Density Estimation by Spatially Explicit Capture-Recapture: Likelihood-Based Methods
Density Estimation by Spatially Explicit Capture-Recapture: Likelihood-Based Methods
复制标题
DOI：
10.1007/978-0-387-78151-8_11
复制
发表时间：
2009-01-01
期刊：
影响因子：
--
通讯作者：
Byrom, Andrea E.
中科院分区：
文献类型：
--
作者：
Efford, Murray G.;Borchers, David L.;Byrom, Andrea E.
种群密度是一个关键的生态变量，最近的研究表明，如何对几个密集时间间隔内的诱捕器阵列进行建模，以提供种群密度的估计值（Borchers和Efford，2007年）。模型的细节取决于陷阱的性质（更一般地说是“检测器”）。我们提供了一个简洁的描述，新开发的基于似然的方法，并将其扩展到包括“接近检测器”，不限制动物的运动后检测。这类检测器包括被动DNA采样和相机陷阱。空间检测历史的概率模型包括用于归属范围中心分布的子模型（e. G. 2-D泊松）和检测子模型（e. G.距离中心和陷阱之间的距离的半正规函数）。该模型可以通过最大化完全可能性或以观察到的动物数量为条件的可能性来拟合。对检测概率的各种各样的其他影响可以包括在使用协变量或混合模型的可能性中，并且也可以对地点之间或时间之间的密度差异进行建模。我们应用该方法的数据在新西兰山毛榉林确定的微卫星DNA从头发样本中的白鼬鼬erminea。该方法假设在一个检测器处一次可以记录多个个体。正式扩展到“单捕”陷阱是困难的，但在我们的模拟中，“多捕”模型产生了几乎无偏的估计密度为中等水平的陷阱饱和度（
Population density is a key ecological variable, and it has recently been shown how captures on an array of traps over several closely-spaced time intervals may be modelled to provide estimates of population density (Borchers and Efford 2007). Specifics of the model depend on the properties of the traps (more generally 'detectors'). We provide a concise description of the newly developed likelihood-based methods and extend them to include 'proximity detectors' that do not restrict the movements of animals after detection. This class of detector includes passive DNA sampling and camera traps. The probability model for spatial detection histories comprises a submodel for the distribution of home-range centres (e. g. 2-D Poisson) and a detection submodel (e. g. halfnormal function of distance between a range centre and a trap). The model may be fitted by maximising either the full likelihood or the likelihood conditional on the number of animals observed. A wide variety of other effects on detection probability may be included in the likelihood using covariates or mixture models, and differences in density between sites or between times may also be modelled. We apply the method to data on stoats Mustela erminea in a New Zealand beech forest identified by microsatellite DNA from hair samples. The method assumes that multiple individuals may be recorded at a detector on one occasion. Formal extension to 'single-catch' traps is difficult, but in our simulations the 'multi-catch' model yielded nearly unbiased estimates of density for moderate levels of trap saturation (