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Updated: Jun 8, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
Toward a conceptual framework for blending social and biophysical attributes in conservation planning: a case-study
Lorena Pasquini1, Chasca Twyman, John Wainwright
1Department of Geography, University of Sheffield, UK. lorena.pasquini@gmail.com
Abstract:
There has been increasing recognition within systematic conservation planning of the need to include social data alongside biophysical assessments. However, in the approaches to identify potential conservation sites, there remains much room for improvement in the treatment of social data. In particular, few rigorous methods to account for the diversity of less-easily quantifiable social attributes that influence the implementation success of conservation sites (such as willingness to conserve) have been developed. We use a case-study analysis of private conservation areas within the Little Karoo, South Africa, as a practical example of the importance of incorporating social data into the process of selecting potential conservation sites to improve their implementation likelihood. We draw on extensive data on the social attributes of our case study obtained from a combination of survey questionnaires and semi-structured interviews. We discuss the need to determine the social attributes that are important for achieving the chosen implementation strategy by offering four tested examples of important social attributes in the Little Karoo: the willingness of landowners to take part in a stewardship arrangement, their willingness to conserve, their capacity to conserve, and the social capital among private conservation area owners. We then discuss the process of using an implementation likelihood ratio (derived from a combined measure of the social attributes) to assist the choice of potential conservation sites. We conclude by summarizing our discussion into a simple conceptual framework for identifying biophysically-valuable sites which possess a high likelihood that the desired implementation strategy will be realized on them.
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