Evaluation of Biodiversity Value Based on Wildness: A Study of the Western Northwoods, Upper Great Lakes, USA

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Article

Publication Date

2001

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Abstract

Regional conservation plans are developed and implemented to protect areas from human land uses that adversely affect biological diversity. Several different criteria have been used to identify locations with high biodiversity value; among these is "wildness." We evaluated the extent of wildness of the Upper Great Lakes region of the United States (the "Northwoods") to identify areas for inclusion in a regional conservation plan. Using a geographic information system to organize data, we evaluated wildness using six different data layers in raster format: land cover classification, forest cover type, land ownership, human population density, road density, and wolf (Canis lupus L.) habitat suitability. For each data layer, we quantified wildness (more vs. less wild) based on threshold criteria. We classified pixels within data layers as more wild if: (1) forest is present, (2) the forest has the same general species composition since European settlement, (3) the area is in public ownership, (4) human population density is < 1 person per km2, (5) road density is < 0.5 km of road per km2, and (6) the probability that wolves would colonize the area exceeds 75%. We found all these measures of wildness to be significantly associated with each other based on chi-square tests of independence (adjusted P ? 0.001). We created a new data layer by combining five existing layers (all except the wolf colonization layer) using an overlay procedure. This new layer shows a gradient of wildness across the region, with only 5.7% of all pixels meeting all five wildness criteria. The probability of wolves colonizing an area increases with its wildness. The methods we use to evaluate wildness here can be used in conjunction with other methods of regional biodiversity assessment to guide acquisition and easement efforts, zoning policies, and land management activities as opportunities arise.

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