Seed rain dynamics reveals strong dispersal limitation, different reproductive strategies and responses to climate in a temperate forest in northeast China
Bibliographic record
Abstract
Abstract Questions Species co‐existence may be achieved by limited dispersal due to the reduced chance of inter‐specific competition. But the timing of seed release also provides clues for species co‐existence because asynchronous reproduction can alleviate inter‐specific competition for dispersal agents, while synchronous reproduction can facilitate overall seed dispersal. How strong is seed dispersal limitation, do co‐existing species release seeds synchronously or asynchronously, and what is the relationship between seed production and main meteorological measures? Location A 25‐ha plot in a temperate forest, Changbai Mountain, Northeast China. Methods We calculated Jaccard coefficients between seed rain composition and neighbouring adult tree composition, analysed long‐term seed rain dynamics for both the whole community and different species, and regressed seed rain density with meteorological measures using autoregressive models. Results The Jaccard coefficient dropped sharply as neighbourhood radius increased to about 10 m, indicating severe dispersal limitation. Both synchrony and asynchrony in seed release were observed: most species released mature seeds synchronously from August to November, with small segregations in time; one species released seeds in Jun. Bimodal and unimodal seasonal dynamics of seed rain were observed and some species released seeds beyond the main fruiting seasons. The seasonal dynamics of seed release might be driven by different strategies of seed dispersal. Seed rain density is significantly positively related to temperature and precipitation, with a 2‐mo time lag. Conclusions Both dispersal limitation and timing of seed release by co‐existing species may contribute to maintenance of diversity of this forest, but variations in temperature and precipitation considerably alter seed rain density.
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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 | 0.000 | 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) | 0.000 | 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".