At their most basic level, landscape models consider spatial heterogeneity, or the geographic distribution of landscape characteristics. This attention to spatial heterogeneity is a defining attribute of landscape ecology, and a large class of landscape models is concerned solely with mapping spatial patterns of ecological resources. These include environmental gradient models of the distribution of temperature, water, and soils; habitat models that use these environmental patterns to describe and predict distributions of individuals, populations, and communities; and conservation models of potential hotspots in rarity and endemism. The growth of these models and the field of spatial statistics, in general, has exploded as more and more data have become available in a spatially explicit format. Geographic information systems (GIS) and remote-sensing platforms are at the center of this expansion (spatial models and GIS). Continued improvements in techniques for gathering geor-eferenced field data (e.g., cheaper, more accurate global positioning systems) and for statistical analysis of spatial patterns (e.g., nonparametric ordination, classification analyses, and wavelets) have also played prominent roles in improving mapping capabilities.
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