资源选择信息与空间捕获-再捕获的集成
Integrating Resource Selection Information with Spatial Capture-Recapture
摘要：
了解空间利用和资源选择是许多动物种群研究的主要焦点。通常，这样的研究是基于从遥测获得的位置数据，和资源选择函数（RSF）用于推理。野生动物研究的另一个重要焦点是估计和建模种群规模和密度。最近开发的空间捕获-再捕获（SCR）模型实现了这一目标，使用个人遇到的历史数据与辅助的空间信息捕获的位置。SCR模型包括与RSFs直观相关的遭遇概率函数，但迄今为止，还没有人扩展SCR模型以允许对空间使用和资源选择进行显式推断。我们开发了一个统计框架，联合建模空间利用，资源选择，人口密度，通过整合SCR数据，如从相机陷阱，雾网，或传统的捕捉陷阱，与遥测个人的资源选择数据。我们提供了一个框架，估计基于边际似然，其中我们同时估计的SCR和RSF模型的参数。我们的方法导致增加的精度估计人口密度和普通SCR模型的参数。重要的是，我们还发现，SCR模型可以单独估计参数的资源选择功能，因此，SCR方法可以用作研究空间使用的唯一来源，然而，精度会更高时，遥测数据。最后，我们发现，SCR模型使用标准的对称和固定的遭遇概率模型产生有偏估计的密度时，动物的空间使用相关的景观协变量。因此，如果可能的话，在侧重于使用捕获-再捕获方法估计密度的研究中，必须考虑到空间使用情况。
英文摘要：
Understanding space usage and resource selection is a primary focus of many studies of animal populations. Usually, such studies are based on location data obtained from telemetry, and resource selection functions (RSF) are used for inference. Another important focus of wildlife research is estimation and modeling population size and density. Recently developed spatial capture-recapture (SCR) models accomplish this objective using individual encounter history data with auxiliary spatial information on location of capture. SCR models include encounter probability functions that are intuitively related to RSFs, but to date, no one has extended SCR models to allow for explicit inference about space usage and resource selection. We develop a statistical framework for jointly modeling space usage, resource selection, and population density by integrating SCR data, such as from camera traps, mist-nets, or conventional catch-traps, with resource selection data from telemetered individuals. We provide a framework for estimation based on marginal likelihood, wherein we estimate simultaneously the parameters of the SCR and RSF models. Our method leads to increases in precision for estimating population density and parameters of ordinary SCR models. Importantly, we also find that SCR models alone can estimate parameters of resource selection functions and, as such, SCR methods can be used as the sole source for studying space-usage; however, precision will be higher when telemetry data are available. Finally, we find that SCR models using standard symmetric and stationary encounter probability models produce biased estimates of density when animal space usage is related to a landscape covariate. Therefore, it is important that space usage be taken into consideration, if possible, in studies focused on estimating density using capture-recapture methods.
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