Abstract
Aim: Satellite tracking studies of marine megafauna have grown over the last few decades. The number of these individual datasets are now at levels which if combined, can infer on population level movement and spatial use. Here we use this approach to quantify distribution and important areas for one of the largest populations of green turtles (Chelonia mydas) in the Indo-Pacific.
Location: Western Australia.
Method: We compiled satellite tracking data for 96 adult, female green turtles from 10 rookeries and two genetic stocks and split the data into nesting, migration and foraging using a state-space model to classify the movement behaviour underlying the track (resident or transient). We then used time (and number of turtles) in area analysis on each of these components to quantify the important areas of use, at both rookery and regional/stock scales. We also assessed the representativeness of the calculated distributions, based on the available sample sizes.
Results: 86% of post-nesting turtles had oceanic or coastal movement to neritic foraging grounds and 14% had local residency to their rookery. The foraging distribution consisted of the inshore waters of most of northwestern Australia. Our sample sizes used for inter-nesting distribution were adequate for 90% of rookeries, but still larger sample sizes were needed for post-nesting distributions.
Main conclusions: Despite some limitation with sample size, our analyses have provided a quantitative and robust approach to designate marine areas of importance for an endangered species. The spatial extent of the inter-nesting areas was encompassed by existing spatial protection for green turtles during the breeding season, but existing Biologically Important Areas are largely underestimating the foraging areas. Our study highlights the utility of our approach for providing quantitative outputs at scales needed for management (local and regional).
Original language | English |
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Pages (from-to) | 249-266 |
Number of pages | 18 |
Journal | Diversity and Distributions |
Volume | 27 |
Issue number | 2 |
Early online date | 4 Dec 2020 |
DOIs | |
Publication status | Published - Feb 2021 |