Spatially explicit maximum likelihood methods for capture-recapture studies
Spatially explicit maximum likelihood methods for capture-recapture studies
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DOI：
10.1111/j.1541-0420.2007.00927.x
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发表时间：
2008-06-01
期刊：
影响因子：
1.9
通讯作者：
Efford, M. G.
中科院分区：
文献类型：
--
作者：
Borchers, D. L.;Efford, M. G.
对固定陷阱位置的动物种群进行的实时捕获-再捕获研究不可避免地具有空间成分：靠近陷阱的动物比远离陷阱的动物更有可能被捕获。这在传统的封闭式种群丰度估计中没有得到解决，如果没有空间成分，就无法获得严格的密度估计。我们提出了新的、灵活的捕获-再捕获模型，该模型使用捕获位置来估计动物的位置和空间参考捕获概率。这些模型是基于似然的，因此允许使用赤池的信息准则或其他基于似然的模型选择方法。密度是一个显式参数，因此对其依赖于空间或时间协变量的评估是直接的。捕获概率的附加（非空间）变化可以像传统的捕获-再捕获一样建模。用一个捕获概率只取决于相对于陷阱的位置的模型对该方法进行了仿真测试。发现点估计量是无偏的，标准误差估计量几乎是无偏的。利用该方法对美国马里兰州Patuxent研究保护区的雾网数据进行了红眼Vireos （Vireo olivaceus）的密度估计，结果与现有的基于逆预测的空间显式方法的结果一致。各种额外的空间显式模型被拟合；这些包括具有时间分层、行为反应和异质动物栖息地的模型。
Live-trapping capture-recapture studies of animal populations with fixed trap locations inevitably have a spatial component: animals close to traps are more likely to be caught than those far away. This is not addressed in conventional closed-population estimates of abundance and without the spatial component, rigorous estimates of density cannot be obtained. We propose new, flexible capture-recapture models that use the capture locations to estimate animal locations and spatially referenced capture probability. The models are likelihood-based and hence allow use of Akaike's information criterion or other likelihood-based methods of model selection. Density is an explicit parameter, and the evaluation of its dependence on spatial or temporal covariates is therefore straightforward. Additional (nonspatial) variation in capture probability may be modeled as in conventional capture-recapture. The method is tested by simulation, using a model in which capture probability depends only on location relative to traps. Point estimators are found to be unbiased and standard error estimators almost unbiased. The method is used to estimate the density of Red-eyed Vireos (Vireo olivaceus) from mist-netting data from the Patuxent Research Refuge, Maryland, U.S.A. Estimates agree well with those from an existing spatially explicit method based on inverse prediction. A variety of additional spatially explicit models are fitted; these include models with temporal stratification, behavioral response, and heterogeneous animal home ranges.