Testing hypotheses of bison population decline (1970–1999) in Wood Buffalo National Park: synergism between exotic disease and predation
Bibliographic record
Abstract
One hypothesis to explain the decline of bison (Bison bison (L., 1758)) abundance in Wood Buffalo National Park, Canada, is the "disease–predation" hypothesis where tuberculosis (Mycobacterium bovis Karlson and Lessel, 1970) and brucellosis (Brucella abortus (Schmidt, 1901)) reduce bison survival and reproduction, thus shifting bison abundance from a high-density, food-regulated equilibrium to a low-density, predator-regulated equilibrium. We use historical data on bison abundance and stochastic population simulation to examine this hypothesis. A decline in only one area of the park would discount disease as a factor, because exotic disease is present throughout the park; however, we found that decline rates were similar in the two main populations of bison. Using simulation, we found a high (68.5%) probability that a tuberculosis- and brucellosis-infected bison population experiencing predation by wolves (Canis lupus L., 1758) would stabilize at low densities (<0.83 bison/km2), which increased to 93.6% when anthrax and drowning were considered to be mortality sources. In the absence of tuberculosis and brucellosis, there was a low probability that bison would persist at this low density (<8%). These simulations suggest that an interaction between tuberculosis, brucellosis, and predation may account for the decline of bison abundance in Wood Buffalo National Park from 1970 to 1999.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".