These five resident turtles inhabit heavily fished waters and are vulnerable to a variety of anthropogenic threats. Of the six turtles, five completed their migration and maintained residency at the same foraging ground near the coastal waters of Accra, Ghana until transmission was lost. Turtles exhibited a combination of coastal and oceanic migrations pathways that ranged from 957 km to 1,131 km. Migratory pathways and foraging grounds were identified by applying a switching state space model to locational data, which provides daily position estimates to identify shifts between migrating and foraging behavior. Track lengths of 20–198 days were analyzed, for a total of 536 movement days for the six turtles.
Dr Edem Lafayette Indiana Free Of All
NPI record contains FOIA-disclosable NPPES Editor: Graeme Hays, Deakin University, AUSTRALIAReceived: February 18, 2019 Accepted: June 6, 2019 Published: June 21, 2019This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The 1144575960 NPI number is assigned to the healthcare provider DINESH LAXMINARA EDEM, practice location address at 2600 FERRY ST LAFAYETTE, IN, 47904-3055. PLoS ONE 14(6):This webpage represents 1144575960 NPI record.
It also gives insight into migration and habitat use patterns across multiple taxa and has revealed behavioral patterns across taxonomically distinct species such as similarities in prey pursuit and predator avoidance behaviors. Animal tracking is often critical in assessing the overlap between human threats and vulnerable wildlife habitats, and therefore can indicate the level of human impact on species that may be otherwise unknown. The insights gained from these studies can inform policy and management by providing detailed data on species distribution and delineating habitats used during important life history stages, such as breeding, foraging, and nesting areas. This technology has produced a more comprehensive understanding of the movements and spatial ecology of marine, terrestrial, and avian species that had previously been difficult to track due to the length of their migrations and inaccessibility of frequently used habitats. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Competing interests: The authors have declared that no competing interests exist.Long distance animal migrations are becoming increasingly well-studied with the advent of reliable, individual-level tracking technology.
Consequently, protecting migratory corridors and foraging grounds could have widespread and long-term benefits for entire populations of green turtles. Green turtles typically show fidelity to foraging grounds and post-nesting migratory routes are similar year after year. Adult green sea turtles have been known to migrate hundreds to thousands of kilometers between nesting seasons. It has provided insights into migratory behaviors, locations of foraging grounds and migratory corridors, oceanographic influences on movement patterns, as well as identified locations with high potential for human impact that may contribute to mortality. While tracking multiple individuals across many years can reveal population-level shifts in behavior, these sample sizes are difficult to achieve, and smaller sample sizes, particularly in under-studied populations, are not only more feasible, but are critical in identifying previously unknown habitats and observing variations in movement patterns on a smaller scale.Satellite telemetry has been used to track the in-water movements and distribution of all seven species of sea turtle. Satellite telemetry has become one of the most reliable and widely used tracking technologies, especially in marine research.
Green sea turtles have been classified as endangered by the IUCN since 1982, however despite their international protection and conservation status, they are highly threatened by intentional harvest and incidental bycatch in fisheries. Since in water habitats come with a variety of unique threats, including resource mining, fishing, and anthropogenic pollution, understanding oceanic habitat use and migration patterns is imperative to designing effective marine conservation strategies. However, a number of studies have shown plasticity in migratory behavior among green turtles traveling toward similar destinations, with some individuals taking indirect routes, including both open ocean and coastal pathways, while other individuals of the same population take more direct routes.
Current estimates of this population range from 454–649 nesting females/year however it has seen an estimated 78% decline since the 1940’s. These threats highlight the need to study migration patterns and foraging ground locations of sea turtles to better understand their vulnerabilities.Bioko Island, Equatorial Guinea is home to the second largest nesting rookery for green turtles in Africa, and as such studying this population could have widespread benefits for green turtles throughout the entire region. Oil and gas development has also rapidly intensified in the Gulf of Guinea in recent years , and poses diverse, but difficult to measure, threats to sea turtle populations, with an increase in channel dredging, ship traffic, oil leaks, and chemical pollution, which can affect adult turtles that forage or travel close to offshore platforms.
This model also selects one location per day per turtle to standardize the data across multiple turtles.Individual tracks were then mapped using ArcGIS 10.2 (Esri, Redlands, CA). Behavioral mode 1 is considered transiting behavior, and behavioral mode 2 is considered area restricted search (foraging) behavior. This model returns a behavioral mode of 1 (MCMC mean values 1.5). The model was fit with a total of 5,000 Markov Chain Monte Carlo (MCMC) samples after 5,000 were discarded as burn-in, and every 10 th sample was retained. Was applied to the turtle tracks, using a hierarchical switching first-difference correlated random walk model (hDCRWS). The ‘bsam’ package, based on the Bayesian switching state space model developed by Jonsen et al.
Tracks were also overlaid with ocean surface current data from the Ocean Surface Current Analysis Real-Time (OSCAR) from NASA. Tracks were overlaid with a map of marine and land Exclusive Economic Zones to show country boundaries.
0 kommentar(er)
