Bridging science and traditional knowledge to assess cumulative impacts of stressors on ecosystem health

Mantyka-Pringle, Chrystal, Jardine, Timothy, Bradford, Lori , Bharadwaj, Lalita, Kythreotis, Andrew, Fresque-Baxter, Jennifer, Kelly, Erin, Somers, Gila, Doig, Lorne, Jones, Paul and Lindenschmidt, K.-E. (2017) Bridging science and traditional knowledge to assess cumulative impacts of stressors on ecosystem health. Environment International, 102 . pp. 125-137. ISSN 0160-4120

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Cumulative environmental impacts driven by anthropogenic stressors lead to disproportionate effects on indigenous communities that are reliant on land and water resources. Understanding and counteracting these effects requires knowledge from multiple sources. Yet the combined use of Traditional Knowledge (TK) and Scientific Knowledge (SK) has both technical and philosophical hurdles to overcome, and suffers from inherently imbalanced power dynamics that can disfavour the very communities it intends to benefit. In this article, we present a ‘two-eyed seeing’ approach for co-producing and blending knowledge about ecosystem health by using an adapted Bayesian Belief Network for the Slave River and Delta region in Canada's Northwest Territories. We highlight how bridging TK and SK with a combination of field data, interview transcripts, existing models, and expert judgement can address key questions about ecosystem health when considerable uncertainty exists. SK indicators (e.g., bird counts, mercury in fish, water depth) were graded as moderate, whereas TK indicators (e.g., bird usage, fish aesthetics, changes to water flow) were graded as being poor in comparison to the past. SK indicators were predominantly spatial (i.e., comparing to other locations) while the TK indicators were predominantly temporal (i.e., comparing across time). After being populated by 16 experts (local harvesters, Elders, governmental representatives, and scientists) using both TK and SK, the model output reported low probabilities that the social-ecological system is healthy as it used to be. We argue that it is novel and important to bridge TK and SK to address the challenges of environmental change such as the cumulative impacts of multiple stressors on ecosystems and the services they provide. This study presents a critical social-ecological tool for widening the evidence-base to a more holistic understanding of the system dynamics of multiple environmental stressors in ecosystems and for developing more effective knowledge-inclusive partnerships between indigenous communities, researchers and policy decision-makers. This represents new transformational empirical insights into how wider knowledge discourses can contribute to more effective adaptive co-management governance practices and solutions for the resilience and sustainability of ecosystems in Northern Canada and other parts of the world with strong indigenous land tenure.

Keywords:Bayesian networks, Birds, Blending, Decision making, Environmental impact, Fish, Flow of water, Health, Integration, Knowledge management, Philosophical aspects, Sustainable development, Water resources, Anthropogenic stressors, Co-management, Environmental stressors, Indigenous community, Multiple stressors, Scientific knowledge, Social-ecological systems, Traditional knowledge, Ecosystems, adaptive management, Bayesian analysis, ecosystem health, environmental change, environmental impact assessment, environmental stress, traditional knowledge, Article, Bayes theorem, ecosystem resilience, environmental sustainability, evidence based practice, field study, human, indigenous people, interview, knowledge, Northwest Territories, probability, scientist, spatial analysis, temporal analysis, traditional knowledge, ecosystem, environmental monitoring, environmental protection, procedures, Canada, Slave Delta, Slave River, Aves, Bayes Theorem, Conservation of Natural Resources
Subjects:F Physical Sciences > F850 Environmental Sciences
F Physical Sciences > F851 Applied Environmental Sciences
L Social studies > L723 Political Geography
Divisions:College of Science > School of Geography
ID Code:34057
Deposited On:27 Nov 2018 15:20

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