Body size, rarity, and phylogenetic community structure: insights from diving beetle assemblages of Alberta
Bibliographic record
Abstract
ABSTRACT Although it is implicit that interactions between species depend on their traits, studies on the probability of finding related species in a community are in their infancy. Community composition and species richness of predaceous diving beetles (Dytiscidae: Coleoptera) have been used as indicators of freshwater ecosystem function yet no incorporation of phylogenetic relationships of coexisting dytiscids has been attempted to date. Improved knowledge of phylogenetic relationships and phylogenetic community structure analysis methods may provide additional insight into the relationships between community composition and species richness, thus impacting our interpretation of aquatic indicator species metrics. Here, we use museum records of dytiscid beetles in 53 lakes of Alberta, Canada to: (1) compile a supertree of dytiscid beetles that live in the province, (2) examine whether coexisting dytiscids tend to be more or less related than expected by chance, and (3) examine whether phylogenetic structuring depends on species richness or mean size of coexisting species. We find that, although the majority of dytiscid assemblages exhibited phylogenetic clustering, the extent to which this occurred depended on the mean size of dytiscids. We discuss the potential mechanisms and implications of the observed patterns in phylogenetic clustering, along with data that would further improve our understanding of community dynamics in dytiscid beetles.
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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 | 0.002 | 0.002 |
| 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".