Incorporating multidimensional behavior into a risk management tool for a critically endangered and migratory species

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dc.contributor.authorBarbour, Nicole
dc.contributor.authorShillinger, George L.
dc.contributor.authorGurarie, Eliezer
dc.contributor.authorHoover, Aimee L.
dc.contributor.authorGaspar, Philippe
dc.contributor.authorTemple-Boyer, Julien
dc.contributor.authorCandela, Tony
dc.contributor.authorFagan, William F.
dc.contributor.authorBailey, Helen
dc.date.accessioned2023-10-05T19:12:53Z
dc.date.available2023-10-05T19:12:53Z
dc.date.issued2023-05-19
dc.description.abstractConservation of migratory species exhibiting wide-ranging and multidimensional behaviors is challenged by management efforts that only utilize horizontal movements or produce static spatial–temporal products. For the deep-diving, critically endangered eastern Pacific leatherback turtle, tools that predict where turtles have high risks of fisheries interactions are urgently needed to prevent further population decline. We incorporated horizontal–vertical movement model results with spatial–temporal kernel density estimates and threat data (gear-specific fishing) to develop monthly maps of spatial risk. Specifically, we applied multistate hidden Markov models to a biotelemetry data set (n = 28 leatherback tracks, 2004–2007). Tracks with dive information were used to characterize turtle behavior as belonging to 1 of 3 states (transiting, residential with mixed diving, and residential with deep diving). Recent fishing effort data from Global Fishing Watch were integrated with predicted behaviors and monthly space-use estimates to create maps of relative risk of turtle–fisheries interactions. Drifting (pelagic) longline fishing gear had the highest average monthly fishing effort in the study region, and risk indices showed this gear to also have the greatest potential for high-risk interactions with turtles in a residential, deep-diving behavioral state. Monthly relative risk surfaces for all gears and behaviors were added to South Pacific TurtleWatch (SPTW) (https://www.upwell.org/sptw), a dynamic management tool for this leatherback population. These modifications will refine SPTW's capability to provide important predictions of potential high-risk bycatch areas for turtles undertaking specific behaviors. Our results demonstrate how multidimensional movement data, spatial–temporal density estimates, and threat data can be used to create a unique conservation tool. These methods serve as a framework for incorporating behavior into similar tools for other aquatic, aerial, and terrestrial taxa with multidimensional movement behaviors.
dc.description.urihttps://doi.org/10.1111/cobi.14114
dc.identifierhttps://doi.org/10.13016/dspace/0z5z-c0uo
dc.identifier.citationBarbour, N., Shillinger, G. L., Gurarie, E., Hoover, A. L., Gaspar, P., Temple-Boyer, J., Candela, T., Fagan, W. F., & Bailey, H. (2023). Incorporating multidimensional behavior into a risk management tool for a critically endangered and migratory species. Conservation Biology, 37, e14114.
dc.identifier.urihttp://hdl.handle.net/1903/30702
dc.language.isoen_US
dc.publisherWiley
dc.relation.isAvailableAtCollege of Computer, Mathematical & Physical Sciencesen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtBiologyen_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.subjectbiotelemetry
dc.subjectecological risk management
dc.subjecthidden Markov models
dc.subjectleatherback turtle
dc.subjectmigratory species
dc.subjectmultidimensional behavior
dc.subjectbiotelemetría
dc.subjectcomportamiento multidimensional
dc.subjectespecie migratoria
dc.subjectgestión de riesgos ecológicos
dc.subjectmodelos ocultos de Markov
dc.subjecttortuga laúd
dc.titleIncorporating multidimensional behavior into a risk management tool for a critically endangered and migratory species
dc.typeArticle
local.equitableAccessSubmissionNo

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