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Published on: July 24, 2016
Centering geospatial data uncertainty and the potential for injustice in pastoralist rangeland conservation
Ryan R Unks1,2
1Institut de Ciència i Tecnologia Ambientals (ICTA-UAB), Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain.
Abstract:
Procedural, distributional, recognitional, and epistemic justice aspects of conservation interventions are well documented in contexts where pastoralism is a key livelihood and way of life. Geospatial analyses and representations of wildlife conservation and restoration that are increasingly applied in pastoralist rangeland socioecological systems create the potential to make these injustices invisible. Current global-extent publicly-available data and analysis methods cannot be used to accurately characterize or produce generalizations about diverse, variable pastoralist practices or their relations with rangeland ecosystems. In particular, these geospatial data do not adequately represent complex livestock mobility practices or pastoralists' diverse ways of relating to land and wildlife. When they are used to provide evidence for conservation interventions, the inherent uncertainties and limitations of geospatial data and analyses, coupled with ongoing political marginalization of pastoralists, creates a high risk of perpetuating historical injustices and extending new injustices. Epistemic injustices will likely remain a concern even with technological advancement because conservation science itself lacks a sufficient conceptual basis to adequately recognize pastoralists' land use, ecological relations, decision-making processes, and knowledges. When geospatial analysis is used to inform conservation policy and practical applications in pastoralist rangelands, its use should be critically examined with respect to: whether it can technically account for ecological and social variability; how compound uncertainties arise when multiple data layers are used to produce knowledge about socioecological interactions; and how uncertainties become masked by epistemic biases, creating a deceptive sense of certainty and generalizability.
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