2026/04/29 by Ryan R. Unks · 1 voice
Environmental Science · #Rangeland Management and Livestock Ecology #Rangeland and Wildlife Management #Wildlife Ecology and Conservation
paper · doi:10.1111/cobi.70279
openalex publication_date 2026/04/29 · openalex created_date 2026/04/30 · openalex updated_date 2026/07/27
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.