Diversity of plant assemblages dampens the variability of the growing season phenology of wetland ecosystems
Bibliographic record
Abstract
Abstract Background: The functioning of ecosystems is highly variable through space and time. Climatic and edaphic factors are forcing ecological communities to converge, whereas the diversity of plant assemblages dampens these effects by allowing communities’ dynamics to diverge. This study evaluated whether the growing season phenology of plant communities within wetland ecosystems is determined by the climatic/edaphic factors of contrasted regions, by the species richness of plant communities, or by the diversity of plant assemblages. From 2013 to 2016, we monitored the phenology and floristic composition of 118 plant communities across five wetland ecosystems distributed along a gradient of edaphic and climatic conditions in the Province of Quebec, Canada. Results: Growing season phenology of wetlands was driven by differences among plant assemblage within ecosystems, and not by the species richness of each individual community (<1% of the explained variation). Variation in the growing season length of wetlands reflected a balance between the effects of climatic and edaphic factors on green-up dates and the dampening effects of plant assemblage diversity on green-down dates. Conclusions: The latter dampening effect may be particularly important in the context of increasing anthropogenic activities, which are predicted to impair the ability of wetlands to adapt to fluctuating environmental conditions. Our findings suggest that stakeholders should not necessarily consider local monospecific plant communities of lower conservation value to the global functioning of wetland ecosystems.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".