A comparison of nested analysis of variance (ANOVA) and variograms for characterizing grassland spatial structure under a limited sampling budget

Abstract

It is often necessary to estimate fine-scale grassland heterogeneity in situ using ground-based spectral radiometers. However, the sampling techniques used to describe spatiotemporal heterogeneity will strongly influence perceived landscape structure. We hypothesize that nested sampling schemes based on random-effects analysis of variance (ANOVA) will provide a more reliable characterization of spatial structure than that provided by spatial dependence models of geostatistics, whose basic assumption of stationarity is often violated over patchy landscapes. To test this hypothesis, we simulated a variety of nonstationary landscapes with varying complexity of patchiness, then compared the consistency of findings of both approaches. Our results showed that significance tests by distance classes of nested ANOVA consistently provided a more stable characterization of structure than that provided by variogram parameters for all landscapes. Despite its limited scope, this simulation suggests that much more attention should be paid to approaches to pattern description when sampling real landscapes.