Network analysis of sea turtle movements and connectivity: A tool for conservation prioritization

Abstract

Aim: Understanding the spatial ecology of animal movements is a critical element in conserving long-lived, highly mobile marine species. Analyzing networks developed from movements of six sea turtle species reveals marine connectivity and can help prioritize conservation efforts. Location: Global.Methods: We collated telemetry data from 1235 individuals and reviewed the literature to determine our dataset's representativeness. We used the telemetry data to develop spatial networks at different scales to examine areas, connections, and their geographic arrangement. We used graph theory metrics to compare networks across regions and species and to identify the role of important areas and connections.Results: Relevant literature and citations for data used in this study had very little overlap. Network analysis showed that sampling effort influenced network structure, and the arrangement of areas and connections for most networks was complex.However, important areas and connections identified by graph theory metrics can be different than areas of high data density. For the global network, marine regions in the Mediterranean had high closeness, while links with high betweenness among marine regions in the South Atlantic were critical for maintaining connectivity. Comparisons among species-specific networks showed that functional connectivity was related to movement ecology, resulting in networks composed of different areas and links.Main conclusions: Network analysis identified the structure and functional connectivity of the sea turtles in our sample at multiple scales. These network characteristics could help guide the coordination of management strategies for wide-ranging animals throughout their geographic extent. Most networks had complex structures that can contribute to greater robustness but may be more difficult to manage changes when compared to simpler forms. Area-based conservation measures would benefit sea turtle populations when directed toward areas with high closeness dominating network function. Promoting seascape connectivity of links with high betweenness would decrease network vulnerability.

The Poorly Known Northeast Atlantic Regional Management Unit for Loggerhead (<i>Caretta caretta</i>)

“…Similar migratory patterns exist in the Pacific [19], with loggerhead juveniles migrating from Australia to Peru [19] [22] using the South Pacific gyre [23]. In fact, longer distances are covered by loggerhead sea turtles, although not as long as those travelled by leatherback and green turtles [24]. This information supports trans-oceanic migrations for C. caretta, and these patterns probably occur in all oceans in which they are present [19].…”

I still call Australia home: Satellite telemetry informs the protection of flatback turtles in Western Australian waters

“…The analysis of the tracks of 280 flatback turtles, one of the largest single‐species datasets in the world, confirms that this species is resident to Australian waters, with individuals spending 99.5% of the time that they were tracked within the Australian EEZ. Most species of marine turtles across the globe undertake movements that cross international boundaries (Kot et al, 2022). In WA, green (Ferreira et al, 2021) and hawksbill (Fossette, Ferreira, et al, 2021) turtles have previously been tracked moving between the Australian EEZ and the waters of neighboring countries.…”

Loggerhead turtle oceanic-neritic habitat shift reveals key foraging areas in the Western Indian Ocean

“…Understanding how marine megafauna species respond to their environment is crucial to design effective conservation strategies and reduce anthropogenic threats such as fisheries bycatch (Hazen et al, 2018). In recent years, advances in satellite telemetry have considerably enhanced our knowledge of sea turtle ecology (Hays and Hawkes, 2018) and, more generally, of marine megafauna's movements, habitats, and connectivity (Hussey et al, 2015;Sequeira et al, 2018;Kot et al, 2022). This has important implications in behavioral ecology (e.g., Guzman et al, 2019;Chimienti et al, 2020) and oceanography (Bousquet et al, 2020;McMahon et al, 2021) but also in the context of dynamic ocean management (Maxwell et al, 2015;Sequeira et al, 2019).…”