Identifying latent behavioural states in animal movement with M4, a nonparametric Bayesian method
Identifying latent behavioural states in animal movement with M4, a nonparametric Bayesian method
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DOI：
10.1111/2041-210x.13745
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发表时间：
2021-10-31
影响因子：
6.6
通讯作者：
Valle, Denis
中科院分区：
文献类型：
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作者：
Cullen, Joshua A.;Poli, Caroline L.;Valle, Denis
了解动物的运动通常依赖于遥测和生物记录设备。这些数据经常被用来估计潜在的行为状态，以帮助理解动物在景观中移动的原因。虽然有多种方法可以从生物遥测数据进行行为推断，但这些方法的某些特征（例如，分析单个数据流，使用参数分布）可能会限制其可靠区分行为状态的一般性。为了解决现有行为状态估计模型的一些局限性，我们引入了一个非参数贝叶斯框架，称为运动的混合成员方法（M4），它可以在开源的bayesmove R包中使用。该框架可以分析多个数据流（例如步长，转弯角度，加速度），而不依赖于参数分布，这可能比当前方法更成功地捕获复杂行为。我们使用模拟轨迹测试了我们的贝叶斯框架，并将模型性能与两种分割方法（行为变点分析（BCPA）和segclust2d），一种机器学习方法[期望最大化二进制聚类（EMBC）]和一种类型的状态空间模型[隐马尔可夫模型（HMM）]进行了比较。我们还说明了这一贝叶斯框架使用的少年蜗牛风筝Rostrhamus sociabilis在佛罗里达，美国的运动。贝叶斯框架估计断点更准确地比其他分割方法的不同长度的轨道。同样，贝叶斯框架提供了更准确的行为估计比其他状态估计方法时，模拟生成的不太频繁考虑的分布（如截断正态，β，均匀）。三种行为状态估计从蜗牛风筝运动，这被标记为“扎营”，“区域限制搜索”和“过境”。随着时间的推移，这些行为的变化与已知的传播事件从巢址，以及运动和可能的繁殖地点。我们的非参数贝叶斯框架估计的行为状态具有可比性或上级的准确性相比，其他方法时，步长和转向角的模拟产生的不太频繁考虑的分布。由于最合适的参数分布可能不是先验的，因此对底层分布不可知的方法（如M4）可以提供强大的替代方案来解决运动生态学中的问题。
Understanding animal movement often relies upon telemetry and biologging devices. These data are frequently used to estimate latent behavioural states to help understand why animals move across the landscape. While there are a variety of methods that make behavioural inferences from biotelemetry data, some features of these methods (e.g. analysis of a single data stream, use of parametric distributions) may limit their generality to reliably discriminate among behavioural states. To address some of the limitations of existing behavioural state estimation models, we introduce a nonparametric Bayesian framework called the mixed-membership method for movement (M4), which is available within the open-source bayesmove R package. This framework can analyse multiple data streams (e.g. step length, turning angle, acceleration) without relying on parametric distributions, which may capture complex behaviours more successfully than current methods. We tested our Bayesian framework using simulated trajectories and compared model performance against two segmentation methods (behavioural change point analysis (BCPA) and segclust2d), one machine learning method [expectation-maximization binary clustering (EMbC)] and one type of state-space model [hidden Markov model (HMM)]. We also illustrated this Bayesian framework using movements of juvenile snail kites Rostrhamus sociabilis in Florida, USA. The Bayesian framework estimated breakpoints more accurately than the other segmentation methods for tracks of different lengths. Likewise, the Bayesian framework provided more accurate estimates of behaviour than the other state estimation methods when simulations were generated from less frequently considered distributions (e.g. truncated normal, beta, uniform). Three behavioural states were estimated from snail kite movements, which were labelled as 'encamped', 'area-restricted search' and 'transit'. Changes in these behaviours over time were associated with known dispersal events from the nest site, as well as movements to and from possible breeding locations. Our nonparametric Bayesian framework estimated behavioural states with comparable or superior accuracy compared to the other methods when step lengths and turning angles of simulations were generated from less frequently considered distributions. Since the most appropriate parametric distributions may not be obvious a priori, methods (such as M4) that are agnostic to the underlying distributions can provide powerful alternatives to address questions in movement ecology.