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Fig. 11 in The Ecology And Migrations Of Sea Turtles 8. Tests Of The Developmental Habitat Hypothesis

Fig. 11. Geographic distribution of 88 foreign tag returns (numbers in circles) through 2005 of Chelonia mydas originally tagged in Bermuda. The star indicates the only known nesting by a C. mydas tagged in Bermuda. This turtle was tagged in November 1992 and nested near Cancun, Mexico, during the summer of 2006.

Fig. 4 in The Ecology And Migrations Of Sea Turtles 8. Tests Of The Developmental Habitat Hypothesis

Fig. 4. Study site at Zapatilla Cays, Bocas del Toro Province, Panama. Solid circles indicate sites sampled with nets between 1990 and 2005. Point O' Reef, Peachy, and Comfort are in the Caribbean Sea and were fished with ''ocean sets.'' All remaining sites are within Chiriqui Lagoon and were fished with standard set nets (see Methods).

Fig. 6 in The Ecology And Migrations Of Sea Turtles 8. Tests Of The Developmental Habitat Hypothesis

Fig. 6. Carapace length (SCLmin), weight, and maturity status for 131 Chelonia mydas from Bermuda that were examined laparoscopically. Minimum adult size, indicated by the dashed line, is based on laparoscopy of 178 C. mydas in Bocas del Toro, Panama (this study; Meylan and Meylan, unpubl. data). For explanation of stages, see Methods.

Fig. 10 in The Ecology And Migrations Of Sea Turtles 8. Tests Of The Developmental Habitat Hypothesis

Fig. 10. Satellite transmission histories of four large subadult Chelonia mydas from Bermuda. Argos locations displayed are the minimum redundant distance (MRD) output of the Douglas Argos filter algorithm. This output includes points that have a consecutive or near-consecutive neighbor within 6 km. Adaptive kernel density percent volume contours, calculated from the MRD dataset, are also displayed. A, Locations (n 5 103) and volume contours for PTT 07665 (70.4 cm SCLmin). B, Locations (n 5 103) and volume contours for PTT 11676 (72.0 cm SCLmin). C, Locations (n 5 141) and volume contours for PTT 11677 (71.8 cm SCLmin). D, Locations (n 5 253) and volume contours for PTT 60810 (70.0 cm SCLmin).

Fig. 7 in The Ecology And Migrations Of Sea Turtles 8. Tests Of The Developmental Habitat Hypothesis

Fig. 7. Average number of Chelonia mydas caught per set of the entrapment net at Bermuda by month. Mean and one standard deviation are shown for all sets from January 1992–August 2005. Sample size above each bar is for the number of sets made during each month.

Fig. 8 in The Ecology And Migrations Of Sea Turtles 8. Tests Of The Developmental Habitat Hypothesis

Fig. 8. The number of Chelonia mydas caught at Bermuda per set of the entrapment net as a function of water temperature. Data shown are for 258 samples from January 1992–August 2005.

opencc-by-4.0Feb 2010View details →

Fig. 1 in Turtle cleaners: reef fishes foraging on epibionts of sea turtles in the tropical Southwestern Atlantic, with a summary of this association type

Fig. 1. Reef fishes cleaning sea turtles' hard and soft parts in the Southwestern Atlantic. A porkfish (Anisotremus virginicus) and a group of blue tangs (Acanthurus coeruleus) feed on epibionts on the shell of a moving hawksbill turtle (Eretmochelys imbricata); a barely visible doctorfish (Acanthurus chirurgus) nibbles at the posterior portion of the turtle's shell, and two blue tangs nibble at the left hind limb (a). Photo by M. Granville. One Zelinda's parrotfish (Scarus zelindae) and three blue tangs feed on algae growth on the shell of a male loggerhead turtle (Caretta caretta) near a shipwreck; two Spanish hogfishes (Bodianus rufus) also inspect the turtle (b). Photo by Z. Matheus. Four Spanish hogfish inspect and forage on epibionts on the shell of the same loggerhead turtle; one blue tang and one Zelinda's parrotfish also "escort" the slowly moving turtle (c). Photo by Z. Matheus. A green turtle (Chelonia mydas) remain motionless on the bottom, while a Brazilian blenny (Ophioblennius trinitatis) forages on algae growth on the left lateral portion of the shell; a few smallmouth grunts (Haemulon chrysargyreum) also capitalize upon this situation, and nibble at the turtle's shell (d). Photo by C. Sazima. A sergeant major (Abudefduf saxatilis) nibbles at an algae patch on the anterior part of the shell of a posing and hovering green turtle (e). Photo by Z. Matheus. The herbivorous Rocas damselfish (Stegastes rocasensis) nibbles at the right hind limb of a green turtle posing near algae turfs tended by this damselfish (f). Photo by Z. Matheus.

dryad40/100

Stranded marine mammals, sea turtles and seabirds in Paraná and Santa Catarina from August 2018 to August 2023

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publicDec 2023View details →

dryad40/100

Individual plasticity in response to rising sea temperatures contributes to an advancement in green turtle nesting phenology

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publicJan 2025View details →

opencc-zeroOct 2019View details →

Strandings of marine mammals, sea turtles and seabirds along the northern São Paulo coast, Brazil, from 2015 to 2018.

To assess the potential impacts on seabirds, turtles and marine mammals from oil and gas production from the oceanic Pre-Salt province at Brazil's Santos Basin, the Brazilian environmental agency (IBAMA) required PETROBRAS, the main oil company in the basin, to implement the "Programa de Monitoramento de Praias da Bacia de Santos" (Santos Basin Beach Monitoring Program - PMP-BS). Since 2015 PMP-BS has been operating along the states of Santa Catarina, Paraná, São Paulo and Rio de Janeiro, to collect biological data from live and dead stranded animals. This dataset comprises the first three years of monitoring (September 2015 to August 2018) along the northern São Paulo State coastline, from 23°22'17.366" S 44°47'0.479" W to 23°45'24.046" S 45°50'23.335" W. During this period, 5450 animals of 47 species were recorded, 82.11% of which were dead and 17.89% alive when first observed. This dataset represents the first high-intensity monitoring effort in the area and is essential to establish baselines for future work that seek to better understand the impacts of human activities on marine ecosystems.

opencc-zeroOct 2019View details →

Strandings of marine mammals, sea turtles and seabirds along central Santa Catarina coast, Brazil, from 2015 to 2018.

To assess the potential impacts on seabirds, turtles and marine mammals from oil and gas production from the oceanic Pre-Salt province at Brazil's Santos Basin, the Brazilian environmental agency (IBAMA) required PETROBRAS, the main oil company in the basin, to implement the "Programa de Monitoramento de Praias da Bacia de Santos" (Santos Basin Beach Monitoring Program - PMP-BS). Since 2015 PMP-BS has been operating along the states of Santa Catarina, Paraná, São Paulo and Rio de Janeiro, to collect biological data from live and dead stranded animals. This dataset comprises the first three years of monitoring (September 2015 to August 2018) along Santa Catarina state's central coast, from 27°23'7.357" S 48°26'2.519" W to 27°50'24.231" S 48°33'48.706" W. During this period, 5497 animals of 46 species were recorded, 86.90% of which were dead and 13.10% alive when first observed This dataset represents the first high-intensity monitoring effort in the area and is essential to establish baselines for future work that seek to better understand the impacts of human activities on marine ecosystems.

opencc-zeroOct 2019View details →

Strandings of marine mammals, sea turtles and seabirds along the central-south coast of São Paulo, Brazil, from 2015 to 2018.

To assess the potential impacts on seabirds, turtles and marine mammals from oil and gas production from the oceanic Pre-Salt province at Brazil's Santos Basin, the Brazilian environmental agency (IBAMA) required PETROBRAS, the main oil company in the basin, to implement the "Programa de Monitoramento de Praias da Bacia de Santos" (Santos Basin Beach Monitoring Program - PMP-BS). Since 2015 PMP-BS has been operating along the states of Santa Catarina, Paraná, São Paulo and Rio de Janeiro, to collect biological data from live and dead stranded animals. This dataset comprises the first three years of monitoring (September 2015 to August 2018) along the central-south São Paulo state coastline, from 24°1'1.248" S 46°23'52.026" W to 24°26'49.147" S 47°4'49.505" W. During this period, 3790 animals of 47 species were recorded, 94.88% of which were dead and 5.12% alive when first observed. This dataset represents the first high-intensity monitoring effort in the area and is essential to establish baselines for future work that seek to better understand the impacts of human activities on marine ecosystems.

opencc-zeroDec 2018View details →

Data from: A convolutional neural network for detecting sea turtles in drone imagery

1. Marine megafauna are difficult to observe and count because many species travel widely and spend large amounts of time submerged. As such, management programs seeking to conserve these species are often hampered by limited information about population levels. 2. Unoccupied aircraft systems (UAS, aka drones) provide a potentially useful technique for assessing marine animal populations, but a central challenge lies in analyzing the vast amounts of data generated in the images or video acquired during each flight. Neural networks are emerging as a powerful tool for automating object detection across data domains and can be applied to UAS imagery to generate new population-level insights. To explore the utility of these emerging technologies in a challenging field setting, we used neural networks to enumerate olive ridley turtles (Lepidochelys olivacea) in drone images acquired during a mass-nesting event on the coast of Ostional, Costa Rica. 3. Results revealed substantial promise for this approach; specifically, our model detected 8% more turtles than manual counts while effectively reducing the manual validation burden from 2,971,554 to 44,822 image windows. Our detection pipeline was trained on a relatively small set of turtle examples (N=944), implying that this method can be easily bootstrapped for other applications, and is practical with real-world UAS datasets. 4. Our findings highlight the feasibility of combining UAS and neural networks to estimate population levels of diverse marine animals and suggest that the automation inherent in these techniques will soon permit monitoring over spatial and temporal scales that would previously have been impractical.

opencc-zeroNov 2023View details →

Data from: Climate warming and sea turtle sex ratios across the globe

Climate warming and the feminisation of populations due to temperature-dependent sex determination may threaten sea turtles with extinction. To identify sites of heightened risk, we examined sex ratio data and patterns of climate change over multiple decades for 64 nesting sites spread across the globe. Over the last 62 years the mean change in air temperature was 0.85 °C per century (SD = 0.65 °C, range = -0.53 to +2.5 °C, n = 64 nesting sites). Temperatures increased at 40 of the 64 study sites. Female-skewed hatchling or juvenile sex ratios occurred at 57 of the 64 sites, with skews > 90% female at 17 sites. We did not uncover a relationship between the extent of warming and sex ratio (r62 = -0.03, p = 0.802, n = 64 nesting sites). Hence, our results suggest that female-hatchling sex ratio skews are not simply a consequence of recent warming but have likely persisted at some sites for many decades. So other factors aside from recent warming must drive these variations in sex ratios across nesting sites, such as variations in nesting behaviour (e.g., nest depth), substrate (e.g., sand albedo), shading available and rainfall patterns. While overall across sites recent warming is not linked to hatchling sex ratio, at some sites there is both is a high female skew and high warming, such as Raine Island (Australia; 99% female green turtles; 1.27 °C warming per century), nesting beaches in Cyprus (97.1% female green turtles; 1.68 °C warming per century), and in the Dutch Caribbean (St Eustatius; 91.5% female leatherback turtles; 1.15 °C warming per century). These may be among the first sites where management intervention is needed to increase male production. Continued monitoring of sand temperatures and sex ratios are recommended to help identify when high incubation temperatures threaten population viability.

opencc-zeroMar 2024View details →

Green and hawksbill sea turtle nesting in the Gulf of Guinea: A 9-year survey

Sea turtles are critical components of marine ecosystems, and their conservation is important for Ocean Governance and Global Planet Health. However, there is limited knowledge of their ecology in the Gulf of Guinea. To fill this knowledge gap, this study presents the first integrative assessment of green and hawksbill turtles in the region, combining nesting area surveys over 9 years, and telemetry data, to offer insights into these population dynamics, and behaviours including nesting preferences, morphological and reproductive parameters, diving patterns and inter-nesting core-use areas. Green turtles are likely making a recovery in São Tomé, potentially driven by sustained conservation efforts. In contrast, the status of the hawksbill turtle remains less clear. There are preliminary indications of recovery, but we interpret this cautiously. Coupled with satellite tracking, this study estimated that 482 to 736 green turtles, and 135 to 217 hawksbills, nest on the beaches of São Tomé. Their movements overlap significantly with a proposed Marine Protected Area (MPA), which suggests they may be well placed for conservation if managed appropriately. However, the presence of artisanal fisheries and emerging threats, such as sand mining and unregulated tourism, highlight the urgent need for robust management strategies that align global conservation objectives with local socioeconomic realities. This study significantly enhances our understanding of the ecology and conservation needs of the green and hawksbill turtles in the Gulf of Guinea. The insights gleaned here can contribute to the development of tailored conservation strategies that benefit these populations and the ecosystem services upon which they depend.

opencc-by-4.0Mar 2024View details →

Results from simulated drift trajectories of driting Fish Aggregating Devices used in Figures for paper Escalle et al., (2024) Simulating drifting fish aggregating device trajectories to identify potential interactions with endangered sea turtles

Escalle et al., (2024) Simulating drifting fish aggregating device trajectories to identify potential interactions with endangered sea turtles <a href="../doi/10.5281/zenodo.10815559">https://zenodo.org/doi/10.5281/zenodo.10815559</a> Results data description The above doi contains simulation output data generated from passive drift simulations of drifting FADs in the Pacific ocean. We refer the reader to the main text of the paper for details and definitions of the different zones and simulation experiments. All data files are matrices stored in comma-delimited files (.csv), where the first provides the column names of the matrix, and the first column provides the row names. The first cell in row 1, column 1 is simply a placement value that describes the nature of the matrix.  When this value is a hash symbol (#), it denotes that the matrix represents a spatial density of virtual particles under a particular drift scenario and over a particular period of time. Column names provide the latitude indices of each cell, and row names are the longitude indices. Values are the proportion of all particules in the domain that passed through this cell, during the drift-time window of this results file (see below). When the first cell value is a hash followed by a code (e.g. # EqZ), it denotes that the file represents a connectivity matrix between the zones given by the code (e.g. Equatorial Zones EqZ or Fishing Zones FZ) and defined in the row names of the matrix, and the turtle zones defined by the column names. Values are the proportion of particules beginning the zone defined by the row name at the start of the simulation, which are now present in the zone defined by the column name. Files name follow a convention describing the deployment and drift-time scenario they represent. For spatial density matrices: [Origin Zones]_[Subset]_Density_[Drift Time]_....csv For connectivity/transition matrices: [Origin Zones]_[Drift Time]_[Number of Simulations Run]_TransitionMatrix_....csv

opencc-by-4.0May 1988View details →

Fig. 2 in SYNOPSIS OF THE BIOLOGICAL DATA ON THE LOGGERHEAD SEA TURTLE CARETTA CARETTA (LINNAEUS 1758)

Fig. 2. Ventral view of plastron of adult loggerhead (Pritchard and Trebbau 1984).

opencc-by-4.0May 1988View details →

Fig. 4 in SYNOPSIS OF THE BIOLOGICAL DATA ON THE LOGGERHEAD SEA TURTLE CARETTA CARETTA (LINNAEUS 1758)

Fig. 4. Forearm of adult loggerhead (Romer 1956).

opencc-by-4.0May 1988View details →

Fig. 1 in SYNOPSIS OF THE BIOLOGICAL DATA ON THE LOGGERHEAD SEA TURTLE CARETTA CARETTA (LINNAEUS 1758)

Fig. 1. Dorsal view of carapace of adult loggerhead (Deraniyagala 1939).