Spatial autocorrelation (SAC) is the dependence of a given variable's values on the values of the same variable recorded at neighboring locations (Cliff and Ord 1973; Fortin and Dale 2005). When high values are associated with relatively high values at neighboring locations, SAC is said to be positive and, conversely, where high values correspond to relatively low values at neighboring locations, SAC is negative. SAC can be a property of the variable itself (inherent or intrinsic SAC) or it can arise due to the dependence of the variable of interest on another spatially autocorrelated variable (induced SAC) (Legendre et al. 2002; Fortin and Dale 2005). Because it lies at the core of most spatial models, SAC is a fundamental concept of spatial analysis (Getis 2008).
During the last 2 decades, mostly after Pierre Legendre published his seminal paper “Spatial autocorrelation: trouble or new paradigm” (Legendre 1993), SAC received considerable attention from ecologists—in particular biogeographers investigating macroecological patterns of species distributions (Kissling and Carl 2008)—and from population geneticists investigating small-scale spatial genetic structure of populations (Guillot et al. 2009). These circumstances prompted Arthur Getis, in a review on the evolution of the SAC concept, to conclude that “Nearly all the major journals that concern themselves with the ecological aspects of their subjects print articles having a spatial autocorrelation foundation” (Getis 2008). In stark contrast, the issue of SAC has hitherto been largely ignored in behavioral ecology, despite the fact that many studies in this field deal with a spatial component. Thus, despite its recognized importance in adjacent fields of ecological research, even very basic topics in behavioral ecology remain unexplored with respect to SAC, and we could only find a handful of studies (van der Jeugd and McCleery 2002; Laiolo and Tella 2006; Duraes et al. 2007; Aarts et al. 2008; Giesselmann et al. 2008; Holdo et al. 2009), which included SAC in their research paradigm.
The general aim of this paper is to draw the attention of behavioral ecologists to the phenomenon of SAC. Specifically, we aim 1) to provide examples of spatially autocorrelated variables, indicating that SAC is widespread in variables commonly used in behavioral ecology studies, 2) to show why it is important to take SAC into account, and 3) to point to some tools to explore and model it.