Optimizing sampling design, data integration, and methods for understanding population dynamics and predicting ecological changes

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My long-term objective is to evaluate, develop and apply analytical methods to quantify ecological processes describing the interplay among biotic, abiotic, and anthropogenic drivers of population and ecosystem dynamics, particularly in northern regions. Given the current drastic ecological changes, improvements in our ability to predict the dynamics of ecological systems are urgently needed. The development of reliable predictive tools is indispensable to better anticipate climate-induced impacts on ecosystems. My short-term research objectives seek to 1) improve sampling designs and methods for better integrating multiple data sources to model drivers of population and ecosystem dynamics, 2) foster new methods to combine indigenous and scientific knowledge to enhance our understanding of ecological changes in the North, and 3) develop models to produce near-term (seasonal/annual) iterative forecasts to better anticipate population and ecosystem changes and adapt management actions in real time. My program is embedded within the Near-Term Iterative Forecasting (NTIF) framework. NTIF uses models integrating data from diverse sources and temporal scales (day, season, year) to describe ecosystem responses. It then produces daily to annual scale forecasts that are updated as new data become available. Building upon several ongoing long-term studies, including some that have already collected extensive data on state variables and drivers, my group will advance knowledge at all steps required to reach useful ecological forecasts. We will assess how study designs combining new alternative methods (camera-traps, individual identification from hair DNA, Citizen Science sampling) can reduce the uncertainty of estimates and improve the efficiency of data collection at large spatiotemporal scales. We will develop process-oriented models, combining multiple data sources to unravel which mechanisms drive ecological changes. For instance, we will test whether climatic or anthropogenic processes regulate black bear population dynamics. We will develop robust analytical methods to model qualitative data, allowing us to use indigenous knowledge to test the hypothesis that climate-induced mechanisms synchronize population dynamics among key Arctic species. We will develop NTIF models and evaluate their limitations for predicting future ecosystem states. These models will examine climate impacts on the northward spread of winter ticks (Québec) and defoliating moths (Norway), and their respective consequences on moose populations and birch forests dynamics (mortality, regeneration). My program will train innovative HQP who will accelerate scientific learning by shifting from the current focus on measuring impacts towards an adaptive approach where monitoring data is used iteratively to refine hypotheses to better understand mechanisms. These methods will improve predictions of near-term ecological changes to better anticipate impacts and undesirable future states.