Data from: Population closure and the bias-precision trade-off in Spatial Capture-Recapture

1. Spatial capture-recapture (SCR) is an increasingly popular method for
estimating ecological parameters. This method often relies on data
collected over relatively long sampling periods. While longer sampling
periods can yield larger sample sizes and thus increase 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 tradeoff. Yet few studies have studied this tradeoff and
none has done so for SCR models. 2. In this study, we explored the
influence of the length and timing of the sampling period on the
bias-precision tradeoff 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. 3.
While longer sampling periods benefit the study of slow-living species
(increased precision and lower bias), they lead to pronounced
over-estimation of population size for fast living species. In addition,
we show that both bias and uncertainty increase when the sampling period
overlaps the species’ reproductive season. 4. 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 SCR (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.