Equivalence between Step Selection Functions and Biased Correlated Random Walks for Statistical Inference on Animal Movement
Equivalence between Step Selection Functions and Biased Correlated Random Walks for Statistical Inference on Animal Movement
复制标题
DOI：
10.1371/journal.pone.0122947
复制
发表时间：
2015-04-21
期刊：
影响因子：
3.7
通讯作者：
Rivest, Louis-Paul
中科院分区：
文献类型：
--
作者：
Duchesne, Thierry;Fortin, Daniel;Rivest, Louis-Paul
动物运动对种群和群落结构和动态有着根本性的影响。有偏相关随机游动（BCRW）和步长选择函数（SSF）通常用于研究运动。由于没有研究对比了这两种拉格朗日方法下构建的模型的参数和估计量的统计特性，因此尚不清楚它们是否允许类似的推断。首先，我们利用弱大数定律证明了估计BCRW模型参数的对数似然函数可以近似为SSF的对数似然函数。其次，我们通过将BCRW与最大似然和SSF拟合到虚拟环境中的模拟运动数据和野牛（野牛野牛L.）的轨迹来说明这两种方法之间的联系。自然景观中的小径。使用模拟和经验数据，我们发现，直接从最大似然估计的BCRW和通过拟合SSF的参数是非常相似的。运动分析越来越多地被用作了解景观特性对动物分布影响的工具。在快速发展的运动生态学领域，管理和保护生物学家必须决定他们应该实施哪种方法来准确评估动物运动的决定因素。我们表明，BCRW和SSF可以提供类似的见解影响动物运动的环境特征。这两种技术都有优势。BCRW已经扩展到允许多状态建模。然而，与BCRW不同的是，SSF可以使用大多数统计软件包进行估计，它可以同时评估栖息地选择和运动偏差，并且可以很容易地在多个尺度上整合大量的运动税。因此，SSF提供了一种简单而有效的统计技术来识别运动出租车。
Animal movement has a fundamental impact on population and community structure and dynamics. Biased correlated random walks (BCRW) and step selection functions (SSF) are commonly used to study movements. Because no studies have contrasted the parameters and the statistical properties of their estimators for models constructed under these two Lagrangian approaches, it remains unclear whether or not they allow for similar inference. First, we used the Weak Law of Large Numbers to demonstrate that the log-likelihood function for estimating the parameters of BCRW models can be approximated by the log-likelihood of SSFs. Second, we illustrated the link between the two approaches by fitting BCRW with maximum likelihood and with SSF to simulated movement data in virtual environments and to the trajectory of bison (Bison bison L.) trails in natural landscapes. Using simulated and empirical data, we found that the parameters of a BCRW estimated directly from maximum likelihood and by fitting an SSF were remarkably similar. Movement analysis is increasingly used as a tool for understanding the influence of landscape properties on animal distribution. In the rapidly developing field of movement ecology, management and conservation biologists must decide which method they should implement to accurately assess the determinants of animal movement. We showed that BCRW and SSF can provide similar insights into the environmental features influencing animal movements. Both techniques have advantages. BCRW has already been extended to allow for multi-state modeling. Unlike BCRW, however, SSF can be estimated using most statistical packages, it can simultaneously evaluate habitat selection and movement biases, and can easily integrate a large number of movement taxes at multiple scales. SSF thus offers a simple, yet effective, statistical technique to identify movement taxis.