Bias in estimation of adult survival and asymptotic population growth rate caused by undetected capture heterogeneity

Abstract

Summary 1.Mark-recapture studies are often used to estimate adult survival probability / ð Þ, which is an important demographic parameter for long-lived species, as it can have a large impact on the population growth rate. We consider the impact of variation in capture probability among individuals (capture heterogeneity) on the estimation of / from a mark-recapture study and thence on estimation of the asymptotic population growth rate k ð Þ. 2. We review the mechanisms by which capture heterogeneity arises, methods of allowing for it in the analysis, and use simulation to assess the power of detecting three types of capture heterogeneity (two-group heterogeneity, trap-response and temporary emigration) using standard mark-recapture lack-of-fit tests. 3. We use simulation to assess the bias that can arise in the estimation of / from a mark-recapture study when we do not allow for capture heterogeneity. Using a generic population model, we assess the effect this bias has on estimation of k. 4. We use our results on the power of the lack-of-fit tests, together with a measure of the size of the bias relative to the standard error of the estimate of /, to assess which situations might lead to an important level of undetected bias. Our results suggest that undetected bias is not likely to be an issue when there is trap-response, owing to the lack-of-fit tests having sufficient power to detect any trap-response that could lead to non-negligible bias. For two-group heterogeneity, the worst bias generally occurs when the difference between the capture probabilities for the two groups is moderate and both capture probabilities are low. For temporary emigration, the worst bias generally occurs when the rate of emigration and the capture probability are both low. 5. We illustrate the issues for conservation management using data from studies of Hector's dolphin (Cephalorhynchus hectori) in New Zealand and wolves (Canis lupus) in France. 6. Previous studies have suggested that capture heterogeneity will generally lead to a relatively small bias in the estimate of /. However, given the high sensitivity of the asymptotic population growth rate to adult survival, a small bias in / might lead to nontrivial bias in the estimate of k.

Recapture Heterogeneity in Cliff Swallows: Increased Exposure to Mist Nets Leads to Net Avoidance

“…As heterogeneity increases, so too does the bias associated with demographic estimates such as apparent survival [43]–[46]; in the case of severe capture heterogeneity, this may lead to an inaccurate inference of age effects on apparent survival [13]. A failure to accurately account for detection heterogeneity among individuals can additionally lead to flawed estimates of animal abundance [6], [45], [47], population growth and size [44], [48], [49], or species diversity [50]–[52], and may make it difficult to detect environmental drivers of demography [53], to infer the form of natural selection [2], to measure survival differences among groups of individuals [54], or to test for evidence of senescence among older animals [55], [56].…”

Revisiting the Effect of Capture Heterogeneity on Survival Estimates in Capture-Mark-Recapture Studies: Does It Matter?

“…More recently, however, concerns about the assumption that capture heterogeneity in the estimation of survival and/or population size can safely be ignored have been voiced [14]–[19]. At the same time, the development in hierarchical models for analysing capture-mark-recapture data has made it possible to address this issue as capture heterogeneity can now be modelled in various ways [15], [20]–[22].…”

Estimating demographic parameters using a combination of known‐fate and open N‐mixture models

“…The finding of some sample bias is not surprising because adults that are less likely to be lost from the population through emigration are commonly targeted for capture and marking. Bias induced by capture heterogeneity (Fletcher et al 2012) or individual heterogeneity (Lindberg et al 2013) can have implications for assessing population growth rates. We expect…”