Université de Montréal Département de sciences biologiques
Timothée Poisot (Université de Montréal)
Dominique Gravel (Université de Sherbrooke)
Ecological interaction networks and climate change: inference and modeling using machine learning techniques
Interspecific interactions (e.g. parasitism, pollination, predation) in a biological community form a complex network for which data collection in the wild requires a sustained allocation of human and financial resources. Consequently, published empirical data on species interactions are almost exclusively at fine local and temporal scales, and are scarce in many regions of the world. These regions, which include the Arctic, Northern Canada and North Africa, coincide with the areas that will be most severely affected by climate change. The description and analysis of ecological interaction networks typical of these regions are therefore essential to adequately model the impact of climate change on the structure of the biological communities located there. We infer species interaction networks in the above-mentioned regions and simulate the effects of climate change scenarios on the structure of ecological networks on a global scale.