Clement Matthew J, Converse Sarah J, Royle J Andrew
U.S. Geological Survey Patuxent Wildlife Research Center Laurel MD USA.
Arizona Game and Fish Department Phoenix AZ USA.
Ecol Evol. 2017 Aug 8;7(18):7304-7310. doi: 10.1002/ece3.3284. eCollection 2017 Sep.
If animals are independently detected during surveys, many methods exist for estimating animal abundance despite detection probabilities <1. Common estimators include double-observer models, distance sampling models and combined double-observer and distance sampling models (known as mark-recapture-distance-sampling models; MRDS). When animals reside in groups, however, the assumption of independent detection is violated. In this case, the standard approach is to account for imperfect detection of groups, while assuming that individuals within groups are detected perfectly. However, this assumption is often unsupported. We introduce an abundance estimator for grouped animals when detection of groups is imperfect and group size may be under-counted, but not over-counted. The estimator combines an MRDS model with an N-mixture model to account for imperfect detection of individuals. The new MRDS-Nmix model requires the same data as an MRDS model (independent detection histories, an estimate of distance to transect, and an estimate of group size), plus a second estimate of group size provided by the second observer. We extend the model to situations in which detection of individuals within groups declines with distance. We simulated 12 data sets and used Bayesian methods to compare the performance of the new MRDS-Nmix model to an MRDS model. Abundance estimates generated by the MRDS-Nmix model exhibited minimal bias and nominal coverage levels. In contrast, MRDS abundance estimates were biased low and exhibited poor coverage. Many species of conservation interest reside in groups and could benefit from an estimator that better accounts for imperfect detection. Furthermore, the ability to relax the assumption of perfect detection of individuals within detected groups may allow surveyors to re-allocate resources toward detection of new groups instead of extensive surveys of known groups. We believe the proposed estimator is feasible because the only additional field data required are a second estimate of group size.
如果在调查过程中能独立检测到动物个体,那么即便检测概率小于1,仍有许多方法可用于估计动物数量。常见的估计方法包括双观察者模型、距离抽样模型以及双观察者与距离抽样相结合的模型(即标记重捕 - 距离抽样模型;MRDS)。然而,当动物以群体形式存在时,独立检测的假设就会被打破。在这种情况下,标准方法是考虑群体检测不完美的情况,同时假设群体内的个体能被完美检测到。但这种假设往往缺乏依据。我们引入了一种针对群体动物的数量估计方法,该方法适用于群体检测不完美且群体大小可能被低估但不会被高估的情况。此估计方法将MRDS模型与N - 混合模型相结合,以考虑个体检测不完美的情况。新的MRDS - Nmix模型所需的数据与MRDS模型相同(独立的检测历史记录、到样带距离的估计值以及群体大小的估计值),另外还需要第二位观察者提供的群体大小的第二个估计值。我们将该模型扩展到群体内个体的检测概率随距离下降的情况。我们模拟了12个数据集,并使用贝叶斯方法比较新的MRDS - Nmix模型与MRDS模型的性能。MRDS - Nmix模型生成的数量估计偏差极小,覆盖水平接近标称值。相比之下,MRDS的数量估计偏低且覆盖效果不佳。许多具有保护价值的物种以群体形式存在,可能会从能更好地考虑不完美检测情况的估计方法中受益。此外,放宽对已检测群体内个体完美检测的假设,可能使调查人员将资源重新分配到检测新群体上,而不是对已知群体进行广泛调查。我们认为所提出的估计方法是可行的,因为所需的唯一额外野外数据是群体大小的第二个估计值。