Emily G. Webster

Olive ridley sea turtles ( Lepidochelys olivacea ) exhibit two nesting behaviors: solitary nesting and synchronized mass-nesting, known as arribada , with females able to switch between behaviors rather than being exclusive to one strategy. Although arribadas are thought to be driven by environmental and/or social cues that promote synchronized breeding, it remains unclear to what extent they can shape the spatial and temporal dynamics of nesting olive ridleys. Our study documents how nesting behavior influences the frequency of inter-beach movements and the length of within-season inter-nesting periods (IP). We examined records of 4,273 tagged and 555 recaptured individuals from 2016 to 2019 at Corozalito, a nascent arribada nesting beach on the Costa Rican Pacific, and its three neighboring solitary-nesting beaches. Inter-beach movements were recorded for 99 turtles (17.8%), primarily from solitary-nesting beaches towards Corozalito during arribadas. IPs of females nesting in arribadas were not consistent across seasons while IPs between consecutive solitary nesting events were stable across years and comparable to values reported for other olive ridley rookeries. We suggest that arribadas at Corozalito prompt inter-beach movements of olive ridleys that nest on solitary neighboring beaches, attracting them to the mass nesting location. Additionally, breeding synchrony may extend the IP of turtles that have already laid their first nest by delaying oviposition until the arribada begins. However, this delay appears to be context-dependent, likely influenced by the timing of arribadas and individual nesting histories. These findings contribute to better understanding the processes involved in the formation of arribadas and their associated nesting dynamics.

A detailed understanding of how protected species use their habitats can guide management interventions in areas of high human use. For marine turtles, different food availability and physical habitat characteristics can underpin turtle presence at anthropogenically modified compared to unmodified sites. We develop telemetry‐based habitat models with boosted regression trees to identify the environmental characteristics underpinning foraging habitat suitability for green turtles in the Great Barrier Reef region. We fit models to green turtle Fastloc GPS tracks from both modified and unmodified inshore foraging sites and using pseudo‐absences (simulated correlated random walks). We assess model performance by the ability to predict known foraging areas, true skill statistic, explanatory power (percent deviance explained) and predictive skill (AUC) of the models. We then predict potentially suitable foraging areas for green turtles in the Great Barrier Reef region using the model for unmodified habitats. Our model highlights shallow nearshore environments and midshelf reefs as important foraging areas for green turtles. These areas are likely affected by dynamic floods, development, and turbidity. In 2022, 46.6% of predicted suitable habitat fell within habitat protection zones, and 16.5% in Marine National Park Zones of the Great Barrier Reef Marine Park. A detailed foraging distribution of the species has not previously been compiled at this regional scale. Identifying biophysical drivers of habitat suitability can inform identification of possible foraging habitat in less data rich regions in Australia and overseas. Evaluating changes over time in habitat distribution provides insights into the degree to which broad‐scale environmental changes and anthropogenic activities influence the condition and function of habitats, even within protected area boundaries.

A detailed understanding of how protected species use their habitats can guide management interventions in areas of high human use. For marine turtles, different food availability and physical habitat characteristics can underpin turtle presence at anthropogenically modified compared to unmodified sites. We develop telemetry-based habitat models with boosted regression trees to identify the environmental characteristics underpinning foraging habitat suitability for green turtles in the Great Barrier Reef region. We fit models to green turtle Fastloc GPS tracks from both modified and unmodified inshore foraging sites and using pseudo-absences (simulated correlated random walks). We assess model performance by the ability to predict known foraging areas, true skill statistic, explanatory power (percent deviance explained) and pedictive skill (AUC) of the models. We then predict potentially suitable foraging areas for green turtles in the Great Barrier Reef region using the model for unmodified habitats. Our model highlights shallow nearshore environments and midshelf reefs as important foraging areas for green turtles. These areas are likely affected by dynamic floods, development and turbidity. In 2022, 46.6% of predicted suitable habitat fell within habitat protection zones, and 16.5% in Marine National Park Zones of the Great Barrier Reef Marine Park. A detailed foraging distribution of the species has not previously been compiled at this regional scale. Identifying biophysical drivers of habitat suitability can inform identification of possible foraging habitat in less data rich regions in Australia and overseas. Evaluating changes over time in habitat distribution provides insights into the degree to which broad-scale environmental changes and anthropogenic activities influence the condition and function of habitats, even within protected area boundaries.

Foraging animals move through the environment to satisfy their requirements for food, rest, reproduction and risk-avoidance. Understanding how animals respond to changing environmental conditions can help to characterise favourable habitat and determine whether they might be motivated to depart when those habitats become unsuitable. Foraging green turtles are typically residents that scarcely move in response to environmental changes or disturbances. Some individuals though, exhibit high mobility at fine scales. We developed an analysis of Fastloc GPS tracks of 61 green turtles using cox regression models and generalised linear mixed models to investigate the influence of a suite of environmental characteristics on the length of residence time and probability of turtles transitioning between two behavioural modes, “stay” or “go”. Decisions to move (“go”) were influenced by short-term changes in the local environmental conditions. Individuals were more likely to “stay” when temperature increased during their stay and were more likely to “go” when turbidity decreased, and they entered deeper habitats. This result implies that foraging and resting (“staying”) primarily occurs in benthic, shallow, warm habitats, while transit (“going”) is facilitated in deeper, clear water. We also determined that individuals within the green turtle foraging aggregation respond differently to environmental cues to move and hypothesise that a diversity of strategies within a foraging aggregation could confer its resilience to disturbance events. Our study provides new evidence of the factors influencing movements in green turtles and can aid in predicting how they may respond to future changes and enhance risk mitigation efforts through dynamic and adaptive planning.

Foraging animals move through the environment to satisfy their requirements for food, rest, reproduction and risk-avoidance. Understanding how animals respond to changing environmental conditions can help to characterise favourable habitat and determine whether they might be motivated to depart when those habitats become unsuitable. Foraging green turtles are typically residents that scarcely move in response to environmental changes or disturbances. Some individuals though, exhibit high mobility. We developed an analysis of Fastloc GPS tracks of 61 green turtles using cox regression models and generalised linear mixed models to investigate the influence of a suite of environmental characteristics on the length of residence time and probability of turtles transitioning between two behavioural modes, “stay” or “go”. Decisions to move (“go”) were influenced by short-term changes in the local environmental conditions. Individuals were more likely to “stay” when temperature increased during their stay and were more likely to “go” when turbidity decreased, and they entered deeper habitats. This result implies that foraging and resting (“staying”) primarily occurs in benthic, shallow, warm habitats, while travel (“going”) is facilitated in deeper, clear water. We also determined that individuals within the green turtle foraging aggregation respond differently to environmental cues to move and hypothesise that a diversity of strategies within a foraging aggregation could confer its resilience to disturbance events. Our study provides new evidence of the factors influencing movements in green turtles and can aid in predicting how they may respond to future changes and enhance risk mitigation efforts through dynamic and adaptive planning.

Marine turtles encounter different threats during various life-history stages. Therefore, understanding their movements and spatial distribution is crucial for effectively managing these long-lived migratory organisms. This study combines satellite telemetry data with long-term capture-mark-recapture data derived from flipper tag studies to determine distribution patterns of endangered loggerhead turtles (Caretta caretta) during post-nesting migrations from different eastern Australian nesting sites. Individuals from the K’gari-Fraser Island and Great Barrier Reef island rookeries typically migrated northward, whereas individuals from mainland rookeries migrated equally northward and southward. Despite this difference in foraging distribution, loggerheads from the different rookeries did not differ substantially in their migration duration or distance travelled. The foraging distribution identified from successful satellite tag deployments represented 50% of the foraging distribution identified from a large long-term flipper tag recovery database. However, these satellite telemetry results have identified new migration and foraging habitats not previously recognised for loggerhead turtles nesting in eastern Australia. Additionally, they support the conclusion from a past study using flipper tag recovery data that the mainland nesting turtles migrate to different foraging grounds than the turtles nesting on Great Barrier Reef islands. Collectively, the two data sources provide valuable data on the migration route, habitat distribution and ecological range for a threatened genetic stock of loggerhead turtles.

Increasing overlap between anthropogenic activities and wildlife can lead to problematic human–wildlife interactions. To manage these, an understanding of animal space‐use patterns, with sufficient temporal and spatial detail is required. Satellite telemetry can provide such detailed data; however, the cost of tracking units places a significant limitation on sample size. Satellite tracks for 72 green turtles were consolidated through collaboration with multiple entities over 8 years at seven sites within a large industrial port contributing to an ecological monitoring initiative to minimize impacts of planned developments. This study aims to determine the minimum number of satellite‐tracked green turtles required to represent spatial distribution patterns in the foraging ground and to evaluate factors underpinning differences in distribution and site fidelity metrics to inform appropriate management strategies. An autocorrelated kernel density estimator was used to construct 95% utilization distributions for individual turtles during each calendar season. Percentage overlap between pairs of seasonal utilization distributions was calculated as a measure of short‐term site fidelity. Mechanistic range shift analysis was applied to detect significant deviations from range residency behaviour. Green turtles exhibited spatially confined ranges and remained faithful to their foraging area for periods of up to 260 days. Range size was significantly different between microhabitats and study years. Only 16 individuals (22% of tracked turtles) performed significant range shifts, indicating that occupied areas represent important habitats, and most turtles are unlikely to adjust their space‐use in response to anthropogenic or natural disturbances. Although this dataset represents an atypically large sample of satellite tracked individuals, representative data were obtained at only two key sites. This study highlights the importance of evaluating clear objectives when sampling animals for satellite telemetry studies to obtain representation of sites, periods of interest, or age and sex cohorts.