Conservation strategies frequently require prioritization of targets due to limited budgets and personnel. Prioritization involves choosing those areas that return the most conservation value for the time and money invested. Hence, the process of prioritization involves evaluating multiple conservation values and the uneven spatial distribution of those values across a landscape of concern. The goal of this study was to help conservation organizations improve decision making for implementation of prioritization-based strategies for land protection using a GIS-based, multi-criteria decision support system (GIS-MCDS).

Geographic Information Systems (GIS) can assist conservation planners in quantifying the relative desirability of one area over another, therefore enabling better business and ecological decisions. GIS analyses for planning are routinely undertaken over large geographic extents such as ecoregions to identify priority areas. These analyses often summarize priority by pixel values in a raster image. Implementation of conservation strategy often takes place at the much larger parcel level. Therefore, aggregating pixel-based results by parcels is a prerequisite to implementation of a purchase or easement strategy. I developed a spatial decision support system in an attempt to quantify private land holdings in the High Knob area of Virginia for their relative conservation value, as defined by the Clinch Valley Program of The Nature Conservancy. It utilizes a proxy approach for measuring conservation values and an analytical hierarchy process to aggregate the results by privately held real estate parcels.

Simple prioritizations are often based on parcel size alone, rather than consideration of the many conservation values that characterize land parcels. Though it is much quicker and easier to prioritize parcels in this manner, such simplicity risks missing important smaller areas for conservation while prioritizing larger parcels with less value. I compared this simple "bigger is better" ranking method to the GIS-based multi-criteria method developed for TNC. There was a 0.57 correlation between the ranked lists produced by the two models, suggesting that parcel size alone does partially explain the complexity modeled by the multi-criteria method. However, the more complex method did produce different top priority parcels, which could significantly change an organization's implementation strategy. I conclude that both methods have their applications, though the multi-criteria method is better for long-term implementations of strategic acquisition and easement.

A secondary goal was to identify to what extent land trust organizations are prepared to implement a multi-criteria type analysis like the one considered in this study. I conducted an online survey of conservation professionals on how their organization currently uses GIS and their satisfaction with GIS analyses to meet their organizational mission. Sixty-one responses were collected and analyzed. The overwhelming majority of conservation organizations recognize the benefits that GIS bring and have already developed some level of internal expertise, though many barriers to using GIS were also identified. From these results, I conclude that most land trust conservation organizations are not currently utilizing the insights that multi-criteria GIS prioritization is capable of offering, but that their previous positive experience with GIS makes such analyses an attractive proposition for those on the cutting edge of the land conservation movement.