Using fisheries observation data to develop a predictive species distribution model for endangered sea turtles

Jennie Hannah Degenford, Dong Liang, Helen Bailey, Aimee L. Hoover, Patricia Zarate, Jorge Azócar, Daniel Devia, Joanna Alfaro-Shigueto, Jeffery C. Mangel, Nelly de Paz, Javier Quinones Davila, David Sarmiento Barturen, Juan M. Rguez-Baron, Amanda S. Williard, Christina Fahy, Nicole Barbour, George L. Shillinger

Research output: Contribution to journalArticlepeer-review

Abstract

The Eastern Pacific leatherback turtle population (Dermochelys coriacea) has declined precipitously in recent years. One of the major causes is bycatch from coastal and pelagic fisheries. Fisheries observations are often underutilized, despite strong potential for this data to affect policy. In this study, we created a spatiotemporal species distribution model that synthesizes fisheries observations with remotely sensed environmental data. The model will be developed into a dynamic management tool for the Eastern Pacific leatherback population. We obtained leatherback observation data from multiple fisheries that have operated in the Southeast Pacific (2001–2018). A dynamic Poisson point process model was applied to predict leatherback intensity (observation per unit area) as a function of dynamic environmental covariates. This model serves as a tool for application by managers and stakeholders toward the reduction of leatherback turtle bycatch and provides a modeling framework for analyzing fisheries observations from other vulnerable populations and species.

Original languageEnglish
Article numbere349
JournalConservation Science and Practice
Volume3
Issue number2
DOIs
StatePublished - Feb 2021
Externally publishedYes

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