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PreprintCited by 3Environmental DNA in Biodiversity StudiesFish Ecology and Management StudiesGenetic diversity and population structure

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A molecular specimen bank for contemporary and future study captures landscape-scale biodiversity baselines before Klamath River dam removal

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Keel, Dylan J., Karpenko, Katie, Blankenship, Scott M., Schumer, Gregg, O’Rourke, Oshun, Ostberg, Carl O., Chase, Daniel A., Duda, Jeffrey J.·2025·Scientific Reports
DOI 10.21203/rs.3.rs-5575262/v1PMC PMC12217707

Preprint — published as 10.1038/s41598-025-07042-1 · Paper page

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Abstract

Global restoration and conservation of freshwater biodiversity are represented in practice by works such as the Klamath River Renewal Project (KRRP), the largest dam removal and river restoration in the United States, which has reconnected 640 river kilometers. With dam removals, many biological outcomes remain understudied due to a lack of pre-impact data and complex ecosystem recovery timeframes. To avoid this, we created the KRRP molecular library, an environmental specimen bank, for long-term curation of environmental nucleic acids collected from the restoration project. We used these initial samples, environmental DNA metabarcoding, and generalized linear mixed-effects models to evaluate patterns of pre-dam removal fish richness and diversity. Demonstrating the suitability to resolve biological differences, the baseline shows that tributary and mainstem streams had greater native fish diversity and 2.3–10.7 times greater native fish species richness than reservoirs. These and future sampling efforts should, at a minimum, allow tracking of fish community response to ecosystem restoration. Anticipating the acceleration of omics innovation, we preserved samples for long-term storage and identified requisite phases for sustained function and adaptation of the molecular library: securing a physical storage facility for genetic material, establishing a governance structure, and confirming support for archive management.

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  1. Lynch, A. J. et al. People need freshwater biodiversity. WIREs Water. 10, e1633 (2023). · DOI
  2. Dudgeon, D. et al. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol. Rev. 81, 163 (2006). · DOI · Paper page
  3. Jelks, H. L. et al. Conservation status of imperiled North American freshwater and diadromous fishes. Fisheries 33, 372–407 (2008). · DOI
  4. Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019). · DOI · Paper page
  5. Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010). · DOI · Paper page
  6. Maasri, A. et al. A global agenda for advancing freshwater biodiversity research. Ecol. Lett. 25, 255–263 (2022). · DOI · Paper page
  7. Arthington, A. H. et al. Accelerating environmental flow implementation to bend the curve of global freshwater biodiversity loss. Environ. Rev. 32, 387–413 (2023). · DOI
  8. Birnie-Gauvin, K. et al. The RACE for freshwater biodiversity: Essential actions to create the social context for meaningful conservation. Conserv. Sci. Pract. 5, e12911 (2023). · DOI
  9. Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 355, eaai9214 (2017). · DOI · Paper page
  10. Poff, N. L. et al. The natural flow regime. BioScience 47, 769–784 (1997). · DOI · Paper page
  11. Beechie, T. J. et al. Process-based principles for restoring river ecosystems. BioScience 60, 209–222 (2010). · DOI
  12. Silva, A. T. et al. The future of fish passage science, engineering, and practice. Fish. Fish. 19, 340–362 (2018). · DOI
  13. Thieme, M. et al. Measures to safeguard and restore river connectivity. Environ. Rev. 32, 366–386 (2024). · DOI
  14. O’Connor, J. E., Duda, J. J. & Grant, G. E. 1000 dams down and counting. Science 348, 496–497 (2015). · DOI
  15. Magilligan, F. J. et al. River restoration by dam removal: Enhancing connectivity at watershed scales. Elem. Sci. Anthr. 4, 000108 (2016). · DOI · Paper page
  16. Bellmore, J. R. et al. Status and trends of dam removal research in the United States. WIREs Water. 4, e1164 (2017). · DOI · Paper page
  17. McCaffery, R., Duda, J. J., Soissons, L., Roussel, J. M. & Editorial Large-scale dam removal and ecosystem restoration. Front. Ecol. Evol. 12, 1471146 (2024). · DOI
  18. Riva, F. et al. Toward a cohesive understanding of ecological complexity. Sci. Adv. 9, eabq4207 (2023). · DOI
  19. Sparrow, B. D. et al. Effective ecosystem monitoring requires a multi-scaled approach. Biol. Rev. 95, 1706–1719 (2020). · DOI
  20. Cavender-Bares, J. et al. Integrating remote sensing with ecology and evolution to advance biodiversity conservation. Nat. Ecol. Evol. 6, 506–519 (2022). · DOI
  21. Bernatchez, L., Ferchaud, A. L., Berger, C. S., Venney, C. J. & Xuereb, A. Genomics for monitoring and understanding species responses to global climate change. Nat. Rev. Genet. 25, 165–183 (2024). · DOI
  22. Nicholson, E. et al. Scientific foundations for an ecosystem goal, milestones and indicators for the post-2020 global biodiversity framework. Nat. Ecol. Evol. 5, 1338–1349 (2021). · DOI
  23. Clark, D. R. et al. Streams of data from drops of water: 21st century molecular microbial ecology. WIREs Water 5, (2018). · DOI
  24. Levi, T. et al. Environmental DNA for the enumeration and management of Pacific salmon. Mol. Ecol. Resour. 19, 597–608 (2019). · DOI
  25. Miyata, K. et al. Comparative environmental RNA and DNA metabarcoding analysis of river algae and arthropods for ecological surveys and water quality assessment. Sci. Rep. 12, 19828 (2022). · DOI
  26. Carraro, L., Blackman, R. C. & Altermatt, F. Modelling environmental DNA transport in rivers reveals highly resolved spatio-temporal biodiversity patterns. Sci. Rep. 13, 8854 (2023). · DOI
  27. Evans, N. T., Shirey, P. D., Wieringa, J. G., Mahon, A. R. & Lamberti, G. A. Comparative Cost and Effort of Fish Distribution Detection via Environmental DNA Analysis and Electrofishing. Fisheries 42, 90–99 (2017). · DOI
  28. Goldberg, C. S. et al. Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods Ecol. Evol. 7, 1299–1307 (2016). · DOI
  29. Sutter, M. & Kinziger, A. P. Rangewide tidewater goby occupancy survey using environmental DNA. Conserv. Genet. 20, 597–613 (2019). · DOI
  30. Miya, M. & Environmental DNA metabarcoding: A novel method for biodiversity monitoring of marine fish communities. Annu. Rev. Mar. Sci. 14, 161–185 (2022). · DOI
  31. Adams, A. J. et al. From eDNA to decisions using a multi-method approach to restoration planning in streams. Sci. Rep. 14, 14335 (2024). · DOI
  32. Dyson, K. et al. Coupling remote sensing and eDNA to monitor environmental impact: A pilot to quantify the environmental benefits of sustainable agriculture in the Brazilian Amazon. PLOS ONE. 19, e0289437 (2024). · DOI
  33. Duda, J. J. et al. Environmental DNA is an effective tool to track recolonizing migratory fish following large-scale dam removal. Environ. DNA. 3, 121–141 (2021). · DOI
  34. Muha, T. P., Rodriguez-Barreto, D., O’Rorke, R., De Leaniz, G., Consuegra, S. & C. & Using eDNA metabarcoding to monitor changes in fish community composition after barrier removal. Front. Ecol. Evol. 9, 629217 (2021). · DOI
  35. Huang, C. S. et al. Early detection and recovery of river herring spawning habitat use in response to a mainstem dam removal. PLOS ONE. 18, e0284561 (2023). · DOI
  36. Neufeld, J. D. Migrating SSU rRNA gene surveys to the metagenomics era. Environ. Microbiol. Rep. 9, 23–24 (2017). · DOI
  37. Trench-Fiol, S. & Fink, P. Metatranscriptomics from a small aquatic system: Microeukaryotic community functions through the diurnal cycle. Front. Microbiol. 11, 1006 (2020). · DOI
  38. Küster, A., Becker, P. R., Kucklick, J. R., Pugh, R. S. & Koschorreck, J. The international environmental specimen banks—let’s get visible. Environ. Sci. Pollut Res. 22, 1559–1561 (2015). · DOI
  39. Díaz, C. et al. Aquatic suspended particulate matter as source of eDNA for fish metabarcoding. Sci. Rep. 10, 14352 (2020). · DOI
  40. Krehenwinkel, H. et al. Environmental DNA from archived leaves reveals widespread temporal turnover and biotic homogenization in forest arthropod communities. eLife 11, e78521 (2022). · DOI
  41. Sullivan, A. R., Karlsson, E., Svensson, D. & Brindefalk, B. Airborne eDNA captures three decades of ecosystem biodiversity.bioRxiv. (2023).
  42. Zizka, V. M. A., Koschorreck, J., Khan, C. C. & Astrin, J. J. Long-term archival of environmental samples empowers biodiversity monitoring and ecological research. Environ. Sci. Eur. 34, 40 (2022). · DOI
  43. Dysthe, J. C. et al. Repurposing environmental DNA samples—detecting the western pearlshell (Margaritifera falcata) as a proof of concept. Ecol. Evol. 8, 2659–2670 (2018). · DOI
  44. Jarman, S. N., Berry, O. & Bunce, M. The value of environmental DNA biobanking for long-term biomonitoring. Nat. Ecol. Evol. 2, 1192–1193 (2018). · DOI
  45. Auer, H. et al. The effects of frozen tissue storage conditions on the integrity of RNA and protein. Biotech. Histochem. 89, 518–528 (2014). · DOI
  46. Coudy, D., Colotte, M., Luis, A., Tuffet, S. & Bonnet, J. Long term conservation of DNA at ambient temperature. Implications for DNA data storage. PLOS ONE. 16, e0259868 (2021). · DOI · Paper page
  47. Anderson, M. K. & Lake, F. K. California Indian ethnomycology and associated forest management. J. Ethnobiol. 33, 33–85 (2013). · DOI
  48. Lake, F. K. et al. Returning fire to the land: celebrating traditional knowledge and fire. J. For. 115, 343–353 (2017).
  49. Marks-Block, T., Lake, F. K. & Curran, L. M. Effects of understory fire management treatments on California Hazelnut, an ecocultural resource of the Karuk and Yurok Indians in the Pacific Northwest. Ecol. Manag. 450, 117517 (2019). · DOI
  50. England, J. et al. Best practices for monitoring and assessing the ecological response to river restoration. Water 13, 3352 (2021). · DOI
  51. Lindenmayer, D. B. & Likens, G. E. The science and application of ecological monitoring. Biol. Conserv. 143, 1317–1328 (2010). · DOI
  52. Hart, D. D. et al. Dam Removal: Challenges and Opportunities for Ecological Research and River Restoration. BioScience 52, 669 (2002). · DOI
  53. Doyle, M. W. et al. Stream ecosystem response to small dam removal: Lessons from the Heartland. Geomorphology 71, 227–244 (2005). · DOI
  54. Foley, M. M. et al. Dam removal: Listening in. Water Resour. Res. 53, 5229–5246 (2017). · DOI
  55. Jo, T. & Yamanaka, H. Meta-analyses of environmental DNA downstream transport and deposition in relation to hydrogeography in riverine environments. Freshw. Biol. 67, 1333–1343 (2022). · DOI
  56. Palmer, M. A. et al. Standards for ecologically successful river restoration. J. Appl. Ecol. 42, 208–217 (2005). · DOI · Paper page
  57. Curtis, A. N., Tiemann, J. S., Douglass, S. A., Davis, M. A. & Larson, E. R. High stream flows dilute environmental DNA (eDNA) concentrations and reduce detectability. Divers. Distrib. 27, 1918–1931 (2021). · DOI
  58. Thompson, N. F. et al. A complex phenotype in salmon controlled by a simple change in migratory timing. Science 370, 609–613 (2020). · DOI
  59. Hamilton, J. B., Curtis, G. L., Snedaker, S. M. & White, D. K. Distribution of anadromous fishes in the Upper Klamath River Watershed prior to hydropower dams—a synthesis of the historical evidence. Fisheries 30, 10–20 (2005). · DOI
  60. Sutton, R. & Soto, T. Juvenile coho salmon behavioral characteristics in Klamath River summer thermal refugia. River Res. Appl. 28, 338–346 (2012). · DOI
  61. Miya, M. et al. Use of a filter cartridge for filtration of water samples and extraction of environmental DNA. J Vis. Exp. JoVE (2016).
  62. Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species. R Soc. Open. Sci. 2, 150088 (2015). · DOI
  63. O’Donnell, J. L., Kelly, R. P., Lowell, N. C. & Port, J. A. Indexed PCR primers induce template-specific bias in large-scale DNA sequencing studies. PLOS ONE. 11, e0148698 (2016). · DOI
  64. Porter, T. M., Hajibabaei, M. & MetaWorks A flexible, scalable bioinformatic pipeline for high-throughput multi-marker biodiversity assessments. PLOS ONE. 17, e0274260 (2022). · DOI
  65. Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990). · DOI · Paper page
  66. Carraro, L., Mächler, E., Wüthrich, R. & Altermatt, F. Environmental DNA allows upscaling spatial patterns of biodiversity in freshwater ecosystems. Nat. Commun. 11, 3585 (2020). · DOI
  67. Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer New York, 2009). · DOI · Paper page
  68. Pollock, H. S. et al. Long-term monitoring reveals widespread and severe declines of understory birds in a protected Neotropical forest. Proc. Natl. Acad. Sci. 119, e2108731119 (2022).
  69. Lindgren, F., Rue, H. & Lindström, J. An explicit link between Gaussian fields and Gaussian Markov random fields: The stochastic partial differential equation approach: link between Gaussian fields and Gaussian Markov random fields. J. R Stat. Soc. Ser. B Stat. Methodol. 73, 423–498 (2011). · DOI · Paper page
  70. Zimmerman, D. L. & Ver Hoef, J. M. The Torgegram for fluvial variography: Characterizing spatial dependence on stream networks. J. Comput. Graph Stat. 26, 253–264 (2017). · DOI
  71. Rue, H., Martino, S. & Chopin, N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R Stat. Soc. Ser. B Stat. Methodol. 71, 319–392 (2009). · DOI · Paper page
  72. Lindgren, F. & Rue, H. Bayesian spatial modelling with R - INLA. J Stat. Softw 63, (2015). · DOI · Paper page
  73. Robin, X. et al. pROC: An open-source package for R and S + to analyze and compare ROC curves. BMC Bioinform. 12, 77 (2011). · DOI · Paper page
  74. NRC (National Research Council). Endangered and Threatened Fishes in the Klamath River Basin: Causes of Decline and Strategies for Recovery10838 (National Academies, 2004).
  75. Carraro, L., Hartikainen, H., Jokela, J., Bertuzzo, E. & Rinaldo, A. Estimating species distribution and abundance in river networks using environmental DNA. Proc. Natl. Acad. Sci. 115, 11724–11729 (2018).
  76. Winkowski, J. J., Olden, J. D. & Brown, S. Integrating spatial stream network models and environmental DNA to estimate current and future distributions of nonnative Smallmouth Bass. Trans. Am. Fish. Soc. 153, 180–199 (2024). · DOI
  77. Ver Hoef, J. M., Peterson, E. E., Clifford, D. & Shah, R. SSN : An R Package for Spatial Statistical Modeling on Stream Networks. J Stat. Softw 56, (2014).
  78. Struthers, D. P. et al. Statistical stream temperature modelling with SSN and INLA: an introduction for conservation practitioners. Can. J. Fish. Aquat. Sci. 81, 417–432 (2024). · DOI
  79. Deiner, K. & Altermatt, F. Transport Distance of Invertebrate Environmental DNA in a Natural River. PLoS ONE. 9, e88786 (2014). · DOI
  80. Pess, G. R., Quinn, T. P., Gephard, S. R. & Saunders, R. Re-colonization of Atlantic and Pacific rivers by anadromous fishes: linkages between life history and the benefits of barrier removal. Rev. Fish. Biol. Fish. 24, 881–900 (2014). · DOI
  81. Waldman, J. R. & Quinn, T. P. North American diadromous fishes: Drivers of decline and potential for recovery in the Anthropocene. Sci. Adv. 8, eabl5486 (2022). · DOI
  82. Munsch, S. H. et al. Dam removal enables diverse juvenile life histories to emerge in threatened salmonids repopulating a heterogeneous landscape. Front. Ecol. Evol. 11, 1188921 (2023). · DOI
  83. Fraik, A. K. et al. The impacts of dam construction and removal on the genetics of recovering steelhead (Oncorhynchus mykiss) populations across the Elwha River Watershed. Genes 12, 89 (2021). · DOI
  84. Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016). · DOI · Paper page
  85. Zydlewski, J. et al. Seven dam challenges for migratory fish: Insights from the Penobscot River. Front. Ecol. Evol. 11, 1253657 (2023). · DOI
  86. Hitt, N. P., Eyler, S. & Wofford, J. E. B. Dam removal increases American eel abundance in distant headwater streams. Trans. Am. Fish. Soc. 141, 1171–1179 (2012). · DOI
  87. Leigh, D. M. et al. Best practices for genetic and genomic data archiving. Nat. Ecol. Evol. 8, 1224–1232 (2024). · DOI
  88. Nicholson, A. et al. An analysis of metadata reporting in freshwater environmental DNA research calls for the development of best practice guidelines. Environ. DNA. 2, 343–349 (2020). · DOI
  89. Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data. 3, 160018 (2016). · DOI · Paper page
  90. Bergström, A. Improving data archiving practices in ancient genomics. Sci. Data. 11, 754 (2024). · DOI
  91. ISBER Best Practices: Recommendations for Repositories. (International Society for Biological and Environmental Repositories, (2023).
  92. Babel, M. et al. Compared DNA and RNA quality of breast cancer biobanking samples after long-term storage protocols in – 80°C and liquid nitrogen. Sci. Rep. 10, 14404 (2020). · DOI

Metadata

Authors
Keel, D. J., Karpenko, K., Blankenship, S. M., Schumer, G., O’Rourke, O., Ostberg, C. O., Chase, D. A., & Duda, J. J.
Affiliations
  • Resource Environmental Solutions LLC.
  • Cramer Fish Sciences (United States) (US)
  • Yurok Tribal Fisheries Department
  • Western Fisheries Research Center (US)
Corresponding author
  • Keel, Dylan J.
  • Chase, Daniel A.
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2025-04-03
Volume
15
Issue
1
Pages
20679-20679
ISSN
2045-2322 (ISSN-L)
DOI
10.21203/rs.3.rs-5575262/v1
PMC
PMC12217707
OpenAlex
W4411873054
Cited by
3
References
98
Citations by year
2026: 3
FWCI
1.58
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by-nc-nd)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Environmental Science › Ecology › Environmental DNA in Biodiversity Studies
  • Physical Sciences › Environmental Science › Nature and Landscape Conservation › Fish Ecology and Management Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
Keywords
environmental specimen bank, dam removal, river restoration, freshwater biodiversity, eDNA metabarcoding, reservoirs, biodiversity monitoring
MeSH terms
DNA, Environmental, Animals, Conservation of Natural Resources, Fishes, Ecosystem, Biological Specimen Banks, Biodiversity, Rivers, DNA Barcoding, Taxonomic

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