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).

Accounting for heterogeneity when estimating stopover duration, timing and population size of red knots along the Luannan Coast of Bohai Bay, China

“…Our findings are in agreement with previous studies that showed that heterogeneity in encounter probabilities can severely bias population size estimates (Carothers, 1973;Cubaynes et al, 2010;Pledger et al, 2010), and to a lesser extent survival probabilities (Abadi et al, 2013). Bias in parameter estimates was particularly severe when a small group of birds had high p (scenario 1, Table 3), and most severe when this same group also had relatively high ϕ (scenario 5, Table 3).…”

“…Finite mixtures enable the modeling of hidden classes of individuals with contrasting encounter and/or staying (or survival) probabilities and have previously been shown to adequately remove bias in parameter estimates of CJS models (Abadi, Botha, & Altwegg, 2013;Pledger, Pollock, & Norris, 2003) and Jolly-Seber models (Pledger, Pollock, & Norris, 2010) in the presence of individual heterogeneity.…”

An integrated population model sheds light on the complex population dynamics of a unique colonial breeder

“…Recapture rate p was modeled with a mean and individual random effect to account for any overdispersion not accounted for by the multi‐state survival model (Abadi, Botha, & Altwegg, ; Kéry & Schaub, ): where p i is individual recapture, β overall mean logit recapture, and γ( i ) the individual random effect on the logit scale. The individual random term is assumed to have a Normal distribution: γ( i ) ~ Norm(0, σ 2 γ ).…”

A Test of Positive Association for Detecting Heterogeneity in Capture for Capture–Recapture Data

“…Although survival estimates have been shown to be fairly robust, even small biases can lead to flawed inference or have an impact on management strategies (Prévot-Julliard et al 1998;Cubaynes et al 2010;Fletcher et al 2012;Abadi et al 2013). For example, Fletcher et al (2012) and Abadi et al (2013) both observed negative biases in the survival estimates when fitting models that did not account for heterogeneity in capture. It is also known that ignoring heterogeneity in capture leads to underestimating abundance, whether the framework is closed or open populations (see for example Morgan and Ridout 2008;Cubaynes et al 2010;Pledger et al 2010).…”