期刊
MARINE ECOLOGY PROGRESS SERIES
卷 601, 期 -, 页码 215-226出版社
INTER-RESEARCH
DOI: 10.3354/meps12693
关键词
Connectivity; Mixed stock analysis; Dispersal; Behavior; Chelonia mydas; Deepwater Horizon
资金
- National Oceanic and Atmospheric Administration (NOAA)
- National Marine Fisheries Service (NMFS)
- Florida sea turtle license plate
Dispersal is a fundamental driver of population dynamics and connectivity in marine organisms but is often poorly characterized due to the cryptic nature of pelagic life stages. The initial 'lost year' model proposed for surface-pelagic juvenile marine turtles assumed that they passively drifted following a brief swim-frenzy stage. However, mounting evidence indicates that these juveniles engage in directed swimming that affects their trajectories. Dispersal modeling (DM) offers an inferential approach to estimate distributions and connectivity, but model validation remains challenging with sparse empirical data. We sequenced mitochondrial DNA from 121 surface-pelagic juvenile green turtles Chelonia mydas collected in the northern Gulf of Mexico (GoM) from 2009 to 2015 and conducted mixed stock analyses (MSAs) to compare contribution estimates with published DM predictions assuming passive drift. MSA indicated that a large majority of juveniles originated from local nesting populations within the GoM, with contributions markedly divergent from published DM predictions assuming passive drift. DM predictions for western GoM rookeries fell well below their MSA 95% credible intervals (DM: 2%, MSA point estimates: 49-58%), whereas the DM predictions for Caribbean Mexico (Quintana Roo) were larger than the MSA 95% credible intervals (DM: 51-65%, MSA point estimates: <= 5%). Therefore, directed swimming by surface-pelagic green turtles, recently demonstrated via telemetry, likely has profound consequences for their dispersal at the population scale. These results emphasize the value of additional in situ studies of this life stage, as well as the need to integrate swimming behavior into DM to refine fine-scale predictions.
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