Understanding the functional components of predator-prey response to habitat restoration: A Bayesian approach
Bibliographic record
Abstract
Effective action planning for recovering endangered populations of boreal caribou ( Rangifer tarandus caribou ) requires an understanding of the functional interactions between: (1) responses by predators to current and prospective future habitat conditions, (2) responses of prey to population and habitat conditions influencing apparent competition, and (3) residual effects of past population bottlenecks. Monitoring recovery trajectories when human-altered habitats are restored requires consideration of many cause-effect linkages operating among multiple species and across multiple ecological scales. We developed a Bayesian Belief Network (BBN) to help frame potential functional responses of 2 predators (wolves, bears) and 2 prey (moose, caribou) to a large-scaled, silviculturally-based, habitat restoration experiment conducted within the Cold Lake caribou herd area in the Alberta oil sands. The full BBN consists of three general components: (1) those related to predator and prey movements, including use of non-restored and restored habitat features as those opposed conditions affect travel speed and search rates of predators; (2) those related to daily and seasonal use of habitat and how that may affect encounter probabilities between predators and prey; and (3) those related to the probability of kill given an encounter between predator and prey. These components structured in a BBN architecture support the application of the basic parameters in Holling’s disc equation, particularly: seasonal predator search rates and probability of a kill given an encounter (i.e., the area of effective search). We used the BBN to map the functional response spatially and to assess the dynamics of the predators and prey (demographics and response to restoration treatments). Our understanding of these hypothetical responses will, in the future, help shape management actions designed to reduce predator density and prey risk. To demonstrate the management utility of this approach, we plan to set BBN prior probabilities for each model component using 4 types of data: (1) habitat conditions measured semi-annually; (2) GPS relocation data from 128 individual predators (77 wolves; 51 bears) and 34 prey (25 moose and 9 caribou); (3) kill site investigations; and (4) DNA analyses of prey species density.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 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".