Linking theory and data to advance knowledge on species distribution

将理论和数据联系起来以增进对物种分布的了解

• 批准号: RGPIN-2021-03943
• 负责人: Blanchet, Guillaume
• 金额: $ 2.4万
• 依托单位: Université de Sherbrooke
• 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=744994
• 关键词: Linking; theory; data; advance; knowledge

Ecology is by its very nature a multivariate science because species interact with each other and the environment in non-random ways. This particularity of ecology has been investigated theoretically through (meta-)community theory. However, it has been challenging for empirical ecology to test the ideas proposed theoretically in a non-experimental setting or for conservation biologists to apply these theoretical developments in practice mainly because there are no statistical models that explicitly make the link between theory and data. The long-term objective of my research is to forge stronger links between ecological theory and data to advance our knowledge of ecological systems and the practice of conservation by developing multivariate model specifically designed to efficiently use the wealth of biological data being generated on species distributions and interactions. My group and I will focus on three specific objectives spanning ecological theory, statistical ecology and conservation. (1) I will theoretically relate local and regional processes across and test this idea on survey data. (2) I will advance multivariate spatiotemporal point process models (MSTPPM) for them to explicitly account for multiple data sources and sampling effort. (3) Using recently developed multivariate spatiotemporal statistical methods, we will apply (meta-)community theory to the conservation of an endangered species.  I will integrate mathematical and statistical approaches to investigate theory and rely on simulations, empirical systems and conservation problems to test and apply theory to real-world situations and thus reach my objectives.  In Objective 1, I will redefine the realized Huthinsonian niche by theoretically linking local (Lotka-Volterra) and regional (metapopulation) processes. I will also include environmental instability (e.g. resulting from climate change) in metacommunity theory models. I will develop a multivariate statistical model explicitly designed to test these theoretical developments in empirical systems.  In Objective 2, I will advance MSTPPMs, which are designed for punctual data, to account for sampling effort and to integrate information from survey data. In Objective 3, I will propose ways to account for biotic interaction in conservation practice using multivariate spatiotemporal models to better protect and save from extinction endangered fish species in the Richelieu River in Québec and endangered marine bird species on Réunion Island.  This program has multiple strength. It will propose multivariate statistical methodology designed with ecological problematic in mind, thus allowing to undertake new research avenues. It initiates solid foundations rooted in ecological theory and statistics to advance ecology both theoretically and empirically. Lastly, the developments I propose have broad applications within and outside ecology.

生态学本质上是一门多元科学，因为物种之间以及与环境之间的相互作用是非随机的。生态学的这种特殊性已通过（元）社区理论进行了理论上的研究。然而，经验生态学在非实验环境中测试理论上提出的想法，或者保护生物学家在实践中应用这些理论发展一直是一个挑战，主要是因为没有统计模型明确地建立理论和数据之间的联系。我研究的长期目标是在生态理论和数据之间建立更牢固的联系，通过开发专门设计的多元模型来提高我们对生态系统和保护实践的认识，以有效地利用关于物种分布和相互作用产生的丰富生物数据。我和我的团队将重点关注生态理论、统计生态学和保护等三个具体目标。 (1) 我将从理论上将地方和区域进程联系起来，并在调查数据上测试这个想法。 (2) 我将推进多元时空点过程模型 (MSTPPM)，以便他们明确考虑多个数据源和采样工作。 （3）利用最近开发的多元时空统计方法，我们将应用（元）群落理论来保护濒危物种。  我将整合数学和统计方法来研究理论，并依靠模拟、经验系统和守恒问题来测试理论并将其应用于现实世界，从而实现我的目标。  在目标 1 中，我将通过理论上联系本地（Lotka-Volterra）和区域（集合种群）过程来重新定义已实现的哈辛森生态位。我还将在元社区理论模型中包括环境不稳定（例如气候变化引起的）。我将开发一个明确设计的多元统计模型，用于测试经验系统中的这些理论发展。  在目标 2 中，我将推进 MSTPPM，它是为准时数据而设计的，以考虑抽样工作并整合调查数据中的信息。在目标 3 中，我将提出使用多元时空模型来解释保护实践中生物相互作用的方法，以更好地保护魁北克省黎塞留河的濒危鱼类和留尼旺岛的濒危海洋鸟类并使其免遭灭绝。  该计划具有多重优势。它将提出考虑到生态问题而设计的多元统计方法，从而允许采取新的研究途径。它为生态理论和统计学奠定了坚实的基础，以在理论和实证上推进生态学。最后，我提出的发展在生态学内外都有广泛的应用。