MINIREVIEW Quantifying Landscape Spatial Pattern: What Is the State of the Art?

Landscape ecology is based on the premise that there are strong links between ecological pattern and ecological function and process. Ecological sys-tems are spatially heterogeneous, exhibiting consid-erable complexity and variability in time and space. This variability is typically represented by categori-cal maps or by a collection of samples taken at specific spatial locations (point data). Categorical maps quantize variability by identifying patches that are relatively homogeneous and that exhibit a relatively abrupt transition to adjacent areas. Alter-natively, point-data analysis (geostatistics) assumes that the system property is spatially continuous, making fewer assumptions about the nature of spatial structure. Each data model provides capabili-ties that the other does not, and they should be considered complementary. Although the concept of patches is intuitive and consistent with much of ecological theory, point-data analysis can answer two of the most critical questions in spatial pattern analysis: what is the appropriate scale to conduct the analysis, and what is the nature of the spatial structure? I review the techniques to evaluate cat-egorical maps and spatial point data, and make observations about the interpretation of spatial pat-tern indices and the appropriate application of the techniques. Pattern analysis techniques are most useful when applied and interpreted in the context of the organism(s) and ecological processes of inter-est, and at appropriate scales, although some may be useful as coarse-filter indicators of ecosystem func-tion. I suggest several important needs for future research, including continued investigation of scal-ing issues, development of indices that measure specific components of spatial pattern, and efforts to make point-data analysis more compatible with ecological theory. Key words: spatial pattern; index; indices; spatial heterogeneity; patchiness; landscape ecology; scale; geostatistics; autocovariation; spatial models