Insights into the representativeness of biodiversity assessment in large reservoir through eDNA metabarcoding

S2 Fig. Hydrodynamic modeling for the entire Três Irmãos (TI) reservoir. Maximum cur- rent speed as measured during the A) rainy and B) dry seasons. (PNG) S3 Fig. Computed water renewal rate for the Três Irmãos (TI) reservoir. The water renewal is shown moving from Nova Avanhandava to Três Irmãos reservoir during A) rainy and B) dry scenarios. (PNG) PLOS ONE eDNA metabarcoding reservoir PLOS ONE | https://doi.org/10.1371/journal.pone.0314210 January 24, 2025 17 / 22

S4 Fig. Probability of identifying fish by sampling for eDNA at different depths. General- ized Linear Model (GLM) showing the probability (y-axis) of sampling fish taxa in different depths (1 meter, 13 meters and 25 meters from the surface) after three samples (x-axis). Each line represents one depth according to the colors in the legend. (PNG) S5 Fig. Statistics from the Discriminant Analysis of Principal Components (DAPC). A) Sum of contribution of each sampled station for the DAPC clustering assignment. Given the differences in sampling size, the pilot sampling is not included. Station abbreviations as in S1 Table. Each bar corresponds to one station, and the value shows the sum of contribu- tion for the six first Principal Components (see Methods). The stations with the highest val- ues are C01, C11 and C10, respectively. B) Rarefaction curve constructed using only samples from C01, C11 and C10, where the curve reaches the plateau curve around eight samples. (PNG) S1 Table. Sampling locations’ information. Description, geographic coordinates, sampling date, water volume, number of replicates, depth and abbreviations. (XLSX) S2 Table. Pair of primers used to amplify the partial sequences of COI, 12SrRNA, and 16SrRNA genes. Sequences and references are shown for each marker. (XLSX) S3 Table. Summary of reads, Amplicon Sequence Variants (ASVs) and Molecular Opera- tional Taxonomic Units (MOTUs) statistics. Based on eDNA analyses of both pilot and sta- tion sampling, values are shown after and before trimming steps. (XLSX)

S4 Table. List of Amplicon Sequence Variants (ASVs) amplified using eDNA metabarcod- ing. For both pilot and sampling stations included, the molecular marker, frequency, number of samples, length and taxonomic classification information are displayed for each ASVs. (XLSX) S5 Table. Biodiversity inventory for non-fish taxa in the Três Irmãos (TI) reservoir. The inventory was built using previous literature, eDNA and GBIF information. For each taxon, the source of the record is indicated, as well as the reference, if needed. (XLSX) S6 Table. Biodiversity inventory for ichthyofauna in the Três Irmãos (TI) reservoir. The inventory was built using previous literature, eDNA and Global Biodiversity Information Facility (GBIF) information. For each taxon, the source of the record is indicated, as well as the reference, if needed. (XLSX) Acknowledgments We thank Ange´lica Beccato, from Environment and Land Manager at Tijoa´ Energia, for her support and collaboration in developing this work. Author Contributions Conceptualization: Danielle do Amaral, Mauro Rebelo. PLOS ONE eDNA metabarcoding reservoir PLOS ONE | https://doi.org/10.1371/journal.pone.0314210 January 24, 2025 18 / 22

Data curation: Thaina´ Cortez, Andre´ Torres. Formal analysis: Thaina´ Cortez, Andre´ Torres, Marcelo Cabral, Gabriel Zielinsky. Funding acquisition: Danielle do Amaral, Mauro Rebelo. Investigation: Thaina´ Cortez, Andre´ Torres, Murilo Guimarães, Henrique Pinheiro, Camila Pereira, Giovanni de Castro, Luana Guerreiro, Juliana Americo, Danielle do Amaral, Mauro Rebelo. Methodology: Thaina´ Cortez, Andre´ Torres, Murilo Guimarães, Henrique Pinheiro, Marcelo Cabral, Gabriel Zielinsky, Juliana Americo, Danielle do Amaral. Project administration: Juliana Americo, Danielle do Amaral, Mauro Rebelo. Resources: Danielle do Amaral, Mauro Rebelo. Supervision: Juliana Americo, Danielle do Amaral, Mauro Rebelo. Validation: Henrique Pinheiro, Camila Pereira, Giovanni de Castro, Luana Guerreiro, Mauro Rebelo. Visualization: Thaina´ Cortez, Murilo Guimarães, Henrique Pinheiro, Camila Pereira, Gio- vanni de Castro, Luana Guerreiro, Danielle do Amaral. Writing – original draft: Thaina´ Cortez. Writing – review & editing: Thaina´ Cortez, Andre´ Torres, Murilo Guimarães, Henrique Pin- heiro, Marcelo Cabral, Gabriel Zielinsky, Camila Pereira, Giovanni de Castro, Luana Guer- reiro, Juliana Americo, Danielle do Amaral, Mauro Rebelo. References 1. Gu W, Swihart RK (2004) Absent or undetected? Effects of non-detection of species occurrence on wildlife—habitat models. Biol Conserv 116:195–203 2. Ma¨chler E, Deiner K, Steinmann P, Altermatt F (2014) Utility of environmental DNA for monitoring rare and indicator macroinvertebrate species. Freshwater Science 33:1174–1183

3. Laramie MB, Pilliod DS, Goldberg CS (2015) Characterizing the distribution of an endangered salmonid using environmental DNA analysis. Biol Conserv 183:29–37 4. Handley LL (2015) How will the ‘molecular revolution’ contribute to biological recording? Biological Jour- nal of the Linnean Society 115:750–766 5. de Faria LC, Domenico M Di, Andrade SCS, dos Santos MC, Fonseca G, Zanol J, Amaral ACZ (2018) The use of metabarcoding for meiofauna ecological patterns assessment. Mar Environ Res 140:160– 168 https://doi.org/10.1016/j.marenvres.2018.06.013 PMID: 29933903 6. Schwentner M, Zahiri R, Yamamoto S, Husemann M, Kullmann B, Thiel R (2021) eDNA as a tool for non-invasive monitoring of the fauna of a turbid, well-mixed system, the Elbe estuary in Germany. PLoS One 16:e0250452 https://doi.org/10.1371/journal.pone.0250452 PMID: 33861810 7. Manandhar P, Manandhar S, Sherchan AM, et al (2023) Environmental DNA (eDNA) based fish biodi- versity assessment of two Himalayan rivers of Nepal reveals diversity differences and highlights new species distribution records. PLOS Water 2:e0000099 8. Erős T, Funk A, Pont D, Hein T, Meulenbroek P, Preiszner B, et al (2024) eDNA metabarcoding reveals the role of habitat specialization and spatial and environmental variability in shaping diversity patterns of fish metacommunities. PLoS One 19:e0296310 https://doi.org/10.1371/journal.pone.0296310 PMID: 38165893 9. Harrison JB, Sunday JM, Rogers SM (2019) Predicting the fate of eDNA in the environment and implica-

tions for studying biodiversity. Proceedings of the Royal Society B: Biological Sciences 286:20191409 https://doi.org/10.1098/rspb.2019.1409 PMID: 31744434 10. Barnes MA, Turner CR (2016) The ecology of environmental DNA and implications for conservation genetics. Conservation Genetics 17:1–17 PLOS ONE eDNA metabarcoding reservoir PLOS ONE | https://doi.org/10.1371/journal.pone.0314210 January 24, 2025 19 / 22

11. Fukaya K, Murakami H, Yoon S, et al (2021) Estimating fish population abundance by integrating quan- titative data on environmental DNA and hydrodynamic modeling. Mol Ecol 30:3057–3067 12. Sinha S, Liu X, Garcia MH (2012) Three-dimensional hydrodynamic modeling of the Chicago River, Illi- nois. Environmental Fluid Mechanics 12:471–494 13. Bush A, Compson ZG, Monk WA, Porter TM, Steeves R, Emilson E, et al (2019) Studying Ecosystems With DNA Metabarcoding: Lessons From Biomonitoring of Aquatic Macroinvertebrates. Front Ecol Evol. 14. Shu L, Ludwig A, Peng Z (2020) Standards for Methods Utilizing Environmental DNA for Detection of Fish Species. Genes (Basel) 11:296 https://doi.org/10.3390/genes11030296 PMID: 32168762 15. Cilleros K, Valentini A, Allard L, Dejean T, Etienne R, Grenouillet G, et al (2019) Unlocking biodiversity and conservation studies in high-diversity environments using environmental DNA (eDNA): A test with Guianese freshwater fishes. Mol Ecol Resour 19:27–46 https://doi.org/10.1111/1755-0998.12900 PMID: 29768738 16. Holman LE, Chng Y, Rius M (2022) How does eDNA decay affect metabarcoding experiments? Envi- ronmental DNA 4:108–116 17. Biber E (2013) The Challenge of Collecting and Using Environmental Monitoring Data. Ecology and Society 18:art68 18. Haase P, Pauls SU, Schindehu¨tte K, Sundermann A (2010) First audit of macroinvertebrate samples from an EU Water Framework Directive monitoring program: human error greatly lowers precision of assessment results. J North Am Benthol Soc 29:1279–1291

19. Shaw JLA, Weyrich L, Cooper A (2017) Using environmental (e)DNA sequencing for aquatic biodiver- sity surveys: a beginner’s guide. Mar Freshw Res 68:20 20. Miya M (2022) Environmental DNA Metabarcoding: A Novel Method for Biodiversity Monitoring of Marine Fish Communities. Ann Rev Mar Sci 14:161–185 https://doi.org/10.1146/annurev-marine- 041421-082251 PMID: 34351788 21. Valdivia-Carrillo T, Rocha-Olivares A, Reyes-Bonilla H, Domı´nguez-Contreras JF, Munguia-Vega A (2021) Integrating eDNA metabarcoding and simultaneous underwater visual surveys to describe com- plex fish communities in a marine biodiversity hotspot. Mol Ecol Resour 21:1558–1574 https://doi.org/ 10.1111/1755-0998.13375 PMID: 33683812 22. Pont D, Meulenbroek P, Bammer V, et al (2023) Quantitative monitoring of diverse fish communities on a large scale combining eDNA metabarcoding and qPCR. Mol Ecol Resour 23:396–409 https://doi.org/ 10.1111/1755-0998.13715 PMID: 36151931 23. Boivin-Delisle D, Laporte M, Burton F, Dion R, Normandeau E, Bernatchez L (2021) Using environmen- tal DNA for biomonitoring of freshwater fish communities: Comparison with established gillnet surveys in a boreal hydroelectric impoundment. Environmental DNA 3:105–120 24. Evans NT, Li Y, Renshaw MA, Olds BP, Deiner K, Turner CR, et al (2017) Fish community assessment with eDNA metabarcoding: effects of sampling design and bioinformatic filtering. Canadian Journal of Fisheries and Aquatic Sciences 74:1362–1374 25. Lucchetta BC, Watanabe FSY, do Carmo NMRB (2023) A spatiotemporal classification approach to

evaluate the impacts of land use and land cover changes before and after the Três Irmãos reservoir for- mation in the Tietê River, Brazil. Model Earth Syst Environ 9:4425–4435 26. de Miranda RB, Mauad FF (2015) Influence of Sedimentation on Hydroelectric Power Generation: Case Study of a Brazilian Reservoir. Journal of Energy Engineering. 27. Lamas BV, Rosman PCC, de Almeida Cruz DO (2018) Modelo Hidrodinaˆ mico Computacional do Reservato´ rio de Sobradinho. 28. Minamoto T, Miya M, Sado T, et al (2021) An illustrated manual for environmental DNA research: Water sampling guidelines and experimental protocols. Environmental DNA 3:8–13 29. Bolyen E, Rideout JR, Dillon MR, et al (2019) Reproducible, interactive, scalable and extensible micro- biome data science using QIIME 2. Nat Biotechnol 37:852–857 https://doi.org/10.1038/s41587-019- 0209-9 PMID: 31341288 30. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP (2016) DADA2: High-resolu- tion sample inference from Illumina amplicon data. Nat Methods 13:581–583 https://doi.org/10.1038/ nmeth.3869 PMID: 27214047 31. Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al (2018) Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Micro- biome 6:90 https://doi.org/10.1186/s40168-018-0470-z PMID: 29773078 32. Lim SJ, Thompson LR (2021) Mitohelper: A mitochondrial reference sequence analysis tool for fish eDNA studies. Environmental DNA 3:706–715 PLOS ONE eDNA metabarcoding reservoir

PLOS ONE | https://doi.org/10.1371/journal.pone.0314210 January 24, 2025 20 / 22

33. Porter TM, Hajibabaei M (2018) Automated high throughput animal CO1 metabarcode classification. Sci Rep 8:4226 https://doi.org/10.1038/s41598-018-22505-4 PMID: 29523803 34. Machida RJ, Leray M, Ho S-L, Knowlton N (2017) Metazoan mitochondrial gene sequence reference datasets for taxonomic assignment of environmental samples. Sci Data 4:170027 https://doi.org/10. 1038/sdata.2017.27 PMID: 28291235 35. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410 https://doi.org/10.1016/S0022-2836(05)80360-2 PMID: 2231712 36. Sayers EW, Beck J, Bolton EE, et al (2021) Database resources of the National Center for Biotechnol- ogy Information. Nucleic Acids Res 49:D10–D17 https://doi.org/10.1093/nar/gkaa892 PMID: 33095870 37. Oksanen Jari, et al. (2013) Package ‘vegan’. Community ecology package, version 2. 9: 1–295. 38. Smith EP, van Belle G (1984) Nonparametric Estimation of Species Richness. Biometrics 40:119 39. Wickham H, Chang W Package ‘ggplot2’. R package version 1.0.0. 40. Kassambara A, Mundt F (2016) Package ‘factoextra”.’ 41. R Core Team (2021) R: A language and environment for statistical ## computing. Foundation for Statis- tical Computing, Vienna, Austria. 42. Jombart T (2008) adegenet: a R package for the multivariate analysis of genetic markers. Bioinformat- ics 24:1403–1405 43. Dixon P (2003) VEGAN, a package of R functions for community ecology. Journal of Vegetation Sci- ence 14:927–930 44. de Almeida FVR, N O Jr., Branco LHZ (2011) Flora de comunidades de macroalgas lo´ ticas de fragmen-

tos florestais remanescentes da região noroeste do Estado de São Paulo, Brasil. Hoehnea 38:553–568 45. Magalhães C, Bueno SLS, Bond-Buckup G, Valenti WC, da Silva HLM, Kiyohara F, et al (2005) Exotic species of freshwater decapod crustaceans in the state of São Paulo, Brazil: records and possible causes of their introduction. Biodivers Conserv 14:1929–1945 46. Stefani PM (2006) Ecologia tro´fica de espe´cies alo´ ctones (Cichla cf. ocellaris e Plagioscion squamosis- simus) e nativa (Geophagus brasiliensis) nos reservato´rios do rio Tietê. 47. Ferraz KMPMDB, Ferraz SFDB, Moreira JR, Couto HTZ, Verdade LM (2007) Capybara (Hydrochoerus hydrochaeris) distribution in agroecosystems: a cross-scale habitat analysis. J Biogeogr 34:223–230 48. Pessotto MA, Nogueira MG (2018) More than two decades after the introduction of Limnoperna fortunei (Dunker 1857) in La Plata Basin. Brazilian Journal of Biology 78:773–784 https://doi.org/10.1590/1519- 6984.180789 PMID: 29340533 49. Goldberg CS, Turner CR, Deiner K, et al (2016) Critical considerations for the application of environ- mental DNA methods to detect aquatic species. Methods Ecol Evol 7:1299–1307 50. Carvalho C. S., De Oliveira M. E., Rodriguez-Castro K. G., Saranholi B. H., & Galetti P. M. Jr (2022). Efficiency of eDNA and iDNA in assessing vertebrate diversity and its abundance. Molecular Ecology Resources, 22(4), 1262–1273. https://doi.org/10.1111/1755-0998.13543 PMID: 34724330 51. Campanha PMGDC, Matsumoto AA, Brazão ML, Basilio LM, Maruyama LS(2019) Length and weight

relationships and biological aspects for 34 fish species from Três Irmãos reservoir, lower Tietê river basin, SP - Brazil. Boletim do Instituto de Pesca. 52. Kullander SO, de Lucena CAS (2006) A review of the species of Crenicichla (Teleostei: Cichlidae) from the Atlantic coastal rivers of southeastern Brazil from Bahia to Rio Grande do Sul States, with descrip- tions of three new species. Neotropical Ichthyology 4:127–146 53. Maruyama A., Sugatani K., Watanabe K., Yamanaka H., & Imamura A. (2018). Environmental DNA analysis as a non-invasive quantitative tool for reproductive migration of a threatened endemic fish in rivers. Ecology and Evolution, 8(23), 11964–11974. https://doi.org/10.1002/ece3.4653 PMID: 30598791 54. Troth C. R., Sweet M. J., Nightingale J., & Burian A. (2021). Seasonality, DNA degradation and spatial heterogeneity as drivers of eDNA detection dynamics. Science of the Total Environment, 768, 144466. https://doi.org/10.1016/j.scitotenv.2020.144466 PMID: 33736342 55. Stoeckle B. C., Kuehn R., & Geist J. (2016). Environmental DNA as a monitoring tool for the endan- gered freshwater pearl mussel (Margaritifera margaritifera L.): a substitute for classical monitoring approaches?. Aquatic Conservation: Marine and Freshwater Ecosystems, 26(6), 1120–1129. 56. Ha¨nfling B, Handley LL, Read DS, Hahn C, Li J, Nichols P, et al (2016) Environmental DNA metabar- coding of lake fish communities reflects long-term data from established survey methods. Mol Ecol 25:3101–3119 https://doi.org/10.1111/mec.13660 PMID: 27095076

57. Zhang S, Lu Q, Wang Y, Wang X, Zhao J, Yao M (2020) Assessment of fish communities using environ- mental DNA: Effect of spatial sampling design in lentic systems of different sizes. Mol Ecol Resour 20:242–255 https://doi.org/10.1111/1755-0998.13105 PMID: 31625686 PLOS ONE eDNA metabarcoding reservoir PLOS ONE | https://doi.org/10.1371/journal.pone.0314210 January 24, 2025 21 / 22

58. Turner CR, Uy KL, Everhart RC (2015) Fish environmental DNA is more concentrated in aquatic sedi- ments than surface water. Biol Conserv 183:93–102 59. Lacoursière-Roussel A, Rosabal M, Bernatchez L (2016) Estimating fish abundance and biomass from eDNA concentrations: variability among capture methods and environmental conditions. Mol Ecol Resour 16:1401–1414 https://doi.org/10.1111/1755-0998.12522 PMID: 26946353 60. Ficetola G. F., Pansu J., Bonin A., Coissac E., Giguet-Covex C., De Barba M.,. . . et al. (2015). Replica- tion levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data. Molecular ecology resources, 15(3), 543–556. https://doi.org/10.1111/1755-0998.12338 PMID: 25327646 61. Maruyama A, Nakamura K, Yamanaka H, Kondoh M, Minamoto T (2014) The Release Rate of Environ- mental DNA from Juvenile and Adult Fish. PLoS One 9:e114639 https://doi.org/10.1371/journal.pone. 0114639 PMID: 25479160 62. Carraro L, Stauffer JB, Altermatt F (2021) How to design optimal eDNA sampling strategies for biomoni- toring in river networks. Environmental DNA 3:157–172 63. Rourke ML, Fowler AM, Hughes JM, Broadhurst MK, DiBattista JD, Fielder S, et al (2022) Environmen- tal DNA (eDNA) as a tool for assessing fish biomass: A review of approaches and future considerations for resource surveys. Environmental DNA 4:9–33 64. Goutte A, Molbert N, Gue´ rin S, Richoux R, Rocher V (2020) Monitoring freshwater fish communities in large rivers using environmental DNA metabarcoding and a long-term electrofishing survey. J Fish Biol

97:444–452 https://doi.org/10.1111/jfb.14383 PMID: 32412670 65. Pont D, Rocle M, Valentini A, Civade R, Jean P, Maire A, et al (2018) Environmental DNA reveals quan- titative patterns of fish biodiversity in large rivers despite its downstream transportation. Sci Rep 8:10361 https://doi.org/10.1038/s41598-018-28424-8 PMID: 29991759 66. Sales NG, Wangensteen OS, Carvalho DC, Deiner K, Præbel K, Coscia I, et al (2021) Space-time dynamics in monitoring neotropical fish communities using eDNA metabarcoding. Science of The Total Environment 754:142096 https://doi.org/10.1016/j.scitotenv.2020.142096 PMID: 32898783 67. Hayami K, Sakata MK, Inagawa T, et al (2020) Effects of sampling seasons and locations on fish envi- ronmental DNA metabarcoding in dam reservoirs. Ecol Evol 10:5354–5367 https://doi.org/10.1002/ ece3.6279 PMID: 32607158 68. da Costa ID, de Carvalho Freitas CE (2013) Trophic ecology of the ichthyofauna of a stretch of the Urucu River (Coari, Amazonas, Brazil). Acta Limnologica Brasiliensia 25:54–67 69. Calazans AM, Martinez PA, Jacobina UP (2021) Lentic and lotic environments affect morphological diversity in characiformes fishes in the Neotropical São Francisco River Basin, Brazil. Environ Biol Fishes 104:977–987 70. Rivera SF, Vasselon V, Bouchez A, Rimet F (2023) eDNA metabarcoding from aquatic biofilms allows studying spatial and temporal fluctuations of fish communities from Lake Geneva. Environmental DNA 5:570–581 PLOS ONE eDNA metabarcoding reservoir PLOS ONE | https://doi.org/10.1371/journal.pone.0314210 January 24, 2025 22 / 22