The number of phenology patterns, not species richness, affects the greening season length of freely assembled plant communities
Bibliographic record
Abstract
Abstract Questions Plant greening phenology is a key response trait that drives numerous ecosystem functions such as carbon storage and flowering. Plant communities with a diversity of phenology responses could show a longer greening season due to more complete occupation of the temporal window available for growth. However, it is unclear how species composition and richness affect the phenology of local plant communities. Location This study was conducted in wetland landscapes of the St. Lawrence River in the province of Québec, Canada (46.07°N; 73.17°W). Methods We used close‐range digital imagery to monitor the greening phenology and species richness of 20 herbaceous plant communities from 2013 to 2016. We quantified the number of greening phenology patterns observed each year within each plant community using singular value decomposition of close‐range image time series. Results The number of plant species within plant communities was independent of the number of phenology patterns, or the length of the greening season. However, the number of phenology patterns correlated positively to the greening season length in all four years of monitoring. Conclusions The relationship between the number of phenology patterns and the greening season length suggests a complementary use of the temporal window available for growth within plant communities. Species richness was a poor indicator of the diversity of phenology responses in wet meadow communities. The absence of a positive relationship between the number of plant species and the diversity of greening phenology patterns, or the length of the greening season, suggests that other descriptors of plant communities are of importance. Species richness is more often than not a weak predictor of the functioning of local plant communities in the wild.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".