Revisiting the effect of capture heterogeneity on survival estimates in capture-mark-recapture studies: does it matter?

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).

Figure 1. Different scenarios of heterogeneity in detection probabilities.

a) Symmetric individual detection probabilities, b) right-skewed (most individuals had a lower detection probability), c) left-skewed (most individuals had a higher detection probability), and d) two-group heterogeneity (individuals with low and high detection probabilities).

Figure 2. Relative bias and precision in the estimate of survival probability.

The relative bias (panel 1) in the estimate of survival probability and precision in terms of standard deviation (panel 2) from a model that ignores (solid circle) and accounts for heterogeneity (open circle) in detection probability under different scenarios: a) symmetric individual heterogeneity, b) right skewed, c) left skewed, and d) two-group heterogeneity.

Figure 3. Example of parts of the capture-resighting histories for a subset of the African White-backed Vultures.

The rows correspond to individuals and the column to months. If a particular bird was seen in a given months, its sighting history contains a ‘1′ in the corresponding column, and ‘0′ otherwise. The capture histories suggest strong heterogeneity in resighting probabilities, probably due to individual differences in movement patterns.

Figure 4. Estimates of survival probability.

Mean survival estimates along with the 95% credible interval obtained from a model that ignores heterogeneity (solid symbols and lines) and a model that allows for heterogeneity (open symbols and broken lines) for (a) the African White-backed Vultures data, and (b) the African Penguins data.