Predators in natural fragments: foraging ecology of wolves in British Columbia's central and north coast archipelago
Bibliographic record
Abstract
Abstract Aim Predator–prey dynamics in fragmented areas may be influenced by spatial features of the landscape. Although little is known about these processes, an increasingly fragmented planet underscores the urgency to predict its consequences. Accordingly, our aim was to examine foraging behaviour of an apex mammalian predator, the wolf ( Canis lupus ), in an archipelago environment. Location Mainland and adjacent archipelago of British Columbia, Canada; a largely pristine and naturally fragmented landscape with islands of variable size and isolation. Methods We sampled 30 mainland watersheds and 29 islands for wolf faeces in summers 2000 and 2001 and identified prey remains. We examined broad geographical patterns and detailed biogeographical variables (area and isolation metrics) as they relate to prey consumed. For island data, we used Akaike Information Criteria to guide generalized linear regression model selection to predict probability of black‐tailed deer (main prey; Odocoileus hemionus ) in faeces. Results Black‐tailed deer was the most common item in occurrence per faeces (63%) and occurrence per item (53%) indices, representing about 63% of mammalian biomass. Wolves consumed more deer on islands near the mainland (65% occurrence per item) than on the mainland (39%) and outer islands (45%), where other ungulates (mainland only) and small mammals replaced deer. On islands, the probability of detecting deer was influenced primarily by island distance to mainland (not by area or inter‐landmass distance), suggesting limited recolonization by deer from source populations as a causal mechanism. Main conclusions Although sampling was limited in time, consistent patterns among islands suggest that population dynamics in isolated fragments are less stable and can result in depletion of prey. This may have important implications in understanding predator–prey communities in isolation, debate regarding wolf–deer systems and logging in temperate rain forests, and reserve design.
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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".