Landscape Pattern Analysis -- Characterizing Landscape Patterns - Conceptual Foundation

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variable size across the landscape one cell at a time. The window size and form should be selected such that it reflects the scale and manner in which the organism perceives or responds to pattern. If this is unknown, the user can vary the size of the window over several runs and empirically determine which scale the organism is most responsive to. The window moves over the landscape one cell at a time, calculating the selected metric within the window and returning that value to the center cell. The result is a continuous surface which reflects how an organism of that perceptual ability would perceive the structure of the landscape as measured by that metric. The surface then would be available for combination with other such surfaces in multivariate models to predict, for example, the distribution and abundance of an organism continuously across the landscape. (3) Global landscape structure.ÐThe traditional application of landscape metrics involves characterizing the structure of the entire landscape with one or more landscape metrics. For example, traditional landscape pattern analysis would measure the total contrast-weighted edge density for the entire landscape. This would be a global measure of the average property of that landscape. This is a Òlandscape-centricÓ perspective on landscape patterns in which the scope of analysis is restricted to the characterization of the entire patch mosaic in aggregate. In this case, the landscape is characterized according to one or more landscape-level metrics (see below). The results of a global landscape structure analysis is typically given in the form of a vector of measurements, where each element represents a separate landscape metric. 8.7  4. Levels of Heterogeneity Patches form the basis (or building blocks) for categorical maps. Depending on the method used to derive patches (and therefore the data available), they can be characterized compositionally in terms of variables measured within them. This may include the mean (or mode, central, or max) value and internal heterogeneity (variance, range). However, in most applications, once patches have been established, the within-patch heterogeneity is ignored. Landscape pattern metrics instead focus on the spatial character and distribution of patches. While individual patches possess relatively few fundamental spatial characteristics (e.g., size, perimeter, and shape), collections of patches may have a variety of aggregate properties, depending on whether the aggregation is over a single class (patch type) or multiple classes, and whether the aggregation is within a specified subregion of a landscape or across the entire landscape. Thus, the common hierarchical organization of categorical maps is patch!class!landscape. However, the fundamental spatial unit in a grid data model is the cell. Therefore, for grid representations of categorical patterns, the cell represents an additional (and finest) level of heterogeneity. (1) Cell-level metrics.Ð cell-level metrics are defined for individual cells, and characterize the spatial context or ecological neighborhood of each cell without explicit regard to any patch or class affiliation. In other words, cell metrics are not patch-centric. Cell metrics provide the finest spatial unit of resolution for characterizing spatial patterns. Each cell has a spatial context defined by the composition and configuration of its neighborhood, and that context may 8.8  influence the ecological properties of the focal cell. For example, an individual organism dispersing from its natal habitat interacts with the structure of the landscape in the neighborhood surrounding that initial location. Thus, the ability to traverse across the landscape from that location may be a function of the landscape character within some ecological neighborhood defined by dispersal distance. Cell metrics may be computed for a targeted set of focal cells representing specific locations of interest (e.g., nest sites, capture locations, etc.), in which case the standard output would consist of a vector of cell-based measurements reported in tabular form (i.e., one record for each focal cell). Cell metrics may also be computed exhaustively for every cell in the landscape, in which case the standard output would consist of a continuous surface grid or map. (2) Patch-level metrics.Ðpatch-level metrics are defined for individual patches, and characterize the spatial character and context of patches. In most applications, patch metrics serve primarily as the computational basis for several of the landscape metrics, for example by averaging patch attributes across all patches in the class or landscape; the computed values for each individual patch may have little interpretive value. However, sometimes patch indices can be important and informative in landscape-level investigations. For example, many vertebrates require suitable habitat patches larger than some minimum size (e.g., Robbins et al. 1989), so it would be useful to know the size of each patch in the landscape. Similarly, some species are adversely affected by edges and are more closely associated with patch interiors (e.g., Temple 1986), so it would be useful to know the size of the core area for each patch in the landscape. The probability of occupancy and persistence of an organism in a patch may be related to patch insularity (sensu Kareiva 1990), so it would be useful to know the nearest neighbor of each patch and the degree of contrast between the patch and its neighborhood. The utility of the patch characteristic information will ultimately depend on the objectives of the investigation. (3) Class-level metrics.ÐClass-level metrics are integrated over all the patches of a given type (class). These may be integrated by simple averaging, or through some sort of weighted-averaging scheme to bias the estimate to reflect the greater contribution of large patches to the overall index. There are additional aggregate properties at the class level that result from the unique configuration of patches across the landscape. In many applications, the primary interest is in the amount and distribution of a particular patch type. A good example is in the study of habitat fragmentation. Habitat fragmentation is a landscape-level process in which contiguous habitat is progressively sub-divided into smaller, geometrically more complex (initially, but not necessarily ultimately), and more isolated habitat fragments as a result of both natural processes and human land use activities (McGarigal and McComb 1999). This process involves changes in landscape composition, structure, and function and occurs on a backdrop of a natural patch mosaic created by changing landforms and natural disturbances. Habitat loss and fragmentation is the prevalent trajectory of landscape change in several human-dominated regions of