Stability of facial scale patterns on green sea turtles Chelonia mydas over time: A validation for the use of a photo-identification method

Carpentier, Alice S.; Jean, Claire; Barret, Mathieu; Chassagneux, Agathe; Ciccione, Stéphane

doi:10.1016/j.jembe.2015.12.003

Cited by 32 publications

(20 citation statements)

References 29 publications

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“…Other studies have re-identified individuals 3, 4 and 6 and 11 years later (Reisser et al, 2008;Jean et al, 2010;Goncalves and Loureiro, 2013;Carpentier et al, 2016, respectively). For greens there is some evidence to suggest facial scale stability exists, although pigmentation patterns can change (Féliz et al, 2010;Araujo et al, 2016;Carpentier et al, 2016). Long-term photo series from known individuals are required to quantify the rates and implications of changes to facial scales, but we think it is reasonable to assume that facial scutes remain stable over the 5-year period discussed here.…”

Section: Photo-identification and Population Structurementioning

confidence: 93%

“…Left and right facial profiles of each animal were visually inspected and classified using a threedigit code to describe each scute on the turtle's head, posterior from the eye to the neck, and from the line of the upper jaw to the top of the turtle's head, following Jean et al (2010) (this process required < 2 min per profile). To our knowledge, the recognition software used in TORSOOI has only been tested within green and hawksbill turtles (Jean et al, 2010;Carpentier et al, 2016). Suitability for loggerheads, was tested by comparing manual visual identification against TORSOOI matching.…”

Section: Photo-identification Analysismentioning

confidence: 99%

“…An additional technique which could yield valuable information in foraging grounds is photographic identification of individuals. The low cost, low-tech, and non-invasive nature of Photo-ID in contrast to other techniques (Fastloc, Argos or acoustic tagging), has propelled its adoption as a tool for in-water monitoring of animals (Schofield et al, 2008;Jean et al, 2010;Dunbar et al, 2014;Carpentier et al, 2016), particularly in developing countries where application of alternative technologies was hindered by resource limitations. Photo-ID can potentially provide insight into use of neritic coastal areas by sea turtles as foraging grounds or migratory corridors and may further enhance understanding on late stage juveniles and ontogenetic space use.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Distribution and Residency of Green and Loggerhead Sea Turtles Using Coastal Reef Habitats in Southern Mozambique

et al. 2017

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Sea turtles spend the majority of their immature and adult lives in foraging grounds, yet few studies have examined their abundance and condition in these areas when compared to more accessible nesting beach habitats. Here, a 5-year dive log, photo-identification (photo-ID) and surface encounter datasets were used to investigate the abundance, individual movements and distribution of sea turtles along 40 km of coastal reefs in southern Mozambique. A generalized linear model (GLM) was constructed with turtle sightings as the response variable. Habitat type, year and day of the year, as well as underwater visibility, were significant predictors of turtle sightings. However, only 8% of the total variance was explained by the model, indicating that other variables have a significant influence on turtle movement and distribution. Photo-ID differentiated 22 individual green turtles Chelonia mydas and 42 loggerhead turtles Caretta caretta from 323 photo-ID encounters. A majority (64%) of the photos could be used to identify the individual. Although residency times of up to 1152 days were calculated for juvenile green turtles, a low overall resighting rate indicates that individual turtles either had large home ranges or were transient to the area. Surface encounter data revealed a preference for nearshore shallow waters and an increased abundance close to reef systems. Sea turtles' preferences for shallow, nearshore habitats are likely to increase the encounter risk with opportunistic and targeted artisanal fishers who catch sea turtles.

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Section: Photo-identification and Population Structurementioning

confidence: 93%

Section: Photo-identification Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial Distribution and Residency of Green and Loggerhead Sea Turtles Using Coastal Reef Habitats in Southern Mozambique

et al. 2017

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“…However, photographic recognition also has some limitations. First, this method only works well for species that have a defined pattern but its application is challenging in species having uniform colouration (Carafa and Biondi 2004;Martin-Smith 2011;Rocha et al 2013;Carpentier et al 2016). In addition, it does not allow monitoring the development of young individuals into adults in species where the pattern changes during ontogenesis (Lanza et al 2006).…”

Section: Introductionmentioning

confidence: 99%

On the stability of the dorsal pattern of European cave salamanders (genus Hydromantes)

Lunghi¹,

Romeo²,

Mulargia³

et al. 2019

Herpetozoa

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Photographic identification is an emerging method for recognising wild animals. This harmless methodology allows researchers to identify "naturally marked" individuals and therefore study their specific ecology and behaviour. However, before incurring potential data loss, it is recommended to test the methodology on the target species and evaluate the pros and cons. We assessed the reliability of photographic identification in adult Hydromantes salamanders from three species. Specifically, we assessed whether the dorsal pattern of adult salamanders changed over time, thus evaluating its potential use as a reliable marking methodology. We used capture-mark-recapture and controlled conditions (i.e. individuals kept in fauna boxes) to evaluate potential changes in the dorsal pattern of Hydromantes through time. We did not observe any change in the dorsal pattern in the three species during the study period. Photographic identification might be a useful marking technique for these endangered species. However, these animals are usually found in environments generally lacking light and thus, researchers must be careful in setting up proper light conditions to produce suitable pictures for individual identification of Hydromantes.

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“…Due to its fundamental research role, photo-ID is an active research area for many species, e.g. green sea turtles [10], gorillas [11], and dolphins [12]. Much of the imagery used for photo-identification of dwarf minke whales in recent years has come from tourists and crew aboard swim-with whales dive tourism vessels [8].…”

Section: Introductionmentioning

confidence: 99%

Individual Minke Whale Recognition Using Deep Learning Convolutional Neural Networks

Konovalov¹,

Hillcoat²,

Williams³

et al. 2018

GEP

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The only known predictable aggregation of dwarf minke whales (Balaenoptera acutorostrata subsp.) occurs in the Australian offshore waters of the northern Great Barrier Reef in May-August each year. The identification of individual whales is required for research on the whales' population characteristics and for monitoring the potential impacts of tourism activities, including commercial swims with the whales. At present, it is not cost-effective for researchers to manually process and analyze the tens of thousands of underwater images collated after each observation/tourist season, and a large data base of historical non-identified imagery exists. This study reports the first proof of concept for recognizing individual dwarf minke whales using the Deep Learning Convolutional Neural Networks (CNN).The "off-the-shelf" Image net-trained VGG16 CNN was used as the feature-encoder of the per-pixel sematic segmentation Automatic Minke Whale Recognizer (AMWR). The most frequently photographed whale in a sample of 76 individual whales (MW1020) was identified in 179 images out of the total 1320 images provided. Training and image augmentation procedures were developed to compensate for the small number of available images. The trained AMWR achieved 93% prediction accuracy on the testing subset of 36 positive/MW1020 and 228 negative/not-MW1020 images, where each negative image contained at least one of the other 75 whales. Furthermore on the test subset, AMWR achieved 74% precision, 80% recall, and 4% false-positive rate, making the presented approach comparable or better to other state-of-the-art individual animal recognition results.

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Stability of facial scale patterns on green sea turtles Chelonia mydas over time: A validation for the use of a photo-identification method

Cited by 32 publications

References 29 publications

Spatial Distribution and Residency of Green and Loggerhead Sea Turtles Using Coastal Reef Habitats in Southern Mozambique

Spatial Distribution and Residency of Green and Loggerhead Sea Turtles Using Coastal Reef Habitats in Southern Mozambique

On the stability of the dorsal pattern of European cave salamanders (genus Hydromantes)

Individual Minke Whale Recognition Using Deep Learning Convolutional Neural Networks

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