Abstract
We simulated multistate capture histories (CHs) by varying state survival (ϕ), detection (p) and transition (ψ), number of total capture occasions and releases per capture occasion and then modified these scenarios to mimic false rejection error (FRE), a common misidentification error, resulting from the failure to match samples of the same individual. We then fit a multistate model and estimated accuracy, bias and precision of state-specific ϕ, p and ψ to better understand the effects of FRE on different simulation scenarios. As expected, ϕ, and p, decreased in accuracy with FRE, with lower accuracy when CHs were simulated under a shorter-term study and a lower number of releases per capture occasion (lower sample size). Accuracy of ψ estimates were robust to FRE except in those CH scenarios simulated using low sample size. The effect of FRE on bias was not consistent among parameters and differed by CH scenario. As expected, ϕ was negatively biased with increased FRE (except for the low ϕ low p CH scenario simulated with a low sample size), but we found that the magnitude of bias differed by scenario (high p CH scenarios were more negatively biased). State transition was relatively unbiased, except for the low p CH scenarios simulated with a low sample size, which were positively biased with FRE, and high p CH scenarios simulated with a low sample size. The effect of FRE on precision was not consistent among parameters and differed by scenario and sample size. Precision of ϕ decreased with FRE and was lowest with the low ϕ low p CH scenarios. Precision of p estimates also decreased with FRE under all scenarios, except the low ϕ high p CH scenarios. However, precision of ψ increased with FRE, except for those CH scenarios simulated with a low sample size. Our results demonstrate how FRE leads to loss of accuracy in parameter estimates in a multistate model with the exception of ψ when estimated using an adequate sample size.
Highlights
Knowledge of wildlife population dynamics is a crucial step towards species conservation and necessary if we wish to improve our understanding of the potential effects of climate change and future land use change
Survival of capture histories (CHs) simulated with 10 capture occasions decreased in accuracy with increased false rejection error (FRE) and was lowest in those CH scenarios simulated with high p (Fig 1; left panels)
Survival estimates were more negatively biased with increased FRE, but we found that the magnitude of bias differed by the CH scenario simulated (Fig 1; center panels)
Summary
Knowledge of wildlife population dynamics is a crucial step towards species conservation and necessary if we wish to improve our understanding of the potential effects of climate change and future land use change. Survival (φ) can be estimated using capture-mark-recapture (CMR) models, but accuracy of the estimates relies on meeting model assumptions, which include: 1) individuals in a population all have an equal probability of being marked and recaptured; and 2) marks are permanent and they are observed, identified and recorded accurately at recaptures [1,2,3]. Photo and genotype-based CMR are less invasive, not requiring physical capture, but, photo-based CMR depends on focal species having unique visual markings [4,5] and genotype-based CMR relies on the presence of highly polymorphic molecular markers [6,7,8]. Individual capture histories (CHs) can be inferred from photo matches, which is required data input for CMR models. Individual identification is possible with polymorphic molecular markers, such as microsatellite loci or single nucleotide polymorphisms [8,18]
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