Tracking wetland community evolution using Diptera taxonomic, functional and phylogenetic structure
Bibliographic record
Abstract
Abstract Different processes drive spatial variation in community composition. Standard measures of composition are useful in species‐based conservation and ecology, but they may be less informative in the context of evolutionary history and functional diversity. Functional and phylogenetic approaches are increasingly used to test mechanisms driving biodiversity patterns. We studied 28 families of flies (Diptera) with a range of functional characteristics in three wetland classes (bogs, swamps, marshes) in Quebec, Canada. We examined taxonomic, phylogenetic and functional structure of communities and assessed whether rarity is deterministic or stochastic. Beta‐ and phylobeta‐diversity were also examined for relatedness to local environmental conditions, patch area, and/or surrounding landscape. Phylogenetic community structure analyses had high value and complementarity to standard measures. Environmental filtering acted on bog communities during assembly, as they emerged from a slow peat accumulation process and the plant composition is characteristic as few species can survive in these acidic and low nutrient conditions. Subsequently, community assembly happened randomly. Neutral processes of community assembly are more important in marshes and swamps, as dispersal limitation explained species abundance dynamics of small and common Diptera species. The assembly of marsh communities is a balance between neutral processes and environmental filtering, while assembly in swamps can be seen as neutral. Clustering increased with environmental extremes, indicating environmental filtering. Rare species tended to be less closely related to common species. They have unique habitat requirements, and the high diversity is maintained by temporal turnover of species with similar traits filtered by the environment.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".