Lundberg, J. G., Kottelat, M., Smith, G. R., Stiassny, M. L. J. & Gill, A. C. So many fishes, so little time: An overview of recent ichthyological discovery in continental waters. Ann. Mo. Bot. Gard. 87, 26–62 (2000).
Google Scholar
Relyea, R. A. The impact of insecticides and herbicides on the biodiversity and productivity of aquatic communities. Ecol. Appl. 15, 618–627 (2005).
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
Clare, A. I. M. et al. Beyond biodiversity: Can environmental DNA (eDNA) cut it as a population genetics tool?. Genes 10, 192 (2019).
Google Scholar
Tsuji, S., Shibata, N., Sawada, H. & Ushio, M. Quantitative evaluation of intraspecific genetic diversity in a natural fish population using environmental DNA. Mol. Ecol. Resour. 20, 1323–1332 (2020).
Google Scholar
Miya, M., Gotoh, R. O. & Sado, T. MiFish metabarcoding: A high-throughput approach for simultaneous detection of multiple fish species from environmental DNA and other samples. Fish. Sci. 86, 939–970 (2020).
Google Scholar
Dagosta F. C. P. & de Pinna, M. C. C. The fishes of the Amazon: Distribution and biogeographical patterns, with a comprehensive list of species. Bull. Am. Mus. Nat. Hist, 431, 1–163 (2019).
Jézéquel, C., Tedesco, P. A. & Bigorne, R. A database of freshwater fish species of the Amazon Basin. Sci. Data 7, 96 (2020).
Google Scholar
Reis, R. E., Kullander, S. O. & Ferraris, C. J. Check List of the Freshwater Fishes of South and Central America. (Edipucrs, 2003).
Tedesco, P. et al. A global database on freshwater fish species occurrence in drainage basins. Sci. Data 4, 170141 (2017).
Google Scholar
Brito, P. M., Meunier, F. J. & Leal, M. E. C. Origine et diversification de líchthyofaune Neotropical: Une revue. Cybium 31, 139–153 (2007).
Lowe-McConnell, R. H. Ecological Studies in Tropical Fish Communities (Cambridge University Press, 1987).
Google Scholar
Bloom, D. D. & Lovejoy, N. R. On the origins of marine derived fishes in South America. J. Biogeogr. 44, 1927–1938 (2017).
Google Scholar
de Santana, C. D. et al. Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator. Nat. Commun. 10, 4000 (2019).
Google Scholar
Carvalho, L. N., Zuanon, J. & Sazima, I. Natural history of Amazon fishes. In Tropical Biology and Natural Resources Theme (ed. Del-Claro, K.), K. Del-Claro & R. J. Marquis (Session Eds. the Natural History Session), Encyclopedia of Life Support Systems (EOLSS) (Eolss Publishers, 2007).
Cardoso, Y. P. et al. A continental-wide molecular approach unraveling mtDNA diversity and geographic distribution of the Neotropical genus Hoplias. PLoS ONE 13, e0202024 (2018).
Google Scholar
Hebert, P. D. N., Cywinska, A., Ball, S. L. & de Waard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. Ser. B Biol. Sci. 270, 313–321 (2003).
Google Scholar
Baldwin, C. C., Castillo, C. I., Weigt, L. A. & Victor, B. C. Seven new species within western Atlantic Starksia atlantica, S. lepicoelia, and S. sluiteri (Teleostei, Labrisomidae), with comments on congruence of DNA barcodes and species. ZooKeys 79, 21–27 (2011).
Google Scholar
Robertson, D. R. et al. Deep-water bony fishes collected by the B/O Miguel Oliver on the shelf edge of Pacific Central America: An annotated, illustrated and DNA-barcoded checklist. Zootaxa 4348, 1–125 (2017).
Google Scholar
Weigt, L. A. et al. Using DNA barcoding to assess Caribbean reef fish biodiversity: Expanding taxonomic and geographic coverage. PLoS ONE 7, e41059 (2012).
Google Scholar
Seberg, O. et al. Global genome biodiversity network: Saving a blueprint of the tree of life—a botanical perspective. Ann. Bot. 118, 393–399 (2016).
Google Scholar
Parenti, L. R. et al. Fishes collected during the 2017 MarineGEO assessment of Kāne‘ohe Bay, O‘ahu, Hawai‘i. J. Mar. Biol. Assoc. UK 100, 607–637 (2020).
Google Scholar
Droege, G. et al. The Global Genome Biodiversity Network (GGBN) Data Standard specification. Database https://doi.org/10.1093/database/baw125 (2016).
Google Scholar
Marques, V. et al. Blind assessment of vertebrate taxonomic diversity across spatial scales by clustering environmental DNA metabarcoding sequences. Ecography 43, 1779–1790 (2020).
Google Scholar
Leray, M., Knowlton, N., Shien-Lei, H., Nguyen, B. N. & Machida, R. J. GenBank is a reliable resource for 21st biodiversity research. Proc. Natl. Acad. Sci. U.S.A. 116, 22651–22656 (2019).
Google Scholar
Dillman, C. B. et al. Forensic investigations into a GenBank anomaly: Endangered taxa and the importance of voucher specimens in molecular studies. J. Appl. Ichthyol. 30, 1300–1309 (2014).
Google Scholar
Locatelli, N. S., McIntyre, P. B., Therkildsen, N. O. & Baetscher, D. S. GenBank’s reliability is uncertain for biodiversity researchers seeking species-level assignment for eDNA. Proc. Natl. Acad. Sci. U.S.A. 117, 32211–32212 (2020).
Google Scholar
Jerde, C. L., Wilson, E. A. & Dressler, T. L. Measuring global fish species richness with eDNA metabarcoding. Mol. Ecol. Resour. 19, 19–22 (2019).
Google Scholar
Nobile, A. B. et al. DNA metabarcoding of Neotropical ichthyoplankton: Enabling high accuracy with lower cost. Metabarcoding Metagenom. 3, 35060 (2019).
Google Scholar
Cilleros, K. et al. Unlocking biodiversity and conservation studies in high diversity environments using environmental DNA (eDNA): A text 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 1, 119–130 (2019).
Google Scholar
Jackman, J. M. C. et al. eDNA in a bottleneck: Obstacles to fish metabarcoding studies in megadiverse freshwater systems. Environ. DNA https://doi.org/10.1002/edna3.191 (2021).
Google Scholar
Valentini, A. et al. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol. Ecol. 25, 929–942 (2016).
Google Scholar
McElroy, M. E. et al. Calibrating environmental DNA metabarcoding to conventional surveys for measuring fish species richness. Front. Ecol. Evol. 8, 276 (2020).
Google Scholar
Dudgeon, D. Freshwater Biodiversity: Status (Cambridge University Press, 2020).
Google Scholar
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Austral Ecol. 18, 117–143 (1993).
Google Scholar
Milan, D. T., Mendes, I. S. & Carvalho, D. C. New 12S metabarcoding primers for enhanced Neotropical freshwater fish biodiversity assessment. Sci. Rep. 10, 17966 (2020).
Google Scholar
Deagle, B. E., Jarman, S. N., Coissac, E., Pompanon, F. & Taberlet, P. DNA metabarcoding and the cytochrome c oxidase subunit I marker: Not a perfect match. Biol. Lett. 10, 20140562 (2014).
Google Scholar
Collins, R. A. et al. Non-specific amplification compromises environmental DNA metabarcoding with COI. Methods Ecol. Evol. 10, 1985–2001 (2019).
Google Scholar
Antich, A. et al. To denoise or to cluster, that is not the question: optimizing pipelines for COI metabarcoding and metaphylogeography. BMC Bioinf. 22, 177 (2021).
Google Scholar
Vieira, T. B. et al. A multiple hypothesis approach to explain species richness patterns in neotropical stream-dweller fish communities. PLoS ONE 13, e0204114 (2018).
Google Scholar
Zuanon, J., Bockmann, F. A. & Sazima, I. A remarkable sand-dwelling fish assemblage from central Amazonia, with comments on the evolution of psammophily in South American freshwater fishes. Neotrop. Ichthyol. 4, 107–118 (2006).
Google Scholar
Sazima, I., Carvalho, L. N., Mendonça, F. P. & Zuanon, J. Fallen leaves on the water-bed: Diurnal camouflage of three night-active fish species in an Amazonian streamlet. Neotrop. Ichthyol. 4, 119–122 (2006).
Google Scholar
Espírito-Santo, H. M. V. & Zuanon, J. Temporary pools provide stability to fish assemblages in Amazon headwater streams. Ecol. Freshw. Fish 26, 475–483 (2017).
Google Scholar
de Pinna, M. C. C., Zuanon, J., Rapp-Py-Daniel, L. R. & Petry, P. A new family of neotropical freshwater fishes from deep fossorial Amazonian habitat, with a reappraisal of morphological characiform phylogeny (Teleostei: Ostariophysi). Zool. J. Linn. Soc. 182, 76–106 (2018).
Google Scholar
López-Rojas, H., Lundberg, J. G. & Marsh, E. Design and operation of a small trawling apparatus for use with dugout canoes. N. Am. J. Fish. Manag. 4, 331–334 (1984).
Google Scholar
Marrero, C. & Taphorn, D. C. Notas sobre la historia natural y la distribution de los peces Gymnotiformes in la cuenca del Rio Apure y otros rios de la Orinoquia. Biollania 8, 123–142 (1991).
Cox-Fernandes, C., Podos, J. & Lundberg, J. G. Amazonian ecology: Tributaries enhance the diversity of electric fishes. Science 305, 1960–1962 (2004).
Google Scholar
Peixoto, L. A. W., Dutra, G. M. & Wosiack, W. B. The electric. Glassknife fishes of the Eigenmannia trilineata group (Gymnotiformes: Sternopygidae): Monophyly and description of seven new species. Zool. J. Linn. Soc. 175, 384–414 (2015).
Google Scholar
de Santana, C. D. & Vari, R. P. Electric fishes of the genus Sternarchorhynchus (Teleostei, Ostariophysi, Gymnotiformes); phylogenetic and revisionary studies. Zool. J. Linn. Soc. 159, 223–371 (2010).
Google Scholar
Castro, R. M. C. Evolução da ictiofauna de riachos sul-americanos: Padrões gerais e possíveis processos causais. In Ecologia de peixes de riachos (eds Caramaschi, E. P., Mazzoni, R., & Peres-Neto, P. R.) Série Oecologia Brasiliensis volume VI, PPGE-UFRJ, Rio de Janeiro, 139–155 (1999).
Mojica, J. I., Castellanos, C. & Lobón-Cerviá, J. High temporal species turnover enhances the complexity of fish assemblages in Amazonian Terra firme streams. Ecol. Freshw. Fish 18, 518–526 (2009).
Google Scholar
de Oliveira, R. R., Rocha, M. M., Anjos, M. B., Zuanon, J. & Rapp Py-Daniel, L. H. Fish fauna of small streams of the Catua-Ipixuna Extractive Reserve, State of Amazonas, Brazil. Check List 5, 154–172 (2009).
Google Scholar
Caramaschi E., Mazzoni, P. R., Bizerril, C. R. S. F. & Peres-Neto, P. R. Ecologia de Peixes de Riachos: Estado Atual e Perspectivas. Oecologia Brasiliensis, v. VI, Rio de Janeiro (1999).
Anjos, M. B. & Zuanon, J. Sampling effort and fish species richness in small Terra firme forest streams of central Amazonia, Brazil. Neotrop. Ichthyol. 5, 45–52 (2007).
Google Scholar
Mojica, J. I., Lobón-Cerviá, J. & Castellanos, C. Quantifying fish species richness and abundance in Amazonian streams: Assessment of a multiple gear method suitable for Terra firme stream fish assemblages. Fish. Manag. Ecol. 21, 220–233 (2014).
Google Scholar
Barros, D. F. et al. The fish fauna of streams in the Madeira-Purus interfluvial region, Brazilian Amazon. Check List 7, 768–773 (2011).
Google Scholar
Escobar-Camacho, D., Barriga, R. & Ron, S. R. Discovering hidden diversity of characins (Teleostei: Characiformes) in Ecuador’s Yasuní National Park. PLoS ONE 10, e0135569 (2015).
Google Scholar
Ramirez, J. L. et al. Revealing hidden diversity of the underestimated neotropical ichthyofauna: DNA barcoding in the recently described genus Megaleporinus (Characiformes: Anostomidae). Front. Genet. 8, 149 (2017).
Google Scholar
Crampton, W. G. R., de Santana, C. D., Waddell, J. C. & Lovejoy, N. R. The Neotropical electric fish genus Brachyhypopomus (Ostariophysi: Gymnotiformes: Hypopomidae): taxonomy and biology, with descriptions of 15 new species. Neotrop. Ichthyol. 14, 639–790 (2016).
Google Scholar
Abel, R. Conservation biology for the biodiversity crisis: A freshwater follow-up. Conserv. Biol. 5, 1435–1437 (2002).
Google Scholar
Dudgeon, D. Prospects for sustaining freshwater biodiversity in the 21st century: Linking ecosystem structure and function. Curr. Opin. Environ. Sustain. 5, 422–430 (2010).
Google Scholar
Jenkins, M. Prospects for biodiversity. Science 302, 1175–1177 (2003).
Google Scholar
Bunn, S. E. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010).
Google Scholar
Albert, J. S. et al. Scientists’ warning to humanity on the freshwater biodiversity crisis. Ambio 50, 85–94 (2020).
Google Scholar
Gilbert, M. T. P. et al. The isolation of nucleic acids from fixed, paraffin-embedded tissues–which methods are useful when?. PLoS ONE 2, e537 (2007).
Google Scholar
Campos, P. F. & Gilbert, T. M. DNA extraction from formalin-fixed material. In Ancient DNA 81–85 (Humana Press, 2012).
Hykin, S. M., Bi, K. & McGuire, J. A. Fixing formalin: A method to recover genomic-scale DNA sequence data from formalin-fixed museum specimens using high-throughput sequencing. PLoS ONE 10, e0141579 (2015).
Google Scholar
Hagedorn, M. M. et al. Cryopreservation of fish spermatogonial cells: The future of natural history collections. Sci. Rep. 8, 6149 (2018).
Google Scholar
Albert, J. & Reis, R. E. Historical Biogeography of Neotropical Freshwater Fishes (University of California Press, 2011).
Google Scholar
Sabaj Pérez, M. H. Where the Xingu bends and will soon break. Am. Sci. 103, 395–403 (2015).
Google Scholar
Amigo, I. When will the Amazon hit a tipping point?. Nature 578, 505–507 (2020).
Google Scholar
Murienne, J. et al. Aquatic DNA for monitoring French Guiana biodiversity. Biodivers. Data J. 7, 37518 (2019).
Google Scholar
McDevitt, A. D. et al. Environmental DNA metabarcoding as an effective and rapid tool for fish monitoring in canals. J. Fish Biol. 95, 679–682 (2019).
Google Scholar
Fernandes, G. W. et al. Dismantling Brazil’s science threatens global biodiversity heritage. Perspect. Ecol. Conserv. 15, 239–243 (2017).
Alves, R. J. V. et al. Brazilian legislation on genetic heritage harms Biodiversity Convention goals and threatens basic biology research and education. An. Acad. Bras. Ciênc. 90, 1279–1284 (2018).
Google Scholar
Overbeck, G. E. et al. Global biodiversity threatened by science budget cuts in Brazil. Bioscience 68, 11–12 (2018).
Google Scholar
Miya, M. et al. Use of a filter cartridge for filtration of water samples and extraction of environmental DNA. J. Vis. Exp. 117, 54741 (2016).
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
Google Scholar
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
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
Google Scholar
Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).
Google Scholar
Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).
Google Scholar
Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: More models, new heuristics and parallel computing. Nat. Methods 9, 772 (2012).
Google Scholar
Ronquist, F. et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).
Google Scholar
Miller, M. A. et al. A RESTful API for access to phylogenetic tools via the CIPRES science gateway. Evol. Bioinf. 11, 43–48 (2015).
Google Scholar
Ciccarelli, F. D. et al. Toward automatic reconstruction of a highly resolved tree of life. Science 311, 1283–1287 (2006).
Google Scholar
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020). https://www.Rproject.org/.
Wickham, H. Ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
Google Scholar
Oksanen, J., Kindt, R. & O’Hara, B. Package VEGAN. Community Ecology Package, Version 2 (2013).
Fox, J. & Weisberg, S. An R Companion to Applied Regression 3rd edn. (Sage, 2019).
Adler D., Nenadic, O. & Zucchini, W. rgl: 3D visualization device system (OpenGL). R package version 0.93.945. http://CRAN.R-project.org/package=rgl (2013).
Gu, Z. Circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).
Google Scholar
Schiettekatte, N. M. D., Brandl, S. J. & Casey, J. M. Fishualize: Color Palettes Based On Fish Species. CRAN version 0.2.0 (2019).
Chao, A. Estimating population size for sparse data in capture-recapture experiments. Biometrics 45, 427 (1989).
Google Scholar
Hsieh T. C., Ma, K. H. & Chao, A. iNEXT: Interpolation and Extrapolation for Species Diversity. R package version 2.0.20 (2020).
Chao, A., Chazdon, R. L., Colwell, R. K. & Shen, T.-J. A new statistical approach for assessing compositional similarity based on incidence and abundance data. Ecol. Lett. 8, 148–215 (2005).
Google Scholar
Olds, B. P. et al. Estimating species richness using environmental DNA. Ecol. Evol. 6, 4214–4226 (2016).
Google Scholar
Chao A., Ma, K. H., Hsieh, T. C. & Chiu, C. H. SpadeR (Species-richness Prediction and Diversity Estimation in R): An R package in CRAN. Program and User’s Guide also published at http://chao.stat.nthu.edu.tw/wordpress/software_download/ (2016).
Source: Ecology - nature.com
