Movement upscaled: From behavioural responses to population dynamics of interacting animals species

动物之间的种间相互作用，如竞争、捕食和互惠共生，是它们当地种群和群落动态的关键。在与物种分布、生物多样性格局和保护相关的群落水平上，相互作用通常被认为是物种密度相互作用的聚合效应。然而，互动首先发生在个人层面上，并通过行为发生。要全面了解人口动态并建立有效的预测模型，我们必须将这两个视角结合起来。在个体层面上，我们可以通过记录动物物种在其自然栖息地的运动，破译它们相互之间的动态时空反应，并将这些与空间和栖息地的使用以及健康成分联系起来，来研究动物物种之间的相互作用。虽然这些考虑已经提高了我们对捕食者-猎物相互作用的理解，但对于生态相似的物种来说，这些考虑是罕见的。这是令人惊讶的，因为人们对促进物种共存的机制和通过物种间信息转移避免开发和干扰、竞争或促进的精细种间反应的潜在相关性很感兴趣。最有可能的是，此类研究的缺乏取决于与数据收集、数据分析和升级过程相关的一系列挑战，它限制了我们量化和理解社区层面竞争的能力。拟议的项目将通过以下方式克服这些挑战：(I)开发新的统计方法，根据运动数据量化细微规模的互动；(Ii)分析两个生态上相似的鸣禽物种作为模式物种的互动运动的独特数据集，以及(Iii)通过基于个体的建模，调查群落层面的细微规模互动的后果及其与物种互动的基于密度的聚合公式的相关性。

Interspecific interactions between animals, such as competition, predation, and mutualism, are key to their local population and community dynamics. At the community level, which is pertinent to species distributions, biodiversity patterns, and conservation, interactions are typically considered aggregated as effects of species’ densities on each other. However, interactions occur first and foremost at the individual level and through behaviour. To understand population dynamics fully and to build effective predictive models, we must synergize the two perspectives. At the individual level, we can study interactions between animal species in their natural habitats by recording their movements, deciphering their dynamic spatiotemporal responses to each other, and relating these to space and habitat use and fitness components. While such considerations have already improved our understanding of predator-prey interactions, they are rare for ecologically similar species. This is surprising given a wide interest in the mechanisms that facilitate species coexistence and the potential relevance of fine-scale interspecific responses for avoidance of exploitation and interference competition or facilitation through interspecific information transfer. Most likely, the scarcity of such studies hinges on a combination of challenges related to data collection, data analysis, and the process of upscaling, and it limits our ability to quantify and understand competition at the community level. The proposed project will overcome these challenges by (i) developing new statistical methodology for quantifying fine-scale interactions based on movement data, (ii) analyzing a unique data set on interactive movements of two ecologically similar songbird species as model species in relation to fitness components, and (iii) investigating the consequences of fine-scale interactions at the community level and their relevance for aggregated density-based formulations of species interactions through individual-based modelling.