Animal use is a dynamic phenomenon, emerging from the movements of animals responding to a changing environment. Interactions between animals are reflected in patterns of joint space use, which are also dynamic. High frequency sampling associated with GPS telemetry provides detailed data that capture space use through time. However, common analyses treat joint space use as static over relatively long periods, masking potentially important changes. Furthermore, linking temporal variation in interactions to covariates remains cumbersome. We propose a novel method for analyzing the dynamics of joint space use that permits straightforward incorporation of covariates. This method builds upon tools commonly used by researchers, including kernel density estimators, utilization distribution intersection metrics, and extensions of linear models. We treat the intersection of the utilization distributions of two individuals as a time series. The series is linked to covariates using copula-based marginal beta regression, an alternative to generalized linear models. This approach accommodates temporal autocorrelation and the bounded nature of the response variable. Parameters are easily estimated with maximum likelihood and trend and error structures can be modeled separately. We demonstrate the approach by analyzing simulated data from two hypothetical individuals with known utilization distributions, as well as field data from two coyotes (Canis latrans) responding to appearance of a carrion resource in southern Texas. Our analysis of simulated data indicated reasonably precise estimates of joint space use can be achieved with commonly used GPS sampling rates (s.e.=0.029 at 150 locations per interval). Our analysis of field data identified an increase in spatial interactions between the coyotes that persisted for the duration of the study, beyond the expected duration of the carrion resource. Our analysis also identified a period of increased spatial interactions before appearance of the resource, which would not have been identified by previous methods. We present a new approach to the analysis of joint space use through time, building upon tools commonly used by ecologists, that permits a new level of detail in the analysis of animal interactions. The results are easily interpretable and account for the nuances of bounded serial data in an elegant way.

中文翻译：

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动物关节空间使用动力学：时间序列方法的新应用

动物使用是一种动态现象，源于动物对不断变化的环境做出反应的运动。动物之间的相互作用反映在关节空间使用的模式中，这也是动态的。与 GPS 遥测相关的高频采样提供了详细的数据，这些数据可以捕获随时间变化的空间使用情况。然而，常见的分析将关节空间的使用视为相对较长时期内的静态，掩盖了潜在的重要变化。此外，将交互的时间变化与协变量联系起来仍然很麻烦。我们提出了一种分析联合空间使用动态的新方法，该方法允许直接合并协变量。该方法建立在研究人员常用的工具之上，包括核密度估计器、利用率分布交叉指标和线性模型的扩展。我们将两个人的利用率分布的交集视为时间序列。该系列使用基于 copula 的边际 beta 回归与协变量相关联，这是广义线性模型的替代方法。这种方法适应时间自相关和响应变量的有界性质。参数很容易用最大似然估计，趋势和误差结构可以单独建模。我们通过分析来自两个具有已知利用率分布的假设个体的模拟数据，以及来自德克萨斯州南部腐肉资源出现的两只土狼 (Canis latrans) 的现场数据来演示该方法。我们对模拟数据的分析表明，可以通过常用的 GPS 采样率（se=0. 029，每个间隔 150 个位置）。我们对现场数据的分析发现，在研究期间持续存在的土狼之间的空间相互作用增加，超出了腐肉资源的预期持续时间。我们的分析还确定了在资源出现之前空间交互增加的时期，这是以前的方法无法识别的。我们提出了一种通过时间分析联合空间使用的新方法，该方法以生态学家常用的工具为基础，允许在分析动物相互作用时达到新的细节水平。结果易于解释，并以优雅的方式解释了有界串行数据的细微差别。超出腐肉资源的预期持续时间。我们的分析还确定了在资源出现之前空间交互增加的时期，这是以前的方法无法识别的。我们提出了一种通过时间分析联合空间使用的新方法，该方法以生态学家常用的工具为基础，允许在分析动物相互作用时达到新的细节水平。结果易于解释，并以优雅的方式解释了有界串行数据的细微差别。超出腐肉资源的预期持续时间。我们的分析还确定了在资源出现之前空间交互增加的时期，这是以前的方法无法识别的。我们提出了一种通过时间分析联合空间使用的新方法，该方法以生态学家常用的工具为基础，允许在分析动物相互作用时达到新的细节水平。结果易于解释，并以优雅的方式解释了有界串行数据的细微差别。这允许在分析动物相互作用时达到新的细节水平。结果易于解释，并以优雅的方式解释了有界串行数据的细微差别。这允许在分析动物相互作用时达到新的细节水平。结果易于解释，并以优雅的方式解释了有界串行数据的细微差别。