‘WildLift’: An Open-Source Tool to Guide Decisions for Wildlife Conservation
Bibliographic record
Abstract
A recurring challenge for resource managers and decision makers is quantifying the trade-offs associated with alternative recovery actions for threatened species. Structured decision-making approaches can help evaluate such complex problems by formalizing objectives and constraints into functions that quantify the benefits and costs associated with each action. Yet many of the scientific tools necessary to implement structured decision making require extensive literature review and often involve complex algorithms that make them inaccessible to managers. To address these issues, we integrated available information and developed a decision-support tool that managers can readily use to compare costs and benefits associated with alternative recovery actions for threatened species. Our software can be used to quantitatively estimate and compare the costs and demographic benefits of recovery actions for an iconic threatened species, woodland caribou (Rangifer tarandus caribou). While we use caribou as a case study, our approach to developing this management tool is transferable to other threatened taxa. The tool consists of a generalized matrix population model that is parametrized based on information from the published literature or ongoing experiments. Users can input population parameters (e.g., population size and survival rates) or choose from pre-set caribou subpopulations to estimate changes to populations from implementing recovery actions. The tool estimates the trade-offs associated with seven alternative recovery actions: Linear Feature Restoration, Linear Feature Deactivation, Maternal Penning, Conservation Breeding, Predator Exclosure, Wolf Reduction, and Moose Reduction. We demonstrate our software by comparing recovery actions for the East Side Athabasca River caribou subpopulation and discuss how this tool can be used under a structured decision-making framework. This case study suggests that our open-source tool can be useful to guide wildlife conservation decisions by explicitly estimating costs and benefits associated with recovery actions, which ultimately helps to bridge the gap between management and science via increased accessible application of current knowledge.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".