Ecology in spatially structured environments

空间结构环境中的生态学

• 批准号: RGPIN-2020-07188
• 负责人: PeresNeto, Pedro
• 金额: $ 4.01万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=745848
• 关键词: Ecology; spatially; structured; environments

Determining the processes, and their relative importances, underlying complex spatial patterns of biodiversity has been one of the most popular, vibrant, challenging and well-cited areas of research in ecology. Complexities in spatial patterns of biodiversity are recognized, in large part, to be a product of the interactions between the processes that influence species distributions at different spatial scales, and spatial and temporal environmental variation (heterogeneity). Remarkable progress has been made in our understanding of how ecological communities are structured in space, yet major gaps and related challenges remain. Environmental heterogeneity is composed of multiple components even within the same spatial scale. These components should act and interact with different ecological and evolutionary mechanisms to influence the large variety of spatial biodiversity patterns we observe in nature. Spatial environmental heterogeneity is widely recognized as a mechanism underlying the structure of ecological communities. To date, however, research on spatial heterogeneity has almost exclusively focused on the variation (how much) component. For example, how do species composition among different geographical locations vary as a function of their environmental differences (variation)? Similar levels of environmental variation among locations, however, are often structured differently in space (where variation occurs), likely affecting species distributions and the ways they interact. It is critical to stress that environmental heterogeneity has random and non-random spatial components. And that these components differ across important environment features as well as in the way these features correlate in space. The structural components of environmental features can be directly equated to how abruptly or smoothly they change across geographical localities. Because species vary in their responses to and abilities to cope with variation in their environments (e.g. dispersal and life history differences), species distributions and their coexistence should be affected by the spatial structure of their environments. This research program seeks to answer novel and fundamental questions about how the spatial structure of the environment influence species distributions and their interactions. We will combine fieldwork, statistical and theoretical approaches to understand and quantify how the patterns of spatial structure in the environment affect spatial patterns of biodiversity. My research team will work on various taxa, as their differences should allow to generate insights about how they are influenced by different components of environmental heterogeneity. The proposed program will generate a training environment that builds highly transferable skills, linking conceptual, statistical and empirical approaches. This research will also have relevance for our ongoing research with government partners on climate change and ecosystem services.

确定生物多样性复杂空间格局的过程及其相对重要性，一直是生态学研究中最受欢迎，最具活力，最具挑战性和引用最多的领域之一。人们认识到，生物多样性空间格局的复杂性在很大程度上是影响不同空间尺度物种分布的过程与空间和时间环境变化（异质性）之间相互作用的产物。我们在了解生态群落在空间的结构方面取得了显著进展，但仍然存在重大差距和相关挑战。 环境异质性在同一空间尺度上也是由多个成分组成的。这些组成部分应该与不同的生态和进化机制相互作用，以影响我们在自然界中观察到的各种各样的空间生物多样性模式。环境空间异质性是生态群落结构的内在机制。然而，迄今为止，对空间异质性的研究几乎完全集中在变化（多少）组件。例如，不同地理位置的物种组成如何因其环境差异（变异）而变化？然而，不同地点之间类似程度的环境变化在空间（发生变化的地方）的结构往往不同，可能影响物种分布及其相互作用的方式。必须强调，环境异质性具有随机和非随机的空间成分。而且这些组成部分在重要的环境特征以及这些特征在空间中相互关联的方式上有所不同。环境特征的结构成分可以直接等同于它们在地理位置上的突然或平稳变化。由于物种对环境变化的反应和科普能力各不相同（例如扩散和生活史差异），物种分布及其共存应受到其环境空间结构的影响。该研究项目旨在回答有关环境的空间结构如何影响物种分布及其相互作用的新的和基本的问题。我们将结合联合收割机实地调查，统计和理论方法来了解和量化的空间结构模式在环境中如何影响生物多样性的空间格局。我的研究团队将研究各种分类群，因为它们的差异应该可以让我们了解它们如何受到环境异质性不同成分的影响。拟议的计划将产生一个培训环境，建立高度可转移的技能，连接概念，统计和实证方法。这项研究也将与我们正在进行的气候变化和生态系统服务政府合作伙伴的研究相关。