作者
Bryan P Wallace, Andrew D DiMatteo, Brendan J Hurley, Elena M Finkbeiner, Alan B Bolten, Milani Y Chaloupka, Brian J Hutchinson, F Alberto Abreu-Grobois, Diego Amorocho, Karen A Bjorndal, Jerome Bourjea, Brian W Bowen, Raquel Briseño Dueñas, Paolo Casale, BC Choudhury, Alice Costa, Peter H Dutton, Alejandro Fallabrino, Alexandre Girard, Marc Girondot, Matthew H Godfrey, Mark Hamann, Milagros López-Mendilaharsu, Maria Angela Marcovaldi, Jeanne A Mortimer, John A Musick, Ronel Nel, Nicolas J Pilcher, Jeffrey A Seminoff, Sebastian Troëng, Blair Witherington, Roderic B Mast
发表日期
2010/12/17
期刊
Plos one
卷号
5
期号
12
页码范围
e15465
出版商
Public Library of Science
简介
Background
Resolving threats to widely distributed marine megafauna requires definition of the geographic distributions of both the threats as well as the population unit(s) of interest. In turn, because individual threats can operate on varying spatial scales, their impacts can affect different segments of a population of the same species. Therefore, integration of multiple tools and techniques — including site-based monitoring, genetic analyses, mark-recapture studies and telemetry — can facilitate robust definitions of population segments at multiple biological and spatial scales to address different management and research challenges.
Methodology/Principal Findings
To address these issues for marine turtles, we collated all available studies on marine turtle biogeography, including nesting sites, population abundances and trends, population genetics, and satellite telemetry. We georeferenced this information to generate separate layers for nesting sites, genetic stocks, and core distributions of population segments of all marine turtle species. We then spatially integrated this information from fine- to coarse-spatial scales to develop nested envelope models, or Regional Management Units (RMUs), for marine turtles globally.
Conclusions/Significance
The RMU framework is a solution to the challenge of how to organize marine turtles into units of protection above the level of nesting populations, but below the level of species, within regional entities that might be on independent evolutionary trajectories. Among many potential applications, RMUs provide a framework for identifying data gaps …
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