Distiller Greg, Borchers David L
Statistics in Ecology, Environment and Conservation (SEEC) Department of Statistical Sciences University of Cape Town Private Bag X3 Rondebosch 7701 South Africa.
School of Mathematics and Statistics Centre for Research into Ecological and Environmental Modelling University of St Andrews The Observatory, Buchanan Gardens Fife KY16 9LZ UK.
Ecol Evol. 2015 Oct 19;5(21):5075-87. doi: 10.1002/ece3.1748. eCollection 2015 Nov.
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.
单捕陷阱常用于小型哺乳动物的活体捕捉研究。到目前为止，单捕陷阱的似然函数一直难以捉摸，通常使用多捕陷阱的似然函数对此类数据进行空间明确的捕获-重捕（SECR）分析。先前的研究发现，多捕似然函数能提供平均密度的稳健估计量。我们基于最近开发的用于SECR的连续时间模型，推导出单捕陷阱的似然函数。我们利用这个似然函数开发了一个基于观察到的捕获时间的估计量，并通过模拟将其性能与多捕估计量在各种密度表面非恒定情况下的性能进行比较。虽然发现多捕估计量是平均密度的一个出人意料的稳健估计量，但其性能会随着陷阱高饱和度和密度梯度增加而变差。此外，还发现它对检测函数高度的估计效果不佳。相比之下，密度、分布和检测函数参数的单捕估计量在所有考虑的情况下被发现是无偏或几乎无偏的。这种优势是以更高的方差为代价的。如果对解释检测函数参数本身不感兴趣，并且预计密度在调查区域内相当恒定，那么多捕估计量在单捕陷阱中表现良好。然而，如果对检测函数的准确估计感兴趣，或者预计密度在空间上会有很大变化，那么当陷阱饱和度高于约60%时，使用单捕估计量是有价值的。如果注意放置陷阱以涵盖影响动物分布的变量范围，估计量的性能会得到改善。由于目前具有未知捕获时间的单捕似然函数仍然难以处理，使用单捕陷阱的研究人员应旨在将计时装置与陷阱结合使用。
