New multi-scale landscape indices for spatial pattern analysis
A major component of Landscape Ecology is studying how spatial patterns of vegetation affect ecological processes and individual organisms. A reoccurring issue within the field has always been that the analysis of this process/pattern relationship always depends on the scale of analysis undertaken in the study.
Many studies that attempt to relate species occurrence to the spatial patterns of landscapes typically have narrowly relevant results, since the spatial pattern is analyzed at a single spatial scale. The scale of analysis has important ramifications for the conclusions of the study and often the patterns that are described at one spatial scale can be very different at another spatial scale. This issue of scale has long hampered the application of Landscape Ecology analyses because patterns of habitat use for individual organisms can vary widely from broader trends found within, and between, populations. SILVIS post-doc Avi Bar Massada, however, has developed a technique that will allow for multiple spatial scales to be incorporated simultaneously into the analysis to more accurately describe how patterns change across these scales. The hope is that by incorporating multiple scales into analyses of spatial pattern, they will generate more accurate predictions about the distribution or abundance of a particular species within an area.
Bar Massada calls this technique Multiscale Contextual Spatial Pattern Analysis (MCSPA) and feels that it addresses two major short-comings of traditional methods of spatial pattern analysis: 1) it incorporates multiple scales into the analysis, thereby giving a spatial context to every pixel in a landscape map, and 2) it addresses the fact that not all habitat/non-habitat patches within a given scale are created equal. Going back to the warbler/early successional forest example, a narrow strip of successional forest protruding from a large patch of the same forest type, and surrounded on most sides by agriculture will have very different use patterns by the birds than the core forest it protrudes from. Traditional analysis methods treat contiguous patches of forest as a single entity, and thus would define all parts of the forest patch as suitable habitat (i.e., the basic level of analysis is the patch, regardless of its shape, and the assumption is that it is internally homogeneous). Using Bar Massada's MCSPA, however, the surrounding areas of agriculture and wetland would indicate that the narrow strip is less suitable habitat for Golden-winged Warblers, since its spatial context is very different than the one of a large block of succesional forest. Such a prediction fits well with known habitat use patterns of the birds.
Bar Massada tested these methods in collaboration with another SILVIS member, Ph. D. candidate Eric Wood, on the distribution an abundance of migrating bird species within Wood's study site at Fort McCoy, Wisconsin. Ft. McCoy is a patchy landscape with large areas of oak savanna that are important habitat for a variety of migratory songbirds (including the above-mentioned Golden-winged Warbler). Bar Massada applied MCSPA to a habitat map of Ft. McCoy and compared the results to Wood's field data on species richness and abundance. The MCSPA metrics were correlated with bird species richness and abundance as well as, or better than, existing, single-scale metrics of spatial pattern analysis. The standard deviation of the scalograms explained variability of bird species richness better than single-scale metrics quantifying the percentage of habitat cover, number of patches within a given area, or NDVI (an index that measures the density of healthy green vegetation), all of which have been applied in the past to try and predict species richness.