Influence of intraspecific competition on the distribution of a wide‐ranging, non‐territorial carnivore
Bibliographic record
Abstract
Abstract Aim It is generally held that the dominant competitors in a population will occupy high‐quality habitat while forcing subordinates into lower‐quality habitats through interference competition. We examined the distribution of a non‐territorial apex carnivore relative to foraging habitat to assess the effect of two types of interference: competitive asymmetries in predatory ability and conspecific predation risk. Location B eaufort S ea, C anada. Methods The quality of foraging habitat was modelled using resource selection functions to relate the locations of seals killed by polar bears ( U rsus maritimus ) to attributes of the sea ice. We used estimated seal biomass as a sample weight to reflect the energetic return of different sizes of kills. To test the effect of sample weighting, locations at which polar bears were captured were used to compare habitat quality modelled with kills weighted equally and kills weighted by their biomass. The distributions of different demographic classes of polar bears were then compared with the general predictions of unequal‐competitor models. Results Polar bear distribution was correlated with the quality of the foraging habitat as determined by the kill biomass model ( r s = 0.90, P = 0.04), but not in the unweighted design ( P = 0.75). No difference was detected in use of the highest‐quality foraging habitat by subadults and adults. Females with cubs‐of‐the‐year used lower‐quality foraging habitat relative to the rest of the population. Main conclusions Weighting the habitat model with biologically relevant information improved its fit to species distribution, and suggested that density of use alone was insufficient to define habitat quality. Intraspecific competition had a varying influence on the distribution: unequal competitors coexisted, while the avoidance of conspecific predation risk resulted in semi‐truncation.
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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 | 0.001 |
| 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".