for each primer pair.
63
Sample processing and analyses Environmental DNA was captured from each 50 ml water sample by filtering it through a 1.2 µm glass fibre filter (MicroScience, Taren Point, Australia). Negative equipment controls (NEC) were obtained for each sample by filtering 500 mL of UV-sterilized water through the sterilized equipment prior to filtering samples. Environmental DNA was extracted using the PowerWater DNA Extraction Kit (MoBio Laboratories, Carlsbad, USA) in the trace DNA laboratory at the University of Canberra (Australia). For each sampling time, one NEC was included in the extraction protocol to test for cross-contamination occurring during either the filtrations or DNA extraction phase. DNA extracts were transferred to 2 mL screw-cap tubes and stored at -20 °C for further downstream analyses. Quantitative Real-Time PCR (qPCR) was used to determine eDNA concentrations. The absence of goldfish DNA from all negative control samples (i.e. water samples collected from the experimental tanks prior to introducing goldfish, water samples from the NCT and NEC’s) was confirmed by performing six qPCR replicates for both the short COI and ITS fragment. For each water sample, six qPCR replicates were performed for each primer set. Due to the large number of qPCR analyses and the costs of probe-based qPCR assays, a SYBR® Green chemistry was used with previous reaction conditions scaled down to a final reaction volume of 15 µL containing 1.5 µL of DNA extract (see chapter 2 page 44). All qPCR reactions were set-up in a dedicated room within the trace DNA laboratory in 384-well plates using the epMotion® 5075 Liquid Handling Workstation (Eppendorf, Hamburg, Germany). For each plate six non-template controls (NTC) and a standard curve, consisting of six qPCR replicates for each concentration with concentration ranging from 3 x 101 to 3 x 106 molecules per reaction, was included. Synthetic gBlock® fragments (IDT, Coralville, USA) were used to construct standard curves. Synthetic fragments contained bp mismatches with the sequences of the experimental animals so that contamination during the PCR set-up stage could be detected when sequencing PCR amplicons (Appendix 2.A.). All qPCR analyses were performed using the Viia7 Real-Time PCR System (Applied Biosystems, Foster City, USA) using thermal cycling conditions described in the ‘Primer design and testing’ section. Amplification and melt curves were visually inspected and replicates were excluded from further analyses if the amplification curve did not show a clear exponential phase and/or the melt curve deviated from those obtained with genomic goldfish DNA (see Appendix 2.B. and Appendix 2.C.). Finally, for each fragment a subset of the PCR
64
amplicons (ca. 10% of all samples showing a positive amplification) were purified using the MinElute PCR Purification Kit and Sanger sequenced at the ARCF Biomolecular Resource Facility (Australian National University). Statistical analyses Environmental DNA concentrations were expressed as copy numbers per µL of DNA extract and negative qPCR replicates were assigned a concentration of zero copies per µL. As bp mismatches were observed between the mitochondrial primers and the template sequences from two experimental animals, and the quality of the qPCR data from the medium and long ITS fragments were insufficient, they were excluded from further analyses (see the ‘Results’ section for a more details). All analyses were performed using natural log-transformed eDNA concentrations (equation 3.1.) with the packages tidyverse and nlme in R version 3.4.1 (R Development Core Team 2010; Pinheiro et al. 2016; Wickham 2016). [𝑒𝐷𝑁𝐴] = log 𝑒 ((𝑒𝐷𝑁𝐴 𝑐𝑜𝑝𝑖𝑒𝑠⁄𝜇𝐿) + 1)
3.1.
Initial inspection of the data prior to fish removal revealed that all eDNA fragments reached equilibrium concentrations after approximately 24 h (Appendix 2.D. and Appendix 2.E.). Consequently, estimates of the equilibrium eDNA concentrations were obtained using the data from 24 h until fish were removed (i.e. sampling time ≥ 24 h and sampling time ≤ 336 h). We fitted a linear mixed-effect model to the data with log-transformed eDNA concentrations as the response variable, fish density and fragment type as fixed effects, and individual tanks and individual samples as random effects to correct for pseudo replication (i.e. samples nested within tanks). Differences in eDNA equilibrium concentrations between density treatments and fragment types were estimated relative to a reference class set to zero (COI (096bp) in the HD treatment). Decay rates for eDNA have previously been obtained assuming a constant rate of decay (Thomsen et al. 2012b; Sassoubre et al. 2016; Jo et al. 2017). Under this assumption, the change in eDNA concentration over time, C(t), can be modelled with a first-order exponential decay function, with C0 representing the equilibrium eDNA concentration prior to fish removal and k being the decay constant (equation 3.2.), with larger values of k indicating a faster rate of decay.
65
𝐶(𝑡) = C0 × 𝑒 −𝑘𝑡
3.2.
While this first-order decay model may be a simplification of the true decay process, it allows us to quantify the decay rate of eDNA fragments with a single parameter, k, and to compare how decay rates vary under different conditions (Turner et al. 2014a; Strickler et al. 2014). Decay rates were estimated using the data obtained at least 6 hours after fish removal (i.e. sampling time ≥ 342 h). This was done to avoid a spike in eDNA concentrations immediately after fish removal in a single tank possibly caused by increased stress levels in the experimental animal and/or resuspension of sediment during fish removal. Additionally, sampling times for which all qPCR replicates tested negative were excluded from the analyses. We estimated the parameters of the exponential decay model by fitting linear models using log-transformed eDNA concentrations as the response variable. Sampling time, fish density and fragment type were included as fixed effects with two-way interactions between sampling time and fish density, and sampling time and fragment type (significant interactions imply differing slopes and hence differing values of the decay constant, k, for density and fragment type). Individual tanks and individual samples were included as random effects to correct for pseudo replication (i.e. samples nested within tanks). Differences in decay rate between density treatments and fragment types were estimated relative to a reference class set to zero (COI (096bp) in the HD treatment). We also fitted the model separately for each fragment by density treatment combination and used the Akaike Information Criterion (AIC) as a measure of how well the model fitted the data. Nevertheless, decay rates may vary over time as not all eDNA fragments will be equally susceptible to environmental decay (e.g. fragments present within cells versus free floating DNA). To test if eDNA decay rates vary over time for each treatment by fragment combination, we fitted a Weibull decay model to the decay data (equation 3.3.). The Weibull parameter (β) in this model allows for a non-constant rate of decay, with values smaller than one indicating decreasing decay rates over time and values larger than one indicating increasing decay rates over time. When β equals one, however, the model reduces to the firstorder exponential decay function. 𝐶(𝑡) = C0 × 𝑒 (−𝑘 × 𝑡
66
𝛽)
3.3.
We estimated parameters for the Weibull model for each fragment by density treatment combination as above (i.e. using the data obtained at least 6 hours after fish removal and sampling times for which all replicates tested negative for eDNA excluded). We used the AIC as a measure of how well the model fitted the data for each treatment by fragment combination, and compared AIC values from the Weibull model with those obtained from fitting the first-order exponential decay model to assess which model best described the data. Results Primer design and testing All primer pairs successfully passed the in silico evaluation and did not show off-target amplification in co-occurring species. However, it is important to note that poor quality sequences were obtained for the ITS (280bp) amplicons generated from the eDNA samples suggesting this primer pair is unsuitable for future eDNA applications (for more details see the ‘Analyses of the nuclear eDNA fragments’ section). Analyses of the mitochondrial eDNA fragments The COI barcode sequences (660bp in length) obtained from all experimental animals revealed that two mitochondrial haplotypes were present. While the majority of the COI sequences had a 100% match with C. auratus voucher specimens, which were used as a basis for primer development, the COI barcodes of two experimental animals only had a 98% match with C. auratus voucher specimens. A total of four, five and one bp mismatches were present in the primer binding regions for the COI (096bp), COI (285bp) and COI (515bp) fragments, respectively. Consequently, the goldfish specific COI primers showed reduced or failed amplification in two of the experiment tanks. The data obtained from these replicate tanks was thus excluded from further analyses (i.e. removal of one LD and one HD replicate tank). Finally, all tanks were initially free from goldfish DNA and there was no evidence of cross-contamination (i.e. no positive amplification was observed in the negative controls).
67
As expected, the estimates of equilibrium eDNA concentrations decreased with decreasing fish density (Figure 3.1. and Figure 3.2.). The mean eDNA concentration in the MD and LD treatments was on average 0.43 and 0.17 times that of the HD treatment, respectively. Progressively longer mitochondrial fragments also had lower equilibrium concentrations (Figure 3.1. and Figure 3.2.). The COI (285bp) and COI (515bp) fragments had, on average, equilibrium concentrations that were 0.77 and 0.33 times that of the COI (096bp) fragment, respectively. The decay rates, measured by the first-order exponential decay model, decreased as fish density declined: the HD treatment had the fastest decay rate followed by the MD and LD treatments (Figure 3.3. and Figure 3.4.). No clear differences were observed in the estimated decay rate for the three mitochondrial fragments of different length (Figure 3.3.).
Figure 3.1. Effects of varying density and fragment length on the environmental DNA (eDNA) equilibrium concentration. Differences in log-transformed concentration are expressed with respect to a reference class (high density (HD) treatment and COI (096bp) fragment), which is set to zero. Solid points show the mean and the lines represent the 95% confidence intervals for the mean difference in the log transformed concentration relative to the reference class.
68
Figure 3.2. The estimated mean equilibrium concentrations (solid black line) for each density treatment and all environmental DNA (eDNA) fragments plotted against the actual data for the equilibrium eDNA concentrations (grey points) (i.e. 24h ≤ Sampling time ≤ 336h). Plots are labelled with the density treatments (i.e. Low Density (LD) - 1 fish / 60 L, Medium Density (MD) – 1 fish / 30L and High Density (HD) – 1 fish / 10L) and the eDNA fragments given as the genetic target region (i.e. mitochondrial cytochrome c oxidase subunit I (COI) gene and nuclear internal transcribed spacer (ITS) region) with the fragment size in between brackets expressed as the total number of base pairs (bp). 69
Figure 3.3. Effects of varying density and fragment length on the decay rate as estimated from the first-order decay model. Differences in decay rate are shown with respect to a reference class (high density (HD) treatment and COI (096bp) fragment), which is set to zero. Solid points show the mean and the lines represent the 95% confidence intervals for the mean difference in decay rate relative to the reference class.
70
Figure 3.4. The best fit of the first-order exponential decay model (solid black line) for each density treatment and all environmental DNA (eDNA) fragments plotted against the actual data obtained after fish removal (grey points) (i.e. Sampling time ≥ 342 h). Plots are labelled with the density treatments (i.e. Low Density (LD) - 1 fish / 60 L, Medium Density (MD) – 1 fish / 30L and High Density (HD) – 1 fish / 10L) and the eDNA fragments given as the genetic target region (i.e. mitochondrial cytochrome c oxidase subunit I (COI) gene and nuclear internal transcribed spacer (ITS) region) with the fragment size in between brackets expressed as the total number of bas pairs (bp). 71
Analyses of the nuclear eDNA fragments The sequences obtained for the ITS region from two experimental animals (with differening COI haplotypes) were identical and used as a basis for primer development. Consequently, the goldfish specific ITS primers showed consistent amplification in all replicate tanks. Nonetheless, the data obtained from the two tanks showing reduces and/or failed amplification for the COI primers was excluded from further analyses to allow for comparisons between the mitochondrial and nuclear data. An initial evaluation of the data from the remaining tanks revealed some abnormalities that need to be addressed. First, the data suggested concentrations of the ITS (280bp) fragment were higher than the ITS (095bp) fragment (Appendix 2.D.). Sanger sequencing of the ITS (280bp) amplicons, however, produced poor quality sequences thus suggesting that the ITS (280bp) data are unreliable even though the melt-curve analyses did not indicate abnormalities (Appendix 2.C.). Second, the concentrations of the ITS (476bp) fragment were very low throughout the duration of the experiment and dropped below the limits of detection on several sampling occasions (Appendix 2.D.). Due to the unreliability of the data obtained from the ITS (280bp) fragment and the very limited data available for the ITS (476bp) fragment, these data were excluded from further analyses. The limited data did not allow us to evaluate the effect of fragment length on the relative abundance and degradation of nuclear eDNA fragments. Nevertheless, a comparison between the data obtained from the short nuclear and mitochondrial fragment is possible. The estimates of the equilibrium eDNA concentrations showed that the ITS (095bp) fragment was less abundant than the COI (096bp) fragment with the equilibrium concentration of the nuclear fragment being 0.30 times that of the mitochondrial (Figure 3.1. and Figure 3.2.). The first-order decay constant as estimated from the nuclear data showed a slower mean decay rate for the ITS (095bp) fragment than the COI (096bp) fragment (Figure 3.3.).
72
Comparison of eDNA decay models To evaluate whether decay rates were constant over time for all treatment by fragment combinations, the estimates for the Weibull parameters (log(β)) obtained from fitting equation 2 to the data were plotted (Figure 3.5.). The mean estimate of log(β) was less than zero (implying β 10 indicate random dispersion). The overall sensitivity of the targeted eDNA monitoring survey as a function of the mean eDNA concentrations at a site is shown in Figure 7.3. The results show that the currently used sampling design (i.e. eight 2 L water samples with maximum 6 PCR replicates per sample) achieves a detection sensitivity of 95% or greater when redfin perch eDNA concentrations at site are equal to or higher than 2.5 molecules per litre (Figure 7.3.).
148
Figure 7.2. The estimated concentrations of redfin perch (Perca fluviatilis) eDNA for four sites within the Blakney Creek catchment sampled during autumn 2015 and spring 2015. Mean concentration are shown as solid points while the thick and thin lines represent the 50% and 95% credible intervals, respectively.
eDNA metabarcoding The MiSeq01 run generated a total of 12,315,650 sequences from 259 uniquely labelled amplicon libraries (i.e. an average sequencing depth of ca. 47,000 reads per library) and had a Phred Q30 score ≥ 91.17. The yield of the MiSeq02 run was a total of 17,050,364 sequence reads from 260 samples (i.e. an average sequencing depth of ca. 65,000 reads per sample). The quality of the MiSeq02 run was lower (i.e. Phred Q30 score ≥ 79.40) which could be explained by the inclusion of amplicon libraries with a shorter length. After the bioinformatics filtering of the MiSeq01 sequences on per sample basis on average 28,918 (± 6,638) reads were assigned to fish species. The second bio-informatics filtering process contained sequences reads from both the MiSeq01 and MiSeq02 run and yielded an average of 294,826 (± 42,500) fish sequences per sampling site.
149
Figure 7.3. The overall probability of detection (sensitivity) of the targeted eDNA survey used in the current study as a function of the mean redfin perch (Perca fluviatilis) eDNA concentrations (solid curve). The probability of detection was calculated assuming a random dispersion of the eDNA molecules at the sampling sites.
No effect was observed for the amount of template eDNA used per PCR replicate during library construction phase using both presence/absence (i.e. R2 = 0.00028, p-value = 0.857) and proportional abundance data (i.e. R2 = 0.00292, p-value = 0.573). The effect of sampling season on the other hand was significant for both datasets used (i.e. R2 = 0.03993, p-values = 0.001 for the presence/absence data and R2 = 0.17156, p-value = 0.001 for the proportional abundance data). The overall community dissimilarity for the presence/absence data was lower compared to the community dissimilarity for the proportional read abundance data (Figure 7.4.A.). While no consistent patterns were observed in the relative contributions of the different species to the community dissimilarity based on the presence/absence data, the proportional abundance data revealed consistent higher proportions of SPP sequences in the samples collected during the spring survey (Figure 7.4.B.). Finally, the species accumulation curves did not show consistent patterns for the different template eDNA volumes used and sampling season although for the majority of sites the detected species richness was lower for the spring sampling season (Figure 7.5.).
150
Figure 7.4. The results of the overall community dissimilarity between the autumn and spring sample collections (A) and a heat map showing the average contribution of each species to the overall dissimilarity (B). The community dissimilarity was evaluated for eight sampling sites within the Blakney Creek catchment using presence/absence data (light grey bars in plot A and upper panel in plot B) and proportional read abundance data (dark grey bars in plot A and lower panel in plot B). The y-axis of the heat map contains the labels of the sampling sites with the total number of available samples for the different seasons in between brackets (autumn/spring). Labels within the heat map show the number of samples testing positive (upper panel) and the average proportion of sequence reads (lower panel) for the different species during the autumn/spring sampling seasons.
151
Figure 7.5. Species accumulation curves (SAC) for eights sampling sites within the Blakney Creek catchment. The different coloured curves show the SAC for the samples collected during the two different sampling seasons (black lines: autumn 2015; grey lines: spring 2015) while the different line types represent the data derived from amplicon libraries constructed with different amounts of template eDNA (solid lines: 4 µL per PCR replicate; dashed lines: 8 µL per PCR replicate).
152
Targeted eDNA monitoring vs. eDNA metabarcoding The detection probabilities as a function of the mean eDNA concentrations for both the targeted eDNA and the eDNA metabarcoding survey are shown in Figure 7.6. A marginal R2value of 0.79 was obtained suggesting an overall good fit of the model. The results show that in the current system and for the number of sample replicates used, the eDNA metabarcoding survey will fail to detect the presence of redfin perch when eDNA concentrations are below 2.5 molecules per litre (Figure 7.6.). Furthermore, the redfin perch eDNA concentrations needed to obtain a 0.25-0.75 detection probability are approximately two times higher for the eDNA metabarcoding survey compared to the targeted eDNA monitoring approach (Figure 7.6.). At concentrations higher than approximately 9 DNA molecules per litre, no differences in the detection probabilities are observed for both surveys methods.
Figure 7.6. The probability of detection as a function of the mean redfin perch (Perca fluviatilis) eDNA concentrations for both the targeted eDNA survey (black line) and the eDNA metabarcoding survey (grey line). The probability of detection was estimated considering only sample replicates and solid points show the proportions of samples showing a positive detection for redfin perch eDNA.
153
The impact of invasive fish species A summary of the fish community data per sampling site derived from the samples collected during spring 2015 and spring 2016 can be found in Appendix 6.D. Prior to constructing the NMDS-plot the detections of SPP (N. australis) and Galaxias sp. were removed from the data as they were detected at every sampling location during both sampling seasons (Appendix 6.D.). The NMDS-plot reveals a clear difference in fish communities between the Blakney Creek sampling sites and those from the adjoining Urumwalla Creek (Figure 7.7.). In particular, the presence of Hypseleotris sp. ‘Lake’s carp gudgeon’ in Urumwalla Creek and the absence of this species in the Blakney Creek sampling sites seems to drive the community dissimilarity (Figure 7.7. and Appendix 6.D.).
Figure 7.7. The non-metric multidimensional scaling plot of the presence/absence fish community data per sampling site (solid points) for the spring 2015 and spring 2016 sampling season (R2 = 0.999, Stress = 0.034).
154
The linear regression analyses for all native fish species showed a negative correlation between the logit-transformed proportion abundances of SPP and the number of species detected per sampling site (Figure 7.8.A.). The regression analyses performed to evaluate the effect of the invasive species show a similar pattern where only a native correlation is observed for the proportional read abundance of SPP (Figure 7.8.B. and Figure 7.8.C.). While these results may be indicative of a negative interaction between the invasive fish species and SPP, additional care is needed when interpreting these results.
155
Figure 7.8. The estimated regression slopes for the relationship between the logit-transformed proportional read abundances for the native species and the number of species detected (A) and the proportional read abundances of both invasive species (B) (i.e. common carp (Cyprinus carpio) and redfin perch (Perca fluviatilis)). The linear regression model for the relationship between the logit-transformed abundance data for southern pygmy perch (Nannoperca australis) and both invasive species was fitted to data (C). The 95% confidence intervals around the mean estimated regression slopes are shown as a line (A and B) and the dashed lines show the 95% confidence interval around the best fitting model (C).
156
Discussion Clear seasonal and species-specific patterns in eDNA abundance were observed for both the targeted eDNA survey and the eDNA metabarcoding results. The estimated concentrations of redfin perch eDNA were overall lower during the spring sampling season when a continuous flow was present in the system (Figure 7.2.). The only sampling site deviating from this pattern (i.e. BC10) is located on the edge of the redfin perch distribution and the increased eDNA concentration may thus reflect an increased colonization of the sampling site by redfin perch. The reduction in redfin perch eDNA concentrations during spring may result from either increased water flow or a reduction in metabolic rates due to low temperatures. The most likely explanation for the pattern observed here is the increased dilution of eDNA as a result of a higher water flow as previous studies have found reduced eDNA concentrations during high flow events while eDNA production rates do not appear to be influenced by water temperature (Klymus et al. 2014; Jane et al. 2015). By contrast, the eDNA metabarcoding data show evidence of increased eDNA concentrations of SPP during the spring sampling season (Figure 7.4.). This observation is likely to reflect SPP breeding activity as the spring sampling period coincided with the expected spawning period (i.e. September to January) (Llewellyn 1974; Lintermans 2007) and juvenile SPP have been collected from the BC catchment during the first half of October (Pearce L., pers. comm.). This observed increase in the proportion of SPP sequences appears to negatively influence the performance of the eDNA metabarcoding as for the majority of the sampling sites species accumulation curves were shallower for the spring sampling season compared to autumn (Figure 7.5.). Overall, the seasonal comparisons suggest that for intermittent river systems eDNA sampling during periods of low flow is preferred and will maximize the performance of both targeted eDNA monitoring surveys and eDNA metabarcoding. However, further studies are needed to confirm the generality of these findings. The direct comparison of the probability of detecting redfin perch eDNA, through either a targeted approach or eDNA metabarcoding, confirmed the hypothesis that targeted eDNAbased monitoring has a lower limit of detection compared to eDNA metabarcoding (Figure 7.6.). Consequently, the results presented here indicate that rare species may remain undetected during eDNA metabarcoding surveys. This is supported by the failure to detect redfin perch at the BC10, UC01 and UC03 sampling sites using an eDNA metabarcoding approach, while targeted eDNA monitoring indicated the presence of redfin perch DNA at these locations (Appendix 6.C.). Although eDNA metabarcoding is commonly believed to be 157
more prone to false negative detection, this study provided the first direct comparison of the two methods currently used for eDNA-based monitoring of aquatic biodiversity (Comtet et al. 2015; Hatzenbuhler et al. 2017). Additional comparative studies of targeted eDNA-based monitoring of multiple species with eDNA metabacoding results could be used to quantitatively evaluate the effect of laboratory protocols (e.g. primer choice) and bioinformatics filtering processes (e.g. removal of low abundant sequence records) (Alberdi et al. 2017). To ensure the uptake of eDNA-based monitoring by environmental management agencies, the strengths and limitations of both approaches need to be well understood (Goldberg et al. 2014). Furthermore, the eDNA metabarcoding data and the linear regression analyses show evidence of a negative interaction between the proportional read abundances of SPP and both invasive species. While it is tempting to conclude that this is indicative of a negative impact of common carp and redfin perch on the abundance of SPP, a number of biases need to be taken into consideration. Firstly, species-specific differences in amplification efficiency (i.e. due to primer-template mismatches) will influence the proportional read abundances (Elbrecht & Leese 2015; Pinol et al. 2015). Nonetheless, several studies have indicated that read abundances are to some extent correlated with species biomass (Evans et al. 2015; Hänfling et al. 2016; Stoeckle et al. 2017). Second, differences in the number of species detected per sampling unit will inherently bias proportional abundance data. Finally, as invasive species spread, become established and increase in abundance they will contribute more towards the total eDNA pool. As such, the relative increase in invasive species DNA may by itself reduce the proportional read abundance of all native species even if their absolute abundance did not decrease. The data from Retropinna semoni only may suffer from reduced amplification efficiency as relatively high primer-template mismatches are found for this species (chapter 4). This could also explain the patchy detections for this species within BC (Appendix 6.D.). The relatively low primer-template mismatches for the remaining species (i.e. two bpmismatches for Hypseleotris sp. and one mismatch for all other species near the 5’-end of the forward primer) suggests that primer biases may not have a very strong impact. Furthermore, the absence of any negative correlation for the proportional read abundances of all native species, excluding SPP, suggests that the number of detected species and the relative increase in invasive species eDNA do not inherently reduce the proportion abundance data (Figure 7.8.A. and Figure 7.8.B.). Consequently, the negative correlation found between the proportional reads of SPP and the proportional reads of the invasive species provides strong
158
evidence of a negative interaction between the abundance of SPP and the abundance of common carp and/or redfin perch. Although the relative contribution of the two invasive fish species cannot be assessed from the current data, previous surveys within the BC catchment have found that predation by redfin perch is the most likely cause for the decrease in the abundance and distribution of SPP (Pearce 2015). Nonetheless, the presence of common carp may also exert a negative pressure on the abundance of SPP by increasing water turbidity, through their feeding behaviour, which in turn reduces the abundance of aquatic macrophytes (i.e. the preferred habitat for SPP) (Vilizzi et al. 2014; Price et al. 2016). Finally, when evaluating the fish community structure within the catchment, a clear difference in fish communities was found between Blakney Creek and the adjoining Urumwalla Creek (Figure 7.7.). The finding that the upstream sites within UC only contain Hypseleotris sp. ‘Lake’s carp gudgeon’ and none of the other Hypseleotris species is of particular interest as all individuals of the species are believed to represent F1 hemi-clones, which require the presences of a sexually reproducing species to carry on their lineage (Bertozzi et al. 1997; Schmidt et al. 2011). Consequently, the population present in the upper reaches of UC may represent a remnant sexually reproducing population of Hypseleotris sp. ‘Lake’s carp gudgeon’ although further research is needed to confirm/refute this hypothesis. Conclusion The results presented here reveal that the performance of eDNA based monitoring in intermittent streams is influenced by seasonal variations in eDNA concentrations. Surveys aimed at detecting a single-species will be more effective when eDNA samples are collected during low flow periods or periods of reproductive activity. Environmental DNA collections for metabarcoding analyses on the other hand should avoid periods of reproductive activity as the increased eDNA concentrations of a restricted number of species appears to reduce the detection of other co-occurring species. The current findings also show that eDNA metabarcoding surveys can be particularly useful to study species interactions and can reveal unexpected distribution patterns. However, a targeted eDNA monitoring approach will be preferred when fine-scale distribution data is needed, which is often the case for decision making processes in environmental management.
159
160
CHAPTER 8.
General discussion
The objective of this thesis was to evaluate the potential of eDNA-based monitoring technologies, and in particular, eDNA metabarcoding for freshwater fishes within the MurrayDarling Basin (MDB). The research presented here provides a solid basis for the future implementation of eDNA surveys in standard monitoring efforts. Over the course of this PhD the interest of environmental management agencies in utilising eDNA-based monitoring has increased. Consequently, a stronger collaborative effort between academic research and environmental managers is urgently needed so that the application of this technology can ensure better management of our natural resources. While the current research has focussed on the freshwater fish biodiversity within the MDB, the results have broader implications (i.e. selection of optimal barcodes, design of appropriate sampling strategies and determining the best approach to address ecological/management issues). The following sections provide a brief overview of the major findings and the potential avenues for future research. Selecting optimal DNA barcodes for eDNA-based monitoring After the release of DNA by an organism it is generally believed that nuclear DNA will degrade rapidly and is therefore less suitable for eDNA studies (Murgia et al. 1992; Foran 2006; Allentoft et al. 2012). The findings of chapter 2 showed that aquatic eDNA can contain very high concentrations of nuclear DNA fragments during the reproductive period of a species. Furthermore, detecting this change in eDNA concentrations can be used to determine spawning periods and habitats of the species of interest. The ability to amplify nuclear eDNA fragments also opens up opportunities to target additional genetic markers, such as microsatellites and/or single nucleotide polymorphisms. These markers could possibly be used to characterize the genetic diversity of spawning aggregates and determine the effective population size. A widely recognized limitation of eDNA metabarcoding surveys is that it is often difficult to distinguish between closely related species using only short barcode sequences (Coissac et al. 2012; Valentini et al. 2016; Sigsgaard et al. 2017). The results from chapter 3 have shown that longer eDNA fragments are less abundant than shorter ones. However, the rate of degradation for the different sized eDNA fragments was not significantly different. This suggests that some proportion of the aquatic eDNA is relatively intact and accessible for 161
eDNA-based monitoring. These findings are consistent with the broader literature which has indicated that aquatic eDNA mainly occurs within whole cells and mitochondria (Turner et al. 2014a; Wilcox et al. 2015) and even entire mitochondrial genomes can be amplified from aquatic eDNA samples (Deiner et al. 2017b). Longer barcoding regions can thus be amplified from eDNA samples and used to improve the taxonomic assignments in eDNA metabarcoding studies. However, the relative lower abundance of longer DNA fragments may reduce the detection of rare species, although this has been refuted by a recent study by Piggott (2016). Nonetheless, there is still relatively limited information available about the impact of eDNA fragment length on the probability of detecting low abundant species. Consequently, future studies providing a thorough evaluation of this relationship, preferably relying on field based survey results, will be extremely valuable. Evaluation of metabarcoding primers and sampling strategies Within the current literature most eDNA metabarcoding protocols require an initial amplification of barcoding regions. This initial step is known to cause significant biases and has led multiple authors to conclude that eDNA metabarcoding should only rely on presenceabsence data (Valentini et al. 2009a; Elbrecht & Leese 2015; Pinol et al. 2015). Nevertheless, some studies have suggested that eDNA metabarcoding can provide (semi-)quantitative data (Evans et al. 2015; Hänfling et al. 2016; Ushio et al. 2018). The results from chapter 4 confirm that primer biases can have a strong influence on both the detected species and the proportional read abundance data. However, the use of the newly developed primer pair appears to reduce the impact of amplification biases on the proportion read abundance data. Future research aimed at improving our understanding of amplification biases can be valuable to determine optimal correction factors and may lead to more robust (semi-)quantitative data from eDNA metabarcoding surveys. The development of protocols to obtain quantitative eDNA metabarcoding data is likely to benefit from the use of universal primers without degenerate bases. While degenerate primers are often used in eDNA metabarcoding studies and can be highly effective (Pinol et al. 2015; Elbrecht & Leese 2017), degenerate bases in primers add an extra level of complexity to the PCR reaction. Hybridisation based approaches, such as targeted enrichment, have also been used to avoid amplification biases and could provide further improvements (Dowle et al. 2016). However, using read counts as a quantitative measure is likely to remain controversial. Embracing the full molecular character of eDNA-based monitoring and moving towards estimates of genetic diversity may instead be a more valuable approach. Recent research has shown that aquatic eDNA samples can be used 162
to assess the mitochondrial haplotype diversity of fish aggregates (Sigsgaard et al. 2016). While other genetic markers such microsatellite or SNP can theoretically be extracted and analysed from eDNA samples, more exploratory research is needed to evaluate the potential application of these approaches. Studies in lentic freshwater systems have revealed both a homogeneous (Thomsen et al. 2012b; Evans et al. 2017) and heterogeneous distribution of eDNA (Klobucar et al. 2017; Sato et al. 2017). Evidence from lotic systems suggests that eDNA is likely to have a patchy distribution (Sigsgaard et al. 2015; Furlan et al. 2016). The results from chapter 5 suggest that the distribution of eDNA in riverine systems may be strongly influenced by river morphology (i.e. river width and water flow) and ecological interactions between the different species present at a sampling location (i.e. niche differentiation). The latter finding is of particular interest as it suggests that the spatial variation in eDNA concentrations could reflect species distribution patterns on a relatively small-scale. In order to assess whether the distribution of eDNA reflects ecologically relevant distribution patterns, more extensive sampling surveys are needed. In particular, surveys in lowland riverine systems with a low flow rate and spatial differences in structural habitat will be highly valuable. Improving IAS management through eDNA-based monitoring Environmental DNA has been proven to be a highly valuable tool for monitoring invasive species (Ficetola et al. 2008; Jerde et al. 2013; Takahara et al. 2013; Piaggio et al. 2014; Dougherty et al. 2016). Chapter 6 highlights the potential of eDNA-based monitoring to make well informed management decisions and improve the outcomes of management actions directed at invasive species. Although much of the current research has focussed on vertebrate species, one of the major strengths of eDNA monitoring is that the collected samples can also provide valuable information about the distribution of pathogens (Schmidt et al. 2013; Hall et al. 2016). Since invasive vertebrates are often carriers of invasive pathogens, which can also negatively impact native species, eDNA surveys can thus be used to simultaneously assess the distribution of invasive vertebrate and pathogen species. Furthermore, quantitative surveys may provide more detailed information on the causative agent of declining populations of native species. Seasonal variation in biotic and abiotic factors is known to influence the eDNA concentrations found in water bodies (De Souza et al. 2016; Buxton et al. 2017; Sigsgaard et
163
al. 2017; Takahashi et al. 2017). The results in chapter 7 provide a first thorough evaluation of seasonal and species-specific effects on the eDNA concentrations in an intermittent river. More importantly, the method presented in chapter 7 enables a direct comparison of detection probabilities for targeted eDNA surveys and eDNA metabarcoding analyses. Broader implementations of this approach will be highly valuable to gain more insights into the advantages and limitations of both eDNA monitoring methods. Particularly, combining quantitative targeted eDNA monitoring of multiple species with eDNA metabarcoding analyses could be used to quantitatively evaluate the performance of metabarcoding primers through field surveys. The future of molecular monitoring methods Within recent years there have been some interesting practical and technological advancements which open up new opportunities for molecular monitoring tools. This final section outlines some of the potential future applications of molecular monitoring methods. The trade of ornamental species is an important pathway for future introductions of invasive species (Collins et al. 2012; García-Díaz & Cassey 2014). Consequently, rigorous border control and quarantine procedures can reduce the risk of future incursions and thus represents an important measure in protecting native biodiversity. Earlier studies have already indicated that an eDNA based single species detection method can be effective in biosecurity surveillance procedures for ornamental fish species (Collins et al. 2013). However, the feasibility of using eDNA technology for border control has only recently become feasible with the increased miniaturization of PCR and sequencing technologies (Ahrberg et al. 2016; Goodwin et al. 2016). The preference of a targeted eDNA approach or eDNA metabarcoding workflow for border surveillance will strongly depend on the import legislation. Targeted eDNA monitoring will be useful for countries that employ a limited list of unpermitted highrisk species (blacklist), such as the United States and United Kingdom (Collins et al. 2012). In addition to use of standard PCR thermocyclers, isothermal amplification of eDNA for the detection of unpermitted species may facility the implementation of this technology (Williams et al. 2017). In Australia and New Zealand, border control and biosecurity permits species that are included in a list of manageable species (whitelist) (Whittington & Chong 2007; Collins et al. 2012). As a result, the number of prohibited species is much higher and an eDNA metabarcoding method relying on universal fish primers, in combination with blocking primers for those permitted species, might be more feasible.
164
One of the major advantages of molecular monitoring tools is the possibility of automation. A number of autonomous sampling and analytical devices have been or are under development (e.g. the Environmental Sampling Processor (ESP) and the Biomeme two3TM) (Greenfield et al. 2008). The ability of remotely monitoring aquatic biodiversity opens up a range of applications. In freshwater and marine systems, such monitoring devices can be deployed to continuously assess recreational areas for noxious species (e.g. monitoring cyanobacteria or jellyfish blooms). Remote monitoring technologies will also be highly valuable for industrial applications. In particular, the early detection of disease causing agents in the aquaculture industry will allow farmers to undertake appropriate actions prior to mass mortalities. In recent years a number of studies have utilized both eDNA and environmental RNA (eRNA) for metabarcoding surveys (Pochon et al. 2015; Pawlowski et al. 2016; Laroche et al. 2017a,b). The advantage of using eRNA for biodiversity monitoring is that eRNA will only be obtained from living cells and it will therefor provide a snapshot of living/active biodiversity (Laroche et al. 2017b,a). While each of these studies has focussed on either micro-organisms or meiofauna of marine ecosystems, eRNA monitoring could also be a valuable tool for the monitoring of macro-organisms. In freshwater systems, aquatic invertebrates are commonly used to assess stream health and the potential of eDNA metabarcoding to improve routine surveys has been broadly recognized (Elbrecht et al. 2017). The use of eRNA could further revolutionize this field as it is theoretically possible to evaluate both the diversity and the functioning of invertebrate communities. Over the last few years molecular monitoring tools are increasingly used in biodiversity research. Future research avenues, as highlighted above, are likely to provide further improvements and diversify the range of applications. However, promoting the uptake of these technologies by environmental management agencies and/or industries will be highly important in the near future to ensure continued investments and improvements.
165
166
References Abell, R., Thieme, M.L., Revenga, C., Bryer, M., Kottelat, M., Bogutskaya, N., Coad, B., Mandrak, N., Balderas, S.C., Bussing, W., Stiassny, M.L.J., Skelton, P., Allen, G.R., Unmack, P., Naseka, A., Ng, R., Sindorf, N., Robertson, J., Armijo, E., Higgins, J. V, Heibel, T.J., Wikramanayake, E., Olson, D., López, H.L., Reis, R.E., Lundberg, J.G., Sabaj Pérez, M.H., Petry, P., Pérez, M.H.S. & Petry, P. (2008). Freshwater ecoregions of the world: A new map of biogeographic units for freshwater biodiversity conservation. BioScience, 58, 403–414. Adams, M., Raadik, T.A., Burridge, C.P. & Georges, A. (2014). Global biodiversity assessment and hyper-cryptic species complexes: More than one species of elephant in the room? Systematic Biology, 63, 518–533. Ahrberg, C.D., Ilic, B.R., Manz, A. & Neužil, P. (2016). Handheld real-time PCR device. Lab on a Chip, 16, 586–592. Alberdi, A., Aizpurua, O., Gilbert, M.T.P. & Bohmann, K. (2017). Scrutinizing key steps for reliable metabarcoding of environmental samples. Methods in Ecology and Evolution, 9, 134–147. Allentoft, M.E., Collins, M., Harker, D., Haile, J., Oskam, C.L., Hale, M.L., Campos, P.F., Samaniego, J.A., Gilbert, M.T.P., Willerslev, E., Zhang, G., Scofield, R.P., Holdaway, R.N. & Bunce, M. (2012). The half-life of DNA in bone: measuring decay kinetics in 158 dated fossils. Proceedings of The Royal Society: Biological sciences, 279, 4724–33. Alonso, A., Martín, P., Albarrán, C., García, P., García, O., de Simón, L.F., GarcíaHirschfeld, J., Sancho, M., de La Rúa, C. & Fernández-Piqueras, J. (2004). Real-time PCR designs to estimate nuclear and mitochondrial DNA copy number in forensic and ancient DNA studies. Forensic science international, 139, 141–149. Andersen, K., Bird, K.L., Rasmussen, M., Haile, J., Breuning-Madsen, H., Kjær, K.H., Orlando, L., Gilbert, M.T.P. & Willerslev, E. (2012). Meta-barcoding of ‘dirt’ DNA from soil reflects vertebrate biodiversity. Molecular Ecology, 21, 1966–1979. Anderson, C.B., Lencinas, M.V., Wallem, P.K., Valenzuela, A.E.J., Simanonok, M.P. & Pastur, G.M. (2014). Engineering by an invasive species alters landscape-level ecosystem function, but does not affect biodiversity in freshwater systems (R. Keller, Ed.). Diversity and Distributions, 20, 214–222. Auguie, B. (2012). gridExtra: functions in Grid graphics. R package version 0.9, 1. Baird, D.J. & Hajibabaei, M. (2012). Biomonitoring 2.0: a new paradigm in ecosystem assessment made possible by next-generation DNA sequencing. Molecular ecology, 21, 2039–2044. De Barba, M., Miquel, C., Boyer, F., Mercier, C., Rioux, D., Coissac, E. & Taberlet, P. (2014). DNA metabarcoding multiplexing and validation of data accuracy for diet assessment: application to omnivorous diet. Molecular ecology resources, 14, 306–323.
167
Barnes, M.A. & Turner, C.R. (2016). The ecology of environmental DNA and implications for conservation genetics. Conservation Genetics, 17, 1–17. Barnes, M.A., Turner, C.R., Jerde, C.L., Renshaw, M.A., Chadderton, W.L. & Lodge, D.M. (2014). Environmental Conditions Influence eDNA Persistence in Aquatic Systems. Environmental Science & Technology, 48, 1819–1827. Barnosky, A.D., Matzke, N., Tomiya, S., Wogan, G.O.U., Swartz, B., Quental, T.B., Marshall, C., McGuire, J.L., Lindsey, E.L., Maguire, K.C., Mersey, B. & Ferrer, E.A. (2011). Has the Earth’s sixth mass extinction already arrived? Nature, 471, 51–57. Bates, D., Maechler, M., Bolker, B. & Walker, S. (2014). lme4: Linear mixed-effects models using Eigen and S4. R package version, 1, 1–23. Beebee, T.J.C. (1996). Ecology and conservation of amphibians. Springer Science & Business Media. Bellemain, E., Carlsen, T., Brochmann, C., Coissac, E., Taberlet, P. & Kauserud, H. (2010). ITS as an environmental DNA barcode for fungi: an in silico approach reveals potential PCR biases. BMC microbiology, 10, 189. Bellemain, E., Swenson, J., Tallmon, D., Brunberg, S. & Taberlet, P. (2005). Estimating population size of elusive animals with DNA from hunter collected feces: Four methods for brown bears. Conservation Biology, 19, 150–161. Bertozzi, T., Adams, M. & Walker, K.F. (1997). Species boundaries in carp gudgeons (Eleotrididae : Hypseleotris) from the River Murray, South Australia: evidence for multiple species and extensive hybridization. Marine and Freshwater Research, 51, 805–815. Bickford, D., Lohman, D.J., Sodhi, N.S., Ng, P.K.L., Meier, R., Winker, K., Ingram, K.K. & Das, I. (2007). Cryptic species as a window on diversity and conservation. Trends in ecology & evolution, 22, 148–155. Billman, H.G., St-Hilaire, S., Kruse, C.G., Peterson, T.S. & Peterson, C.R. (2011). Toxicity of the piscicide rotenone to Columbia spotted frog and boreal toad tadpoles. Transactions of the American Fisheries Society, 140, 919–927. Bista, I., Carvalho, G.R., Walsh, K., Seymour, M., Hajibabaei, M., Lallias, D., Christmas, M. & Creer, S. (2017). Annual time-series analysis of aqueous eDNA reveals ecologically relevant dynamics of lake ecosystem biodiversity. Nature Communications, 8, 14087. Blackman, R.C., Constable, D., Hahn, C., Sheard, A.M., Durkota, J., Hänfling, B. & Handley, L.L. (2017). Detection of a new non-native freshwater species by DNA metabarcoding of environmental samples – first record of Gammarus fossarum in the UK. Aquatic Invasions, 12, 177–189. Boessenkool, S., Epp, L.S., Haile, J., Bellemain, E., Edwards, M., Coissac, E., Willerslev, E. & Brochmann, C. (2012). Blocking human contaminant DNA during PCR allows amplification of rare mammal species from sedimentary ancient DNA. Molecular ecology, 21, 1806–1815.
168
Bohmann, K., Evans, A., Gilbert, M.T.P., Carvalho, G.R., Creer, S., Knapp, M., Yu, D.W. & de Bruyn, M. (2014). Environmental DNA for wildlife biology and biodiversity monitoring. Trends in Ecology & Evolution, 29, 358–367. Bolger, A.M., Lohse, M. & Usadel, B. (2014). Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics, 30, 2114–2120. Bower, D.S., Pickett, E.J., Stockwell, M.P., Pollard, C.J., Garnham, J.I., Sanders, M.R., Clulow, J. & Mahony, M.J. (2014). Evaluating monitoring methods to guide adaptive management of a threatened amphibian (Litoria aurea). Ecology and Evolution, 4, 1361– 1368. Boyer, S., Brown, S.D.J., Collins, R. a, Cruickshank, R.H., Lefort, M.-C., Malumbres-Olarte, J. & Wratten, S.D. (2012). Sliding window analyses for optimal selection of minibarcodes, and application to 454-pyrosequencing for specimen identification from degraded DNA. PloS one, 7, e38215. Boyer, F., Mercier, C., Bonin, A., Le Bras, Y., Taberlet, P. & Coissac, E. (2016). obitools: A unix-inspired software package for DNA metabarcoding. Molecular Ecology Resources, 16, 176–182. Britton, J.R., Gozlan, R.E. & Copp, G.H. (2011a). Managing non-native fish in the environment. Fish and Fisheries, 12, 256–274. Britton, J.R., Pegg, J. & Gozlan, R.E. (2011b). Quantifying imperfect detection in an invasive pest fish and the implications for conservation management. Biological Conservation, 144, 2177–2181. Broadhurst, B.T., Ebner, B.C. & Clear, R.C. (2012). A rock-ramp fishway expands nursery grounds of the endangered Macquarie perch (Macquaria australasica). Australian Journal of Zoology, 60, 91–100. Broadhurst, B.T., Ebner, B.C. & Clear, R.C. (2009). Effects of radio-tagging on two-year-old, endangered Macquarie perch (Macquaria australasica : Percichthyidae). Marine and Freshwater Research, 60, 341–345. Bronnenhuber, J.E. & Wilson, C.C. (2013). Combining species-specific COI primers with environmental DNA analysis for targeted detection of rare freshwater species. Conservation Genetics Resources, 5, 971–975. Brown, E.A., Chain, F.J.J., Zhan, A., MacIsaac, H.J. & Cristescu, M.E. (2016). Early detection of aquatic invaders using metabarcoding reveals a high number of nonindigenous species in Canadian ports. Diversity and Distributions, 22, 1045–1059. Buxton, A.S., Groombridge, J.J., Zakaria, N.B. & Griffiths, R.A. (2017). Seasonal variation in environmental DNA in relation to population size and environmental factors. Scientific Reports, 7, 46294.
169
Caffrey, J.M., Baars, J., Barbour, J.H., Boets, P., Boon, P., Davenport, K., Dick, J.T.A., Early, J., Edsman, L., Gallagher, C., Gross, J., Heinimaa, P., Horrill, C., Hudin, S., Hulme, P.E., Hynes, S., Macisaac, H.J., McLoone, P., Millane, M., Moen, T.L., Moore, N., Newman, J., O’Conchuir, R., O’Farrell, M., O’Flynn, C., Oidtmann, B., Renals, T., Ricciardi, A., Roy, H., Shaw, R., Weyl, O., Williams, F. & Lucy, F.E. (2014). Tackling Invasive Alien Species in Europe : the top 20 issues. Management of Biological Invasions, 5, 1–20. Campbell, S.P., Clark, J.A., Crampton, L.H., Guerry, A.D., Leila, T., Hosseini, P.R., Lawler, J.J. & Connor, R.J.O. (2009). An Assessment of Monitoring Efforts in Endangered Species Recovery Plans. Ecological Applications, 12, 674–681. Cannon, M. V, Hester, J., Shalkhauser, A., Chan, E.R., Logue, K., Small, S.T. & Serre, D. (2016). In silico assessment of primers for eDNA studies using PrimerTree and application to characterize the biodiversity surrounding the Cuyahoga River. Scientific reports, 6, 22908. Caporaso, J., Kuczynski, J. & Stombaugh, J. (2010). QIIME allows analysis of highthroughput community sequencing data. Nature Methods, 7, 335–336. Caswell, N.M., Peterson, D.L., Manny, B. a. & Kennedy, G.W. (2004). Spawning by lake sturgeon (Acipenser fulvescens) in the Detroit River. Journal of Applied Ichthyology, 20, 1–6. Chariton, A.A., Court, L.N., Hartley, D.M., Colloff, M.J. & Hardy, C.M. (2010). Ecological assessment of estuarine sediments by pyrosequencing eukaryotic ribosomal DNA. Frontiers in Ecology and the Environment, 8, 233–238. Civade, R., Dejean, T., Valentini, A., Roset, N., Raymond, J.C., Bonin, A., Taberlet, P. & Pont, D. (2016). Spatial Representativeness of Environmental DNA Metabarcoding Signal for Fish Biodiversity Assessment in a Natural Freshwater System. PLoS ONE, 11, 1–19. Clarke, L.J., Beard, J.M., Swadling, K.M. & Deagle, B.E. (2017). Effect of marker choice and thermal cycling protocol on zooplankton DNA metabarcoding studies. Ecology and Evolution, 7, 873–883. Clarke, L.J., Soubrier, J., Weyrich, L.S. & Cooper, A. (2014). Environmental metabarcodes for insects: in silico PCR reveals potential for taxonomic bias. Molecular Ecology Resources, 14, 1160–1170. Coissac, E., Riaz, T. & Puillandre, N. (2012). Bioinformatic challenges for DNA metabarcoding of plants and animals. Molecular ecology, 21, 1834–1847. Collins, R. a., Armstrong, K.F., Holyoake, A.J. & Keeling, S. (2013). Something in the water: biosecurity monitoring of ornamental fish imports using environmental DNA. Biological Invasions, 15, 1209–1215. Collins, R. a, Armstrong, K.F., Meier, R., Yi, Y., Brown, S.D.J., Cruickshank, R.H., Keeling, S. & Johnston, C. (2012). Barcoding and border biosecurity: identifying cyprinid fishes in the aquarium trade. PloS one, 7, e28381.
170
Comtet, T., Sandionigi, A., Viard, F. & Casiraghi, M. (2015). DNA (meta)barcoding of biological invasions: a powerful tool to elucidate invasion processes and help managing aliens. Biological Invasions, 17, 905–922. Cosson, J., Groison, A.L., Suquet, M., Fauvel, C., Dreanno, C. & Billard, R. (2008). Marine fish spermatozoa: Racing ephemeral swimmers. Reproduction, 136, 277–294. Coward, K., Bromage, N.R., Hibbitt, O. & Parrington, J. (2002). Gametogenesis, fertilization and egg activation in teleost fish. Reviews in Fish Biology and Fisheries, 12, 33–58. Creer, S., Deiner, K., Frey, S., Porazinska, D., Taberlet, P., Thomas, w. K., Potter, C. & Bik, H.M. (2016). The ecologist’s field guide to sequence-based identification of biodiversity. Methods in Ecology and Evolution, 7, 1008–1018. Creer, S., Fonseca, V.G., Porazinska, D.L., Giblin-Davis, R.M., Sung, W., Power, D.M., Packer, M., Carvalho, G.R., Blaxter, M.L., Lambshead, P.J.D. & Thomas, W.K. (2010). Ultrasequencing of the meiofaunal biosphere: Practice, pitfalls and promises. Molecular Ecology, 19, 4–20. Cristescu, M.E. (2014). From barcoding single individuals to metabarcoding biological communities: towards an integrative approach to the study of global biodiversity. Trends in Ecology & Evolution, 29, 566–571. Cucherousset, J. & Olden, J.D. (2011). Ecological Impacts of Nonnative Freshwater Fishes. Fisheries, 36, 215–230. Darling, J.A. & Mahon, A.R. (2011). From molecules to management : Adopting DNA-based methods for monitoring biological invasions in aquatic environments. Environmental Research, 111, 978–988. Davidson, S. (2002). Carp crusades. Ecos, 112, 8–12. Davies, P.E., Stewardson, M.J., Hillman, T.J., Roberts, J.R. & Thoms, M.C. (2012). Sustainable Rivers Audit 2. Canberra. Deagle, B.E., Eveson, J.P. & Jarman, S.N. (2006). Quantification of damage in DNA recovered from highly degraded samples - a case study on DNA in faeces. Frontiers in zoology, 3, 11. Deagle, B.E., Jarman, S.N., Coissac, E., Pompanon, F. & Taberlet, P. (2014). DNA metabarcoding and the cytochrome c oxidase subunit I marker : not a perfect match. Biology Letters, 10. Deiner, K. & Altermatt, F. (2014). Transport distance of invertebrate environmental DNA in a natural river. PloS one, 9, e88786. Deiner, K., Bik, H.M., Mächler, E., Seymour, M., Lacoursière-Roussel, A., Altermatt, F., Creer, S., Bista, I., Lodge, D.M., de Vere, N., Pfrender, M.E. & Bernatchez, L. (2017a). Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Molecular Ecology, 26, 5872–5895.
171
Deiner, K., Renshaw, M.A., Li, Y., Olds, B.P., Lodge, D.M. & Pfrender, M.E. (2017b). Longrange PCR allows sequencing of mitochondrial genomes from environmental DNA. Methods in Ecology and Evolution, 8, 1888–1898. Deiner, K., Walser, J.-C., Mächler, E. & Altermatt, F. (2015). Choice of capture and extraction methods affect detection of freshwater biodiversity from environmental DNA. Biological Conservation, 183, 53–63. Dejean, T., Valentini, A., Duparc, A., Pellier-Cuit, S., Pompanon, F., Taberlet, P. & Miaud, C. (2011). Persistence of environmental DNA in freshwater ecosystems. PloS one, 6, e23398. Dejean, T., Valentini, A., Miquel, C., Taberlet, P., Bellemain, E. & Miaud, C. (2012). Improved detection of an alien invasive species through environmental DNA barcoding: the example of the American bullfrog Lithobates catesbeianus. Journal of Applied Ecology, 49, 953–959. Dell’Anno, A. & Corinaldesi, C. (2004). Degradation and Turnover of Extracellular DNA in Marine Sediments : Ecological and Methodological Considerations. Applied and environmental microbiology, 70, 4384–4386. Diana, J.S., Hanchin, P. & Popoff, N. (2015). Movement patterns and spawning sites of muskellunge Esox masquinongy in the Antrim chain of lakes, Michigan. Environmental Biology of Fishes, 98, 833–844. DiBattista, J.D., Darren Coker, B.J., Stat, M., Michael Berumen, B.L. & Michael Bunce, B. (2017). Assessing the utility of eDNA as a tool to survey reef-fish communities in the Red Sea. Coral Reefs, 36, 1245–1252. Doi, H., Uchii, K., Takahara, T., Matsuhashi, S., Yamanaka, H. & Minamoto, T. (2015). Use of Droplet Digital PCR for Estimation of Fish Abundance and Biomass in Environmental DNA Surveys. Plos One, 10, e0122763. Dorazio, R.M. & Erickson, R.A. (2017). eDNAoccupancy : An R Package for Multi-scale Occupancy Modeling of Environmental DNA Data. Molecular Ecology Resources. Dougherty, M.M., Larson, E.R., Renshaw, M.A., Gantz, C.A., Egan, S.P., Erickson, D.M. & Lodge, D.M. (2016). Environmental DNA (eDNA) detects the invasive rusty crayfish Orconectes rusticus at low abundances. Journal of Applied Ecology, 53, 722–732. Dowle, E., Pochon, X., Banks, J., Shearer, K. & Wood, S. (2016). Targeted gene enrichment and high throughput sequencing for environmental biomonitoring: a case study using freshwater macroinvertebrates. Molecular Ecology Resources, 16, 1240–1254. Dudgeon, D., Arthington, A.H., Gessner, M.O., Kawabata, Z.-I., Knowler, D.J., Lévêque, C., Naiman, R.J., Prieur-Richard, A.-H., Soto, D., Stiassny, M.L.J. & Sullivan, C.A. (2006). Freshwater biodiversity: importance, threats, status and conservation challenges. Biological reviews of the Cambridge Philosophical Society, 81, 163–182. Dunn, N., Priestley, V., Herraiz, A., Arnold, R. & Savolainen, V. (2017). Behavior and season affect crayfish detection and density inference using environmental DNA. Ecology and Evolution, 7, 7777–7785. 172
Ebner, B.C., Thiem, J.D., Gilligan, D.M., Lintermans, M., Wooden, I.J. & Linke, S. (2008). Estimating species richness and catch per unit effort from boat electro-fishing in a lowland river in temperate Australia. Austral Ecology, 33, 891–901. Eichmiller, J.J., Best, S.E. & Sorensen, P.W. (2016). Effects of Temperature and Trophic State on Degradation of Environmental DNA in Lake Water. Environmental Science & Technology, 50, 1859–1867. Elbrecht, V. & Leese, F. (2015). Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass-sequence relationships with an innovative metabarcoding protocol. PLoS ONE, 10, 1–16. Elbrecht, V. & Leese, F. (2016). PRIMERMINER: an R package for development and in silico validation of DNA metabarcoding primers. Methods in Ecology and Evolution, 8, 622–626. Elbrecht, V. & Leese, F. (2017). Validation and Development of COI Metabarcoding Primers for Freshwater Macroinvertebrate Bioassessment. Frontiers in Environmental Science, 5, 1–11. Elbrecht, V., Vamos, E.E., Meissner, K., Aroviita, J. & Leese, F. (2017). Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring. Methods in Ecology and Evolution, 8, 1265–1275. Engstedt, O., Engkvist, R. & Larsson, P. (2014). Elemental fingerprinting in otoliths reveals natal homing of anadromous Baltic Sea pike (Esox lucius L.). Ecology of Freshwater Fish, 23, 313–321. Epp, L.S., Boessenkool, S., Bellemain, E.P., Haile, J., Esposito, A., Riaz, T., Erséus, C., Gusarov, V.I., Edwards, M.E., Johnsen, A., Stenøien, H.K., Hassel, K., Kauserud, H., Yoccoz, N.G., Bråthen, K.A., Willerslev, E., Taberlet, P., Coissac, E. & Brochmann, C. (2012). New environmental metabarcodes for analysing soil DNA: potential for studying past and present ecosystems. Molecular ecology, 21, 1821–1833. Erickson, R.A., Rees, C.B., Coulter, A.A., Merkes, C.M., McCalla, S.G., Touzinsky, K.F., Walleser, L., Goforth, R.R. & Amberg, J.J. (2016). Detecting the movement and spawning activity of bigheaded carps with environmental DNA. Molecular Ecology Resources, 16, 957–965. Evans, N.T., Li, Y., Renshaw, M.A., Olds, B.P., Deiner, K., Turner, C.R., Jerde, C.L., Lodge, D.M., Lamberti, G.A. & Pfrender, M.E. (2017). Fish community assessment with eDNA metabarcoding: effects of sampling design and bioinformatic filtering. Canadian Journal of Fisheries and Aquatic Sciences, 74, 1362–1374. Evans, N.T., Olds, B.P., Turner, C.R., Renshaw, M. a., Li, Y., Jerde, C.L., Mahon, A.R., Pfrender, M.E., Lamberti, G. a. & Lodge, D.M. (2015). Quantification of mesocosm fish and amphibian species diversity via eDNA metabarcoding. Molecular Ecology Resources, 16, 29–41. Faircloth, B.C. & Glenn, T.C. (2012). Not all sequence tags are created equal: Designing and validating sequence identification tags robust to indels. PLoS ONE, 7, e42543.
173
Ficetola, G.F., Coissac, E., Zundel, S., Riaz, T., Shehzad, W., Bessière, J., Taberlet, P. & Pompanon, F. (2010). An in silico approach for the evaluation of DNA barcodes. BMC genomics, 11, 434. Ficetola, G.F., Miaud, C., Pompanon, F. & Taberlet, P. (2008). Species detection using environmental DNA from water samples. Biology Letters, 4, 423–425. Ficetola, G.F., Pansu, J., Bonin, A., Coissac, E., Giguet-Covex, C., De Barba, M., Gielly, L., Lopes, C.M., Boyer, F., Pompanon, F., Rayé, G. & Taberlet, P. (2015). Replication levels, false presences, and the estimation of presence / absence from eDNA metabarcoding data. Molecular Ecology Resources, 15, 543–556. Ficetola, G.F., Taberlet, P. & Coissac, E. (2016). How to limit false positives in environmental DNA and metabarcoding? Molecular Ecology Resources, 16, 604–607. Fischer, J.R. & Quist, M.C. (2014). Characterizing lentic freshwater fish assemblages using multiple sampling methods. Environmental Monitoring and Assessment, 186, 4461– 4474. Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. (1994). DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Molecular Marine Biology and Biotechnology, 3, 294–299. Foran, D.R. (2006). Relative degradation of nuclear and mitochondrial DNA: an experimental approach. Journal of Forensic Sciences, 51, 766–770. Foster, Z.S.L., Sharpton, T.J. & Grünwald, N.J. (2017). Metacoder: An R package for visualization and manipulation of community taxonomic diversity data. PLoS Computational Biology, 13, 1–15. Di Franco, A., Coppini, G., Pujolar, J.M., De Leo, G. a, Gatto, M., Lyubartsev, V., Melià, P., Zane, L. & Guidetti, P. (2012). Assessing dispersal patterns of fish propagules from an effective mediterranean marine protected area. PLoS ONE, 7, e52108. Frank, D.N. (2009). BARCRAWL and BARTAB: software tools for the design and implementation of barcoded primers for highly multiplexed DNA sequencing. BMC bioinformatics, 10, 362. Fuller, S., Baverstock, P. & King, D. (1998). Biogeographic origins of goannas (Varanidae): a molecular perspective. Molecular phylogenetics and evolution, 9, 294–307. Furlan, E.M. & Gleeson, D. (2016a). Environmental DNA detection of redfin perch, Perca fluviatilis. Conservation Genetics Resources, 8, 115–118. Furlan, E. & Gleeson, D. (2016b). Improving reliability in environmental DNA detection surveys through enhanced quality control. Marine and Freshwater Research, 68, 388– 395. Furlan, E.M., Gleeson, D., Hardy, C.M. & Duncan, R.P. (2016). A framework for estimating the sensitivity of eDNA surveys. Molecular Ecology Resources, 16, 641–654.
174
García-Díaz, P. & Cassey, P. (2014). Patterns of transport and introduction of exotic amphibians in Australia. Diversity and Distributions, 20, 455–466. Geist, J. (2011). Integrative freshwater ecology and biodiversity conservation. Ecological Indicators, 11, 1507–1516. Genovesi, P. (2005). Eradications of invasive alien species in Europe: a review. Biological Invasions, 7, 127–133. Gilligan, D., McGarry, T. & Carter, S. (2010). A scientific approach to developing habitat rehabilitation strategies in aquatic environments: A case study on the endangered Macquarie perch (Macquaria australasica) in the Lachlan catchment. Batemans Bay. Glenn, T.C. (2011). Field guide to next-generation DNA sequencers. Molecular ecology resources, 11, 759–769. Goldberg, C.S., Pilliod, D.S., Arkle, R.S. & Waits, L.P. (2011). Molecular detection of vertebrates in stream water: a demonstration using Rocky Mountain tailed frogs and Idaho giant salamanders. PloS one, 6, e22746. Goldberg, C.S., Strickler, K.M. & Pilliod, D.S. (2014). Moving environmental DNA methods from concept to practice for monitoring aquatic macroorganisms. Biological Conservation, 183, 1–3. Goldberg, C.S., Turner, C.R., Deiner, K., Klymus, K.E., Thomsen, P.F., Murphy, M.A., Spear, S.F., McKee, A., Oyler-McCance, S.J., Cornman, R.S., Laramie, M.B., Mahon, A.R., Lance, R.F., Pilliod, D.S., Strickler, K.M., Waits, L.P., Fremier, A.K., Takahara, T., Herder, J.E., Taberlet, P. & Gilbert, M. (2016). Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods in Ecology and Evolution, 7, 1299–1307. Goodwin, S., McPherson, J.D. & McCombie, W.R. (2016). Coming of age: ten years of nextgeneration sequencing technologies. Nature Reviews Genetics, 17, 333–351. Gorham, E. & Boyce, F.M. (1989). Influence of lake surface area and depth upon thermal stratification and the depth of the summer thermocline. Journal of Great Lakes Research, 15, 233–245. Gormley, A.M., Forsyth, D.M., Griffioen, P., Lindeman, M., Ramsey, D.S.L., Scroggie, M.P. & Woodford, L. (2011). Using presence-only and presence-absence data to estimate the current and potential distributions of established invasive species. Journal of Applied Ecology, 48, 25–34. Gotelli, N.J. & Colwell, R.K. (2001). Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology letters, 4, 379–391. Gozlan, R.E., Britton, J.R., Cowx, I. & Copp, G.H. (2010). Current knowledge on non-native freshwater fish introductions. Journal of Fish Biology, 76, 751–786. Grant, R.A., Chadwick, E.A. & Halliday, T. (2009). The lunar cycle: a cue for amphibian reproductive phenology? Animal Behaviour, 78, 349–357.
175
Greenfield, D.I., Marin, R., Doucette, G.J., Mikulski, C., Jones, K., Jensen, S., Roman, B., Alvarado, N., Feldman, J. & Scholin, C. (2008). Field applications of the secondgeneration Environmental Sample Processor (ESP) for remote detection of harmful algae: 2006-2007. Limnology and Oceanography: Methods, 6, 667–679. Growns, I.O., Pollard, D. a. & Harris, J.H. (1996). A comparison of electric fishing and gillnetting to examine the effects of anthropogenic disturbance on riverine fish communities. Fisheries Management and Ecology, 3, 13–24. Gunzburger, M.S. (2007). Evaluation of seven aquatic sampling methods for amphibians and other aquatic fauna. Applied Herpetology, 4, 47–63. Hajibabaei, M., Shokralla, S., Zhou, X., Singer, G.A.C. & Baird, D.J. (2011). Environmental barcoding: a next-generation sequencing approach for biomonitoring applications using river benthos. PloS one, 6, e17497. Hall, E.M., Crespi, E.J., Goldberg, C.S. & Brunner, J.L. (2016). Evaluating environmental DNA-based quantification of ranavirus infection in wood frog populations. Molecular Ecology Resources, 16, 423–433. Hänfling, B., Handley, L.L., Read, D.S., Hahn, C. & Li, J. (2016). Environmental DNA metabarcoding of lake fish communities reflects long-term data from established survey methods. Molecular Ecology, 25, 3101–3119. Hankin, D.G. & Reeves, G.H. (1988). Estimating Total Fish Abundance and Total Habitat Area in Small Streams Based on Visual Estimation Methods. Canadian Journal of Fisheries and Aquatic Sciences, 45, 834–844. Hardy, C.M., Adams, M., Jerry, D.R., Court, L.N., Morgan, M.J. & Hartley, D.M. (2011). DNA barcoding to support conservation: species identification, genetic structure and biogeography of fishes in the Murray-Darling River Basin, Australia. Marine and Freshwater Research, 62, 887–901. Hardy, C.M., Krull, E.S., Hartley, D.M. & Oliver, R.L. (2010). Carbon source accounting for fish using combined DNA and stable isotope analyses in a regulated lowland river weir pool. Molecular Ecology, 19, 197–212. Harrison, P.L., Babcock, R.C., Bull, G.D., Oliver, J.K., Wallace, C.C. & Willis, B.L. (1984). Mass spawning in tropical reef corals. Science, 223, 1186–1189. Harvey, C.T., Qureshi, S.A. & MacIsaac, H.J. (2009). Detection of a colonizing, aquatic, nonindigenous species. Diversity and Distributions, 15, 429–437. Hatzenbuhler, C., Kelly, J.R., Martinson, J., Okum, S. & Pilgrim, E. (2017). Sensitivity and accuracy of high-throughput metabarcoding methods for early detection of invasive fish species. Scientific Reports, 7, 46393. Hebert, P.D.N., Ratnasingham, S. & DeWaard, J.R. (2003). Barcoding animal life: cytochrome c oxidase subunit 1 divergences among closely related species. Proceedings of The Royal Society: Biological sciences, 270, S96–S99.
176
Herder, J., Valentini, A. & Kranenbarg, J. (2012). Detectie van grote modderkruipers met behulp van Environmental DNA. H2O, 3, 25–27. Hermoso, V., Clavero, M., Blanco-Garrido, F. & Prenda, J. (2011). Invasive species and habitat degradation in Iberian streams : an analysis of their role in freshwater fish diversity loss. Ecological Applications, 21, 175–188. Hinlo, R., Furlan, E., Suitor, L. & Gleeson, D. (2017). Environmental DNA monitoring and management of invasive fish: comparison of eDNA and fyke netting. Management of Biological Invasions, 8, 89–100. Holland, M. & Parsons, T. (1999). Mitochondrial DNA sequence analysis-validation and use for forensic casework. Forensic Science Review, 11, 21–50. Hulme, P.E. (2009). Trade, transport and trouble: managing invasive species pathways in an era of globalization. Journal of Applied Ecology, 46, 10–18. Ingram, B., Douglas, J. & Lintermans, M. (2000). Threatened fishes of the world: Macquaria australasica Cuvier, 1830 (Percichthyidae). Environmental Biology of Fishes, 59, 68. Islam, M.S. & Akhter, T. (2012). Tale of Fish Sperm and Factors Affecting Sperm Motility: A Review. Advances in Life Sciences, 1, 11–19. Jackson, D.A., Peres-Neto, P.R. & Olden, J.D. (2001). What controls who is where in freshwater fish communities - the roles of biotic, abiotic, and spatial factors. Canadian Journal of Fisheries and Aquatic Sciences, 58, 157–170. Jamieson, B.G.M. (1991). Fish Evolution and Systematics: Evidence from Spermatozoa. Cambridge University Press. Jane, S.F., Wilcox, T.M., McKelvey, K.S., Young, M.K., Schwartz, M.K., Lowe, W.H., Letcher, B.H. & Whiteley, A.R. (2015). Distance, flow, and PCR inhibition: eDNA dynamics in two headwater streams. Molecular Ecology Resources, 15, 216–227. Jerde, D.L., Cadderton, W.L., Mahon, A.R., Renshaw, M.A., Corush, J., Bundy, M.L., Mysorekar, S. & Lodge, D.M. (2013). Detection of Asian carp DNA as part of a basinwide Great Lakes surveillance program. Canadian Journal of Fisheries and Aquatic Sciences, 70, 522–526. Jerde, C.L., Mahon, A.R., Chadderton, W.L. & Lodge, D.M. (2011). “Sight-unseen” detection of rare aquatic species using environmental DNA. Conservation Letters, 4, 150–157. Jeschke, J.M. & Strayer, D.L. (2005). Invasion success of vertebrates in Europe and North America. Proceedings of the National Academy of Sciences of the United States of America, 102, 7198–7202. Jin, X.X., Zhao, S.L. & Wang, R.X. (2013). Universal primers to amplify the complete mitochondrial 12S rRNA gene in marine fish species. Genetics and Molecular Research, 12, 4575–4578.
177
Jo, T., Murakami, H., Masuda, R., Sakata, M., Yamamoto, S. & Minamoto, T. (2017). Rapid degradation of longer DNA fragments enables the improved estimation of distribution and biomass using environmental DNA. Molecular Ecology Resources, 17, 25–33. Johansen, T., Repolho, T., HellebØ, A. & Raae, A.J. (2006). Strict conservation of the ITS regions of the ribosomal RNA genes in Atlantic cod (Gadus morhua L.). DNA Sequence, 17, 107–114. Jurajda, P., Janáč, M., White, S.M. & Ondračková, M. (2009). Small – but not easy: Evaluation of sampling methods in floodplain lakes including whole-lake sampling. Fisheries Research, 96, 102–108. Kearns, J., Tonkin, Z., O’Mahony, J. & Lyon, J. (2012). Identification and protection of key spawning habitats for Macquarie Perch in King Parrot Creek. Heidelberg, Victoria. Kearse, M., Moir, R., Wilson, A., Stones-Havas, S., Cheung, M., Sturrock, S., Buxton, S., Cooper, A., Markowitz, S., Duran, C., Thierer, T., Ashton, B., Meintjes, P. & Drummond, A. (2012). Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics, 28, 1647– 1649. Kellner, K. (2015). jagsUI: a wrapper around rjags to streamline JAGS analyses. R package. Kennard, M.J., Pusey, B.J., Harch, B.D., Dore, E. & Arthington, A.H. (2006). Estimating local stream fish assemblage attributes: sampling effort and efficiency at two spatial scales. Marine and Freshwater Research, 57, 635–653. Kibbe, W.A. (2007). OligoCalc: an online oligonucleotide properties calculator. Nucleic Acids Research, 35, W43–W46. King, A.J., Ward, K.A., O’Connor, P., Green, D., Tonkin, Z. & Mahoney, J. (2010). Adaptive management of an environmental watering event to enhance native fish spawning and recruitment. Freshwater Biology, 55, 17–31. Klobucar, S.L., Rodgers, T.W. & Budy, P. (2017). At the forefront: evidence of the applicability of using environmental DNA to quantify the abundance of fish populations in natural lentic waters with additional sampling considerations. Canadian Journal of Fisheries and Aquatic Sciences, 74, 2030–2034. Klymus, K.E., Richter, C.A., Chapman, D.C. & Paukert, C. (2014). Quantification of eDNA shedding rates from invasive bighead carp Hypophthalmichthys nobilis and silver carp Hypophthalmichthys molitrix. Biological Conservation, 183, 77–84. Ko, H.-L., Wang, Y.-T., Chiu, T.-S., Lee, M.-A., Leu, M.-Y., Chang, K.-Z., Chen, W.-Y. & Shao, K.-T. (2013). Evaluating the accuracy of morphological identification of larval fishes by applying DNA barcoding. PloS one, 8, e53451. Kocher, A., Thoisy, B. De, Catzeflis, F., Huguin, M., Valiere, S., Zinger, L., Bañuls, A.-L. & Murienne, J. (2017). Evaluation of short mitochondrial metabarcodes for the identification of Amazonian mammals. Methods in Ecology and Evolution, 8, 1276– 1283.
178
Koehn, J.D. (2004). Carp (Cyprinus carpio) as a powerful invader in Australian waterways. Freshwater Biology, 49, 882–894. Koehn, J.D. & MacKenzie, R.F. (2004). Priority management actions for alien freshwater fish species in Australia. New Zealand Journal of Marine and Freshwater Research, 38, 457–472. Koenig, C.C., Coleman, F.C., Grimes, C.B., Fitzhugh, G.R., Scanlon, K.M., Gledhill, C.T. & Grace, M. (2000). Protection of fish spawning habitat for the conservation of warm temperate reef fish fisheries of shelf edge reefs of Florida. Bulletin of Marine Science, 66, 593–616. Koster, W.M., Dawson, D.R., Morrongiello, J.R. & Crook, D.A. (2013). Spawning season movements of Macquarie perch (Macquaria australasica) in the Yarra River, Victoria. Australian Journal of Zoology, 61, 386–394. Kuczynski, J., Lauber, C.L., Walters, W.A., Parfrey, L.W., Clemente, J.C., Gevers, D. & Knight, R. (2012). Experimental and analytical tools for studying the human microbiome. Nature Reviews Genetics, 13, 47–58. Kupriyanova, N.S. (2000). Conservation and variation of ribosomal DNA in eukaryotes. Molecular Biology, 34, 637–647. Lacoursière-Roussel, A., Côté, G., Leclerc, V. & Bernatchez, L. (2015). Quantifying relative fish abundance with eDNA: a promising tool for fisheries management. Journal of Applied Ecology, 53, 1148–1157. Lahoz-Monfort, J.J., Guillera-Arroita, G. & Tingley, R. (2016). Statistical approaches to account for false positive errors in environmental DNA samples. Molecular Ecology Resources, 16, 673–685. Laramie, M.B., Pilliod, D.S. & Goldberg, C.S. (2014). Characterizing the distribution of an endangered salmonid using environmental DNA analysis. Biological Conservation, 183, 29–37. Laroche, O., Wood, S.A., Tremblay, L.A., Ellis, J.I., Lear, G. & Pochon, X. (2017a). A crosstaxa study using environmental DNA/RNA metabarcoding to measure biological impacts of offshore oil and gas drilling and production operations. Marine Pollution Bulletin, In review, 97–107. Laroche, O., Wood, S.A., Tremblay, L.A., Lear, G., Ellis, J.I. & Pochon, X. (2017b). Metabarcoding monitoring analysis: the pros and cons of using co-extracted environmental DNA and RNA data to assess offshore oil production impacts on benthic communities. PeerJ, 5, e3347. Lee, M.S.Y. & Jamieson, B.G.M. (1992). The Ultrastructure of the Spermatozoa of 3 Species of Myobatrachid Frogs (Anura, Amphibia) with Phylogenetic Considerations. Acta Zoologica, 73, 213–222. Lefort, M.C., Boyer, S., Barun, A., Emami-Khoyi, A., Ridden, J., Smith, V.R., Sprague, R., Waterhouse, B.R. & Cruickshank, R.H. (2015). Blood, sweat and tears: non-invasive vs. non-disruptive DNA sampling for experimental biology. PeerJ Preprints. 179
Levitan, D., Boudreau, W., Jara, J. & Knowlton, N. (2014). Long-term reduced spawning in Orbicella coral species due to temperature stress. Marine Ecology Progress Series, 515, 1–10. Lintermans, M. (2013a). A review of on-ground recovery actions for threatened freshwater fish in Australia. Marine and Freshwater Research, 64, 775–791. Lintermans, M. (2016). Finding the needle in the haystack: comparing sampling methods for detecting an endangered freshwater fish. Marine and Freshwater Research, 67, 1740– 1749. Lintermans, M. (2002). Fish in the Upper Murrumbidgee Catchment: A Review of Current Knowledge. Canberra, Australia. Lintermans, M. (2007). Fishes of the Murray-Darling Basin: An introductory guide. Lintermans, M. (2013b). Recovering threatened freshwater fish in Australia. Marine and Freshwater Research, 64, 3–6. Lintermans, M. (2013c). The rise and fall of a translocated population of the endangered Macquarie perch, Macquaria australasica, in south-eastern Australia. Marine and Freshwater Research, 64, 838. Llewellyn, L.C. (1974). Spawning, development and distribution of the southern pigmy perch Nannoperca australis australis Gunther from inland waters in eastern australia. Marine and Freshwater Research, 25, 121–149. Lodge, D.M., Turner, C.R., Jerde, C.L., Barnes, M. a, Chadderton, L., Egan, S.P., Feder, J.L., Mahon, A.R. & Pfrender, M.E. (2012). Conservation in a cup of water: estimating biodiversity and population abundance from environmental DNA. Molecular ecology, 21, 2555–2558. Long, E.O. & Dawid, I.B. (1980). Repeated genes in eukaryotes. Annual review of biochemistry, 49, 727–764. Lovell, S.J., Stone, S.F. & Fernandez, L. (2005). The Economic Impacts of Aquatic Invasive Species : A Review of the Literature. Agricultural and Resource Economics Review, 35, 195. MacDougall, A. & Turkington, R. (2005). Are invasive species the drivers or passengers of change in degraded ecosystems? Ecology, 86, 42–55. Maruyama, A., Nakamura, K., Yamanaka, H., Kondoh, M. & Minamoto, T. (2014). The Release Rate of Environmental DNA from Juvenile and Adult Fish. PloS One, 9, e114639. Matsui, K., Honjo, M. & Kawabata, Z. (2001). Estimation of the fate of dissolved DNA in thermally stratified lake water from the stability of exogenous plasmid DNA. Aquatic Microbial Ecology, 26, 95–102. Maxwell, D. & Jennings, S. (2005). Power of monitoring programmes to detect decline and recovery of rare and vulnerable fish. Journal of Applied Ecology, 42, 25–37. 180
McInnes, J.C., Jarman, S.N., Lea, M.-A., Raymond, B., Deagle, B.E., Phillips, R.A., Catry, P., Stanworth, A., Weimerskirch, H., Kusch, A., Gras, M., Cherel, Y., Maschette, D. & Alderman, R. (2017). DNA metabarcoding as a marine conservation and management tool: A circumpolar examination of fishery discards in the diet of threatened albatrosses. Frontiers in Marine Science, 4, 1–22. McKee, A.M., Spear, S.F. & Pierson, T.W. (2014). The effect of dilution and the use of a post-extraction nucleic acid purification column on the accuracy, precision, and inhibition of environmental DNA samples. Biological Conservation, 183, 70–76. McLeod, R. & Norris, A. (2004). Counting the cost: impact of invasive animals in Australia, 2004. Cooperative Research Centre for Pest Animal Control Canberra, Canberra. Merz, J. & Setka, J. (2004). Evaluation of a Spawning Habitat Enhancement Site for Chinook Salmon in a Regulated California River. North American Journal of Fisheries Management, 24, 397–407. Meusnier, I., Singer, G.A.C., Landry, J.-F., Hickey, D.A., Hebert, P.D.N. & Hajibabaei, M. (2008). A universal DNA mini-barcode for biodiversity analysis. BMC genomics, 9, 214. Miller, D.A.W., Weir, L.A., McClintock, B.T., Campbell, G.E.H., Bailey, L.L. & Simons, T.R. (2012). Experimental investigation of false positive errors in auditory species occurrence surveys. Ecological Applications, 22, 1665–1674. Minamoto, T., Fukuda, M., Katsuhara, K.R., Fujiwara, A., Hidaka, S., Yamamoto, S., Takahashi, K. & Masuda, R. (2017). Environmental DNA reflects spatial and temporal jellyfish distribution. Plos One, 12, e0173073. Minamoto, T., Naka, T., Moji, K. & Maruyama, A. (2016a). Techniques for the practical collection of environmental DNA: filter selection, preservation, and extraction. Limnology, 17, 23–32. Minamoto, T., Uchii, K., Takahara, T., Kitayoshi, T., Tsuji, S., Yamanaka, H. & Doi, H. (2016b). Nuclear internal transcribed spacer-1 as a sensitive genetic marker for environmental DNA studies in common carp Cyprinus carpio. Molecular Ecology Resources, 81, 324–333. Minamoto, T., Yamanaka, H., Takahara, T., Honjo, M.N. & Kawabata, Z. (2012). Surveillance of fish species composition using environmental DNA. Limnology, 13, 193–197. Miya, M., Sato, Y., Fukunaga, T., Sado, T., Poulsen, J.Y., Sato, K., Minamoto, T., Yamamoto, S., Yamanaka, H., Araki, H., Kondoh, M. & Iwasaki, W. (2015). MiFish , a set of universal PCR primers for metabarcoding environmental DNA from fishes : detection of more than 230 subtropical marine species. Royal Society open science, 2, 150088. Molak, M. & Ho, S.Y.W. (2011). Evaluating the impact of post-mortem damage in ancient DNA: A theoretical approach. Journal of Molecular Evolution, 73, 244–255. Morgan, M.J., Chariton, A.A., Hartley, D.M., Court, L.N. & Hardy, C.M. (2013). Improved Inference of Taxonomic Richness from Environmental DNA. PLoS ONE, 8, e71974. 181
Murgia, M., Pizzo, P., Sandona, D., Zanovello, P., Rizzuto, R. & Virgilioll, F. Di. (1992). Mitochondrial DNA Is Not Fragmented during Apoptosis. The Journal of Biological Chemistry, 267, 10939–10941. Myers, J.H., Simberloff, D., Kuris, A.M. & Carey, J.R. (2000). Eradication revisited: dealing with exotic species. Trends in ecology & evolution, 15, 316–320. Neebling, T.E. & Quist, M.C. (2011). Comparison of Boat Electrofishing, Trawling, and Seining for Sampling Fish Assemblages in Iowa’s Nonwadeable Rivers. North American Journal of Fisheries Management, 31, 390–402. NSW Fisheries and the CRC for Freshwater Ecology. (1997). Fish and rivers in stress: the NSW rivers survey (J.H. Harris & P.C. Gehrke, Eds.). NSW Fisheries, Cronulla. O’Donnell, J.L., Kelly, R.P., Lowell, N.C. & Port, J.A. (2016). Indexed PCR Primers Induce Template-Specific Bias in Large-Scale DNA Sequencing Studies. Plos One, 11, e0148698. Oberdorff, T., Guilbert, E. & Lucchetta, J.-C. (1993). Patterns of fish species richness in the Seine River basin, France. Hydrobiologia, 259, 157–167. Oksanen, J., Kindt, R., Legendre, P., O’Hara, B., Stevens, M.H.H., Oksanen, M.J. & Suggests, M. (2007). The vegan package. Community ecology package, 10, 631–637. Olden, J.D., Kennard, M.J. & Pusey, B.J. (2007). Species invasions and the changing biogeography of Australian freshwater fishes. Global Ecology and Biogeography, 17, 25–37. Olds, B.P., Jerde, C.L., Renshaw, M.A., Li, Y., Evans, N.T., Turner, C.R., Deiner, K., Mahon, A.R., Brueseke, M.A., Shirey, P.D., Pfrender, M.E., Lodge, D.M. & Lamberti, G.A. (2016). Estimating species richness using environmental DNA. Ecology and Evolution, 6, 4214–4226. Olson, Z.H., Briggler, J.T. & Williams, R.N. (2012). An eDNA approach to detect eastern hellbenders (Cryptobranchus a. alleganiensis) using samples of water. Wildlife Research, 39, 629–636. Olson, D.M., Dinerstein, E., Wikramanayake, E.D., Burgess, N.D., Powell, G.V.N., Underwood, E.C., D’amico, J.A., Itoua, I., Strand, H.E. & Morrison, J.C. (2001). Terrestrial Ecoregions of the World: A New Map of Life on Earth: A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience, 51, 933–938. Padilla, D.K. & Williams, S.L. (2004). Beyond Ballast Water: Aquarium and Ornamental Trades as Sources of Invasive Species in Aquatic Ecosystems. Frontiers in Ecology and the Environment, 2, 131–138. Paukert, C.P. (2004). Comparison of electrofishing and trammel netting variability for sampling native fishes. Journal of Fish Biology, 65, 1643–1652.
182
Pauls, S.U., Alp, M., Bálint, M., Bernabò, P., Čiampor, F., Čiamporová-Zaťovičová, Z., Finn, D.S., Kohout, J., Leese, F., Lencioni, V., Paz-Vinas, I. & Monaghan, M.T. (2014). Integrating molecular tools into freshwater ecology: developments and opportunities. Freshwater Biology, 59, 1559–1576. Pawlowski, J., Esling, P., Lejzerowicz, F., Cordier, T., Visco, J., Martins, C., Kvalvik, A., Staven, K. & Cedhagen, T. (2016). Benthic monitoring of salmon farms in Norway using foraminiferal metabarcoding. Aquaculture Environment Interactions, 8, 371–386. Pearce, L. (2013). Macquarie Perch refuge project - Final report for the Lachlan CMA. Pearce, L. (2015). Surveys, monitoring and conservation status of Southern Pygmy Perch (Nannoperca australis) within Blakney and Pudman Creeks. Pejchar, L. & Mooney, H.A. (2009). Invasive species, ecosystem services and human wellbeing. Trends in ecology & evolution, 24, 497–504. Pereira, H.M., Navarro, L.M. & Martins, I.S. (2012). Global Biodiversity Change: The Bad, the Good, and the Unknown. Annual Review of Environment and Resources, 37, 25–50. Piaggio, A.J., Engeman, R.M., Hopken, M.W., Humphrey, J.S., Keacher, K.L., Bruce, W.E. & Avery, M.L. (2014). Detecting an elusive invasive species: a diagnostic PCR to detect Burmese python in Florida waters and an assessment of persistence of environmental DNA. Molecular Ecology Resources, 14, 374–380. Pidgeon, R. (2004). A review of options for monitoring freshwater fish biodiversity in the Darwin Harbour catchment. Palmerston, NT, Australia. Pietramellara, G., Ascher, J., Borgogni, F., Ceccherini, M.T., Guerri, G. & Nannipieri, P. (2009). Extracellular DNA in soil and sediment: fate and ecological relevance. Biology and Fertility of Soils, 45, 219–235. Piggott, M.P. (2016). Evaluating the effects of laboratory protocols on eDNA detection probability for an endangered freshwater fish. Ecology and Evolution, 6, 2739–2750. Pilliod, D.S., Goldberg, C.S., Arkle, R.S. & Waits, L.P. (2013). Estimating occupancy and abundance of stream amphibians using environmental DNA from filtered water samples. Canadian Journal of Fisheries and Aquatic Sciences, 10, 1123–1130. Pilliod, D.S., Goldberg, C.S., Arkle, R.S. & Waits, L.P. (2014). Factors influencing detection of eDNA from a stream-dwelling amphibian. Molecular ecology resources, 14, 109–116. Pimentel, D., Lach, L., Zuniga, R. & Morrison, D. (2000). Environmental and Economic Costs of Nonindigenous Species in the United States. BioScience, 53. Pinheiro, J., Bates, D., DebRoy, S., Heisterkammp, S. & Van Willigen, B. (2016). Package ‘nlme’. R package. Pinol, J., Mir, G., Gomez-Polo, P. & Agusti, N. (2015). Universal and blocking primer mismatches limit the use of high-throughput DNA sequencing for the quantitative metabarcoding of arthropods. Molecular Ecology Resources, 15, 819–830.
183
Platts, W.S. (1979). Relationships among Stream Order, Fish Populations, and Aquatic Geomorphology in an Idaho River Drainage. Fisheries, 4, 5–9. Pochon, X., Wood, S.A., Keeley, N.B., Lejzerowicz, F., Esling, P., Drew, J. & Pawlowski, J. (2015). Accurate assessment of the impact of salmon farming on benthic sediment enrichment using foraminiferal metabarcoding. Marine Pollution Bulletin, 100, 370–382. Porazinska, D.L., Giblin-Davis, R.M., Faller, L., Farmerie, W., Kanzaki, N., Morris, K., Powers, T.O., Tucker, A.E., Sung, W. & Thomas, W.K. (2009). Evaluating highthroughput sequencing as a method for metagenomic analysis of nematode diversity. Molecular ecology resources, 9, 1439–1450. Poretsky, R., Rodriguez-R, L.M., Luo, C., Tsementzi, D. & Konstantinidis, K.T. (2014). Strengths and Limitations of 16S rRNA Gene Amplicon Sequencing in Revealing Temporal Microbial Community Dynamics. PLoS ONE, 9, e93827. Porreca, A.P., Pederson, C.L., Laursen, J.R. & Colombo, R.E. (2013). A comparison of electrofishing methods and fyke netting to produce reliable abundance and size metrics. Journal of Freshwater Ecology, 28, 585–590. Potapov, V. & Ong, J.L. (2017). Examining Sources of Error in PCR by Single- Molecule Sequencing. PLoS One, 12, e0169774. Price, A., Stoffels, R., Weatherman, K., O’Keefe, R. & Müller, W. (2016). Structural habitat selection by Southern pygmy perch (Nannoperca australis). Pritt, J.J. & Frimpong, E.A. (2014). The effect of sampling intensity on patterns of rarity and community assessment metrics in stream fish samples. Ecological Indicators, 39, 169– 178. R Development Core Team. (2010). R : A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, ISBN 3-900051-07-0. Raadik, T.A. (2014). Fifteen from one: A revision of the Galaxias olidus Günther, 1866 complex (Teleostei, Galaxiidae) in south-eastern Australia recognises three previously described taxa and describes 12 new species. Zootaxa, 3898, 1–198. Raupach, M.J., Astrin, J.J., Hannig, K., Peters, M.K., Stoeckle, M.Y. & Wägele, J.-W. (2010). Molecular species identification of Central European ground beetles (Coleoptera: Carabidae) using nuclear rDNA expansion segments and DNA barcodes. Frontiers in zoology, 7, 26. Rees, H.C., Bishop, K., Middleditch, D.J., Patmore, J.R.M., Maddison, B.C. & Gough, K.C. (2014a). The application of eDNA for monitoring of the Great Crested Newt in the UK. Ecology and Evolution, 4, 4023–4032. Rees, H.C., Gough, K.C., Middleditch, D.J., Patmore, J.R.M. & Maddison, B.C. (2015). Applications and limitations of measuring environmental DNA as indicators of the presence of aquatic animals. Journal of Applied Ecology, 52, 827–831.
184
Rees, H.C., Maddison, B.C., Middleditch, D.J., Patmore, J.R.M. & Gough, K.C. (2014b). The detection of aquatic animal species using environmental DNA - a review of eDNA as a survey tool in ecology. Journal of Applied Ecology, 51, 1450–1459. Reid, S.M. & Haxton, T.J. (2017). Backpack electrofishing effort and imperfect detection: Influence on riverine fish inventories and monitoring. Journal of Applied Ichthyology, 33, 1083–1091. Renshaw, M.A., Olds, B.P., Jerde, C.L., McVeigh, M.M. & Lodge, D.M. (2014). The room temperature preservation of filtered environmental DNA samples and assimilation into a Phenol-Chloroform-Isoamyl alcohol DNA extraction. Molecular ecology resources, 15, 168–176. Revenga, C., Campbell, I., Abell, R., de Villiers, P. & Bryer, M. (2005). Prospects for monitoring freshwater ecosystems towards the 2010 targets. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 360, 397–413. Riaz, T., Shehzad, W., Viari, A., Pompanon, F., Taberlet, P. & Coissac, E. (2011). ecoPrimers: inference of new DNA barcode markers from whole genome sequence analysis. Nucleic acids research, 39, e145. Ritchie, E.G., Bradshaw, C.J.A., Dickman, C.R., Hobbs, R., Johnson, C.N., Johnston, E.L., Laurance, W.F., Lindenmayer, D., McCarthy, M.A., Nimmo, D.G., Possingham, H.H., Pressey, R.L., Watson, D.M. & Woinarski, J. (2013). Continental-Scale Governance and the Hastening of Loss of Australia’s Biodiversity. Conservation Biology, 27, 1133–1135. Robertson, D.R. & Smith-vaniz, W.F. (2008). Rotenone : An Essential but Demonized Tool for Assessing Marine Fish Diversity. BioScience, 58, 165–170. Robinson, D. (2014). broom: An R Package for Converting Statistical Analysis Objects Into Tidy Data Frames. arXiv preprint arXiv:1412.3565. Rogers, S.W., Donnelly, M., Peed, L., Kelty, C.A., Mondal, S., Zhong, Z. & Shanks, O.C. (2011). Decay of bacterial pathogens, fecal indicators, and real-time quantitative PCR genetic markers in manure-amended soils. Applied and Environmental Microbiology, 77, 4839–4848. Rose, G.A. (1993). Cod spawning on a migration highway in the north-west Atlantic. Nature, 366, 458–461. Roussel, J.-M., Paillisson, J.-M., Tréguier, A. & Petit, E. (2015). The downside of eDNA as a survey tool in water bodies. Journal of Applied Ecology, 52, 823–826. Rubinoff, D., Cameron, S. & Will, K. (2006). A genomic perspective on the shortcomings of mitochondrial DNA for ‘barcoding’ identification. The Journal of heredity, 97, 581–594. Ruetz, C.R., Uzarski, D.G., Krueger, D.M. & Rutherford, E.S. (2007). Sampling a Littoral Fish Assemblage: Comparison of Small-Mesh Fyke Netting and Boat Electrofishing. North American Journal of Fisheries Management, 27, 825–831.
185
Sarre, S.D., Macdonald, A.J., Barclay, C., Saunders, G.R. & Ramsey, D.S.L. (2013). Foxes are now widespread in Tasmania: DNA detection defines the distribution of this rare but invasive carnivore. Journal of Applied Ecology, 50, 459–468. Sarre, S.D., Macdonald, A.J., Berry, O.F., Barclay, C., Saunders, G.R. & Ramsey, D.S.L. (2014). Defining specificity in DNA detection of wildlife: Response to Gonçalves et al. ‘the risks of using “species-specific” PCR assays in wildlife research: the case of red fox (Vulpes vulpes) identification in Tasmania’. Forensic Science International: Genetics, 13, 206–207. Sassoubre, L.M., Yamahara, K.M., Gardner, L.D., Block, B.A. & Boehm, A.B. (2016). Quantification of Environmental DNA (eDNA) Shedding and Decay Rates for Three Marine Fish. Environmental Science & Technology, 50, 10456–10464. Sato, H., Sogo, Y., Doi, H. & Yamanaka, H. (2017). Usefulness and limitations of sample pooling for environmental DNA metabarcoding of freshwater fish communities. Scientific Reports, 7, 14860. Sawyer, S., Krause, J., Guschanski, K., Savolainen, V. & Paabo, S. (2012). Temporal patterns of nucleotide misincorporations and DNA fragmentation in ancient DNA. PLoS ONE, 7, e34131. Schmidt, B.R. (2005). Monitoring the distribution of pond-breeding amphibians when species are detected imperfectly. Aquatic Conservation: Marine and Freshwater Ecosystems, 15, 681–692. Schmidt, D.J., Bond, N.R., Adams, M. & Hughes, J.M. (2011). Cytonuclear evidence for hybridogenetic reproduction in natural populations of the Australian carp gudgeon (Hypseleotris: Eleotridae). Molecular Ecology, 20, 3367–3380. Schmidt, B.R., Kery, M., Ursenbacher, S., Hyman, O.J. & Collins, J.P. (2013). Site occupancy models in the analysis of environmental DNA presence/absence surveys: A case study of an emerging amphibian pathogen. Methods in Ecology and Evolution, 4, 646–653. Schnell, I.B., Bohmann, K. & Gilbert, M.T.P. (2015). Tag jumps illuminated - reducing sequence-to-sample misidentifications in metabarcoding studies. Molecular Ecology Resources, 15, 1289–1303. Shanmuganathan, T., Pallister, J., Doody, S., McCallum, H., Robinson, T., Sheppard, A., Hardy, C., Halliday, D., Venables, D., Voysey, R., Strive, T., Hinds, L. & Hyatt, A. (2010). Biological control of the cane toad in Australia: a review. Animal Conservation, 13, 16–23. Shaw, J.L.A., Clarke, L.J., Wedderburn, S.D., Barnes, T.C., Weyrich, L.S. & Cooper, A. (2016). Comparison of environmental DNA metabarcoding and conventional fish survey methods in a river system. Biological Conservation, 197, 131–138. Shehzad, W., Riaz, T., Nawaz, M.A., Miquel, C., Poillot, C., Shah, S.A., Pompanon, F., Coissac, E. & Taberlet, P. (2012). Carnivore diet analysis based on next-generation sequencing: application to the leopard cat (Prionailurus bengalensis) in Pakistan. Molecular ecology, 21, 1951–1965. 186
Shelton, A.O., O’Donnell, J.L., Samhouri, J.F., Lowell, N., Williams, G.D. & Kelly, R.P. (2016). A framework for inferring biological communities from environmental DNA. Ecological Applications, 26, 1645–1659. Shogren, A.J., Tank, J.L., Andruszkiewicz, E., Olds, B., Mahon, A.R., Jerde, C.L. & Bolster, D. (2017). Controls on eDNA movement in streams: Transport, Retention, and Resuspension. Scientific Reports, 7, 5065. Shokralla, S., Gibson, J.F., Nikbakht, H., Janzen, D.H., Hallwachs, W. & Hajibabaei, M. (2014). Next-generation DNA barcoding: using next-generation sequencing to enhance and accelerate DNA barcode capture from single specimens. Molecular Ecology Resources, 14, 892–901. Shokralla, S., Spall, J.L., Gibson, J.F. & Hajibabaei, M. (2012). Next-generation sequencing technologies for environmental DNA research. Molecular ecology, 21, 1794–1805. Short, J., Kinnear, J.E. & Robley, A. (2002). Surplus killing by introduced predators in Australia - evidence for ineffective anti-predator adaptations in native prey species? Biological Conservation, 103, 283–301. Sigsgaard, E.E., Carl, H., Møller, P.R. & Thomsen, P.F. (2015). Monitoring the near-extinct European weather loach in Denmark based on environmental DNA from water samples. Biological Conservation, 183, 46–52. Sigsgaard, E.E., Nielsen, I.B., Bach, S.S., Lorenzen, E.D., Robinson, D.P., Knudsen, S.W., Pedersen, M.W., Jaidah, M. Al, Orlando, L., Willerslev, E., Møller, P.R. & Thomsen, P.F. (2016). Population characteristics of a large whale shark aggregation inferred from seawater environmental DNA. Nature Ecology & Evolution, 1, 4. Sigsgaard, E.E., Nielsen, I.B., Carl, H., Krag, M.A., Knudsen, S.W., Xing, Y., Holm-Hansen, T.H., Møller, P.R. & Thomsen, P.F. (2017). Seawater environmental DNA reflects seasonality of a coastal fish community. Marine Biology, 164, 1–15. Smith, T.A. & Kraft, C.E. (2005). Stream Fish Assemblages in Relation to Landscape Position and Local Habitat Variables. Transactions of the American Fisheries Society, 134, 430–440. Smithson, M. & Verkuilen, J. (2006). A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychological Methods, 11, 54–71. Song, H., Buhay, J.E., Whiting, M.F. & Crandall, K.A. (2008). Many species in one: DNA barcoding overestimates the number of species when nuclear mitochondrial pseudogenes are coamplified. Proceedings of the National Academy of Sciences, 105, 13486–13491. De Souza, L.S., Godwin, J.C., Renshaw, M.A. & Larson, E. (2016). Environmental DNA (eDNA) detection probability is influenced by seasonal activity of organisms. PLoS ONE, 11, e0165273. Spalding, M.D., Fox, H.E., Allen, G.R., Davidson, N., Ferdaña, Z.A., Finlayson, M.A.X., Halpern, B.S., Jorge, M.A., Lombana, A.L. & Lourie, S.A. (2007). Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. BioScience, 57, 573–583.
187
Spear, S.F., Groves, J.D., Williams, L. a. & Waits, L.P. (2014). Using environmental DNA methods to improve detectability in a hellbender (Cryptobranchus alleganiensis) monitoring program. Biological Conservation, 183, 38–45. Specziár, A., Takács, P., Czeglédi, I. & Erős, T. (2012). The role of the electrofishing equipment type and the operator in assessing fish assemblages in a non-wadeable lowland river. Fisheries Research, 125–126, 99–107. Spens, J., Evans, A.R., Halfmaerten, D., Knudsen, S.W., Sengupta, M.E., Mak, S.S.T., Sigsgaard, E.E. & Hellström, M. (2016). Comparison of capture and storage methods for aqueous macrobial eDNA using an optimized extraction protocol: advantage of enclosed filter. Methods in Ecology and Evolution, 8, 635–645. Stoeckle, M.Y., Soboleva, L. & Charlop-Powers, Z. (2017). Aquatic environmental DNA detects seasonal fish abundance and habitat preference in an urban estuary. PLoS ONE, 12, e0175186. Strayer, D.L. & Dudgeon, D. (2010). Freshwater biodiversity conservation: recent progress and future challenges. Journal of the North American Benthological Society, 29, 344– 358. Strickler, K.M., Fremier, A.K. & Goldberg, C.S. (2014). Quantifying effects of UV-B, temperature, and pH on eDNA degradation in aquatic microcosms. Biological Conservation, 183, 85–92. Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C. & Willerslev, E. (2012). Towards next-generation biodiversity assessment using DNA metabarcoding. Molecular Ecology, 21, 2045–2050. Takahara, T., Minamoto, T. & Doi, H. (2014). Effects of sample processing on the detection rate of environmental DNA from the Common Carp (Cyprinus carpio). Biological Conservation, 183, 64–69. Takahara, T., Minamoto, T. & Doi, H. (2013). Using environmental DNA to estimate the distribution of an invasive fish species in ponds. PloS one, 8, e56584. Takahara, T., Minamoto, T., Yamanaka, H., Doi, H. & Kawabata, Z. (2012). Estimation of fish biomass using environmental DNA. PloS one, 7, e35868. Takahashi, M.K., Meyer, M.J., Mcphee, C., Gaston, J.R., Venesky, M.D. & Case, B.F. (2017). Seasonal and diel signature of eastern hellbender environmental DNA. The Journal of Wildlife Management, 82, 217–225. Thomas, A.C., Jarman, S.N., Haman, K.H., Trites, A.W. & Deagle, B.E. (2014). Improving accuracy of DNA diet estimates using food tissue control materials and an evaluation of proxies for digestion bias. Molecular Ecology, 23, 3706–3718. Thomsen, P.F., Kielgast, J., Iversen, L.L., Møller, P.R., Rasmussen, M. & Willerslev, E. (2012a). Detection of a Diverse Marine Fish Fauna Using Environmental DNA from Seawater Samples. PloS one, 7, e41732.
188
Thomsen, P.F., Kielgast, J., Iversen, L.L., Wiuf, C., Rasmussen, M., Gilbert, M.T.P., Orlando, L. & Willerslev, E. (2012b). Monitoring endangered freshwater biodiversity using environmental DNA. Molecular Ecology, 21, 2565–2573. Thresher, R.E., Hayes, K., Bax, N.J., Teem, J., Benfey, T.J. & Gould, F. (2013). Genetic control of invasive fish: technological options and its role in integrated pest management. Biological Invasions, 16, 1201–1216. Thuesen, P.A.A., Russell, D.J.A., Thomson, F.E.A. & Pearce, M.G.A. (2011). An evaluation of electrofishing as a control measure for an invasive tilapia (Oreochromis mossambicus) population in northern Australia. Marine and Freshwater Research, 62, 110–118. Tonkin, Z., Kearns, J., O’Mahony, J. & Mahoney, J. (2015). Spatio-temporal spawning patterns of two riverine populations of the threatened Macquarie perch (Macquaria australasica). Marine and Freshwater Research, 67, 1762–1770. Tonkin, Z., Lyon, J. & Pickworth, A. (2010). Spawning behaviour of the endangered Macquarie Perch Macquaria australasica in an upland Australian river. Ecological Management & Restoration, 11, 223–226. Tremblay, J., Singh, K., Fern, A., Kirton, E.S., He, S., Woyke, T., Lee, J., Chen, F., Dangl, J.L. & Tringe, S.G. (2015). Primer and platform effects on 16S rRNA tag sequencing. Frontiers in Microbiology, 6, 771. Tsuji, S., Ushio, M., Sakurai, S., Minamoto, T. & Yamanaka, H. (2017). Water temperaturedependent degradation of environmental DNA and its relation to bacterial abundance. Plos One, 12, e0176608. Tsukamoto, K. (2006). Oceanic biology: spawning of eels near a seamount. Nature, 439, 929. Turner, C.R., Barnes, M.A., Xu, C.C.Y., Jones, S.E., Jerde, C.L. & Lodge, D.M. (2014a). Particle size distribution and optimal capture of aqueous macrobial eDNA. Methods in Ecology and Evolution, 5, 676–684. Turner, C.R., Uy, K.L. & Everhart, R.C. (2014b). Fish environmental DNA is more concentrated in aquatic sediments than surface water. Biological Conservation, 183, 93– 102. Uchii, K., Doi, H., Yamanaka, H. & Minamoto, T. (2017). Distinct seasonal migration patterns of Japanese native and non-native genotypes of common carp estimated by environmental DNA. Ecology and Evolution, 7, 8515–8522. Unmack, P.J. (2013). Biogeography. The Ecology of Australian Freshwater Fish (eds P. Humphries & K. Walker), pp. 25–48. CSIRO PUBLISHING, Melbourne. Ushio, M., Murakami, H., Masuda, R., Sado, T., Miya, M., Sakurai, S., Yamanaka, H., Minamoto, T. & Kondoh, M. (2018). Quantitative monitoring of multispecies fish environmental DNA using high-throughput sequencing. Metabarcoding and Metagenomics, 2, 1–15.
189
Valentini, A., Miquel, C., Nawaz, M.A., Bellemain, E., Coissac, E., Pompanon, F., Gielly, L., Cruaud, C., Nascetti, G., Wincker, P., Swenson, J.E. & Taberlet, P. (2009a). New perspectives in diet analysis based on DNA barcoding and parallel pyrosequencing: the trnL approach. Molecular ecology resources, 9, 51–60. Valentini, A., Pompanon, F. & Taberlet, P. (2009b). DNA barcoding for ecologists. Trends in ecology & evolution, 24, 110–117. Valentini, A., Taberlet, P., Miaud, C., Civade, R., Herder, J., Thomsen, P.F., Bellemain, E., Besnard, A., Coissac, E., Boyer, F., Gaboriaud, C., Jean, P., Poulet, N., Roset, N., Copp, G.H., Geniez, P., Pont, D., Argillier, C., Baudoin, J.-M., Peroux, T., Crivelli, A.J., Olivier, A., Acqueberge, M., Le Brun, M., Møller, P.R., Willerslev, E. & Dejean, T. (2016). Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Molecular Ecology, 25, 929–942. Vestheim, H. & Jarman, S.N. (2008). Blocking primers to enhance PCR amplification of rare sequences in mixed samples - a case study on prey DNA in Antarctic krill stomachs. Frontiers in zoology, 5, 12. Vilizzi, L. & Copp, G.H. (2013). Application of FISK, an invasiveness screening tool for nonnative freshwater fishes, in the Murray-Darling Basin (southeastern Australia). Risk analysis, 33, 1432–1440. Vilizzi, L., Thwaites, L.A., Smith, B.B., Nicol, J.M. & Madden, C.P. (2014). Ecological effects of common carp (Cyprinus carpio) in a semi-arid floodplain wetland. Marine and Freswater Research, 65, 802–817. Waits, L. & Paetkau, D. (2005). Noninvasive genetic sampling tools for wildlife biologists: a review of applications and recommendations for accurate data collection. Journal of Wildlife Management, 69, 1419–1433. Walters, S.P., Yamahara, K.M. & Boehm, A.B. (2009). Persistence of nucleic acid markers of health-relevant organisms in seawater microcosms: Implications for their use in assessing risk in recreational waters. Water Research, 43, 4929–4939. Wang, H.Y., Tsai, M.P., Tu, M.C. & Lee, S.C. (2000). Universal primers for amplification of the complete mitochondrial 12S rRNA gene in vertebrates. Zoological Studies, 39, 61– 66. Wei, Q.W., Kynard, B., Yang, D.G., Chen, X.H., Du, H., Shen, L. & Zhang, H. (2009). Using drift nets to capture early life stages and monitor spawning of the yangtze river chinese sturgeon (Acipenser sinensis). Journal of Applied Ichthyology, 25, 100–106. Weiß, C.L., Schuenemann, V.J., Devos, J., Shirsekar, G., Reiter, E., Gould, B.A., Stinchcombe, J.R., Krause, J. & Burbano, H.A. (2016). Temporal patterns of damage and decay kinetics of DNA retrieved from plant herbarium specimens. Royal Society Open Science, 3, 160239. Whittington, R.J. & Chong, R. (2007). Global trade in ornamental fish from an Australian perspective: the case for revised import risk analysis and management strategies. Preventive veterinary medicine, 81, 92–116.
190
Wickham, H. (2016). tidyverse: Easily Install and Load “Tidyverse” Packages. R package. Wilcove, D.S., Rothstein, D., Dubow, J., Phillips, A. & Losos, E. (1998). Quantifying Threats to Imperiled Species in the United States. BioScience, 48, 607–615. Wilcox, T.M., McKelvey, K.S., Young, M.K., Jane, S.F., Lowe, W.H., Whiteley, A.R. & Schwartz, M.K. (2013). Robust detection of rare species using environmental DNA: the importance of primer specificity. PloS One, 8, e59520. Wilcox, T.M., McKelvey, K.S., Young, M.K., Lowe, W.H. & Schwartz, M.K. (2015). Environmental DNA particle size distribution from Brook Trout (Salvelinus fontinalis). Conservation Genetics Resources, 7, 639–641. Wilcox, T.M., Schwartz, M.K., McKelvey, K.S., Young, M.K. & Lowe, W.H. (2014). Blocking primer increases specificity in environmental DNA detection of bull trout (Salvelinus confluentus). Conservation Genetics Resources, 6, 283–284. Wildman, T.L. & Neumann, R.M. (2003). Comparison of snorkeling and electrofishing for estimating abundance and size structure of brook trout and brown trout in two southern New England streams. Fisheries Research, 60, 131–139. Willerslev, E., Hansen, A.J., Binladen, J., Brand, T.B., Gilbert, M.T.P., Shapiro, B., Bunce, M., Wiuf, C., Gilichinsky, D.A. & Cooper, A. (2003). Diverse plant and animal genetic records from Holocene and Pleistocene sediments. Science, 300, 791–795. Williams, M.R., Stedtfeld, R.D., Engle, C., Salach, P., Fakher, U., Stedtfeld, T., Dreelin, E., Stevenson, R.J., Latimore, J. & Hashsham, S.A. (2017). Isothermal amplification of environmental DNA (eDNA) for direct field-based monitoring and laboratory confirmation of Dreissena sp. PLoS ONE, 12, 1–18. Wilson, C.C., Wozney, K.M. & Smith, C.M. (2016). Recognizing false positives: synthetic oligonucleotide controls for environmental DNA surveillance. Methods in Ecology and Evolution, 7, 23–29. With, K.A. (2002). The landscape ecology of invasive spread. Conservation Biology, 16, 1192–1203. Woodruff, S.P., Johnson, T.R. & Waits, L.P. (2015). Evaluating the interaction of faecal pellet deposition rates and DNA degradation rates to optimize sampling design for DNAbased mark-recapture analysis of Sonoran pronghorn. Molecular Ecology Resources, 15, 843–854. Xu, H., Ding, H., Li, M., Qiang, S., Guo, J., Han, Z., Huang, Z., Sun, H., He, S., Wu, H. & Wan, F. (2006). The distribution and economic losses of alien species invasion to China. Biological Invasions, 8, 1495–1500. Yamamoto, S., Minami, K., Fukaya, K., Takahashi, K., Sawada, H., Murakami, H., Tsuji, S., Hashizume, H., Kubonaga, S., Horiuchi, T., Hongo, M., Nishida, J., Okugawa, Y., Fujiwara, A., Fukuda, M., Hidaka, S., Suzuki, K.W., Miya, M., Araki, H., Yamanaka, H., Maruyama, A., Miyashita, K., Masuda, R., Minamoto, T. & Kondoh, M. (2016). Environmental DNA as a ‘Snapshot’ of Fish Distribution: A Case Study of Japanese Jack Mackerel in Maizuru Bay , Sea of Japan. PloS one, 11, e0149786. 191
Ye, J., Coulouris, G., Zaretskaya, I., Cutcutache, I., Rozen, S. & Madden, T.L. (2012). Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction. BMC Bioinformatics, 13, 134. Yoccoz, N.G. (2012). The future of environmental DNA in ecology. Molecular ecology, 21, 2031–2038. Zhan, A., Bailey, S.A., Heath, D.D. & Macisaac, H.J. (2014). Performance comparison of genetic markers for high-throughput sequencing-based biodiversity assessment in complex communities. Molecular Ecology Resources, 14, 1049–1059. Zhu, B. (2006). Degradation of plasmid and plant DNA in water microcosms monitored by natural transformation and real-time polymerase chain reaction (PCR). Water research, 40, 3231–3238.
192
Derivative Reporter (-Rn)
Appendix 1.
A1
A2
A3
B1
B2
C1
C2
C3
Temperature ( C)
Appendix 1.A. Representative melt curves from the quantitative PCR analyses for the mitochondrial target fragments (A) and nuclear target fragments for both experimental (B) and field (C) samples. Melt curves are shown for standard curve samples (A1, B1 and C1); experimental samples (A2 and B2) and field samples (A3, C2 and C3). Graph C3 shows the typical melt curve of a qPCR replicate in which non-specific amplification was observed and was thus excluded from the analyses.
193
194
Appendix 2. Appendix 2.A. Details of the synthetic gBlock gene fragments used to construct the standard curves for the quantitative Real-Time PCR analyses. Sequences highlighted in italics and underlined represent the primer binding sites while sequences highlighted in bold and underlined show the base-pair mismatches between the synthetic sequences and the sequences obtained from the experimental animals. Target region
gBlock sequence (5’-3’)
Cytochrome c oxidase subunit I
TAGTAGGAACCGCTTTAAGCCTCCTCATCCGAGCTGAACTTAGTCAACCCGGATCACTTCTAGGTGATGA CCAAATTTACAATGTAATTGTTACCGCCCACGCCTTCGTAATAATTTTCTTTATAGTAATGCCTATCCTC ATTGGAGGATTCGGAAACTGACTTGTACCCCTGATAATCGGAGCCCCAGACATGGCATTCCCACGAATAA ATAATATAAGCTTCTGACTTCTTCCCCCATCATTCCTGTTACTACTAGCTTCCTCTGGTGTTGAAGCCGGAG CTGGCACCGGATGGACAGTATACCCCCCTCTTGCAGGAAACCTAGCCCACGCAGGAGCATCAGTAGAC CTAACAATTTTCTCACTACATTTAGCAGGTGTTTCATCAATCCTGGGGGCAATCAACTTCATTACTACAA CCATTAACATAAAACCTCCAGCCATTTCCCAATACCAAACACCCCTATTTGTTTGATCCGTACTTGTAAC CGCCGTCCTCCTTCTCCTATCACTACCTGTTCTAGCTGCC CCGGCGCGGCCTCCGACCCGCGAGAGAGACAGTCGGAACCGGAGGGCTCGAGCGATACGTACCCCTTGG CGCTCCCTCACACCGTGACACCCCAGGGCGTGGTGCGGGGGACGCCAGCCCCGACGGGTGCCCTGCTT GGCCCGGTGGCCTCAACACCCACCGGGACCGTGGGCTCAAAGTCCCAACCTCCTGGGGTGGCGCCCGTT CGGGGTCAAGACCCCCTTTTCATTCCCATACCCCTTGTCTGCGGCTAAAGGCCTCGATACCTCTAACAAAA AAAGAGTACAACTCTTACCGGTGGATCACTCGGCTCGTGCGTCGATGAAGAACGCAGCTAGCTGCGAG AACTAATGTGAATTGCAGGACACATTGATCATCGACACTTCGAACGCACTTTGCGGCCCCGGGTTCCTC CCGGGGCCACGCCTGTCTGAGGGTCGCTTTCTCATCGATCGGGGCCTCCGGGTCCCGCGGCTGGAGCTT CGTAGGGGTCGCCCCCTCCGTCCTCCTAAGTGCAGACCGCCCCGGGT
Internal transcribed spacer
195
Appendix 2.B. Representative melt curves obtained for the primers pairs used to amplify different sized DNA fragments from the mitochondrial Cytochrome oxidase I (COI) gene (vertical). Melt curves are shown for quantitative Real-Time PCR (qPCR) analyses with the template DNA being: genomic DNA of goldfish (Carassius auratus); environmental DNA from the experimental tanks for which the qPCR data was omitted (eDNA Invalid); environmental DNA from the experimental tanks for which that qPCR data was used (eDNA Valid); the synthetic gBlock fragment used to construct the standard curves (gBlock).
196
Appendix 2.C. Representative melt curves obtained for the primers pairs used to amplify different sized DNA fragments from the nuclear Internal Transcribes Spacer (ITS) region (vertical). Melt curves are shown for quantitative Real-Time PCR (qPCR) analyses with the template DNA being: genomic DNA of goldfish (Carassius auratus); environmental DNA from the experimental tanks for which the qPCR data was omitted (eDNA Invalid); environmental DNA from the experimental tanks for which that qPCR data was used (eDNA Valid); the synthetic gBlock fragment used to construct the standard curves (gBlock).
197
Appendix 2.D. Plots showing raw data obtained from all environmental DNA (eDNA) fragments (horizontal) and experimental tanks (vertical). Experimental tanks were set-up using low densities (CA-ET03-04), medium densities (CA-ET05-07) and high densities (CA-ET08 & CA-ET10) of goldfish (Carassius auratus). Grey dots represent the raw data points, black dots show the mean eDNA concentration at each sampling point and the black line shows the overall pattern in eDNA concentrations over time.
198
Appendix 2.E. Plots showing the raw data used in the statistical analysis. Panels are sorted according to the environmental DNA (eDNA) fragments (horizontal) and the experimental tanks (vertical). Experimental tanks were set-up using low densities (CA-ET03-04), medium densities (CA-ET05-07) and high densities (CA-ET08 & CA-ET10) of goldfish (Carassius auratus). Grey dots represent the raw data points, black dots show the mean eDNA concentration at each sampling point and the black line shows the overall pattern in eDNA concentrations over time.
199
Appendix 2.F. The effect of the density treatments and the environmental DNA (eDNA) fragments on the estimated decay constant (k) after fitting a first-order exponential decay model to the data obtained after fish removal (i.e. Sampling time ≥ 342 h). Points show the mean decay rate and error bars represent the 95% confidence interval calculated as 2 times the standard error. The different density treatments are shown on x-axis (i.e. Low Density (LD) - 1 fish / 60 L, Medium Density (MD) – 1fish / 30L and High Density (HD) – 1fish / 10L) while the different panels show the results of the different eDNA fragments. Labels of the different fragments show the genetic target region (i.e. mitochondrial Cytochrome c oxidase subunit I (COI) gene and nuclear Internal Transcribed Spacer (ITS) region) with the fragment size in between brackets expressed as the total number of base-pairs (bp).
200
Appendix 3. Appendix 3.A. Complete list of freshwater fish species of the Murray-Darling Basin (MDB) for which the 12S ribosomal RNA gene region was PCR amplified and Sanger sequenced. For each species, its origin is given (i.e. native or invasive). The primer pairs used during PCR amplification are given in the PCR amplification column: 12SrRNA-F and 12SrRNA-R (Jin et al. 2013), 12SL and 12SR (Wang et al. 2000), 12SV5-F and 12SV5-R (Riaz et al. 2011), Not-12S-F (5’-TATTTAAAACGTAACACTGAAAATG-3’) and Not-12S-R (5’TCATGATGCAAAAGGTACGAG-3’). *Thermal cycling profiles: 2 min at 95°C; 35 3-step cycles of 1 min at 94°C, 1 min at 50°C and 1 min 30 sec at 72°C; and 10 min at 72°C. # Thermal cycling profiles: 2 min at 95°C; 10 3-step cycles of 1 min at 94°C, 1 min at 60°C (decreasing with 1°C per cycle) and 1 min 30 sec at 72°C; 35 3-step cycles of 1 min at 94°C, 1 min at 50°C and 1 min 30 sec at 72°C; and 10 min at 72°C. For most samples the MT1478H primer was used as an internal sequencing primer to improve the sequencing quality of the 5’ region of the 12S gene region (Fuller et al. 1998). Species name Afurcagobius tamarensis Ambassis agassizii Anguilla australis Anguilla reinhardtii Atherinosoma microstoma Bidyanus bidyanus Carassius auratus Carassius carassius Craterocephalus amniculus Craterocephalus fluviatilis Craterocephalus stercusmuscarum fulvus Cyprinus carpio Gadopsis bispinosus Gadopsis marmoratus
Origin
PCR amplification
Internal sequencing primer *
Native Native Native Native Native Native Invasive Invasive Native Native Native Invasive Native Native
12SrRNA-F + 12SrRNA-R 12SrRNA-F + 12SrRNA-R* 12SL + 12SR* 12SL + 12SR* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SL + 12SR* 12SL + 12SR* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SL + 12SR* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R*
201
MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H
Species name
Origin
Galaxias arcanus Galaxias brevipinnis Galaxias fuscus Galaxias maculatus Galaxias olidus Galaxias oliros Galaxias ornatus
Native Native Native Native Native Native Native
Galaxias rostratus Galaxias tantangara Galaxias truttaceus Gambusia holbrooki Geotria australis Hypseleotris klunzingeri Hypseleotris sp. ‘Midgley’s carp gudgeon’ Hypseleotris sp. ‘Lake’s carp gudgeon’ Hypseleotris sp. ‘Murray-Darling carp gudgeon’ Leiopotherapon unicolor Maccullochella macquariensis Maccullochella peelii
Native Native Native Invasive Native Native Native Native Native Native Native Native
Macquaria ambigua Macquaria australasica Melanotaenia fluviatilis
Native Native Native
PCR amplification *
12SL + 12SR 12SL + 12SR* 12SL + 12SR* 12SrRNA-F + 12SrRNA-R* 12SL + 12SR* 12SL + 12SR* 12SrRNA-F + 12SV5-R# 12SV5-F + 12SR# 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SL + 12SR* 12SrRNA-F + 12SrRNA-R* 12SL + 12SR* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SL + 12SR* 12SrRNA-F + 12SV5-R# 12SV5-F + 12SR# 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R*
202
Internal sequencing primer MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H NA MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H NA MT1478H MT1478H MT1478H
Species name Melanotaenia splendida tatei Misgurnus anguillicaudatus Mogurnda adspersa Mordacia mordax Nannoperca australis Nannoperca obscura Nematalosa erebi Neosilurus hyrtlii Oncorhynchus mykiss Oxyeleotris lineolate Perca fluviatilis Percalates colonorum Percalates novemaculeata Philypnodon grandiceps Philypnodon macrostomus Porochilus rendahli Pseudaphritis urvillii Pseudogobius olorum Retropinna semoni Rutilus rutilus Salmo salar Salmo trutta Salvelinus fontinalis Tandanus tandanus
Origin
PCR amplification
Internal sequencing primer *
Native Invasive Native Native Native Native Native Native Invasive Native Invasive Native Native Native Native Native Native Native Native Invasive Invasive Invasive Invasive Native
12SrRNA-F + 12SrRNA-R 12SL + 12SR* 12SrRNA-F + 12SrRNA-R* 12SL + 12SR* 12SL + 12SR* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* Not-12S-F + Not-12S-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R* 12SrRNA-F + 12SrRNA-R*
203
MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H MT1478H
Species name Tasmanogobius lasti Tinca tinca
Origin
PCR amplification
Internal sequencing primer *
Native Invasive
12SrRNA-F + 12SrRNA-R 12SrRNA-F + 12SrRNA-R*
204
MT1478H MT1478H
Appendix 3.B. List of all major vertebrate families with occurrence records in the Darling River Drainage (Atlas of Living Australia). Class
Order
Family
Actinopterygii
Anguilliformes Clupeiformes Galaxiiformes Osmeriformes Siluriformes Atheriniformes Perciformes Centrarchiformes Gobiiformes Salmoniformes Cypriniformes Cyprinidontiformes
Anguillidae Clupeidae Galaxiidae Retropinnidae Plotosidae Atherinidae; Melanotaeniidae Ambassidae; Bovichtidae; Percidae Percichthyidae; Terapontidae Eleotridae; Gobiidae Salmonidae Cyprinidae; Cobitidae Poeciliidae
Chondrichthyes
Carcharhiniformes
Triakidae; Sphyrnidae; Carcharhinidae; Scyliorhinidae Lamnidae; Mitsukurinidae; Odontaspididae Pristiophoridae Myliobatidae; Dasyatidae Callorhinchidae Hexanchidae Orectolobidae Heterodontidae
Lamniformes Pristiophoriformes Myliobatiformes Chimaeriformes Hexanchiformes Orectolobiformes Heterodontiformes Amphibia
Anura
Myobatrachidae; Microhylidae
Reptilia
Squamata
Scincidae; Agamidae; Diplodactylidae; Elapidae; Gekkonidae; Pygopodidae; Varanidae; Typhlopidae; Carphodactylidae; Boidae; Colubridae; Acrochordidae Chelidae; Cheloniidae; Dermochelyidae Crocodylidae
Testudines Crocodylia
205
Hylidae;
Bufonidae;
Class Aves
Order
Family Meliphagidae; Artamidae; Acanthizidae; Pachycephalidae; Rhipiduridae; Monarchidae; Corvidae; Pardalotidae; Maluridae; Petroicidae; Sturnidae; Hirundinidae; Campephagidae; Climacteridae; Passeridae; Corcoracidae; Estrildidae; Timaliidae; Turdidae; Pomatostomidae; Megaluridae; Nectariniidae; Motacillidae; Fringillidae; Oriolidae; Oreoididae; Neosittidae; Acrocephalidae; Ptilonorhynchidae; Psophodidae; Alaudidae; Cisticolidae; Menuridae; Dasyornithidae; Dicruridae; Orthonychidae; Pittidae; Paradisaeidae; Pycnonotidae; Atrichornithidae; Ploceidae; Sylviidae Psittacidae; Cacatuidae Anatidae; Anseranatidae Accipitridae; Falconidae Charadriidae; Laridae; Scolopacidae; Recurvirostridae; Burhinidae; Haematopodidae; Pedionomidae; Glareolidae; Rostratulidae; Jacanidae; Stercorariidae Columbidae Threskiornithidae; Ardeidae; Ciconiidae Phalacrocoracidae; Pelecanidae; Anhingidae; Phaethontidae; Sulidae; Fregatidae Alcedinidae; Meropidae; Coraciidae Rallidae; Gruidae; Otididae Cuculidae; Centropodidae Podicipedidae Strigidae; Tytonidae Casuariidae; Struthionidae Phasianidae; Megapodiidae; Numididae Aegothelidae; Apodidae Podargidae; Caprimulgidae Turnicidae Procellariidae; Diomedeidae; Oceanitidae Spheniscidae Accipitridae
Passeriformes
Psittaciformes Anseriformes Falconiformes Charadriiformes
Columbiformes Ciconiiformes Pelecaniformes
Coraciiformes Gruiformes Cuculiformes Podicipediformes Strigiformes Struthioniformes Galliformes Apodiformes Caprimulgiformes Turniciformes Procellariiformes Sphenisciformes Accipitriformes
206
Class Mammalia
Order
Family Macropodidae; Phalangeridae; Vombatidae; Pseudocheiridae; Phascolarctidae; Petauridae; Potoroidae; Burramyidae; Acrobatidae; Hypsiprymnodontidae Vespertilionidae; Molossidae; Miniopteridae; Pteropodidae; Emballonuridae; Rhinolophidae; Rhinonycteridae; Megadermatidae; Hipposideridae Canidae; Felidae; Otariidae; Mustalidae; Phocidae Muridae Dasyuridae; Myrmecobiidae Leporidae Bovidae; Cervidae; Suidae; Camelidae Tachyglossidae; Ornithorhynchidae Peramelidae; Thylacomyidae; Chaeropodidae Equidae Delphinidae; Balaenidae; Physeteridae; Ziphiidae; Phocoenidae; Balaenopteridae; Neobalaenidae; Kogiidae Dugongidae
Diprotodontia
Chiroptera
Carnivora Rodentia Dasyuromorphia Lagomorpha Artiodactyla Monotremata Peramelemorphia Perrisodactyla Cetacea
Sirenia
207
Appendix 3.C. The number of internally amplified barcodes for each primer pair plotted against the length of the internal barcode sequences. The data are derived from all sequence records that were successfully assigned to their respective samples and the vertical dashed lines represent the sequence length threshold used to remove short sequence records for each primer pair.
208
Appendix 3.D. Summary of the metabarcoding data obtained from environmental DNA samples collected for two sites within the MurrayDarling Basin (i.e. 8 and 12 samples collected for the Blakney Creek and Murrumbidgee River sites respectively) and analysed with the MiFishU, Teleo and AcMDB07 primer pairs. Results are given as the number of samples testing positive for the different species and the average proportion of sequence reads ± the standard deviation given in between brackets. Species name C. auratus C. carpio G. bispinosus G. holbrooki Galaxias sp. H. klunzingeri H. sp. 'Midgley's' M. ambigua M. anguillicaudatus M. australasica M. macquariensis M. peelii N. australis O. mykiss P. fluviatilis P. grandiceps R. semoni S. trutta
Blakney Creek
Murrumbidgee River
MiFish-U
Teleo
AcMDB07
MiFish-U
Teleo
AcMDB07
0 8 (0.448 ± 0.074) 0 0 8 (0.223 ± 0.065) 0 3 (0.020 ± 0.007) 0 0 0 0 0 8 (0.183 ± 0.048) 0 8 (0.123 ± 0.068) 3 (0.017 ± 0.010) 4 (0.018 ± 0.007) 0
0 8 (0.064 ± 0.019) 8 (0.057 ± 0.036) 0 8 (0.461 ± 0.098) 1 (0.004) 1 (0.008) 0 0 0 0 0 8 (0.231 ± 0.054) 0 8 (0.111 ± 0.058) 3 (0.013 ± 0.004) 8 (0.069 ± 0.043) 0
0 8 (0.317 ± 0.118) 2 (0.044 ± 0.009) 0 8 (0.331 ± 0.095) 0 0 0 0 0 0 0 8 (0.183 ± 0.085) 0 8 (0.101 ± 0.029) 2 (0.044 ± 0.015) 6 (0.061 ± 0.034) 0
1 (0.004) 12 (0.847 ± 0.134) 0 0 11 (0.036 ± 0.017) 8 (0.083 ± 0.195) 0 6 (0.018 ± 0.008) 8 (0.028 ± 0.016) 0 1 (0.015) 11 (0.036 ± 0.022) 0 0 2 (0.013 ± 0.000) 0 2 (0.010 ± 0.009) 0
0 12 (0.369 ± 0.132) 0 2 (0.022 ± 0.012) 12 (0.119 ± 0.070) 11 (0.152 ± 0.243) 0 7 (0.020 ± 0.021) 12 (0.044 ± 0.044) 3 (0.040 ± 0.011) 2 (0.031 ± 0.039) 12 (0.115 ± 0.066) 0 2 (0.055 ± 0.021) 3 (0.014 ± 0.004) 0 12 (0.161 ± 0.105) 3 (0.040 ± 0.017)
0 12 (0.757 ± 0.175) 0 0 8 (0.057 ± 0.030) 6 (0.168 ± 0.277) 0 2 (0.037 ± 0.026) 5 (0.024 ± 0.003) 2 (0.025 ± 0.006) 1 (0.028) 9 (0.043 ± 0.013) 0 0 1 (0.028) 0 10 (0.074 ± 0.048) 1 (0.025)
209
210
Appendix 4. Appendix 4.A. Summary of the eDNA metabarcoding data obtained from the different sampling locations within the five sampling sites along the Murrumbidgee River (Australia). Numbers of samples yielding a positive detection are given for each sampling location (i.e. left bank (LB), mid river (MR) and right bank (RB)). Four samples were collected for each site by location combination except for the MR01 site and the MR location for which only three samples were available. Species name Carassius auratus Cyprinus carpio Galaxias sp. Gambusia holbrooki Hypseleotris klunzingeri Maccullochella macquariensis Maccullochella peelii Macquaria ambigua Macquaria australasica Misgurnus anguillicaudatus Oncorhynchus mykiss Perca fluviatilis Retropinna semoni Salmo trutta
MR01
MR02
MR03
MR04
MR05
LB
MR
RB
LB
MR
RB
LB
MR
RB
LB
MR
RB
LB
MR
RB
0 0 0 0 0 0 0 0 0 0 4 0 0 4
0 0 0 0 0 0 0 0 0 0 3 0 0 3
0 0 0 0 0 0 0 0 0 0 4 0 0 4
0 4 4 1 0 0 0 0 4 0 2 0 0 1
1 4 4 0 0 0 0 0 4 0 1 0 0 2
1 4 4 0 0 0 0 0 4 0 2 0 0 2
0 4 3 0 0 1 1 0 0 1 0 0 0 0
0 4 2 0 0 0 0 0 0 0 0 0 0 0
0 4 3 0 0 0 4 0 0 0 0 0 0 0
0 4 0 0 1 1 3 0 0 2 0 1 0 1
0 4 2 0 0 1 4 0 1 0 0 0 0 0
0 4 0 0 3 1 4 0 1 0 0 0 0 0
0 4 4 0 3 1 4 2 0 3 0 1 2 0
0 4 3 1 2 0 3 1 1 1 0 0 1 1
0 4 4 0 3 2 4 1 1 1 0 1 3 0
211
212
Appendix 5.
Appendix 5.A. Coordinate details and sampling dates for all sampling locations used to assess the distribution of the invasive redfin perch (Perca fluviatilis) through conventional and eDNA-based monitoring. Sampling location Creek
Site
Sampling dates
Latitude
Longitude
Blakney Creek Blakney Creek Blakney Creek Blakney Creek Blakney Creek Blakney Creek Blakney Creek Blakney Creek Blakney Creek Blakney Creek Urumwalla Creek Urumwalla Creek Urumwalla Creek Urumwalla Creek
BC01 BC02 BC03 BC04 BC05 BC06 BC07 BC08 BC09 BC10 UC01 UC02 UC03 UC04
-34.643900 -34.655010 -34.659000 -34.662220 -34.668000 -34.669090 -34.679000 -34.682170 -34.690000 -34.695750 -34.670370 -34.671184 -34.676241 -34.670202
149.036610 149.032700 149.027000 149.021870 149.022000 149.014310 149.011000 149.005870 149.006000 148.999310 149010143 149.009242 148.989909 148.982691
Pudman Creek
PC*
-34.643900
149.03661
Legend: na = not sampled, * negative control site
213
Conventional survey 25/02/2015 na 25/02/2015 na na 24/02/2015 na na 24/02/2015 na 13/03/2015 13/03/2015 13/03/2015 26/02/2015 13/03/2015 na
eDNA survey 24/02/2015 24/02/2015 23/02/2015 23/02/2015 23/02/2015 20/02/2015 20/02/2015 20/02/2015 19/02/2015 19/02/2015 19/05/2015 19/05/2015 19/05/2015 13/03/2015 19/02/2015
Appendix 5.B. Details of all fish species caught by the conventional survey in Blakney Creek (BC) and Urumwalla Creek (UC). For all sites within BC the number of species caught is given. For the sites within UC the species caught with each method is indicated with (✓). Species name
Backpack electrofishing BC01
Cyprinus Carpio Gadopsis marmoratus Galaxias olidus Hypseleotris spp. Nannoperca australis Perca fluviatilis Philypnodon grandiceps Retropinna semoni Species name Cyprinus Carpio Galaxias olidus Gambusia holbrooki Hypseleotris spp. Nannoperca australis
0 1 20 1 10 10 2 0 UC01 ✓ ✓ ✓
BC03
BC06
BC09
BC01
2 0 0 3 1 0 8 0
0 0 0 0 1 0 0 0
UC03
UC04
UC01
✓
✓
2 1 0 0 7 0 18 18 11 1 1 6 4 4 0 1 Dip netting UC02
Unbaited traps BC03
BC06
0 0 0 0 0 0 2 6 0 9 1 15 0 3 0 0 Unbaited traps
BC09 0 0 0 27 0 0 4 0
UC02
UC03
UC04
✓
✓
✓
✓ ✓
✓
✓
✓ ✓
✓ ✓
✓
214
✓ ✓
Appendix 5.C. Results of the environmental DNA analyses performed on the Blank Field Controls (BFC) and Negative Equipment Controls (NEC). Results show the total number of control samples analysed, the total number of PCR replicates performed and the number of valid and positive PCR replicates. Sample BFC
NEC
Sites
Samples analyzed
Total PCRs
Valid PCRs
Positive PCRs
BC01, BC02 BC03, BC04, BC05 BC06, BC07, BC08 BC09, BC10 UC01 UC02 UC03 UC04 BC01 BC02 BC03 BC04 BC05 BC06 BC07 BC08 BC09 BC10 UC01 UC02 UC03 UC04
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 5 4 1
6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 12 6 30 24 6
0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
215
216
Appendix 6. Appendix 6.A. Full details of the sampling locations, sampling dates and the number of eDNA samples used for the targeted eDNA monitoring and the eDNA metabarcoding analyses (* amplicon libraries for eDNA metabarcoding were constructed using both 4 µL and 8 µL of template eDNA per PCR replicate). Site ID
Latitude
Longitude
Sampling date
BC01
-34.64390
149.03661
BC02
-34.65501
149.03270
BC03
-34.65900
149.02700
BC04
-34.66222
149.021878
BC05
-34.66800
149.02200
BC06
-34.66909
149.01431
BC07
-34.67900
149.01100
BC08
-34.68217
149.00587
BC09
-34.69000
149.00600
BC10
-34.69575
148.99931
BC11
-34.70012
148.99418
BC12
-34.70546
148.99414
BC13
-34.71372
148.99415
24/02/2015 25/09/2015 26/10/2016 25/09/2015 26/10/2016 23/02/2015 24/09/2015 26/10/2016 24/09/2015 25/10/2016 23/09/2015 25/10/2016 20/02/2015 23/09/2015 21/10/2016 23/09/2015 21/10/2016 22/09/2015 19/10/2016 19/02/2015 22/09/2015 19/10/2016 19/02/2015 22/09/2015 19/10/2016 21/09/2015 18/10/2016 21/09/2015 18/10/2016 21/09/2015 18/10/2016
217
Targeted monitoring
eDNA metabarcoding
8 8
8* 8* 8 8 8 8* 8* 8 8 8 8 8 8* 8* 8 8 8 8 8 8 7 8 7 8 8 8 8 8 8 8 8
8 8
8 8
8 7 8 8
Site ID
Latitude
Longitude
Sampling date
UC01
-34.67118
149.00924
UC02
-34.67249
149.00270
UC03
-34.67624
148.98991
UC04
-34.67122
148.98314
UC05
-34.67020
148.98269
UC06
-34.66647
148.98013
19/05/2015 01/10/2015 28/10/2016 01/10/2015 28/10/2016 19/05/2015 30/09/2015 28/10/2016 29/09/2015 29/10/2016 13/03/2015 29/09/2015 29/10/2016 28/09/2015 29/10/2016
218
Targeted monitoring
eDNA metabarcoding
8 8
8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
8 8
8 8
Appendix 6.B. The proportion of successful amplifications obtained from a 10-fold dilution series of redfin perch (Perca fluviatilis) DNA as a function of the expected number of molecules per PCR replicate. The inset shows the posterior distribution for Ø (i.e. the probability that a single molecule will amplify) which was derived from the fitted curve.
219
Appendix 6.C. Proportions of negative and positive detections of redfin perch eDNA per site for the autumn and spring 2015 sampling season. The total number of valid PCR replicates per site are 19 (spring: BC09), 21 (spring: BC10), 24 (autumn: BC01, BC03, BC06, BC09, UC01, UC03 & spring: BC01, BC03, BC06), 47 (spring: UC01) and 48 (autumn: BC10, UC05 & spring: UC03, UC05). PCR replicates were considered invalid if no amplification was observed for both the redfin perch and the generic fish Real-Time PCR assay.
220
Appendix 6.D. Summarizing figure for the eDNA metabarcoding data obtained for 19 sites within the Blakney Creek catchment sampled during spring 2015 and spring 2016. The presence of the different species at the sampling locations is indicated by the black point while the area of the point represents the proportional sequence abundance for that species at the particular sampling site.
221
