Diet and lake size are the main drivers of the territorial occupation dynamics of North American beaver
Bibliographic record
Abstract
Beavers, as ecosystem engineers, create crucial wetlands and habitats for other species, altering the structure and function of the surrounding forests and affecting human infrastructure. However, despite these significant economic and ecological implications, the spatiotemporal patterns of beaver feeding strategies remain understudied. This study aimed to evaluate how forest stand type, lake size, and diet influence beaver territorial occupation in eastern Canada. We used a dendroecological approach to measure beaver occupation time and maximum browsing distance around 61 lakes. Around each beaver lodge, we established 1 m² plots along three transects in which we measured distance of browsing from shore and counted annual rings on coppices resulting from beaver presence. PERMANOVA revealed that both maximum browsing distance (F = 8.66, R² = 0.261, p = 0.003, permutations = 999) and temporal occupation (F = 6.55, R² = 0.238, p = 0.006, permutations = 999) differed significantly across lake size categories. The type of forest stand had no impact on beaver dynamics. Stable isotope analysis (δ 13 C and δ 15 N) of beaver carcasses collected from local trappers showed that the beavers’ diet included the consumption of conifer trees and a seasonal shift in food consumption. We found that lakes ranging from 4 to 20 ha were optimal for beavers, as this lake size mattered more than the availability of specific food sources. Beavers exhibited high adaptation skills by using different plant species depending on the season to maximize resource availability and energy cost trade-off. Understanding the factors involved in beaver territorial occupation dynamics is crucial for land managers and conservationists to effectively incorporate this species into forest management plans and mitigate beaver–human conflicts.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".