Modeling multispecies predator–prey dynamics: predicting the outcomes of conservation actions for woodland caribou
Bibliographic record
Abstract
Abstract Woodland caribou is a Threatened or Endangered subspecies across much of Canada. In many cases, these caribou are declining because of human‐mediated predation in the form of apparent competition. Provincial and federal agencies have employed a number of conservation actions to arrest the decline, but insufficient time and limited replication make it difficult to test the efficacy of those activities. We built a three‐species population model that served as a tool to integrate current knowledge, explore uncertainties, and assess activities focused on the recovery of caribou. We applied the model to interconnected populations of caribou, moose, and wolves at two locations in Canada (Chinchaga [British Columbia], Charlevoix [Québec]) and explored the efficacy of conservation actions unique to each population: predator control, restoration of linear features, reduction of habitat for moose, supplementation, maternal pens, and predator exclosures. Results confirmed that caribou at these two locations faced different recovery challenges, dictated by specific population dynamics and threats. Wolf predation, enhanced by seismic lines and resource roads, was responsible for the decline of the simulated Chinchaga population. The most cost‐effective recovery actions for that population were long‐term lethal wolf control (λ1–50 = 1.014, $25,665/caribou), a large‐scale predator exclosure (λ1–50 = 1.015, $170,767/caribou), and the aggressive restoration of linear features (λ1–50 = 1.002, $531,675/caribou). In contrast, simulations suggested that calf predation by black bears, a fixed source of mortality in the model, limited the growth of the Charlevoix population. Assuming high survival of adult caribou and poor recruitment of juveniles, a maternal pen was the most effective action for the recovery of those caribou (λ1–50 = 1.004, $148,473/caribou). Short‐term population supplementation provided only a temporary increase in abundance (λ1–50 = 0.994, $62,143/caribou). The model was limited by ecological and data uncertainties, but served as an effective platform for representing and testing our understanding of the complex interspecific interactions that underlie the recovery of woodland caribou.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".