Convergent diversity and trait composition in temporary streams and ponds
Bibliographic record
Abstract
Abstract Hydrology is the main environmental filter in aquatic ecosystems and may result in shared tolerances and functional traits among species in disparate ecosystems. We analyzed the associations between taxonomic and functional facets of diversity within aquatic ecosystems (ponds vs. streams) across a hydroperiod gradient (1–365 d) to untangle the hydrologic drivers of aquatic invertebrate diversity. We used invertebrate assemblage data from seven arid‐land streams in southeastern Arizona, United States collected over 2 yr and nine temperate woodland ponds in Ontario, Canada collected over 2 yr. Our results showed that although invertebrate assemblages from streams and ponds differ taxonomically, hydroperiod had similar influence on invertebrate trait structure regardless of biogeographic and habitat differences. Streams and ponds independently showed strong positive relationships between functional richness and taxonomic richness; however, the relationship showed a shallower slope in ponds, indicating higher functional redundancy. Intermittent ponds and streams tended to have lower functional and taxonomic richness than their perennial counterparts, but harbored greater beta diversity. Our results suggest that even though ponds and streams are fundamentally different habitats with distinct faunas and unique ecological processes, hydrology produces convergent patterns in both trait composition and diversity patterns.
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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 | 0.001 |
| 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".