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

Abstract

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.

Untapped potential of collective intelligence in conservation and environmental decision making

“…), including in the harnessing of collective local ecological knowledge in groups without a strong numerical background or with different knowledge systems (Mantyka‐Pringle et al. ). More research is also required to establish who should be consulted, the required number of experts, methods for combining judgments, techniques for training and feedback, and tools for independent verification of expert judgments (Martin et al.…”

“…Despite successful applications in, for example, threatened species assessments (McBride et al 2012b), prioritizing management strategies (e.g., Carwardine et al 2019), risk assessments (Smith et al 2015), and estimating population trends (Adams-Hosking et al 2016), most researchers do not use a structured approach (Drescher & Edwards 2019). Expert-elicitation protocols require testing in a wider range of environmental decision making (Hemming et al 2017), including in the harnessing of collective local ecological knowledge in groups without a strong numerical background or with different knowledge systems (Mantyka-Pringle et al 2017). More research is also required to establish who should be consulted, the required number of experts, methods for combining judgments, techniques for training and feedback, and tools for independent verification of expert judgments (Martin et al 2012).…”

A review of macroinvertebrate‐ and fish‐based stream health modelling techniques

“…Representative BBN applications in stream health modelling include the evaluation of cumulative environmental impacts of multiple stressors on ecosystem health using traditional and scientific knowledge (Mantyka-Pringle et al, 2017). The study incorporated biotic factors describing wildlife health, food webs, wildlife populations, fish health, and macroinvertebrate metrics (density, richness, and diversity), among others.…”

Assessing and mapping cultural ecosystem services at community level in the Colombian Amazon

“…Alternatively, there might be overlap between services types, as people cannot easily distinguish between them (Plieninger et al 2013). It demonstrates the challenges related with the attempts of combining traditional and scientific knowledge (Mantyka-Pringle et al 2017) showing the importance of both types of knowledge and the need for studies enabling researchers to build bridges between them.…”