\ Landscape Ecology vol. 4 no. I pp 21-30 (1990) SPB Academic Publishing bv, The Hague Spatial and temporal analysis of landscape patterns Monica G. Turner Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 Keywords: geographic information systems, GIS, spatial pattern analysis, scale, neutral model, distu-rbance Abstract Introduction materials such as sediment or nutrients (e.g., Peter- john and Correll 1984 Ryszkowski and Kedziora A variety of ecological questions now require the study of large regions and the understanding of spa- tial heterogeneity. Landscape ecology seeks to un- derstand the ecological function of large areas and hypothesizes that the spatial arrangement of eco- systems, habitats, or communities has ecological implications. For example, landscape patterns may influence the spread of disturbance (e.g., Romme and Knight 1982 Franklin and Forman 1987 Turn- er 1987a), the distribution and persistence of popu- lations (e.g., Van Dorp and Opdam 1987 Fahrig and Paloheimo 1988), ���large herbivore foraging (e.g., Senft et al. 1987), the horizontal flow of A variety of ecological questions now require the study of large regions and the understanding of spatial heterogeneity. Methods for spatial-temporal analyses are becoming increasingly important for ecological studies. A grid cell based spatial analysis program (SPAN) is described and results of landscape pattern analy- sis using SPAN are presentedd. Several ecological topics in which geographic information systems (GIS) can play an important role (landscape pattern analysis, neutral models of pattern and process, and extrapolation across spatial scales) are reviewed. To study the relationship between observed landscape patterns and ecolog- ical processes, a neutral model approach is recommended. For example, the expected pattern (i.e., neutral model) of the spread of disturbance across a landscape can be generated and then tested using actual landz scape data that are stored in a GE. Observed spatial or temporal patterns in ecological data may also be in- fluenced by scale. Creating a spatial data base frequently requires integrating data at different scales. Spatial scale is shown to influence landscape pattern analyses, but extrapolation of data across spatial scales may be possible if the grain and extent of the data are specified. The continued development and testing of new methods for spatial-temporal analysis will contribute to a general understanding of landscape dynamics. 1987), and other ecologically important processes such as net primary production (e.g., Turner 1987b Sala et al. 1988). Landscape-level pheno- mena are also receiving increasing attention as questions of global change become more promi- nent. Therefore, methods to analyze and interpret heterogeneity at broad spatial scales are becoming increasingly important for ecological studies. The need to consider spatial and temporal scale in ecological analyses has often been noted (e.g., Allen and Starr 1982 Delcourt et al. 1983 O���Neill et al. 1986 Addicott et al. 1987 Getis and Franklin 1987 Meentemeyer and Box 1987 Morris 1987

Urban et al. 1987). Given the dramatic expansion of the range of scales at which ecological problems are posed, this need may be greater than ever. Para- meters and processes important at one scale are frequently not important or predictive at another scale, and information is often lost as spatial da- ta are considered at ���coarser scales of resolution (Henderson-Sellers et al. 1985 Meentemeyer and Box 1987). Ecological problems may also require the extrapolation of fine-scale measurement for the analysis of broad-scale phenomena. Therefore, the development of methods that will preserve infor- mation across scales or quantify the loss of infor- mation with changing scales has become a critical task. Such methods are necessary before ecological insights can, be extrapolated between spatial and temporal scales. Sharpe 198 1 Forman and Godron 1981, 1986 Krummel et al. 1987 Turner and Ruscher 1988). Considerable progress has been made in landscape pattern analysis (e.g., Milne 1988 O���Neill et al. 1988 Turner and Ruscher 1988). Many studies em- ploy user-generated computer programs to perform the analyses rather than commercially available GIS. User-generated programs allow the inclusion of customized analytical methods and easy linkages to other programs such as spatial simulation models. Such programs generally lack the advanced graphics capabilities of commercially available GIS, but may have the ability to run on almost any computer. I will describe a spatial analysis program that I developed in FORTRAN and briefly review some of its applications. Geographical information systems (GIS) of vary- ing complexity have emerged as useful tools in ad- dressing landscape-level research questions. Many current ecological problems can be addressed more easily by using some type of GIS. Such questions might include: How has landscape structure chang- ed through time? What factors control landscape patterns? How does landscape pattern affect eco- logical processes? Can measures of landscape pat- tern be directly related to ecological function? How does landscape pattern affect the spread of distur- bance? Can landscape changes be predicted using simulation models? How does spatial scale in- fluence the analysis of landscape pattern? The objectives of this paper are to review several topics in which GIS can play an important role and to highlight current research results. In particular, I will focus on the analysis of landscape data, the use of neutral models of pattern and process, and extrapolation across spatial scales. Landscape pattern analysis Spatial analysis program (SPAN) J SPAN is a grid-cell based analysis program that can be applied to any kind of categorical data (note that SPAN is not related to the commercially available geographic information system, SPANS). The pro- gram was developed to quantify landscape patterns and their changes in an ecologically meaningful manner (Turner and Ruscher 1988) and to evaluate the predictions of a spatial simulation model (Turn- er 1987c, 1988). SPAN can be used with any kind of categorical data that can be rasterized at an ap- propriate level of resolution. The program provides printed output with some summary statistics and computerized output in the form of data files that can be statistically analyzed using SAS. SPAN incorporates a series of measures of spa- tial pattern (Table 1). The fraction of the land- scape, p, occupied by each type of data (e.g., cover type) is calculated. Nearest neighbor probabilities, sj are then calculated, representing the probability of cells of land use type i being adjacent to cells of land use type j. The qij values are calculated by Before the interaction between landscape structure dividing the number of cells of type i that are adja- and ecological processes can be understood, land- cent to typej by the total number of cells of type i. scape patterns must be identified and quantified in Nearest neighbor probabilities can be calculated meaningful ways. Landscape mosaics are mixtures both vertically and horizontally (even diagonally) of natural and human-managed patches that vary such that anisotropism, or directionality, in the in size, shape, and arrangement (e.g., Burgess and spatial pattern can be measured. The degree of