Carolyn T. Hunsaker

A factor analysis of landscape pattern and structure metrics

Fifty-five metrics of landscape pattern and structure were calculated for 85 maps of land use and land cover. A multivariate factor analysis was used to identify the common axes (or dimensions) of pattern and structure which were measured by a reduced set of 26 metrics. The first six factors explained about 87% of the variation in the 26 landscape metrics. These factors were interpreted as composite measures of average patch compaction, overall image texture, average patch shape, patch perimeter-area scaling, number of attribute classes, and large-patch density-area scaling. We suggest that these factors can be represented in a simpler way by six univariate metrics - average perimeter-area ratio, contagion, standardized patch shape, patch perimeter-area scaling, number of attribute classes, and large-patch density-area scaling.

Hierarchical Approaches to the Study of Water Quality in RiversSpatial scale and terrestrial processes are important in developing models to translate research results to management practices

and-use change may be the single greatest factor affecting ecological resources. Allan and Flecker (1993), who have identified six major factors threatening the destruction of river ecosystems, state that various transformations of the landscape-hydrologic changes to streams and rivers resulting from changes in land use, habitat alteration, and nonpoint source pollution-are probably the most widespread and potent threats to the well-being of lotic ecosystems. Measures of landscape structure are necessary to monitor change and assess the risks it poses to ecological resources (Graham et al. 1991, Hunsaker et al. 1990). Landscape ecologists seek to better understand the relationships between landscape structure and ecosystem processes at various spatial scales (Forman and Godron 1986, Risser 1987, Turner 1987, 1989). We use the word structure to refer to the spatial relationships of ecosystem characteristics such as vegetation, animal distributions, and soil types. Processes or function refers to the interactions-that is, the flow of energy, materials, and organismsbetween the spatial elements. Because landscapes are spatially het-

Scale problems in reporting landscape pattern at the regional scale

Remotely sensed data for Southeastern United States (Standard Federal Region 4) are used to examine the scale problems involved in reporting landscape pattern for a large, heterogeneous region. Frequency distributions of landscape indices illustrate problems associated with the grain or resolution of the data. Grain should be 2 to 5 times smaller than the spatial features of interest. The analyses also reveal that the indices are sensitive to the calculation scale,i.e., the unit area or extent over which the index is computed. This “sample area” must be 2 to 5 times larger than landscape patches to avoid bias in calculating the indices.

Monitoring environmental quality at the landscape scale

ver the past century, technological advances have greatly improved the standard of living in the United States. But these same advances have caused sweeping environmental changes, often unforeseen and potentially irreparable. Ethical stewardship of the environment requires that society monitor and assess environmental changes at the national scale with a view toward the conservation and wise management of our natural resources. Some of the most important environmental changes occur a t the spatial scale of landscapes. Obvious examples include clearcutting for lumber, urbanization, the loss of wetlands, and the conversion of forest and prairies into crop and grazing systems. Decisions about how to change land cover may be made by individual landowners, but their im-

Assessing ecological risk on a regional scale

Society needs a quantitative and systematic way to estimate and compare the impacts of environmental problems that affect large geographic areas. This paper presents an approach for regional risk assessment that combines regional assessment methods and landscape ecology theory with an existing framework for ecological risk assessment. Risk assessment evaluates the effects of an environmental change on a valued natural resource and interprets the significance of those effects in light of the uncertainties identified in each component of the assessment process. Unique and important issues for regional risk assessment are emphasized; these include the definition of the disturbance scenario, the assessment boundary definition, and the spatial heterogeneity of the landscape.

Issues related to the detection of boundaries

Ecotones are inherent features of landscapes, transitional zones, and play more than one functional role in ecosystem dynamics. The delineation of ecotones and environmental boundaries is therefore an important step in land-use management planning. The delineation of ecotones depends on the phenomenon of interest and the statistical methods used as well as the associated spatial and temporal resolution of the data available. In the context of delineating wetland and riparian ecosystems, various data types (field data, remotely sensed data) can be used to delineate ecotones. Methodological issues related to their detection need to be addressed, however, so that their management and monitoring can yield useful information about their dynamics and functional roles in ecosystems. The aim of this paper is to review boundary detection methods. Because the most appropriate methods to detect and characterize boundaries depend of the spatial resolution and the measurement type of the data, a wide range of approaches are presented: GIS, remote sensing and statistical ones.

Communicating Ecological Indicators to Decision Makers and the Public

Introduction EMAP’s Indicators A Region as a Case Study Development of Common-language Indicators Testing the Common-language Indicators From “values” to “valued aspects” Testing CLIs in relation to valued aspects of the environment Discussion Final thoughts Responses to this Article Acknowledgments Literature Cited Appendix 1 Appendix 2 Appendix 3

Sampling to characterize landscape pattern

Current reseach suggests that metrics of landscape pattern may reflect ecological processes operating at different scales and may provide an appropriate indicator for monitoring regional ecological changes. This paper examines the extent to which a 1/16 areal subset of the landscape using equally spaced 40-km2 hexagons can characterize the spatial extent of land cover types and landscape pattern (number of types of edges, patch shape complexity, dominance, and contagion). For 200-m resolution data the hexagon subset gives a reasonable estimate of overall landscape cover but may not be adequate for monitoring uncommon land cover types such as wetlands. For agriculture and forest, their proportion of the full landscape units is only outside the 95% confidence interval of the hexagon estimate 4–8% of the time, whereas the proportions for wetland and barren areas are outside the confidence interval 11–34% of the time. The hexagon subset also does not appear to be adequate as the sole basis for monitoring landscape pattern. The values for contagion, dominance, and shape complexity calculated on the full landscape units are outside the 95% confidence interval of the hexagon estimate 27–76% of the time. Other statistical analyses include regressions between full landscape and hexagon subsets, mean differences and standard errors along with tests on number of positive and negative values, and percent relative error of hexagon estimates.Although the research described in this article has been funded in part by the U.S. Environmental Protection Agency, under Interagency Agreement DW89934921-01-0 with the U.S. Department of Energy under Contract DE-AC05-84OR21400 with Martin Marietta Energy Systems, Inc., it has not been subjected to Agency review. Therefore, it does not necessarily reflect the views of the Agency. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

Spatial partitioning of environmental correlates of avian biodiversity in the conterminous United States

Classification and regression tree (CART) analysis was used to create hierarchically organized models of the distribution of bird species richness across the conterminous United States. Species richness data were taken from the Breeding Bird Survey and were related to climatic and land use data. We were available to the independent variables, yielding an /?2-type goodness of fit metric of 47.5% deviance explained. The resulting model recognized eleven groups of hexagons, with species richness within each group determined by unique sequences of hierarchically constrained independent variables. Within the hierarchy, climate data accounted for more variability in the bird data, followed by land cover proportion, and then pattern metrics. The model was then used to predict species richness in all 12,500 hexagons of the conterminous United States yielding a map of the distribution of these eleven classes of bird species richness as determined by the environmental correlates. The potential for using this technique to interface biogeographic theory with the hierarchy theory of ecology is discussed.

The effects of habitat resolution on models of avian diversity and distributions: a comparison of two land-cover classifications

Quantifying patterns is a key element of landscape analysis. One aspect of this quantification of particular importance to landscape ecologists is the classification of continuous variables to produce categorical variables such as land-cover type or elevation stratum. Although landscape ecologists are fully aware of the importance of spatial resolution in ecological investigations, the potential importance of the resolution of classifications has received little attention. Here we demonstrate the effects of using two different land-cover classifications to predict avian species richness and the occurrences of six individual species across the conterminous United States. We compared models built with a data set based on 14 coarsely resolved land-cover variables to models built with a data set based on 160 finely resolved land-cover variables. In general, comparable models built with the two data sets fit the data to similar degrees, but often produced strikingly different predictions in various parts of the country. By comparing the predictions made by pairs of models, we determined in which regions of the US predictions were most sensitive to differences in land-cover classification. In general, these sensitive areas were different for four of the individual species and for predictions of species richness, indicating that alternate classifications will have different effects in the analyses of different ecological phenomena and that these effects will likely vary geographically. Our results lead us to emphasize the importance of the resolution to which continuous variables are classified in the design of ecological studies.