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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742790
• 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经通过（Meta）社区理论进行了理论研究。然而，它一直是具有挑战性的经验生态学在非实验环境中测试理论上提出的想法，或保护生物学家在实践中应用这些理论的发展，主要是因为没有明确的统计模型，使理论和数据之间的联系。我的研究的长期目标是建立生态理论和数据之间更强的联系，通过开发专门设计用于有效利用物种分布和相互作用产生的丰富生物数据的多变量模型，促进我们对生态系统和保护实践的认识。我和我的团队将专注于生态理论、统计生态学和保护的三个具体目标。(1)我将从理论上把地方和区域的过程联系起来，并在调查数据上测试这个想法。(2)我将提出多变量时空点过程模型（MSTPPM），以明确地说明多个数据源和采样工作。(3)利用最近发展起来的多元时空统计方法，我们将把（Meta）群落理论应用于濒危物种的保护。我将整合数学和统计方法来研究理论，并依靠模拟、经验系统和保护问题来测试和应用理论到现实世界的情况，从而达到我的目标。在目标1中，我将重新定义实现Huthinsonian生态位理论上连接本地（洛特卡-沃尔泰拉）和区域（集合种群）的过程。我还将包括环境不稳定性（例如，由气候变化引起的）在metacompatibility理论模型。我将开发一个多元统计模型，明确地设计来测试这些理论的发展，在实证system. In目标2，我将推进MSTPPM，这是专为准时的数据，占抽样工作，并整合调查数据的信息。在目标3中，我将提出在保护实践中使用多变量时空模型来解释生物相互作用的方法，以更好地保护和拯救魁北克省黎塞留河的濒危鱼类物种和留尼汪岛的濒危海洋鸟类物种。它将提出多变量统计方法，考虑到生态问题，从而允许进行新的研究途径。它开创了植根于生态理论和统计学的坚实基础，在理论和经验上推进生态学。最后，我提出的发展在生态学内外都有广泛的应用。