Global Conservation Priorities for Marine Turtles
2011

Global Conservation Priorities for Marine Turtles

Sample size: 58 publication Evidence: moderate

Author Information

Author(s): Wallace Bryan P., DiMatteo Andrew D., Bolten Alan B., Chaloupka Milani Y., Hutchinson Brian J., Abreu-Grobois F. Alberto, Mortimer Jeanne A., Seminoff Jeffrey A., Amorocho Diego, Bjorndal Karen A., Bourjea Jérôme, Bowen Brian W., Briseño Dueñas Raquel, Casale Paolo, Choudhury B. C., Costa Alice, Dutton Peter H., Fallabrino Alejandro, Finkbeiner Elena M., Girard Alexandre, Girondot Marc, Hamann Mark, Hurley Brendan J., López-Mendilaharsu Milagros, Marcovaldi Maria Angela, Musick John A., Nel Ronel, Pilcher Nicolas J., Troëng Sebastian, Witherington Blair, Mast Roderic B.

Hypothesis

Current global extinction risk assessment frameworks do not adequately assess the conservation status of spatially and biologically distinct marine turtle populations.

Conclusion

The study developed a new assessment framework that evaluates and organizes marine turtle Regional Management Units according to their status and threats, identifying the world's most endangered marine turtle populations.

Supporting Evidence

  • The study identified 11 marine turtle Regional Management Units as the most endangered based on risk and threats scores.
  • Average risk scores for marine turtles were moderate, indicating a concerning trend of population declines.
  • Data deficiencies were noted for pollution and climate change impacts, highlighting the need for better monitoring.

Takeaway

This study helps figure out which types of sea turtles are in the most danger and what we can do to help them.

Methodology

The framework consists of semi-quantitative scoring of criteria related to the status of and threats to individual Regional Management Units (RMUs) based on data from various sources.

Potential Biases

Potential biases may arise from the subjective nature of expert scoring and the reliance on published literature.

Limitations

The assessment relies on available data, which may be incomplete or of varying quality, leading to data deficiencies in some RMUs.

Digital Object Identifier (DOI)

10.1371/journal.pone.0024510

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