Not all ponds are created equal: long-term beaver (<i>Castor canadensis</i>) lodge occupancy in a heterogeneous landscape
Bibliographic record
Abstract
Modeling habitat selection often represents single spatial scales and short time periods. However, for species that dramatically modify environments upon initial colonization, non-linear interactions change original habitat to one where habitat selection is confounded by subsequent modifications by the organism itself. The ability of beavers (Castor canadensis Kuhl, 1820) to dramatically alter habitat components over time makes them a model species for examining habitat selection spatially and temporally. This study identified habitat features selected by a relatively long-lived, colonial species, and how selection influenced site occupancy over varying spatial scales. Remote sensing and field data provided spatial and temporal coverage of four measures of occupancy. Models of occupancy over an 11-year period, combined with environmental variables, identified features influencing occupancy, abandonment, and recolonization at 30, 50, and 100 m scales. Unlike many studies, % deciduous was a poor predictor of occupancy. Instead, pond area, % grassland, distance to a pond that was active at least once, and standard deviation (SD) of slope were important variables. SD of slope was the only variable not influenced by beavers. Examining species–habitat relationships over broader spatial and temporal scales, particularly for ecosystem engineers, enhances understanding of non-linear effects and how they influence perceptions of habitat selection.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".