Forecasting wildlife movement with spatial capture-recapture ...
Wildlife movement is an important process affecting species population biology and community interactions in myriad ways. Studies of wildlife movement have focused on retrospectively estimating movements of small numbers of individuals by outfitting them with GPS and telemetry tags. Recent developme...
| Main Authors: | , , |
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| Format: | Software |
| Language: | unknown |
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Zenodo
2023
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.5281/zenodo.8342429 https://zenodo.org/record/8342429 |
| Summary: | Wildlife movement is an important process affecting species population biology and community interactions in myriad ways. Studies of wildlife movement have focused on retrospectively estimating movements of small numbers of individuals by outfitting them with GPS and telemetry tags. Recent developments in spatial capture-recapture modeling permit the integration of movement models that can estimate the movement of untagged and undetected individuals. Additionally, hidden Markov movement models provide a framework for forecasting individuals' movements, which may be valuable in the conservation of threatened species facing risks that vary across space and time. We describe maximum likelihood estimators for spatial capture–recapture models integrated with simple, biased, and correlated random walk movement models formulated as hidden Markov models. Additionally, we demonstrate how to forecast wildlife movement based on these models and hidden Markov model algorithms. We conducted a simulation study to test the ... : Funding provided by: National Oceanic and Atmospheric Administration Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000192 Award Number: NA16NMF4720319 ... |
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