Journal of Ecology and Environment

pISSN 2287-8327 eISSN 2288-1220


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Published online May 14, 2019

Journal of Ecology and Environment (2019) 43:19

© The Ecological Society of Korea.

Residual spatial autocorrelation in macroecological and biogeographical modeling: a review

Guetchine Gaspard1, Daehyun Kim2,3 and Yongwan Chun4

Department of Geography, University of Kentucky, Lexington, USA; Department of Geography, Seoul National University, Seoul, South Korea; Institute for Korean Regional Studies, Seoul National University, Seoul, South Korea; University of Texas at Dallas, Dallas, USA

Correspondence to:Daehyun Kim

Received: March 11, 2019; Accepted: April 21, 2019

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.


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.

Keywords: Spatial autocorrelation, Residual spatial autocorrelation, Non-stationarity, Missing variables, Sampling design, Scale, Species distribution models

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Journal of Ecology and Environment

pISSN 2287-8327 eISSN 2288-1220