Decision analysis rooted in Indigenous and Western scientific knowledge identifies cost-effective strategies for managing hyperabundant deer to restore keystone places
Bibliographic record
Abstract
The hyperabundance of herbivores—a result of altered human relationality with the land and the extirpation of predators—is leading to large-scale degradation of keystone ecosystems across the globe. Designing and implementing socially acceptable and cost-effective strategies that meaningfully reduce herbivore populations while allowing for the recovery of ecological function and cultural relationality is an inherently complex issue. As a result, decision paralysis is common, leading to delayed or avoided action and continued ecosystem loss and degradation. Using a structured decision-making process that incorporated expert elicitation, population modeling, and cost-effectiveness analyses while honoring multiple knowledge systems, we identified five discrete and four portfolio strategies for managing hyperabundant black-tailed deer (Odocoileus hemionus columbianus) in the Southern Gulf Islands of British Columbia, Canada, with consideration to benefit, feasibility, and cost objectives. Hunting led by local Indigenous Nations was ranked the most cost-effective strategy when benefits were considered in terms of the well-being of peoples and place holistically, and accounted for both Indigenous and Western science worldviews. When only Western perspectives were included, increased licensed hunting by local communities and hiring professional deer reduction specialists were ranked the most cost-effective. However, while increased licensed hunting had a >50% likelihood of project uptake and success (i.e., feasibility), the strategy had <50% likelihood of achieving any benefit objective. In comparison, Indigenous-led hunting, professional deer reduction specialists, and all portfolio strategies had >50% likelihood of meeting at least one benefit objective, although only Indigenous-led hunting also had >50% likelihood of achieving feasibility objectives. We provide a roadmap for decision-makers across the globe to robustly and transparently assess the problem of herbivore hyperabundance and inform solutions within their context. Within the Salish Sea, our work highlights the need to support hunting, and in particular, Indigenous-led hunting, as cost-effective strategies to promote revitalization of wellbeing of peoples and place.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".