Grant: Discovery Grants Program - Individual

Wild commercial fish populations are extremely important to Canadians and ecological management units rely on accurate forecasting of various ecosystem attributes to support management decisions. The forecasts of those attributes form the basis for tactical management advice in the short term and strategic recommendations in long-term management planning. However, complex population dynamics often arise from various spatial and spatiotemporal processes such as interactions among species, interactions with the physical environment, high intrinsic growth rates, density-dependent dynamics, human intervention, and other stochastic process errors. This makes accurate and adaptive predictive models that are capable to analyze complex systems very critical for successful management. Geostatistical modelling is a major tool of linear predictive models that utilizes the spatial nature of ecological time series. Fisheries stock assessment recognizes such importance and has been moving towards spatial stock assessment. Predictive distribution modelling utilizes spatiotemporal models to incorporate environmental covariates and fish movement to make finer projections. On the other hand, it has been found nonlinear dynamics are common in marine fisheries, and the interaction between the natural system and human interventions can limit the potential for predictions given the observed patterns of high dimensionality and the nonlinearity in exploited systems. Recent developments in nonlinear time series forecasting method provide an alternative tool from data science to improve the predictive ability of ecological time series such as Empirical Dynamic models. Thus, it is crucial to test the predictive ability of the two modern methods using the rich ecological time-series exist from both research surveys, commercial catch, and climate monitoring in the Northwest Atlantic region. Specifically, I will focus on the nonlinear forecasting modelling and geostatistical spatiotemporal distribution modelling to investigate the following specific objectives: 1) are the time series of the major commercial fish in the Northwest Atlantic nonlinear in nature and can they be forecasted using nonlinear forecasting methods 2) do those fish species and their community display predictable distribution shifts in both the short term and long term using geostatistical models; 3) can we improve various spatial processes for stock assessment by incorporating predictions from those models; and 4) how the knowledge gained from predictive modelling contribute to better fish stock management to balance the species, the ecosystem, and the industry.