Boreal Caribou Can Coexist with Natural but Not Industrial Disturbances
Bibliographic record
Abstract
ABSTRACT For species at risk, it is important that demographic models be consistent with our most recent knowledge because alternate model versions can have differing predictions for wildlife and natural resource management. To establish and maintain this consistency, we can compare predicted model values to current or past observations and demographic knowledge. When novel predictor information becomes available, testing for consistency between modeled and observed values ensures the best models are used for robust, evidence‐based, wildlife management. We combine novel information on the extent of historical disturbance regimes (industrial and fire) to an existing demographic model and predict historical and projected demographics of woodland caribou ( Rangifer tarandus caribou ). Exploring 6 simulation experiments across 5 populations in Alberta, Canada, we identify the relative importance of industrial disturbance, fire, and population density to observed population size and growth rate. We confirm the onset of significant declines across all 5 populations began approximately 30 years ago, demonstrate these declines have been consistent, and conclude they are more likely due to industrial disturbance from the oil and gas sector within contemporary population ranges than historical fire regimes. These findings reinforce recent research on the cause of woodland caribou declines. Testing for consistency between observations and models prescribed for species recovery is paramount for assessing the cause of declines, projecting population trends, and refining recovery strategies for effective wildlife management. We provide a novel simulation method for conducting these tests. © 2020 The Authors. The Journal of Wildlife Management published by Wiley Periodicals LLC on behalf of The Wildlife Society.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".