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

Connie Y. Kot, Susanne Åkesson, Joanna Alfaro-Shigueto, Diego Fernando Amorocho Llanos, Marina Antonopoulou, George H. Balazs, Warren R. Baverstock, Janice M. Blumenthal, Annette C. Broderick, Ignacio Bruno, Ali Fuat Canbolat, Paolo Casale, Daniel Cejudo, Michael S. Coyne, Corrie Curtice, Sarah DeLand, Andrew DiMatteo, Kara Dodge, Daniel C. Dunn, Nicole EstebanAngela Formia, Mariana M.P.B. Fuentes, Ei Fujioka, Julie Garnier, Matthew H. Godfrey, Brendan J. Godley, Victoria González Carman, Autumn Lynn Harrison, Catherine E. Hart, Lucy A. Hawkes, Graeme C. Hays, Nicholas Hill, Sandra Hochscheid, Yakup Kaska, Yaniv Levy, César P. Ley-Quiñónez, Gwen G. Lockhart, Milagros López-Mendilaharsu, Paolo Luschi, Jeffrey C. Mangel, Dimitris Margaritoulis, Sara M. Maxwell, Catherine M. McClellan, Kristian Metcalfe, Antonio Mingozzi, Felix G. Moncada, Wallace J. Nichols, Denise M. Parker, Samir H. Patel, Nicolas J. Pilcher, Sarah Poulin, Andrew J. Read, ALan F. Rees, David P. Robinson, Nathan J. Robinson, Alejandra G. Sandoval-Lugo, Gail Schofield, Jeffrey A. Seminoff, Erin E. Seney, Robin T.E. Snape, Doğan Sözbilen, Jesús Tomás, Nuria Varo-Cruz, Bryan P. Wallace, Natalie E. Wildermann, Matthew J. Witt, Alan A. Zavala-Norzagaray, Patrick N. Halpin

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)810-829
Number of pages20
JournalDiversity and Distributions
Volume28
Issue number4
DOIs
StatePublished - Apr 2022

Keywords

  • betweenness
  • centrality
  • closeness
  • graph theory
  • marine turtle
  • migratory
  • satellite telemetry
  • tracking

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