Memories of Migrations Past: Sociality and Cognition in Dynamic, Seasonal Environments
Memories of Migrations Past: Sociality and Cognition in Dynamic, Seasonal Environments
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DOI：
10.3389/fevo.2021.742920
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
E. Gurarie;S. Potluri;G. Cosner;R. S. Cantrell;W. Fagan
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作者：
E. Gurarie;S. Potluri;G. Cosner;R. S. Cantrell;W. Fagan
季节性迁徙是动物在大空间尺度上利用周期性和局部资源的一种广泛且广泛成功的策略。长途迁徙是否能够抵御环境破坏，这仍然是一个开放且主要针对具体情况的问题。高水平的流动性表明具有改变范围的能力，从而赋予弹性。另一方面，如果情况发生变化，保守的、固有的冒险行为可能会付出高昂的代价。促进迁移的机制包括对资源、社会性以及空间记忆和学习等认知过程的识别和响应。我们的目标是探索这些因素相互作用的程度，不仅是为了维持迁徙行为，也是为了提供抵御环境变化的能力。我们开发了一种动物运动的扩散平流模型，其中内源性迁徙行为通过记忆过程被最近的经历所改变，并且动物在一系列空间尺度上具有类似社会集群的行为。我们发现这个相对简单的框架能够在广泛的参数值下适应稳定的季节性资源动态。此外，该模型能够随着时间的推移获得自适应迁移行为。然而，该过程的弹性取决于所考虑的所有参数，以及许多复杂的权衡。例如，社会性的空间尺度需要足够大，以捕获资源的变化，但又不能大到导致所获取的集体信息被过度稀释。长期参考记忆对于对冲高度随机的过程很重要，但需要对最近的记忆进行更高的权重以适应资源物候的方向变化。我们的模型提供了一个通用且通用的框架，用于探索记忆、运动、社会和资源动态的相互作用，即使全球环境条件正在经历快速变化。
Seasonal migrations are a widespread and broadly successful strategy for animals to exploit periodic and localized resources over large spatial scales. It remains an open and largely case-specific question whether long-distance migrations are resilient to environmental disruptions. High levels of mobility suggest an ability to shift ranges that can confer resilience. On the other hand, a conservative, hard-wired commitment to a risky behavior can be costly if conditions change. Mechanisms that contribute to migration include identification and responsiveness to resources, sociality, and cognitive processes such as spatial memory and learning. Our goal was to explore the extent to which these factors interact not only to maintain a migratory behavior but also to provide resilience against environmental changes. We develop a diffusion-advection model of animal movement in which an endogenous migratory behavior is modified by recent experiences via a memory process, and animals have a social swarming-like behavior over a range of spatial scales. We found that this relatively simple framework was able to adapt to a stable, seasonal resource dynamic under a broad range of parameter values. Furthermore, the model was able to acquire an adaptive migration behavior with time. However, the resilience of the process depended on all the parameters under consideration, with many complex trade-offs. For example, the spatial scale of sociality needed to be large enough to capture changes in the resource, but not so large that the acquired collective information was overly diluted. A long-term reference memory was important for hedging against a highly stochastic process, but a higher weighting of more recent memory was needed for adapting to directional changes in resource phenology. Our model provides a general and versatile framework for exploring the interaction of memory, movement, social and resource dynamics, even as environmental conditions globally are undergoing rapid change.