No abstract

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-km 2 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.

Landscapes were mapped as clusters of 2 or 3 land cover** types, based on their pattern within the clusters and tendency for a single type to dominate. These landscapes, called Landscape Pattern Types (LPTs), were combined with other earth surface feature data in a Geographic Information System (GIS) to test their utility as analysis units. Road segment density increased significantly as residential and urbanized land cover components increased from absent, to present as patch, to present as matrix (i.e., the dominant land cover type). Stream segment density was significantly lower in LPTs with an urbanized or residential matrix than in LPTs with either a forest or agriculture matrix, suggesting an inverse relationship between stream network density and the prevalence of human development other than agriculture in the landscape. The ratio of average forest patch size to total forest in the LPT unit decreased as agriculture replaced forest, then increased as residential and urban components dominated. Wetland fractal dimension increased as agriculture and residential land cover components of LPTs increased. Comparison of LPT and LUDA land cover area statistics in ecoregions suggested that land cover data alone does not provide information as to its spatial arrangement.

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Common decision support tools and a growing body of knowledge about ecological recovery can help inform and guide large state and federal restoration programs affecting thousands of impaired waters. Under the federal Clean Water Act (CWA), waters not meeting state Water Quality Standards due to impairment by pollutants are placed on the CWA Section 303(d) list, scheduled for Total Maximum Daily Load (TMDL) development, and ultimately restored. Tens of thousands of 303(d)-listed waters, many with completed TMDLs, represent a restoration workload of many years. State TMDL scheduling and implementation decisions influence the choice of waters and the sequence of restoration. Strategies that compare these waters' recovery potential could optimize the gain of ecological resources by restoring promising sites earlier. We explored ways for states to use recovery potential in restoration priority setting with landscape analysis methods, geographic data, and impaired waters monitoring data. From the literature and practice we identified measurable, recovery-relevant ecological, stressor, and social context metrics and developed a restorability screening approach adaptable to widely different environments and program goals. In this paper we describe the indicators, the methodology, and three statewide, recovery-based targeting and prioritization projects. We also call for refining the scientific basis for estimating recovery potential.