Resource availability influences group size but not territory size of North American Beaver ( <i>Castor canadensis</i> ), a territorial social species
Bibliographic record
Abstract
Abstract Resource availability is a key component of habitat quality and an important driver of animal density. For a territorial social species, population density can be determined by group size, territory size, or both. In this study, we investigated the mechanisms by which food resource density affects North American Beaver (Castor canadensis) populations in temperate riparian forest habitats dominated by ash (Fraxinus spp.) in diversified stands. In 2021, we captured 25 beavers from different colonies in Plaisance National Park (Quebec, Canada) and equipped them with a Global Positioning System (GPS) unit to track their movements and delimit their respective territories. We determined group size by counting the beavers in each territory using aerial imagery obtained from a drone. We measured the density of various food resources such as ash trees, poplars (Populus spp.), or aquatic vegetation, and included these data in generalized linear models to assess their effect on group size or territory size. We observed that group size was positively related to ash tree density with a model-averaged regression coefficient of 0.04 (95% confidence interval: 0.01 to 0.06), but we did not find any relationship between territory size and resource density. These results suggest that the cost of territorial defense may be outweighed by the benefit of securing high-resource territories that support larger groups. This pattern may also indicate that beavers respond to the Resource Dispersion Hypothesis, where territory size is shaped by resource distribution. Alternatively, unmeasured factors such as colony establishment order could influence territory size and warrant further investigation.
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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) | 0.002 | 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".