Estimating Survival, Migratory Connectivity and Reencounter Probability in Continuous Space - Towards a Continuous Version of the Multinomial Reencounter Model
47 Pages Posted: 26 Oct 2022
There are 2 versions of this paper
Estimating Survival, Migratory Connectivity and Reencounter Probability in Continuous Space - Towards a Continuous Version of the Multinomial Reencounter Model
Estimating Survival, Migratory Connectivity and Reencounter Probability in Continuous Space - Towards a Continuous Version of the Multinomial Reencounter Model
Date Written: October 13, 2022
Abstract
Understanding spatially varying survival is crucial for understanding the ecology and evolution of migratory animals, which may ultimately help to conserve such species. We developed a modeling framework allowing to estimate spatially varying survival as well as how animals from different original populations distribute themselves across the destination space, i.e., migratory connectivity, based on dead reencounter data of marked animals. The model also accounts for the observation process, i.e., finding and reporting dead marked animals. The density of dead reencounters is interpreted as a mixed binomial point process thinned by a constant reencounter probability. All model parameters, survival, migratory connectivity, and reencounter probability, are continuous functions of space. They are estimated from the density of dead reencounters using linear models. Survival is estimated independently of the other parameters. Plugging in the estimated survival value enables to estimate a joint function of migratory connectivity and reencounter probability, which can be disentangled if the observation process is roughly constant over space and time. The method is implemented in the R-package CONSURE. In a simulation study, the estimators are unbiased but show edge effects in survival and migratory connectivity. Applying the method to a real-world data set of European robins Erithacus rubecula results in biologically reasonable maps for survival and migratory connectivity.
Keywords: Mark-recovery data, Mixed binomial point process, Observation process, Spatially continuous survival estimation, Thinned point process
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