Behavioural landscape ecology: scaling-up from individuals to populations under rapid environmental changes / Écologie comportementale à l'échelle du paysage: de l'individu à la population lors de changements rapides des conditions environnementales

快速的环境变化会导致适应不良的行为反应，当个人未能认识到与特定资源或行为相关的感知和实际健身结果之间的不匹配时。事实上，快速的环境变化可以改变环境线索和适应性之间的历史相关性，并导致个体参与最终降低其繁殖成功或生存的行为。这种适应不良的反应被称为进化陷阱，当涉及到栖息地的选择，作为ecological traps.Wild蜜蜂和农田鸟类，特别是空中食虫的，显示出强烈的人口下降，与最近发生在北美和欧洲的农业集约化（AI）。这种人工智能的快速性以及密集耕种地区保留天然草原和“古老”农田的栖息地属性的事实可能有助于动物使用“现代”农田的适应不良行为反应。事实上，耕地可能呈现出与草原等自然开放生境类似的环境条件，至少在生长季节的早期是如此。因此，耕地可能会吸引人们定居在最终由于收割等机械处理或施用农药造成的毒理学和营养学效应而导致质量急剧下降的地点。更糟糕的是，耕地甚至可能比其他栖息地产生更大的吸引力，尽管这是有害的。因此，人工智能有可能创造进化陷阱，如果扩散不能帮助生物体避免或逃脱这样的陷阱，那么个人的表现以及他们的人口必须依赖于表型可塑性和/或进化responses.My学生和我将研究人工智能如何影响扩散行为，并导致偏离“理想”的栖息地选择在树燕子。我们还将评估景观组成线索的可靠性，作为指标的猎物可用性和农药污染，这下降的空中食虫动物。此外，我们将模拟树燕子的当地人口趋势和当地的变化，在整个北美农药使用之间的时空动态。我们还将研究人工智能如何影响大黄蜂种群的生活史、生产力和生存，并评估其形态的表型可塑性是否可以帮助大黄蜂减轻人工智能的影响。填补这些知识空白是必要的，以提高我们预测进化陷阱更有可能发生的地点、时间和方式，以及如何避免或减轻其影响的能力。

Rapid environmental changes can lead to maladaptive behavioral responses when individuals fail to recognise the mismatch between perceived and actual fitness outcomes associated to specific resources or behaviors. Indeed, rapid environmental changes can alter the historical correlations between environmental cues and fitness, and lead individuals to engage into behaviors that ultimately lower their breeding success or survival. Such maladaptive responses have been referred to as evolutionary traps, and when related to habitat selection, as ecological traps.Wild bees and farmland birds, particularly aerial insectivorous ones, show strong population declines coinciding with the recent agricultural intensification (AI) that occurred in North America and Europe. The rapidity of this AI and the fact that intensively cultivated areas retain habitat attributes of natural grasslands and “ancient” farmlands are likely conducive to maladaptive behavioral responses by animals using “modern” farmlands. Indeed, cultivated fields are likely to present environmental conditions analogous to those found in natural open habitats such as grasslands, at least early in the growing season. Cultivated fields may thereby draw individuals to settle in sites which eventually decrease abruptly in quality because of mechanical treatments, such as harvesting, or toxicological and trophic effects resulting from pesticide applications. Worse, cultivated fields could even exert a greater attraction than alternative habitats despite being detrimental. Hence, AI has the potential to create evolutionary traps and if dispersal cannot help organisms avoid or escape such traps, then individual performance as well as that of their populations must depend on phenotypic plasticity and/or on evolutionary responses.My students and I will investigate how AI can influence dispersal behavior and cause departures from “ideal” habitat selection in the Tree Swallow. We will also assess the reliability of landscape composition cues as indicators of prey availability and contamination by pesticides for this declining aerial insectivore. In addition, we will model the spatiotemporal dynamics between local population trends of Tree Swallows and local changes in pesticide use across North America. We will also investigate how AI affects the life history, productivity and survival of bumblebee colonies, as well as assess if phenotypic plasticity in their morphology could help bumblebees mitigate the impacts of AI. Filling such knowledge gaps is necessary in order to improve our ability to predict where, when, and how evolutionary traps are more likely to occur and how their impacts can be avoided or mitigated.