Fisheries science has traditionally concerned itself with the interplay of fish population abundance, fishing effort, and fishery catch. However, fisheries managers must increasingly cope with changes over time in fish productivity (e.g., changing individual growth and juvenile survival rates). One hypothesis for changing productivity is that interactions among species will be modified by climate change and fishing impacts, and that changes in these interactions cause the parameters of single-species models to be nonstationary. Estimating community dynamics and species interactions has historically been difficult using time-series data. However, recent research suggests that spatio-temporal analyses have greater statistical efficiency than previous time-series approaches because they use spatial variation as a form of replication.
In this talk, I discuss ongoing collaborations to estimate community dynamics and interactions using multivariate spatio-temporal point process models. I start with a global meta-analysis of a classic hypothesis for nonstationary catch rates, i.e., that fish populations collapse to a core habitat during declines in population size. Using bottom trawl data for 120 populations worldwide, colleagues and I estimate a 0.6% decrease in “effective area” for every 10% decline in abundance, but also show that this relationship varies widely among populations and regions. I then use “spatial dynamic factor analysis” to summarize community dynamics for the Eastern Bering Sea. This case study captures the decline and recovery of cod-like species in the mid-2000s, and shows that species with similar evolutionary history have more similar dynamics than unrelated species. Finally, colleagues and I propose a new procedure for estimating the matrix of pairwise species interactions, where this approach bridges between unregulated (“neutral”) and highly-regulated (“niche”) approaches to community ecology. Using the marine community in the Gulf of St. Lawrence as case study, we show a mixture of regulated and unregulated dynamics, where the unregulated component is associated with a recovering grey seal population that is negatively impacting productivity for three prey species of fish.
I conclude by outlining opportunities for future research in statistical ecology. Throughout, I stress that continuing progress will likely combine methodological improvements (e.g., Riemann MCMC) with increased biological realism in models (e.g., advective-diffusive movement in community models). Given the increasing role of statistics in ecological theory (e.g., neutral and maximum entropy theories), I hypothesize that this two-pronged approach will yield improvements in both the theory and practice of fisheries science.