Body size and species richness along geographical gradients in Albertan diving beetle (Coleoptera: Dytiscidae) communities
Bibliographic record
Abstract
Species richness and body size often vary predictably along latitudinal and elevational gradients. Although these patterns have been well documented for a variety of taxa, the vast majority of studies have focused on terrestrial plants and animals. We used species lists of predaceous diving beetles (Coleoptera: Dytiscidae) collected from >400 lentic water bodies in southern Alberta to investigate the influences of latitude and elevation on species richness and body size. Because our data were based on species lists, we used proportion of, and probability of encountering at least one, large (i.e., mean body length >10 mm) diving beetle species as surrogates for the mean body size of diving beetles in a given water body. Species richness did not change with latitude and displayed a hump-shaped relationship with elevation, peaking at mid-elevations. High elevation (>2000 m) water bodies had markedly low species richness. Proportion of large species increased with latitude, although there was no effect on probability of occupancy by large species. Conversely, both measures tended to decrease with elevation, suggesting that large species are less prevalent at high elevations. We discuss potential factors contributing to the observed responses to latitude and elevation, with an emphasis on the potential impacts of oxygen limitation, productivity, and isolation at high elevation.
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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 | 0.001 | 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".