de Souza Dias, V., Pereira da Luz, M., Medero, G. M. & Tarley Ferreira Nascimento, D. An overview of hydropower reservoirs in Brazil: Current situation, future perspectives and impacts of climate change. Water 10, 592 (2018).
Patias, J., Zuquette, L. V. & Rodrigues-Carvalho, J. A. Piezometric variations in the basaltic massif beneath the Itaipu hydroelectric plant (Brazil/Paraguay border): Right Buttress Dam. Bull. Eng. Geol. Environ. 74, 207–231 (2015).
Google Scholar
Agostinho, A. A. Pesquisas, monitoramento e manejo da fauna aquática em empreendimentos hidrelétricos. In Seminário Sobre Fauna Aquática E O Setor Elétrico Brasileiro 38–59 (Brasil, 1994).
Makrakis, S., Gomes, L. C., Makrakis, M. C., Fernandez, D. R. & Pavanelli, C. S. The Canal da Piracema at Itaipu Dam as a fish pass system. Neotrop. Ichthyol. 5, 185–195 (2007).
Dos Reis, R. B., Frota, A., Depra, G. D. C., Ota, R. R. & Da Graca, W. J. Freshwater fishes from Paraná State, Brazil: An annotated list, with comments on biogeographic patterns, threats, and future perspectives. Zootaxa 4868, 451–494 (2020).
Becker, R. A., Sales, N. G., Santos, G. M., Santos, G. B. & Carvalho, D. C. DNA barcoding and morphological identification of neotropical ichthyoplankton from the Upper Paraná and São Francisco. J. Fish Biol. 87, 159–168 (2015).
Google Scholar
Milan, D. T. et al. New 12S metabarcoding primers for enhanced Neotropical freshwater fish biodiversity assessment. Sci. Rep. 10, 1–12 (2020).
Google Scholar
Agostinho, A. A., Pelicice, F. M. & Gomes, L. C. Dams and the fish fauna of the Neotropical region: Impacts and management related to diversity and fisheries. Braz. J. Biol. 68, 1119–1132 (2008).
Google Scholar
Bonar, S. A., Hubert, W. A. & Willis, D. W. Standard methods for sampling North American freshwater fishes. American Fisheries Society, Bethesda, (USA, 2009).
Shaw, J. L. A. et al. Comparison of environmental DNA metabarcoding and conventional fish survey methods in a river system. Biol. Conserv. 197, 131–138 (2016).
Reis, R. E. et al. Fish biodiversity and conservation in South America. J. Fish Biol. 89, 12–47 (2016).
Google Scholar
Baumgartner, G. et al. Peixes do baixo rio Iguaçu. (Eduem, 2012).
Taberlet, P., Bonin, A., Coissac, E. & Zinger, L. Environmental DNA: For Biodiversity Research and Monitoring (Oxford University Press, 2018).
Taberlet, P., Coissac, E., Pompanon, F., Christian, B. & Willerslev, E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol Ecol 33, 2045–2050 (2012).
Ritter, C. D. et al. The pitfalls of biodiversity proxies: Differences in richness patterns of birds, trees and understudied diversity across Amazonia. Sci. Rep. 9, 1–3 (2019).
Google Scholar
Sales, N. G. et al. Space-time dynamics in monitoring neotropical fish communities using eDNA metabarcoding. Sci. Total Environ. 754, 142096 (2021).
Google Scholar
Zinger, L. et al. Body size determines soil community assembly in a tropical forest. Mol. Ecol. 28, 528–543 (2019).
Google Scholar
Baird, D. J. & Hajibabaei, M. Biomonitoring 2.0: A new paradigm in ecosystem assessment made possible by next-generation DNA sequencing. Mol. Ecol. 21, 2039–2044 (2012).
Google Scholar
Zinger, L. et al. Advances and prospects of environmental DNA in neotropical rainforests. Adv. Ecol. Res. 62, 331–373 (2020).
Cilleros, K. et al. 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 (2019).
Google Scholar
Sales, N. G., Wangensteen, O. S., Carvalho, D. C. & Mariani, S. Influence of preservation methods, sample medium and sampling time on eDNA recovery in a neotropical river. Environ. DNA 119–130. https://doi.org/10.1002/edn3.14 (2020).
Google Scholar
Blaxter, M. et al. Defining operational taxonomic units using DNA barcode data. Philos. Trans. R. Soc. B Biol. Sci. 360(1462), 1935–1943. https://doi.org/10.1098/rstb.2005.1725 (2005).
Google Scholar
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
Google Scholar
Edgar, R. C. UNOISE2: Improved error-correction for Illumina 16S and ITS amplicon sequencing. BioRxiv 81257 (2016).
Muha, T. P., Rodriguez-Barreto, D., O’Rorke, R., Garcia de Leaniz, C. & Consuegra, S. Using eDNA metabarcoding to monitor changes in fish community composition after barrier removal. Front. Ecol. Evol. 9, 28 (2021).
Kitano, T., Umetsu, K., Tian, W. & Osawa, M. Two universal primer sets for species identification among vertebrates. Int. J. Legal Med. 121, 423–427 (2007).
Google Scholar
Stoeckle, M. Y., Soboleva, L. & Charlop-Powers, Z. Aquatic environmental DNA detects seasonal fish abundance and habitat preference in an urban estuary. PLoS One 12, e0175186 (2017).
Google Scholar
Bylemans, J. et al. An environmental DNA-based method for monitoring spawning activity: A case study, using the endangered Macquarie perch (Macquaria australasica). Methods Ecol. Evol. 8, 646–655 (2017).
De Souza, L. S., Godwin, J. C., Renshaw, M. A. & Larson, E. Environmental DNA (eDNA) detection probability is influenced by seasonal activity of organisms. PLoS One 11, e0165273 (2016).
Google Scholar
Ritter, C. D. et al. Locality or habitat? Exploring predictors of biodiversity in Amazonia. Ecography (Cop.) 42, 321–333 (2019).
CFMV-Resolução no 1000 de 11 de maio de 2012—Dispõe sobre procedimentos e métodos de eutanásia em animais e dá outras providências. (2012).
Britski, H. A., de Silimon, K. Z. S. & Lopes, B. S. Peixes do Pantanal: manual de identificação, ampl. Brasília, DF, Embrapa Informação Tecnológica (2007).
Ota, R. R., Deprá, G. de C., Graça, W. J. da & Pavanelli, C. S. Peixes da planície de inundação do alto rio Paraná e áreas adjacentes: revised, annotated and updated. Neotrop. Ichthyol. 16(2). https://www.scielo.br/j/ni/a/tScwvm8JLhKnbxKjtBQLPBx/abstract/?lang=en (2018).
Neris, N., Villalba, F., Kamada, D. & Viré, S. Guía de peces del Paraguay/Guide of fishes of Paraguay. Zamphiropolos, (Paraguay, 2010).
Pie, M. R. et al. Development of a real-time PCR assay for the detection of the golden mussel (Limnoperna fortunei, Mytilidae) in environmental samples. An. Acad. Bras. Cienc. 89, 1041–1045 (2017).
Google Scholar
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).
Google Scholar
Boeger, W. A. et al. Testing a molecular protocol to monitor the presence of golden mussel larvae (Limnoperna fortunei) in plankton samples. J. Plankton Res. 29, 1015–1019 (2007).
Google Scholar
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).
Van Rossum, G. & Drake, F. L. Python 3 References Manual. Scotts Valley CA: CreateSpace. (2009).
R Core Team. R: the R project for statistical computing. 2019. https://www.r-project.org/ (accessed 30 Mar 2020).
Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).
Google Scholar
Edgar, R. C. & Flyvbjerg, H. Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics 31, 3476–3482 (2015).
Google Scholar
Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinform. 10, 421 (2009).
Team, Rs. RStudio: integrated development for R. RStudio, Inc., Boston, MA https://www.rstudio.com42, 84 (2015).
Wickham, H. tidyverse: Easily Install and Load “Tidyverse” Packages (Version R package version 1.1. 1). (2017).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
Google Scholar
Tang, Y., Horikoshi, M. & Li, W. ggfortify: Unified interface to visualize statistical results of popular R packages. R J. 8, 474 (2016).
Auguie, B. & Antonov, A. gridExtra: Miscellaneous functions for “grid” graphics (Version 2.2. 1)[Computer software]. (2016).
Kassambara, A. & Kassambara, M. A. Package ‘ggpubr’. (2020).
Oksanen, J. et al. Vegan: Community ecology package. R package version 1.17-4. https://cran.r-project.org. Acesso em 23, 2010 (2010).
McMurdie, P. J. & Holmes, S. Waste not, want not: Why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).
Google Scholar
Jost, L. Entropy and diversity. Oikos 113, 363–375 (2006).
Marcon, E., Herault, B. & Marcon, M. E. Package ‘entropart’. (2021).
Mächler, E., Walser, J.-C. & Altermatt, F. Decision making and best practices for taxonomy-free eDNA metabarcoding in biomonitoring using Hill numbers. BioRxiv (2020).
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
Google Scholar
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Google Scholar
León, A., Reyes, J., Burriel, V. & Valverde, F. Data quality problems when integrating genomic information. In International Conference on Conceptual Modeling 173–182 (Springer, 2016).
Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).
Google Scholar
Stahlhut, J. K. et al. DNA barcoding reveals diversity of hymenoptera and the dominance of parasitoids in a sub-arctic environment. BMC Ecol. 13, 2 (2013).
Google Scholar
Gillet, B. et al. Direct fishing and eDNA metabarcoding for biomonitoring during a 3-year survey significantly improves number of fish detected around a South East Asian reservoir. PLoS One 13, e0208592 (2018).
Google Scholar
Barrett, M. et al. Living planet report 2018: Aiming higher. WWF. Available at: https://www.globallandscapesforum.org/publication/living-planet-report-2018-aiming-higher/ (2018).
Díaz, S. M. et al. The global assessment report on biodiversity and ecosystem services: Summary for policy makers. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. 56, (2019).
Dudgeon, D. Asian river fishes in the Anthropocene: Threats and conservation challenges in an era of rapid environmental change. J. Fish Biol. 79, 1487–1524 (2011).
Google Scholar
Dudgeon, D. Multiple threats imperil freshwater biodiversity in the Anthropocene. Curr. Biol. 29, R960–R967 (2019).
Google Scholar
He, F. et al. Disappearing giants: A review of threats to freshwater megafauna. Wiley Interdiscip. Rev. Water 4, e1208 (2017).
Agostinho, A. A., Thomaz, S. M. & Gomes, L. C. Threats for biodiversity in the floodplain of the Upper Paraná River: Effects of hydrological regulation by dams. (2018). Int. J. Ecohydrol. Hydrobiol Warsaw. 4(3), 267–280 (2004).
Santana, M. L., Carvalho, F. R. & Teresa, F. B. Broad and fine-scale threats on threatened Brazilian freshwater fish: Variability across hydrographic regions and taxonomic groups. Biota Neotrop. 21 (2). https://www.scielo.br/j/bn/a/YqFbWSy5vbfHy3QK9kNpdKp/?format=html&lang=en (2021).
Matthews, W. J. Patterns in Freshwater Fish Ecology. (Springer Science & Business Media, 2012).
de Oliveira Bueno, E., Alves, G. J. & Mello, C. R. Hydroelectricity water footprint in Parana hydrograph region, Brazil. Renew. Energy 162, 596–612 (2020).
Camacho Guerreiro, A. I., Amadio, S. A., Fabre, N. N. & da Silva Batista, V. Exploring the effect of strong hydrological droughts and floods on populational parameters of Semaprochilodus insignis (Actinopterygii: Prochilodontidae) from the Central Amazonia. Environ. Dev. Sustain. 23, 3338–3348 (2021).
Jespersen, H., Rasmussen, G. & Pedersen, S. Severity of summer drought as predictor for smolt recruitment in migratory brown trout (Salmo trutta). Ecol. Freshw. Fish 30, 115–124 (2021).
Pool, T. K., Grenouillet, G. & Villéger, S. Species contribute differently to the taxonomic, functional, and phylogenetic alpha and beta diversity of freshwater fish communities. Divers. Distrib. 20, 1235–1244 (2014).
de Oliveira, E. F., Goulart, E. & Minte-Vera, C. V. Fish diversity along spatial gradients in the Itaipu Reservoir, Paraná, Brazil. Braz. J. Biol. 64, 447–458 (2004).
Google Scholar
Daga, V. S. et al. Homogenization dynamics of the fish assemblages in Neotropical reservoirs: Comparing the roles of introduced species and their vectors. Hydrobiologia 746, 327–347 (2015).
Vitule, J. R. S. Introdução de peixes em ecossistemas continentais brasileiros: revisão, comentários e sugestões de ações contra o inimigo quase invisível. Neotrop. Biol. Conserv. 4, 111–122 (2009).
Mariac, C. et al. Species‐level ichthyoplankton dynamics for 97 fishes in two major river basins of the Amazon using quantitative metabarcoding. Mol. Ecol. https://onlinelibrary.wiley.com/action/showCitFormats?doi=10.1111%2Fmec.15944 (2021).
Jackman, J. M. et al. eDNA in a bottleneck: Obstacles to fish metabarcoding studies in megadiverse freshwater systems. Environ. DNA 3, 837–849 (2021).
Bessey, C. et al. Maximizing fish detection with eDNA metabarcoding. Environ. DNA 2, 493–504 (2020).
Evans, N. T. et al. Fish community assessment with eDNA metabarcoding: Effects of sampling design and bioinformatic filtering. Can. J. Fish. Aquat. Sci. 74, 1362–1374 (2017).
Google Scholar
Prodan, A. et al. Comparing bioinformatic pipelines for microbial 16S rRNA amplicon sequencing. PLoS One 15, e0227434 (2020).
Google Scholar
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
Google Scholar
Elbrecht, V. & Leese, F. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass—sequence relationships with an innovative metabarcoding protocol. PLoS ONE 10, e0130324 (2015).
Google Scholar
Pawluczyk, M. et al. Quantitative evaluation of bias in PCR amplification and next-generation sequencing derived from metabarcoding samples. Anal. Bioanal. Chem. 407, 1841–1848 (2015).
Google Scholar
Holman, L. E., Chng, Y. & Rius, M. How does eDNA decay affect metabarcoding experiments? Environ. DNA https://onlinelibrary.wiley.com/action/showCitFormats?doi=10.1002%2Fedn3.201 (2021).
Edgar, R. C. UNCROSS2: identification of cross-talk in 16S rRNA OTU tables. BioRxiv 400762 (2018).
MacArthur, R. H. Geographical Ecology: Patterns in the Distribution of Species. (Princeton University Press, 1984).
Leray, M. & Knowlton, N. Random sampling causes the low reproducibility of rare eukaryotic OTUs in Illumina COI metabarcoding. PeerJ 5, e3006 (2017).
Google Scholar
Team, Q. D. QGIS geographic information system. Open Source Geospatial Found. Proj. Versão 2, (2015).
Source: Ecology - nature.com