Associate Professor McGill University, MacDonald Campus, Centre for Indigenous Peoples' Nutrition and Environment (CINE), Canada
Background/Question/Methods
Including local, expert, or Indigenous knowledge into science and conservation can inform and improve ecological research, while also increasing local and stakeholder engagement and support for conservation decisions. However, ecological sciences have become increasingly quantitative, with many research and management projects focusing on big data and complex statistical analyses. How then can we reconcile this focus on quantitative data with the growing recognition of the importance of experiential wildlife knowledge? Harmonizing these two forms of ecological information into a single analysis is challenging and more easily encouraged than accomplished. We review 49 peer-reviewed articles between 1993-2020 that have attempted this weaving of quantitative analysis and experiential knowledge weaving, representing studies in every continent, across diverse vertebrate taxa, and focused on population change over time, distribution, and habitat selection. We categorize and assess the most common knowledge elicitation methods, modelling and statistical frameworks, and methods used to assess bias, communicate uncertainty, and assess accuracy. From this assessment we identify and discuss seven benefits and limitations of local, expert, and Indigenous knowledge inclusion into wildlife science, as well as seven recommended improvements for future projects.
Results/Conclusions
The most common form of experiential wildlife knowledge was point observations or habitat preference knowledge of hunters, trappers, and community members, and the most common models were GLMM, GLM, and other regressions to build habitat models. Most articles accounted for bias and uncertainty either in the knowledge elicitation stage through study design or knowledge holder selection, or in the analysis stage through regression methods. Most articles that assessed model success did so through comparison to independently collected telemetry locations or field survey data. There was wide variation in self-reported success, with most authors offering neutral or positive assessments and many discussing study-specific factors contributing to model performance. Identified benefits of experiential wildlife knowledge interweaving include increased trust in science and management, improving equity between knowledge holders and scientists, providing additional or rare data, and improving temporal transferability of models. Identified limitations included addressing uncertainty, bias, and variation in knowledge, matching the scale of knowledge and scientific objectives, and appropriate communication and use of peoples knowledge. Recommended improvements include employing multi-model studies and comparisons, standardized methods of accounting for variation and bias, increased discussion of power disparity and intellectual property rights, and more involvement of knowledge holders in multiple study stages.
