Author ORCID Identifier

Date Available


Year of Publication


Degree Name

Master of Arts (MA)

Document Type

Master's Thesis


Arts and Sciences



First Advisor

Dr. J. Anthony Stallins


Macroecological and biogeographical modelers have predicted the distribution of species across space relying on the relationship between biotic processes and environmental variables. Such a method employs data associated, for instance, with species abundance or presence/absence, climate, geomorphology, and soils. Statistical analyses found in previous studies have highlighted the importance of accounting for the effects of spatial autocorrelation (SAC), which indicates a level of dependence between pairs of nearby observations. A consensus has existed that residual spatial autocorrelation (rSAC) can substantially impact modeling processes and inferences. However, more emphasis should be put on identifying the sources of rSAC and the degree to which rSAC becomes detrimental. In this thesis, we review previous studies to identify various factors that potentially engender the presence of rSAC in macroecological and biogeographical models. Additionally, special attention is paid to the quantification of rSAC by attempting to bring out the magnitude to which the presence of SAC in model residuals impedes the modeling process. The review identified that five categories of factors potentially drive the presence of SAC in model residuals: the type of ecological data and the processes underlying it, scale and distance, missing variables, sampling design, as well as the assumptions and methodological perspectives of the investigator. Furthermore, we concluded that more explicit discussion of rSAC should be carried out in species distribution modeling. We recommend further investigations involving the quantification of rSAC to understand when rSAC can have a negative effect on the modeling process.

Digital Object Identifier (DOI)

Funding Information

Project title: Analyzing the Effects of Spatial Autocorrelation in Geospatial Databases

This project was funded by:

1. The National Science Foundation (Award number: 1560907), 2016-2020

2. The Department of Geography, University of Kentucky, 2016-2021