The Detection of Spatial Structure in Populations and Communities: An Empirical Case Study

The presence of spatial autocorrelation in abundance and richness patterns has been recognised for some time. Evaluation of the tools to quantify patterning often uses simulated data that may be unrealistic or empirical field data where the presence and cause of structuring are unknown. We examine the efficacy of spatial pattern analysis for detecting pattern in empirical data at a fine scale using a field-based mesocosm experiment of a Drosophilidae community associated with decaying fruit. The mesocosm comprised 2 microclimate treatments that generated a particular expected spatial pattern in abundance and species richness. The magnitude of Moran's autocorrelation coefficients (I) was < 0.3 (i.e., low). However, the detected pattern was unaffected. Low Moran's I values did not result from low sample sizes, neither was the significance of Moran's I falsely inflated by large sample sizes. Examination of published I values revealed that autocorrelation values between 0.1 and 0.3 are common in empirical data, particularly at fine (lag distance <= 1 m) spatial scales. Pooling temporal samples strengthened the detected output without affecting the form of spatial pattern. We conclude that "weak" responses provide a valuable basis for mechanistic hypothesis generation, especially at fine spatial scales, and that the strength of spatial structure is likely to be determined by the spatial scale of the study.