Spatiotemporal changes in biodiversity by ecosystem engineers: how beavers structure the richness of large mammals
Bibliographic record
Abstract
Abstract High levels of biodiversity may be needed to maintain ecosystem functioning. By creating niches for other species, ecosystem engineers have the potential to promote biodiversity, but it is unclear how this translates across spatiotemporal scales. We evaluated the long-term impact of ecosystem engineering by beavers ( Castor canadensis ) on the diversity of mobile species. We tested the hypothesis that the spatial distribution of engineered habitats in different states resulting from ecosystem engineering by beavers increases the biodiversity of large mammals across spatial scales. We tested a second hypothesis that engineered habitat in different states resulting from ecosystem engineering by beavers drive the diversity of large mammals. We compared the richness and composition of boreal mammals using camera traps between habitats with and without history of occurrence by beavers within a protected area, where trapping, hunting, and forest exploitation are prohibited. We found that unique species were mostly found in specific engineered habitats, with ponds and wet meadows showing more species than dry meadows. In addition to the increased diversity of dispersal-limited species, our results show that beavers promote the diversity of mobile species at both local and landscape scales, signaling the importance of niche creation in structuring animal communities across scales.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".