The relationship of amphibian abundance to habitat features across spatial scales in the Boreal Plains
Bibliographic record
Abstract
We examined the relationship between amphibian abundance and habitat features at 8 spatial scales in boreal Alberta, Canada. Twenty-three local pond variables and 15 landscape variables at 7 scales (50, 100, 200, 500, 1000, 2000, and 5000 m) were incorporated into a Principal Component Analysis (PCA) for each scale. We analyzed amphibian relative abundance against the PC axis scores using regression for each species and each scale. We found that each species' abundance was best described at different spatial scales. Wood frog (Lithobates sylvaticus) abundance was best predicted by local, pond-linked variables, boreal chorus frog (Pseudacris maculata) abundance by 1000-m-scale landscape variables, and western toad (Anaxyrus boreas) by the 100-m scale. We found significant positive relationships with amphibian relative abundance and dissolved oxygen, deciduous forest cover, mixed forest cover, and urban cover. Pond depth, conductivity, total dissolved solids, aquatic plant density, low-shrub cover, and conifer cover showed negative relationships with abundance. We also investigated relationships between landform type and amphibian relative abundance. All 3 species were most abundant on wetlands in the moraine landform. Our research highlights the importance of developing conservation plans based on knowledge of individual species' biology because amphibians do not all respond to the same spatial scale.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".