Spatio‐temporal models of intermediate complexity for ecosystem assessments: A new tool for spatial fisheries management

Multispecies models are widely used to evaluate management trade-offs arising from species interactions. However, identifying climate impacts and sensitive habitats requires integrating spatial heterogeneity and environmental impacts into multispecies models at fine spatial scales. We therefore develop a spatio-temporal model of intermediate complexity for ecosystem assessments (a "MICE-in-space"), which is fitted to survey sampling data and time series of fishing mortality using maximum-likelihood techniques. The model is implemented in the VAST R package, and it can be configured to range from purely descriptive to including ratio-dependent interactions among species. We demonstrate this model using data for four groundfishes in the Gulf of Alaska using data from 1982 to 2015. Model selection for this case-study shows that models with species interactions are parsimonious, although a model specifying separate density dependence without interactions also has substantial support. The AIC-selected model estimates a significant, negative impact of Alaska pollock (Gadus chalcogrammus, Gadidae) on productivity of other species and suggests that recent fishing mortality for Pacific cod (G. microcephalus, Gadidae) is above the biological reference point (BRP) resulting in 40% of unfished biomass; other models show similar trends but different scales due to different BRP estimates. A simulation experiment shows that fitting a model with fewer species at a coarse spatial resolution degrades estimation performance, but that interactions and biological reference points can still be estimated accurately. We conclude that MICE-in-space models can simultaneously estimate fishing impacts, species trade-offs, biological reference points and habitat quality. They are therefore suitable to forecast short-term climate impacts, optimize survey designs and designate protected habitats.