Linking theory and data to advance knowledge on species distribution

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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.