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将学习纳入快速人为环境变化下的动物活动范围动态。

Integrating learning into animal range dynamics under rapid human-induced environmental change.

机构信息

Center for Earth and Climate Research, Earth and Life Institute, UCLouvain, Louvain-la-Neuve, Belgium.

Laboratoire Écologie, Systématique et Évolution, Université Paris-Saclay, CNRS, AgroParisTech, Gif-sur-Yvette, France.

出版信息

Ecol Lett. 2024 Feb;27(2):e14367. doi: 10.1111/ele.14367.

Abstract

Human-induced rapid environmental change (HIREC) is creating environments deviating considerably from natural habitats in which species evolved. Concurrently, climate warming is pushing species' climatic envelopes to geographic regions that offer novel ecological conditions. The persistence of species is likely affected by the interplay between the degree of ecological novelty and phenotypic plasticity, which in turn may shape an organism's range-shifting ability. Current modelling approaches that forecast animal ranges are characterized by a static representation of the relationship between habitat use and fitness, which may bias predictions under conditions imposed by HIREC. We argue that accounting for dynamic species-resource relationships can increase the ecological realism of range shift predictions. Our rationale builds on the concepts of ecological fitting, the process whereby individuals form successful novel biotic associations based on the suite of traits they carry at the time of encountering the novel condition, and behavioural plasticity, in particular learning. These concepts have revolutionized our view on fitness in novel ecological settings, and the way these processes may influence species ranges under HIREC. We have integrated them into a model of range expansion as a conceptual proof of principle highlighting the potentially substantial role of learning ability in range shifts under HIREC.

摘要

人为引起的快速环境变化(HIREC)正在创造出与物种进化的自然栖息地有很大差异的环境。与此同时,气候变暖正在将物种的气候范围推向提供新生态条件的地理区域。物种的持续存在可能受到生态新颖度和表型可塑性之间相互作用的影响,而这反过来又可能影响到生物体的迁徙能力。目前预测动物分布范围的模型方法的特点是栖息地利用与适应性之间的关系呈现静态,这可能会导致在 HIREC 条件下产生预测偏差。我们认为,考虑到动态物种资源关系可以提高迁徙预测的生态现实性。我们的基本原理基于生态适应性的概念,即个体根据在遇到新条件时所携带的一系列特征,形成成功的新生物联系的过程,以及行为可塑性,特别是学习。这些概念彻底改变了我们对新生态环境中适应性的看法,以及这些过程在 HIREC 下可能如何影响物种分布范围。我们已经将它们整合到一个范围扩展模型中,作为一个概念性的原理证明,强调了在 HIREC 下学习能力在范围转移中可能发挥的重要作用。

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