Photo-identification as a technique for recognition of individual fish: a test with the freshwater armored catfish Rineloricaria aequalicuspis Reis &amp; Cardoso, 2001 (Siluriformes: Loricariidae)

Dala‐Corte, Renato Bolson; Moschetta, Júlia Bortolini; Becker, Fernando Gertum

doi:10.1590/1982-0224-20150074

Cited by 26 publications

(21 citation statements)

References 34 publications

(48 reference statements)

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“…This approach was pioneered for fluke identification in marine mammals (Adams, Speakman, Zolman, & Schwacke, ; Beekmans, Whitehead, Huele, Steiner, & Steenbeek, ; Gilman, Hupman, Stockin, & Pawley, ) and has since been applied on a wide range of taxa, from zebras ( Equus grevyi ) (Crall, Stewart, Berger‐Wolf, Rubenstein, & Sundaresan, ), to elephants ( L. cyclotis ) (Ardovini, Cinque, & Sangineto, ), and box turtles ( Terrapene carolina ) (Cross, Lipps, Sapak, Tobin, & Root, ). These methods are effective in identifying animals with complex markings, such as giraffes ( Giraffa camelopardalis ) (Bolger, Morrison, Vance, Lee, & Farid, ), whale sharks ( Rhincodon typus ) (Arzoumanian et al., ) and catfish ( Rineloricaria aequalicuspis ) (Dala‐Corte, Moschetta, & Becker, ), and range from completely automated (Town, Marshall, & Sethasathien, ), to involving human feedback during matching (Duyck et al., ). Crall et al.…”

Section: Identitymentioning

confidence: 99%

A computer vision for animal ecology

Weinstein

2017

Journal of Animal Ecology

300

257

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A central goal of animal ecology is to observe species in the natural world. The cost and challenge of data collection often limit the breadth and scope of ecological study. Ecologists often use image capture to bolster data collection in time and space. However, the ability to process these images remains a bottleneck. Computer vision can greatly increase the efficiency, repeatability and accuracy of image review. Computer vision uses image features, such as colour, shape and texture to infer image content. I provide a brief primer on ecological computer vision to outline its goals, tools and applications to animal ecology. I reviewed 187 existing applications of computer vision and divided articles into ecological description, counting and identity tasks. I discuss recommendations for enhancing the collaboration between ecologists and computer scientists and highlight areas for future growth of automated image analysis.

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Section: Identitymentioning

confidence: 99%

A computer vision for animal ecology

Weinstein

2017

Journal of Animal Ecology

300

257

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“…Tagging and photo-identification are among the most used tools for markrecapture studies. Photo-identification allows individual recognition of animal species based on natural marks and is an interesting alternative to other more stressful artificial tagging/marking techniques (Dala-Corte et al, 2016). Potential use of photo-identification for individual fish recognition has shown to be a feasible alternative to behaviour studies on freshwater fish.…”

Section: Discussionmentioning

confidence: 99%

Fish movement in an Atlantic Forest stream

et al. 2018

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Given the importance of fish movement to the dynamics and maintenance of stream dwelling fish communities from the Atlantic Forest, we analysed patterns of fish movement in a coastal stream from Southeastern Brazil, using mark-recapture technique. Displacement distance of each species were presented and discussed considering seasonal (rainy and dry) and body size patterns. We marked 10 species along the stream and recaptured 440 (34.6%) of the 1,270 marked fishes. The species with significant number of upstream moving individuals were Astyanax janeiroensis, Characidium interruptum, Astyanax hastatus, Parotocinclus maculicauda and Awaous tajasica. Only Pimelodella lateristriga presented significant differences between resident and moving individuals. Characidium interruptum and A. tajasica demonstrated greater downstream and upstream movement, respectively, moving up to 2,100 m. Even after controlling for species identity we found no significant correlation between fish length and individual displacement distance. Fishes moved longer distances during the rainy season, in accordance to the breeding season. Patterns of fish movement were in agreement to life-history traits of many of the studied species and can be reflecting specific behaviour and morphologies.Keywords: Coastal stream, Migration, Reproduction, Stream dwelling fish.Considerando-se a importância do movimento de peixes para a dinâmica e manutenção das comunidades de peixes de riachos da mata Atlântica, nós analisamos os padrões de movimento de peixes de um riacho costeiro do sudeste do Brasil, usando a técnica da marcação e recaptura. Avaliamos a distância do deslocamento de cada espécie e também se os padrões de movimento apresentavam alguma relação com a estação do ano (chuva e seca) e o tamanho do corpo. Marcamos 10 espécies ao longo do riacho e recapturamos 400 (34.6%) dos 1.270 peixes marcados. As espécies com número significativo de indivíduos se movendo para cima foram Astyanax janeiroensis, Characidium interruptum, Astyanax hastatus, Parotocinclus maculicauda e Awaous tajasica. Apenas Pimelodella lateristriga apresentou diferenças significativas entre os indivíduos residentes e em movimento. Characidium interruptum e A. tajasica foram as espécies com maior movimento tanto para baixo como para cima, respectivamente, se movendo até mais de 2.100 m. Mesmo após controlar a identidade das espécies, não registramos correlação significativa entre o comprimento e a distância individual do deslocamento. Registramos maiores densidades de peixes se movendo para montante durante a época de chuvas, em coincidência com a estação reprodutiva. Os padrões de movimento registrados estão de acordo com os componentes da história de vida de várias das espécies estudadas, e podem estar refletindo componentes morfológicos e comportamentais específicos.Palavras-chave: Migração, Peixes de riacho, Riacho costeiro, Reprodução.

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“…Ever since the release of the program, it has been used for many species of animals, the majority of them with spots (Bolger et al 2012, Bendik et al 2013, Elgue et al 2014, Dala-Corte et al 2016). This study is uncommon in that it is testing the program on a linear pattern.…”

Section: Discussionmentioning

confidence: 99%

Treefrog lateral line as a mean of individual identification through visual and software assisted methodologies

et al. 2017

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Background: Ecological research often requires monitoring of a specific individual over an extended period of time. To enable non-invasive re-identification, consistent external marking is required. Treefrogs possess lateral lines for crypticity. While these patterns decrease predator detection, they also are individual specific patterns. In this study, we tested the use of lateral lines in captive and wild populations of Dryophytes japonicus as natural markers for individual identification. For the purpose of the study, the results of visual and software assisted identifications were compared. Results: In normalized laboratory conditions, a visual individual identification method resulted in a 0.00 rate of false-negative identification (RFNI) and a 0.0068 rate of false-positive identification (RFPI), whereas Wild-ID resulted in RFNI = 0.25 and RFNI = 0.00. In the wild, female and male data sets were tested. For both data sets, visual identification resulted in RFNI and RFPI of 0.00, whereas the RFNI was 1.0 and RFPI was 0.00 with Wild-ID. Wild-ID did not perform as well as visual identification methods and had low scores for matching photographs. The matching scores were significantly correlated with the continuity of the type of camera used in the field. Conclusions: We provide clear methodological guidelines for photographic identification of D. japonicus using their lateral lines. We also recommend the use of Wild-ID as a supplemental tool rather the principal identification method when analyzing large datasets.

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Photo-identification as a technique for recognition of individual fish: a test with the freshwater armored catfish Rineloricaria aequalicuspis Reis & Cardoso, 2001 (Siluriformes: Loricariidae)

Cited by 26 publications

References 34 publications

A computer vision for animal ecology

A computer vision for animal ecology

Fish movement in an Atlantic Forest stream

Treefrog lateral line as a mean of individual identification through visual and software assisted methodologies

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