Modelling and inference for the movement of interacting animals
Modelling and inference for the movement of interacting animals
复制标题
互动动物运动的建模和推理
DOI：
10.1111/2041-210x.13468
复制
发表时间：
2020
影响因子：
6.6
通讯作者：
Milner J
中科院分区：
文献类型：
--
作者：
Milner J
动物运动数据的统计建模是一个快速发展的研究领域。但通常情况下，这些模型是为了分析个体动物的踪迹而开发的，我们忽视了动物的运动行为对彼此的影响。我们的目标是开发一个具有灵活的社会框架的模型，使我们能够捕获这些信息。我们的方法基于社会等级制度的概念，并将其嵌入到模拟一群动物运动的多元扩散过程中。行为状态之间切换的可能性促进了动态的社会行为，我们用采样的状态切换时间来增强观察到的数据，以便在连续时间内自然地模拟动物的行为。此外，这使我们能够在贝叶斯环境中进行精确推理，并具有能够处理规则、不规则和缺失数据的好处。所有运动和行为参数均采用马尔可夫链蒙特卡罗方法进行估计。我们使用模拟数据检查模型的能力，然后将其拟合到五只野生橄榄狒狒（Papio anubis）的 GPS 位置。结果使我们能够确定哪些动物在何时影响其他动物的运动，这为群体的社会行为提供了动态和长期静态的洞察。我们的模型提供了一种连续时间内的灵活方法，用于对动物运动中的社会互动网络进行建模。这样做可以避免离散时间方法造成的限制，并且使我们能够捕获有关群体社会结构的丰富信息，从而在保护和管理决策中得到建设性应用。然而，目前将模型与数据进行拟合是一项计算成本高昂的任务，这反过来又限制了将模型扩展到更富有成效但更复杂的情况，例如空间异质性或个体特征。此外，我们的社会等级方法假设所有相关的动物都被跟踪，并且任何相互作用都有一定的顺序，这两者都缩小了该方法的适用范围。
Statistical modelling of animal movement data is a rapidly growing area of research. Typically though, these models have been developed for analysing the tracks of individual animals and we lose sight of the impact animals have on each other with regards to their movement behaviours. We aim to develop a model with a flexible social framework that allows us to capture that information.Our approach is based on the concept of social hierarchies, and this is embedded in a multivariate diffusion process which models the movement of a group of animals. The possibility of switching between behavioural states facilitates dynamic social behaviours and we augment the observed data with sampled state switching times in order to model the animals' behaviour naturally in continuous time. In addition, this enables us to carry out exact inference in a Bayesian setting with the benefits of being able to handle regular, irregular and missing data. All movement and behaviour parameters are estimated with Markov chain Monte Carlo methods.We examine the capability of our model with simulated data before fitting it to GPS locations of five wild olive baboonsPapio anubis. The results enable us to identify which animals are influencing the movement of others and when, which provides both a dynamic and long‐term static insight into the group's social behaviours.Our model offers a flexible method in continuous time with which to model the network of social interactions within animal movement. Doing so avoids the limitations caused by a discrete‐time approach and it allows us to capture rich information with regards to a group's social structure, leading to constructive applications in conservation and management decisions. However, currently it is a computationally expensive task to fit the model to data, which in turns limits extending the model to more fruitful but complex cases such as heterogeneity in space or individual characteristics. Furthermore, our social hierarchy approach assumes all relevant animals are tracked and that any interactions have some ordering, both of which narrow the scope within which this approach is appropriate.
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影响因子：
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作者：
G. Ramos;J. Morales
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DOI：
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发表时间：
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期刊：
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影响因子：
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作者：
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影响因子：
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作者：
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DOI：
--
发表时间：
2018
期刊：
影响因子：
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作者：
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