Residual spatial autocorrelation in macroecological and biogeographical modeling: a review

Abstract

Macroecologists and biogeographers continue to predict the distribution of species across space based on the relationship between biotic processes and environmental variables. This approach uses data related to, for example, species abundance or presence/absence, climate, geomorphology, and soils. Researchers have acknowledged in their statistical analyses the importance of accounting for the effects of spatial autocorrelation (SAC), which indicates a degree of dependence between pairs of nearby observations. It has been agreed that residual spatial autocorrelation (rSAC) can have a substantial impact on modeling processes and inferences. However, more attention should be paid to the sources of rSAC and the degree to which rSAC becomes problematic. Here, we review previous studies to identify diverse factors that potentially induce the presence of rSAC in macroecological and biogeographical models. Furthermore, an emphasis is put on the quantification of rSAC by seeking to unveil the magnitude to which the presence of SAC in model residuals becomes detrimental to the modeling process. It turned out that five categories of factors can drive the presence of SAC in model residuals: ecological data and processes, scale and distance, missing variables, sampling design, and assumptions and methodological approaches. Additionally, we noted that more explicit and elaborated discussion of rSAC should be presented in species distribution modeling. Future investigations involving the quantification of rSAC are recommended in order to understand when rSAC can have an adverse effect on the modeling process.

Latitudinal and environmental patterns of species richness in lizards and snakes across continental North America

“…Residual spatial autocorrelation varies among our MLR models for individual snake (Figure S5.3) and lizard (Figure S5.4) clades. Residual spatial autocorrelation can bias effect sizes (Gaspard et al., 2019), so our MLR results for clades with greater residual spatial autocorrelation (e.g. Teioidea) should be interpreted with caution.…”

Consequences of ignoring variable and spatially-autocorrelated detection probability in spatial capture-recapture

“…Spatial autocorrelation in detection probability – when detectability is more similar among neighboring than distant detectors – is common in ecological studies (Guélat and Kéry 2018). Observed and unobserved spatially-autocorrelated variation in detection probability could have many causes (Gaspard et al 2019), divided into two broad categories. On the one hand is the nature of the data collection; for example, regional differences in the mobilization of volunteers for non-invasive DNA collections, variation in camera trap efficiency due to inadvertent scent contamination at a cluster of sites, or reduced physical capture success in traps installed by a less-experienced operator in their designated area (Kristensen and Kovach 2018; Bischof et al 2020a; Tourani et al 2020a).…”

Impacts of climate change on aquatic insects in temperate alpine regions: Complementary modeling approaches applied to Swiss rivers

“…The magnitude to which the presence of spatial autocorrelation in the residuals becomes detrimental to the modeling process is still debated (Gaspard et al, 2019). However, spatially explicit models to control for it have been shown to be more realistic in the context of species' range shift (Crase et al, 2014) or expansion (De Marco et al, 2008), or in conservation planning (Domisch et al, 2019).…”