Integrating telemetry and point observations to inform management and conservation of migratory marine species

Liang, Dong; Bailey, Helen; Hoover, Aimee L.; Eckert, Scott A.; Zárate, Patricia; Alfaro‐Shigueto, Joanna; Mangel, Jeffrey C.; Campos, Nelly de Paz; Dávila, Javier Quiñones; Barturen, David Sarmiento; Rguez‐Baron, Juan M.; Fahy, Christina; Rocafuerte, Amanda; Veelenturf, Callie A.; Abrego, Marino; Shillinger, George L.

doi:10.1002/ecs2.4375

Cited by 6 publications

(3 citation statements)

References 91 publications

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“…In Figure 6a, we calculate the proportion of birds in our non-migratory subpopulation using just the telemetry data. This inference is done using a Bayesian approach with subpopulation membership modeled as a Bernoulli random variable with proportion ‫ݓ‬ ଵ of birds in subpopulation 1 having the same prior (22) as in our integrated analysis. As the telemetry data have a large proportion of non-migrating birds, the estimated proportion of birds in this subpopulation is over 95%.…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, there is a growing interest in estimating underlying, or latent, behavioral states based on movement quantities related to locations of an individual through time, typically referred to in the movement literature as hidden Markov-models (McClintock et al 2012). These advances in data collection and analysis have increased our understanding of the ecology and evolution of migratory behavior (Mueller et al 2013; Gu et al 2021), conservation of at risk species and populations (Liang et al 2023), connectivity of migratory populations (Alheit and Bakun 2009; Kot et al 2022), optimal habitats to prioritize for conservation (Yi et al 2022), movement of pathogens (Takekawa et al 2023), and ways in which species respond to climate change (Youngflesh et al 2021; Horton et al 2023).…”

Section: Introductionmentioning

confidence: 99%

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Integrated Movement Models for Individual Tracking and Species Distribution Data

Buderman,

Hanks,

Ruiz-Gutierrez

et al. 2024

Preprint

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While the quantity, quality, and variety of movement data has increased, methods that jointly allow for population- and species-level movement parameters to be estimated are still needed. We present a formal data integration approach to combine individual-level movement and population-level distribution data. We show how formal data integration can be used to improve precision of individual and population level movement parameters and allow additional population level metrics (e.g., connectivity) to be formally quantified.We describe three components needed for an Integrated Movement Model (IMM): a model for individual movement, a model for among-individual heterogeneity, and a model to quantify changes in species distribution. We outline a general IMM framework and develop and apply a specific stochastic differential equation model to a case study of telemetry and species distribution data for golden eagles in western North American during spring migration.We estimate eagle movements during spring migration from data collected between 2011 and 2019. Individual heterogeneity in migration behavior was modeled for two sub-populations, individuals that make significant northward migrations and those that remained in the southern Rocky Mountain region through the summer. As is the case with most tracking studies, the sample population of individual telemetered birds is not representative of the population, and underrepresents the proportion of long-distance migrants in. The IMM was able to provide a more biological accurate subpopulation structure by jointly estimating the structure using the species distribution data. In addition, the integrated approach a) improves accuracy of other estimated movement parameters, b) allows us to estimate the proportion of migratory and non-migratory birds in a given location and time, and c) estimate future spatio-temporal distributions of birds given a wintering location, which provide estimates of seasonal connectivity and migratory routes.We demonstrate how IMMs can be successfully used to address the challenge of estimating accurate population level movement parameters. Our approach can be generalized to a broad range of available movement models and data types, allowing us to significantly improve our knowledge of migration ecology across taxonomic groups, and address population and continental level information needs for conservation and management.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrated Movement Models for Individual Tracking and Species Distribution Data

Buderman,

Hanks,

Ruiz-Gutierrez

et al. 2024

Preprint

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“…The TurtleWatch approach has been used to mitigate Hawaiian pelagic longline fisheries interactions with loggerhead (Caretta caretta) and leatherback (Dermochelys coriacea) turtles (Howell et al, 2008(Howell et al, , 2015. It has been modified to incorporate multiple envi-ronmental predictors and has been applied in other areas to predict residence time of critically endangered eastern Pacific (EP) leatherbacks (Hoover et al, 2019) across their range in the entire southeastern Pacific (South Pacific Turtlewatch [SPTW] [upwell.org/sptw]) (Degenford et al, 2021;Hoover et al, 2019;Liang et al, 2023). The cryptic, highly migratory, and deep diving nature of leatherbacks has created challenges for understanding their movement ecology and protecting them from the most acute threats at sea.…”

Section: Introductionmentioning

confidence: 99%

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

Barbour,

Shillinger,

Gurarie

et al. 2023

Conservation Biology

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Conservation 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.

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Simulating drifting fish aggregating device trajectories to identify potential interactions with endangered sea turtles

Escalle,

Scutt Phillips,

Lopez

et al. 2024

Conservation Biology

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Purse‐seine fishers using drifting fish aggregating devices (dFADs), mainly built with bamboo, plastic buoys, and plastic netting, to aggregate and catch tropical tuna, deploy 46,000–65,000 dFADs per year in the Pacific Ocean. Some of the major concerns associated with this widespread fishing device are potential entanglement of sea turtles and other marine fauna in dFAD netting; marine debris and pollution; and potential ecological damage via stranding on coral reefs, beaches, and other essential habitats for marine fauna. To assess and quantify the potential connectivity (number of dFADs deployed in an area and arriving in another area) between dFAD deployment areas and important oceanic or coastal habitat of critically endangered leatherback (Dermochelys coriacea) and hawksbill (Eretmochelys imbricata) sea turtles in the Pacific Ocean, we conducted passive‐drift Lagrangian experiments with simulated dFAD drift profiles and compared them with known important sea turtle areas. Up to 60% of dFADs from equatorial areas were arriving in essential sea turtle habitats. Connectivity was less when only areas where dFADs are currently deployed were used. Our simulations identified potential regions of dFAD interactions with migration and feeding habitats of the east Pacific leatherback turtle in the tropical southeastern Pacific Ocean; coastal habitats of leatherback and hawksbill in the western Pacific (e.g., archipelagic zones of Indonesia, Papua New Guinea, and Solomon Islands); and foraging habitat of leatherback in a large equatorial area south of Hawaii. Additional research is needed to estimate entanglements of sea turtles with dFADs at sea and to quantify the likely changes in connectivity and distribution of dFADs under new management measures, such as use of alternative nonentangling dFAD designs that biodegrade, or changes in deployment strategies, such as shifting locations.

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Integrating telemetry and point observations to inform management and conservation of migratory marine species

Cited by 6 publications

References 91 publications

Integrated Movement Models for Individual Tracking and Species Distribution Data

Integrated Movement Models for Individual Tracking and Species Distribution Data

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

Simulating drifting fish aggregating device trajectories to identify potential interactions with endangered sea turtles

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