Incorporating non-stationary spatial variability into dynamic species distribution models

Abstract

Abstract Ecologists and fisheries scientists are faced with forecasting the ecological responses of non-stationary processes resulting from climate change and other drivers. While much is known about temporal change, and resulting responses vis-à-vis species distributional shifts, less is known about how spatial variability in population structure changes through time in response to temporal trends in drivers. A population experiencing decreasing spatial variability would be expected to be more evenly spatially distributed over time, and an increasing trend would correspond to greater extremes or patchiness. We implement a new approach for modelling this spatiotemporal variability in the R package sdmTMB. As a real-world application, we focus on a long-term groundfish monitoring dataset, from the west coast of the USA. Focusing on the 36 species with the highest population densities, we compare our model with dynamic spatiotemporal variance to a model with constant spatiotemporal variance. Of the 36 species examined, 13 had evidence to support increasing patchiness, including darkblotched rockfish, lingcod, and petrale sole. Species appearing to be more uniformly spatially distributed over time included: Dover sole, Pacific ocean perch, and Dungeness crab. Letting spatiotemporal variation change through time generally results in small differences in population trend estimates, but larger estimated differences in precision.