Abstract
Recently developed capture-mark-recapture methods allow us to account for capture heterogeneity among individuals in the form of discrete mixtures and continuous individual random effects. In this article, we used simulations and two case studies to evaluate the effectiveness of continuously distributed individual random effects at removing potential bias due to capture heterogeneity, and to evaluate in what situation the added complexity of these models is justified. Simulations and case studies showed that ignoring individual capture heterogeneity generally led to a small negative bias in survival estimates and that individual random effects effectively removed this bias. As expected, accounting for capture heterogeneity also led to slightly less precise survival estimates. Our case studies also showed that accounting for capture heterogeneity increased in importance towards the end of study. Though ignoring capture heterogeneity led to a small bias in survival estimates, such bias may greatly impact management decisions. We advocate reducing potential heterogeneity at the sampling design stage. Where this is insufficient, we recommend modelling individual capture heterogeneity in situations such as when a large proportion of the individuals has a low detection probability (e.g. in the presence of floaters) and situations where the most recent survival estimates are of great interest (e.g. in applied conservation).
Highlights
Survival of animals in the wild is an important fitness component, and unbiased survival estimates are critical for understanding, among other things, the patterns of life histories [1], evolutionary pressures in the wild (e.g. [2]), and for the conservation of populations [3]
Doubts have been raised whether individual heterogeneity can safely be ignored [14,19,40] and methods to account for such heterogeneity have been developed [15,21]
Individual heterogeneity can conveniently be modelled as individual random effects when formulating the model as a state-space process [20,23] but popular software packages offer individual random effects within the classical capture-markrecapture modelling framework
Summary
Survival of animals in the wild is an important fitness component, and unbiased survival estimates are critical for understanding, among other things, the patterns of life histories [1], evolutionary pressures in the wild (e.g. [2]), and for the conservation of populations [3]. Development of sophisticated open capture-mark-recapture models [4] has revolutionized our knowledge of survival in populations of wild animals (reviewed by [5]). In theory, these methods give unbiased survival estimates by incorporating an estimate of the detection probability (i.e. the probability of recapture (or resighting) an individual that is alive and in the population at the time of a survey) into the estimation of survival probability. Most studies find the detection rate to vary among groups of individuals (e.g. age classes and sex), and over time and space This suggests modelling variation in detection probabilities is critical for obtaining unbiased survival estimates from capture-mark-recapture experiments on wild populations [4,7,8]
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