Incorporating non-stationary spatial variability into dynamic species distribution models

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.

sdmTMB: An R Package for Fast, Flexible, and User-Friendly Generalized Linear Mixed Effects Models with Spatial and Spatiotemporal Random Fields

“…Additional functionality in sdmTMB not already mentioned includes interpolating across missing time slices and forecasting, the barrier SPDE model (Bakka, Vanhatalo, Illian, Simpson, and Rue 2019), and time-varying spatiotemporal covariance parameters (Ward, Barnett, Anderson, Commander, and Essington 2022). There are several planned future additions to the sdmTMB model structure.…”

Complex changes in climatic suitability for Cassin’s Sparrow ( Peucaea cassinii ) revealed by retrospective ecological niche modeling

“…We also know that climate change can drive shifts in the spatial distribution of many bird species as they track suitable conditions (Pecl et al 2017). In addition, recent work underscores the importance of nonconstant processes, such as long-term trends, seasonal or temporal cycles, or random variation, which can cause climatic drivers to vary in complex ways over time and space, a phenomenon referred to as non-stationarity (Rollinson et al 2021, Ward et al 2022. It is generally agreed that the most important contemporary driver of non-stationary environmental trends is global climate change (Ward et al 2022).…”

Predatory walls may impair climate warming‐associated population expansion

“…A focal aim of fisheries oceanography is to understand how fish populations respond to climate change, as well as to regional‐ and local‐scale oceanographic variability (Cury et al, 2008). To do so, one can use species distribution models (SDMs) that take into account environmental variables, such as sea temperature, in conjunction with species location (Antão et al, 2022; Durant et al, 2021; Planque et al, 2011; Ward et al, 2022). However, accounting for the biotic interactions of species in the ecosystem remains challenging (Guisan & Thuiller, 2005).…”