Incorporating spatial autocorrelation into species distribution models alters forecasts of climate‐mediated range shifts

Abstract

Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad-scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment-only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment-only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate forecasts lead to ineffective prioritization of conservation activities and potentially to avoidable species extinctions.

A hierarchical Bayesian Beta regression approach to study the effects of geographical genetic structure and spatial autocorrelation on species distribution range shifts

“…In general, spatial HBMs exhibited more conservative patterns of change compared to Maxent and nonspatial HBMs (Figure ). This result is in agreement with other research suggesting that environment‐only models forecast substantially greater range shifts compared to models incorporating spatial effects (Crase et al, ; Swanson et al, ). The rationale is that organisms exhibiting a high SAC in the environmental drivers accounting for their distribution ranges will have larger areas with similar climates, which will also make GCC effects more predictable and homogeneous across space (Nadeau, Urban, & Bridle, ).…”

“…Spatial HBMs, along with Maxent for genetic cluster C4, did not show residual SAC, which is a desirable property to avoid inaccurate parameter estimates and inadequate quantification of uncertainty (Beguin et al, ; Crase et al, ; Latimer et al, ; Record et al, ). In addition, spatial HBMs exhibited lower average model prediction errors than nonspatial HBMs.…”

“…In addition, spatial HBMs exhibited lower average model prediction errors than nonspatial HBMs. Hence, and from a purely statistical viewpoint, the higher rigour of spatial HBMs, in terms of higher accuracy and efficient removal of residual SAC, gives them a clear advantage (Crase et al, ; Swanson et al, ). Spatial HBMs also allowed us to assess the spatial effects on distribution range, which were quite compact and with high intensities at the distribution range centre (Figure ).…”

“…For this reason, genetic clusters or species with continuous distributions not only increase the ease of modelling, but also facilitate assessment of the SAC on distribution range shifts. Overall, it is clear that considering SAC adds realistic biological elements for understanding the long‐term effects of GCC on biodiversity (Cardador, Sardà‐Palomera, Carrete, & Mañosa, ; Crase et al, ; De Marco et al, ; Swanson et al, ). Nevertheless, it must be noted that we are assuming that SAC and its underlying forces remain relatively constant during climate change.…”

Small-scale drivers: the importance of nutrient availability and snowmelt timing on performance of the alpine shrub Salix herbacea

“…Local models and those which incorporate spatial relationships can mean the difference between predicting extinction and predicting persistence of alpine plant species (Randin et al 2009;Crase et al 2014). This is likely an effect of fine-scale temporal and spatial variability in temperature, precipitation, microtopography and geology, which affect both plant abundance and diversity (Moser et al 2005;Stein et al 2014).…”