Authors: Guetchine Gaspard*, UNIVERSITY OF KENTUCKY, Daehyun Kim, Seoul National University, Yongwan Chun, University of Texas at Dallas
Topics: Spatial Analysis & Modeling
Keywords: Spatial autocorrelation, residual spatial autocorrelation, non-stationarity, missing variables, sampling design, scale, species distribution models
Session Type: Poster
Start / End Time: 1:20 PM / 3:00 PM
Room: Napoleon Foyer/Common St. Corridor, Sheraton, 3rd Floor
Presentation File: Download
Biogeographers and ecologists continue to predict the distribution of species across space based on the relationship between biotic processes and environmental variables. This approach uses data gathered, for example, on species abundance or presence/absence, on climate, and on the physics, chemistry and geomorphology of 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 substantial impact on modeling processes and outcomes. However, more attention is yet to 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 biogeographical and ecological 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. Also, we note that more explicit and elaborated discussion should be presented in species distribution modeling. In particular, future investigations are recommended to involve the quantification of rSAC.