A spatially explicit capture–recapture estimator for single‐catch traps

Abstract

Single‐catch traps are frequently used in live‐trapping studies of small mammals. Thus far, a likelihood for single‐catch traps has proven elusive and usually the likelihood for multicatch traps is used for spatially explicit capture–recapture (SECR) analyses of such data. Previous work found the multicatch likelihood to provide a robust estimator of average density. We build on a recently developed continuous‐time model for SECR to derive a likelihood for single‐catch traps. We use this to develop an estimator based on observed capture times and compare its performance by simulation to that of the multicatch estimator for various scenarios with nonconstant density surfaces. While the multicatch estimator is found to be a surprisingly robust estimator of average density, its performance deteriorates with high trap saturation and increasing density gradients. Moreover, it is found to be a poor estimator of the height of the detection function. By contrast, the single‐catch estimators of density, distribution, and detection function parameters are found to be unbiased or nearly unbiased in all scenarios considered. This gain comes at the cost of higher variance. If there is no interest in interpreting the detection function parameters themselves, and if density is expected to be fairly constant over the survey region, then the multicatch estimator performs well with single‐catch traps. However if accurate estimation of the detection function is of interest, or if density is expected to vary substantially in space, then there is merit in using the single‐catch estimator when trap saturation is above about 60%. The estimator's performance is improved if care is taken to place traps so as to span the range of variables that affect animal distribution. As a single‐catch likelihood with unknown capture times remains intractable for now, researchers using single‐catch traps should aim to incorporate timing devices with their traps.

Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance

“…The first marten to deposit hair while visiting a station often appeared to rip the staple holding one part of the glue-strip off of the bait structure, precluding deposition by subsequent martens. This limits individual cross-contamination (Pauli et al 2008), however, it is problematic because it implies a type of detection competition between individuals recognized as leading to underestimating density using SCR in live-trapping studies (Distiller and Borchers 2015). In fact, this potential bias might be stronger when sampling with single-catch hair detectors rather than single-catch live traps because one individual can prevent the detection of other individuals at multiple locations, and our hair-based estimates of marten density were notably smaller than estimates produced by cameras only or multiple methods.…”

Counting chirps: acoustic monitoring of cryptic frogs

“…Correcting variance estimates is possible via a simulation approach if call rate data are available (see Stevenson et al 2015). Although the effect of violation of the assumption of spatial uniformity with SCR estimators has not been thoroughly investigated, there are a number of studies that suggest that while violation of assumptions can result in bias in some parameters of the SCR model, and to biased inferences about distribution in space, SCR estimates of density itself appear to be remarkably robust to violation of the assumption (Efford, Borchers & Byrom 2009;Distiller & Borchers 2015).…”

Variation in space and time: a long-term examination of density-dependent dispersal in a woodland rodent

“…First, Bayesian SECR 140 models are inappropriate for trapping data that are derived from single-catch traps such as those 141 used in our study (Gerber and Parmenter 2015). Second, when using frequentist SECR models 142 the multi-catch estimator can only be used if density is relatively constant over survey regions 143 (Distiller and Borchers 2015), and this was not the case in our data (see Results). Finally, the 144 single-catch estimator developed by Distiller and Borchers (2015), which allows for frequentist 145 SECR analysis using single catch traps, requires times for all captures which were not available 146 for our historical data.…”