The role of western‐based scientific, Indigenous and local knowledge in wildlife management and conservation
Bibliographic record
Abstract
Abstract Managers of wildlife are faced with decisions and issues that are increasingly complex, spanning natural and human dimensions (i.e. values, preferences, attitudes). A strong evidence base that includes multiple forms and sources of knowledge is needed to support these complex decisions. However, a growing body of literature demonstrates that environmental managers are far more likely to draw on intuition, past experience or opinion to inform important decisions rather than empirical evidence. We set out to assess how decision‐makers and other potential knowledge users (a) perceive, evaluate and use western‐based scientific, Indigenous and local knowledge and (b) the extent to which social, political and economic considerations challenge the integration of different forms of evidence into decision‐making. In 2018, we interviewed members from natural resource management branches of Indigenous governments ( n = 4) and parliamentary governments ( n = 33), as well as representatives from nongovernmental stakeholder groups ( n = 28) involved in wildlife management and conservation in the Canadian province of British Columbia. Contrary to studies that suggest evidence‐based conservation and management are rare, respondents described relying heavily on multiple forms of knowledge. Results revealed that western science is used near‐unanimously, procured from internal (i.e. institutional) sources slightly more than external ones (i.e. peer‐reviewed journals, management agencies in other jurisdictions). However, we found Indigenous and local knowledge use to be much less than western scientific knowledge (approximately half as much) despite being highly valued. Perceived challenges to applying Indigenous and local knowledge include a lack of trust, hesitancy to share knowledge (particularly from Indigenous communities), difficulties in assessing reliability and difficulties discerning knowledge from advocacy. Despite high (and relatively diverse) evidence use, more than 40% of respondents perceived a diminishing role for evidence in final decisions concerning wildlife management and conservation. They associated this with decreases in institutional resources and capacity and increases in socio‐economic and political interference. We encourage transformative change in wildlife management enabling decision‐makers to draw upon multiple forms of knowledge. This transformative change should include direct involvement of knowledge holders, co‐assessment of knowledge and transparency in how (multiple forms of) evidence contribute to decision‐making. A free Plain Language Summary can be found within the Supporting Information of this article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".