McGill, B. J., Dornelas, M., Gotelli, N. J. & Magurran, A. E. Fifteen forms of biodiversity trend in the Anthropocene. Trends Ecol. Evol. 30, 104–113. https://doi.org/10.1016/j.tree.2014.11.006 (2015).
Google Scholar
Pounds, J. A. et al. Widespread amphibian extinctions from epidemic disease driven by global warming. Nature 439, 161–167. https://doi.org/10.1038/nature04246 (2006).
Google Scholar
Thomas, C. D., Franco, A. M. A. & Hill, J. K. Range retractions and extinction in the face of climate warming. Trends Ecol. Evol. 21, 415–416. https://doi.org/10.1016/j.tree.2006.05.012 (2006).
Google Scholar
Young, H. S., McCauley, D. J., Galetti, M. & Dirzo, R. In Annual Review of Ecology, Evolution, and Systematics, Vol. 47 (ed Futuyma, D. J.) 333–358 (Annual Reviews, 2016).
Drew, L. W. Are we losing the science of taxonomy?: As need grows, numbers and training are failing to keep up. Bioscience 61, 942–946. https://doi.org/10.1525/bio.2011.61.12.4 (2011).
Google Scholar
Kim, K. C. & Byrne, L. B. Biodiversity loss and the taxonomic bottleneck: Emerging biodiversity science. Ecol. Res. 21, 794–810. https://doi.org/10.1007/s11284-006-0035-7 (2006).
Google Scholar
Packer, L., Grixti, J. C., Roughley, R. E. & Hanner, R. The status of taxonomy in Canada and the impact of DNA barcoding. Can. J. Zool. 87, 1097–1110. https://doi.org/10.1139/z09-100 (2009).
Google Scholar
Qin, H. W., Li, X., Liang, J., Peng, Y. G. & Zhang, C. S. DeepFish: Accurate underwater live fish recognition with a deep architecture. Neurocomputing 187, 49–58. https://doi.org/10.1016/j.neucom.2015.10.122 (2016).
Google Scholar
Tharwat, A., Hemedan, A. A., Hassanien, A. E. & Gabel, T. A biometric-based model for fish species classification. Fish. Res. 204, 324–336. https://doi.org/10.1016/j.fishres.2018.03.008 (2018).
Google Scholar
Gates, D. M., Keegan, H. J., Schleter, J. C. & Weidner, V. R. Spectral properties of plants. Appl. Opt. 4, 11–000. https://doi.org/10.1364/ao.4.000011 (1965).
Google Scholar
Hutchison, V. H. & Larimer, J. L. Reflectivity of the integuments of some lizards from different habitats. Ecology 41, 199–209. https://doi.org/10.2307/1931954 (1960).
Google Scholar
Asner, G. P. & Martin, R. E. Spectranomics: Emerging science and conservation opportunities at the interface of biodiversity and remote sensing. Glob. Ecol. Conserv. 8, 212–219. https://doi.org/10.1016/j.gecco.2016.09.010 (2016).
Google Scholar
Baldeck, C. A. et al. Operational tree species mapping in a diverse tropical forest with airborne imaging spectroscopy. PLoS ONE 10, e0118403. https://doi.org/10.1371/journal.pone.0118403 (2015).
Google Scholar
Jetz, W. et al. Monitoring plant functional diversity from space. Nat. Plants 2, 1–5. https://doi.org/10.1038/nplants.2016.24 (2016).
Google Scholar
Leblanc, G., Francis, C. M., Soffer, R., Kalacska, M. & de Gea, J. Spectral reflectance of polar bear and other large arctic mammal pelts; potential applications to remote sensing surveys. Remote Sens. 8, 273. https://doi.org/10.3390/rs8040273 (2016).
Google Scholar
Dodd, C. K. Infrared reflectance in chameleons (Chamaeleonidae) from Kenya. Biotropica 13, 161–164. https://doi.org/10.2307/2388120 (1981).
Google Scholar
Pinto, F. et al. Non-invasive measurement of frog skin reflectivity in high spatial resolution using a dual hyperspectral approach. PLoS ONE 8, e73234. https://doi.org/10.1371/journal.pone.0073234 (2013).
Google Scholar
Schwalm, P., Starrett, P. & McDiarmid, R. Infrared reflectance in leaf-sitting neotropical frogs. Science 196, 1225–1226. https://doi.org/10.1126/science.860137 (1977).
Google Scholar
Mielewczik, M., Liebisch, F., Walter, A. & Greven, H. Near-infrared (NIR)-reflectance in insects–phenetic studies of 181 species. Entomologie heute 24, 183–215 (2012).
Bajjouk, T. et al. Detection of changes in shallow coral reefs status: Towards a spatial approach using hyperspectral and multispectral data. Ecol. Ind. 96, 174–191. https://doi.org/10.1016/j.ecolind.2018.08.052 (2019).
Google Scholar
Chennu, A., Faber, P., De’ath, G., de Beer, D. & Fabricius, K. E. A diver-operated hyperspectral imaging and topographic surveying system for automated mapping of benthic habitats. Sci. Rep. 7, 1–12. https://doi.org/10.1038/s41598-017-07337-y (2017).
Google Scholar
Parsons, M., Bratanov, D., Gaston, K. J. & Gonzalez, F. UAVs, hyperspectral remote sensing, and machine learning revolutionizing reef monitoring. Sensors 18, 2026. https://doi.org/10.3390/s18072026 (2018).
Google Scholar
Dumke, I. et al. Underwater hyperspectral imaging as an in situ taxonomic tool for deep-sea megafauna. Sci. Rep. 8, 1–11. https://doi.org/10.1038/s41598-018-31261-4 (2018).
Google Scholar
Akkaynak, D., Siemann, L. A., Barbosa, A. & Mathger, L. M. Changeable camouflage: How well can flounder resemble the colour and spatial scale of substrates in their natural habitats?. R. Soc. Open Sci. 4, 160824. https://doi.org/10.1098/rsos.160824 (2017).
Google Scholar
Chiao, C. C., Wickiser, J. K., Allen, J. J., Genter, B. & Hanlon, R. T. Hyperspectral imaging of cuttlefish camouflage indicates good color match in the eyes of fish predators. Proc. Natl. Acad. Sci. USA. 108, 9148–9153. https://doi.org/10.1073/pnas.1019090108 (2011).
Google Scholar
Hebert, P. D. & Gregory, T. R. The promise of DNA barcoding for taxonomy. Syst. Biol. 54, 852–859 (2005).
Google Scholar
Fricke, R., Eschmeyer, W. N. & Van de Laan, R. Eschmeyer’s Catalog of Fishes: Genera, Species, References. http://researcharchive.calacademy.org/research/ichthyology/catalog/fishcatmain.asp (2019).
Orti, G., Sivasundar, A., Dietz, K. & Jégu, M. Phylogeny of the Serrasalmidae (Characiformes) based on mitochondrial DNA sequences. Genet. Mol. Biol. 31, 343–351 (2008).
Google Scholar
Thompson, A. W., Bentancur-R, R., López-Fernández, H. & Orti, G. A time-calibrated, multi-locus phylogeny of piranhas and pacus (Characiformes: Serrasalmidae) and a comparison of species tree methods. Mol. Phylogenet. Evol. 81, 242–257 (2014).
Google Scholar
Machado, V. N. et al. One thousand DNA barcodes of piranhas and pacus reveal geographic structure and unrecognised diversity in the Amazon. Sci. Rep. 8, 1–12. https://doi.org/10.1038/s41598-018-26550-x (2018).
Google Scholar
Winemiller, K. O. et al. Balancing hydropower and biodiversity in the Amazon, Congo, and Mekong. Science 351, 128–129. https://doi.org/10.1126/science.aac7082 (2016).
Google Scholar
Huie, J. M., Summers, A. P. & Kolmann, M. A. Body shape separates guilds of rheophilic herbivores (Myleinae: Serrasalmidae) better than feeding morphology. Proc. Acad. Natl. Sci. Phila. 166, 1–15. https://doi.org/10.1635/053.166.0116 (2017).
Google Scholar
Schweikert, L. E., Fitak, R. R., Caves, E. M., Sutton, T. T. & Johnsen, S. Spectral sensitivity in ray-finned fishes: Diversity, ecology and shared descent. J. Exp. Biol. 221, jeb189761. https://doi.org/10.1242/jeb.189761 (2018).
Google Scholar
Stockman, A. & Sharpe, L. T. Spectral sensitivities of the middle- and long-wavelength sensitive cones derived from measurements in observers of known genotype. Vis. Res. 40, 1711–1737 (2000).
Google Scholar
Peichl, L., Behrmann, G. & Kröger, R. H. H. For whales and seals the ocean is not blue: A visual pigment loss in marine mammals. Eur. J. Neurosci. 13, 1520–1528 (2001).
Google Scholar
Kelber, A. Bird colour vision—From cones to perception. Curr. Opin. Behav. Sci. 30, 34–40. https://doi.org/10.1016/j.cobeha.2019.05.003 (2019).
Google Scholar
Chikashige, T. & Iwasaka, M. Magnetically-assembled micro/mesopixels exhibiting light intensity enhancement in the (012) planes of fish guanine crystals. AIP Adv. 8, 056704. https://doi.org/10.1063/1.5006135 (2018).
Google Scholar
Churnside, J. H. & McGillivary, P. A. Optical-properties of several pacific fishes. Appl. Opt. 30, 2925–2927. https://doi.org/10.1364/ao.30.002925 (1991).
Google Scholar
Funt, N., Palmer, B. A., Weiner, S. & Addadi, L. Koi fish-scale iridophore cells orient guanine crystals to maximize light reflection. ChemPlusChem 82, 914–923. https://doi.org/10.1002/cplu.201700151 (2017).
Google Scholar
Gur, D., Leshem, B., Oron, D., Weiner, S. & Addadi, L. The structural basis for enhanced silver reflectance in Koi fish scale and skin. J. Am. Chem. Soc. 136, 17236–17242. https://doi.org/10.1021/ja509340c (2014).
Google Scholar
Lythgoe, J. N. & Shand, J. Changes in spectral reflections from the iridophores of the neon tetra. J. Physiol. 325, 23–000. https://doi.org/10.1113/jphysiol.1982.sp014132 (1982).
Google Scholar
Correa, S. B. & Winemiller, K. O. Niche partitioning among frugivore fishes in response to fluctuating resources in Amazonian floodplain forest. Ecology 95, 210–224 (2014).
Google Scholar
Van Nynatten, A., Bloom, D., Chang, B. S. W. & Lovejoy, N. R. Out of the blue: Adaptive visual pigment evolution accompanies Amazon invasion. Biol. Lett. 11, 20150349. https://doi.org/10.1098/rsbl.2015.0349 (2015).
Google Scholar
Shawkey, M. D. & D’Alba, L. Interactions between colour-producing mechanisms and their effects on the integumentary colour palette. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160536. https://doi.org/10.1098/rstb.2016.0536 (2017).
Google Scholar
Jordan, R. et al. Ultraviolet reflectivity in three species of lake Malawi rock-dwelling cichlids. J. Fish Biol. 65, 876–882. https://doi.org/10.1111/j.1095-8649.2004.00483.x (2004).
Google Scholar
Wilkins, L., Marshall, N. J., Johnsen, S. & Osorio, D. Modelling colour constancy in fish: Implications for vision and signalling in water. J. Exp. Biol. 219, 1884–1892. https://doi.org/10.1242/jeb.139147 (2016).
Google Scholar
Andrade, M. C., Fitzgerald, D. B., Winemiller, K. O., Barbosa, P. S. & Giarrizzo, T. Trophic niche segregation among herbivorous serrasalmids from rapids of the lower Xingu River, Brazilian Amazon. Hydrobiologia 829, 265–280. https://doi.org/10.1007/s10750-018-3838-y (2019).
Google Scholar
Rocha, L. A. et al. Specimen collection: An essential tool. Science 344, 814. https://doi.org/10.1126/science.344.6186.814 (2014).
Google Scholar
Alberch, P. Museums, collections and biodiversity inventories. Trends Ecol. Evol. 8, 372–375 (1993).
Google Scholar
Page, L. M., MacFadden, B. J., Fortes, J. A., Soltis, P. S. & Riccardi, G. Digitization of biodiversity collections reveals biggest data on biodiversity. Bioscience 65, 841–842 (2015).
Google Scholar
Peterson, A. T., Soberon, J. & Krishtalka, L. A global perspective on decadal challenges and priorities in biodiversity informatics. BMC Ecol. 15, 15 (2015).
Google Scholar
Singer, R. A., Ellis, S. & Page, L. M. Awareness and use of biodiversity collections by fish biologists. J. Fish Biol. 96, 297–306. https://doi.org/10.1111/jfb.14167 (2020).
Google Scholar
Hoeksema, B. W. et al. Unforeseen importance of historical collections as baselines to determine biotic change of coral reefs: The Saba Bank case. Mar. Ecol. 32, 135–141. https://doi.org/10.1111/j.1439-0485.2011.00434.x (2011).
Google Scholar
Stein, E. D., Martinez, M. C., Stiles, S., Miller, P. E. & Zakharov, E. V. Is DNA barcoding actually cheaper and faster than traditional morphological methods: Results from a survey of freshwater bioassessment efforts in the United States?. PLoS ONE 9, e95525 (2014).
Google Scholar
Johansen, V. E., Onelli, O. D., Steiner, L. M. & Vignolini, S. in Functional Surfaces in Biology III, Vol. 10 (eds Gorb, S. N. & Gorb, E. V.) 53–89 (Springer, 2017).
Wainwright, D. K., Lauder, G. & Weaver, J. C. Imaging biological surface topography in situ and in vivo. Methods Ecol. Evol. 8, 1626–1638. https://doi.org/10.1111/2041-210x.12778 (2017).
Google Scholar
Andrade, M. C., Giarrizzo, T. & Jégu, M. Tometes camunani (Characiformes: Serrasalmidae), a new species of phytophagous fish from the Guiana Shield, Rio Trombetas Basin, Brazil. Neotrop. Ichthyol. 11, 297–306 (2013).
Google Scholar
Généralités, I. Gery, J. Poissons characoïdes des Guyanes. II. Famille des Serrasalmidae. Zoologische Verhandelingen 122, 1–250 (1972).
Jegu, M. & Dos Santos, G. M. Le genre Serrasalmus (Pisces, Serrasalmidae) dans le bas Tocantins (Brésil, Parà), avec la description d’une espèce nouvelle, S. geryi, du bassin Araguaia-Tocantins. Revue d’Hydrobiologie Tropicale 21, 239–274 (1988).
Kolmann, M. A. et al. Phylogenomics of piranhas and pacus (Serrasalmidae) uncovers how dietary convergence and parallelism obfuscate traditional morphological taxonomy. Syst. Biol. 70(3), 576–592 (2021).
Google Scholar
Feller, K. D., Jordan, T. M., Wilby, D. & Roberts, N. W. Selection of the intrinsic polarization properties of animal optical materials creates enhanced structural reflectivity and camouflage. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160336. https://doi.org/10.1098/rstb.2016.0336 (2017).
Google Scholar
Gur, D., Palmer, B. A., Weiner, S. & Addadi, L. Light manipulation by guanine crystals in organisms: Biogenic scatterers, mirrors, multilayer reflectors and photonic crystals. Adv. Funct. Mater. 27, 1603514. https://doi.org/10.1002/adfm.201603514 (2017).
Google Scholar
Elmer, K., Soffer, R., Arroyo-Mora, J. P. & Kalacska, M. ASDToolkit: A novel MATLAB processing toolbox for ASD field spectroscopy data. Data 5, 96. https://doi.org/10.3390/data5040096 (2020).
Google Scholar
Kruse, F. A. et al. The spectral image-processing system (SIPS)—Interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 44, 145–163. https://doi.org/10.1016/0034-4257(93)90013-n (1993).
Google Scholar
Cooksey, C., Tsai, B. K. & Allen, D. A collection and statistical analysis of skin reflectance signatures for inherent variability over the 250 nm to 2500 nm spectral range. Proc. SPIE 9082, 908201–908206. https://doi.org/10.1117/12.2053604 (2014).
Google Scholar
Manolakis, D., Marden, D. & Shaw, G. A. Hyperspectral image processing for automatic target detection applications. Lincoln Lab. J. 14, 79–116 (2003).
Manolakis, D., Lockwood, R., Cooley, T. & Jacobson, J. Is There a Best Hyperspectral Detection Algorithm? Vol. 7334 (SPIE, 2009).
van der Heijden, F., Duin, R., de Ridder, D. & Tax, D. Classification, Parameter Estimation and State Estimation, an Engineering Approach using Matlab (Wiley, 2004).
Google Scholar
Johnson, M. K. & Adelson, E. H. In Cvpr: 2009 IEEE Conference on Computer Vision and Pattern Recognition, Vols 1–4, 1070–1077 (2009).
Harmon, L. J., Weir, J. T., Brock, C. D., Glor, R. E. & Challenger, W. GEIGER: Investigating evolutionary radiations. Bioinformatics 24, 129–131 (2008).
Google Scholar
Wainwright, D. K. & Lauder, G. V. Three-dimensional analysis of scale morphology in bluegill sunfish, Lepomis marochirus. Zoology 119, 182–195 (2016).
Google Scholar
Source: Ecology - nature.com
