Population closure and the bias‐precision trade‐off in spatial capture–recapture

Abstract

Abstract Spatial capture–recapture (SCR) is an increasingly popular method for estimating ecological parameters. SCR often relies on data collected over relatively long sampling periods. While longer sampling periods can yield larger sample sizes and thus increase the precision of estimates, they also increase the risk of violating the closure assumption, thereby potentially introducing bias. The sampling period characteristics are therefore likely to play an important role in this bias‐precision trade‐off. Yet few studies have studied this trade‐off and none has done so for SCR models. In this study, we explored the influence of the length and timing of the sampling period on the bias‐precision trade‐off of SCR population size estimators. Using a continuous time‐to‐event approach, we simulated populations with a wide range of life histories and sampling periods before quantifying the bias and precision of population size estimates returned by SCR models. While longer sampling periods benefit the study of slow‐living species (increased precision and lower bias), they lead to pronounced overestimation of population size for fast‐living species. In addition, we show that both bias and uncertainty increase when the sampling period overlaps the reproductive season of the study species. Based on our findings, we encourage investigators to carefully consider the life history of their study species when contemplating the length and the timing of the sampling period. We argue that both spatial and non‐spatial capture–recapture studies can safely extend the sampling period to increase precision, as long as it is timed to avoid peak recruitment periods. The simulation framework we propose here can be used to guide decisions regarding the sampling period for a specific situation.