Logging‐induced changes in habitat network connectivity shape behavioral interactions in the wolf–caribou–moose system
Bibliographic record
Abstract
Habitat connectivity influences the distribution dynamics of animals. Connectivity can therefore shape trophic interactions, but little empirical evidence is available, especially for large mammals. In forest ecosystems, logging alters functional connectivity among habitat patches, and such activities can affect the spatial game between large herbivores and their predators. We used graph theory to evaluate how harvesting‐induced changes in habitat connectivity influence patch choice and residency time of GPS‐collared caribou (Rangifer tarandus caribou) and moose (Alces alces) in winter in the boreal forest. We then investigated the predator–prey game by assessing how GPS‐collared wolves (Canis lupus) adjusted their movements to changes in landscape properties and in the networks of their prey species. We built prey habitat networks using minimum planar graphs organized around species‐specific, highly selected habitat patches (i.e., network nodes). We found that spatial dynamics of large herbivores were influenced not only by the intrinsic quality of habitat patches, but also by the connectivity of those network nodes. Caribou and moose selected nodes that were connected by a high number of links, and moose also spent relatively more time in those nodes. By limiting node accessibility, human disturbances influenced travel decisions. Caribou and moose avoided nodes that were surrounded by a high proportion of cuts and roads, but once within these nodes, moose stayed longer than in other nodes. Caribou selectively moved among nodes with low distance costs, and their residency time increased with distance costs required to reach the nodes. Wolves selected their prey's nodes, where vegetation consumed by caribou and moose was highly abundant. Furthermore, wolves discriminated among those nodes by selecting the most connected ones. In fact, selection by wolves was stronger for their prey's nodes than for the prey's utilization distribution per se, a difference that increased with the level of human disturbance. Considering the difficulty of keeping track of highly mobile prey, predators may benefit by targeting not only their prey's resource patches, but also the most highly connected patches. Matrix quality and connectivity are therefore key elements shaping the predator–prey spatial game in human‐altered landscapes because of their impact on the spatial dynamics of the interacting species.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".