Combining individual animal movement and ancillary biotelemetry data to investigate population-level activity budgets
Combining individual animal movement and ancillary biotelemetry data to investigate population-level activity budgets
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DOI：
10.1890/12-0954.1
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发表时间：
2013-04-01
期刊：
影响因子：
4.8
通讯作者：
King, Ruth
中科院分区：
文献类型：
--
作者：
McClintock, Brett T.;Russell, Deborah J. F.;King, Ruth
最近的技术进步已经允许收集详细的动物位置和辅助生物遥测数据，这些数据有助于推断动物运动和相关行为。然而，这些丰富的个人信息，位置和生物遥测数据的来源，通常是独立分析，与人口水平的推断仍然主要是事后。我们描述了一种分层建模方法，它能够集成位置和辅助生物遥测（如。例如，在一个实施例中，生理或加速计）数据。因此，我们可以获得（1）种群水平运动参数和（2）一组行为的活动预算的稳健估计，其中动物在响应内部和外部环境变化时会发生转变。测量误差和丢失的数据很容易容纳使用状态空间制定的建议分层模型。使用贝叶斯分析方法，我们证明了我们的建模方法与位置和潜水活动的数据，从17斑海豹（Phoca vitulina）在英国。联合运动和潜水活动的基础上，我们确定了三个不同的运动行为状态：休息，觅食，过境，并估计人口水平的活动预算，这三个国家。由于斑海豹潜水觅食和过境（但通常不是休息），我们比较了这些结果，一个类似的人口水平的分析，只利用位置数据。我们发现，很大一部分的时间步长是错误的行为状态时，从水平轨迹推断单独的，与33%的时间步长表现出大部分的潜水活动分配给休息状态。只有1%的这些时间步骤被分配到休息时，推断出的轨迹和潜水活动数据，使用我们的综合建模方法。越来越多的证据表明，仅根据轨迹推断动物行为的潜在危险，但幸运的是，现在存在许多灵活的分析技术，可以从生物记录技术的最新进展所提供的日益丰富的信息中提取更多信息。
Recent technological advances have permitted the collection of detailed animal location and ancillary biotelemetry data that facilitate inference about animal movement and associated behaviors. However, these rich sources of individual information, location, and biotelemetry data, are typically analyzed independently, with population-level inferences remaining largely post hoc. We describe a hierarchical modeling approach, which is able to integrate location and ancillary biotelemetry (e. g., physiological or accelerometer) data from many individuals. We can thus obtain robust estimates of (1) population-level movement parameters and (2) activity budgets for a set of behaviors among which animals transition as they respond to changes in their internal and external environment. Measurement error and missing data are easily accommodated using a state-space formulation of the proposed hierarchical model. Using Bayesian analysis methods, we demonstrate our modeling approach with location and dive activity data from 17 harbor seals (Phoca vitulina) in the United Kingdom. Based jointly on movement and diving activity, we identified three distinct movement behavior states: resting, foraging, and transit, and estimated population-level activity budgets to these three states. Because harbor seals are known to dive for both foraging and transit (but not usually for resting), we compared these results to a similar population-level analysis utilizing only location data. We found that a large proportion of time steps were mischaracterized when behavior states were inferred from horizontal trajectory alone, with 33% of time steps exhibiting a majority of dive activity assigned to the resting state. Only 1% of these time steps were assigned to resting when inferred from both trajectory and dive activity data using our integrated modeling approach. There is mounting evidence of the potential perils of inferring animal behavior based on trajectory alone, but there fortunately now exist many flexible analytical techniques for extracting more out of the increasing wealth of information afforded by recent advances in biologging technology.