Spatial autocorrelation in biology: 1. Methodology

Abstract

Spatial autocorrelation analysis tests whether the observed value of a nominal, ordinal, or interval variable at one locality is independent of values of the variable at neighbouring localities. The computation of autocorrelation coefficients for nominal, ordinal, and for interval data is illustrated, together with appropriate significance tests. The method is extended to include the computation of correlograms for spatial autocorrelation. These show the autocorrelation coefficient as a function of distance between pairs of localities being considered, and summarize the patterns of geographic variation exhibited by the response surface of any given variable. Autocorrelation analysis is applied to microgeographic variation of allozyme frequencies in the snail Helix aspersa. Differences in variational patterns in two city blocks are interpreted. The inferences that can be drawn from correlograms are discussed and illustrated with the aid of some artificially generated patterns. Computational formulae, expected values and standard errors are furnished in two appendices.

About Ganoderma boninense in oil palm plantations of Sumatra and peninsular Malaysia: Ancient population expansion, extensive gene flow and large scale dispersion ability

“…Finally, we used the Spagedi software version 1.5 (Hardy & Vekemans 2002) to test for allele clustering using spatial autocorrelation. Spatial coordinates for each individual isolate were converted from GPS (deg min 0 sec 00 NSWE) to UTM format (WGS84) and correlation between genetic similarity and spatial distance classes was tested for using Moran's I index of genetic similarity (Sokal & Oden 1978). A total of 20 000 permutations were used for significance testing of Moran's I.…”

Small-scale non-linear geostatistical analysis of Liocarcinus depurator (Crustacea: Brachyura) abundance and size structure in a western Mediterranean population

“…In the absence of spatial autocorrelation, the mathematical expectation of the semivariograms, the sample variance and the semivariogram appears flat (pure nugget effect . The term 'range' indicates the size of the patches in which the population is structured (Sokal & Oden 1978).…”

Habitat partitioning between woodland caribou and moose in Ontario: the potential role of shared prédation risk

“…'s is the randomization assumption in which the locations of the points are randomly distributed over the area. The theory behind these computations can be found in Sokal & Oden (1978), Cliff & Ord (1973) and Upton & Fingleton (1985). We plotted correlograms for all 3 species and cross-correlograms for pairs of species.…”