Analysis of six-decadal seed mass and emergence records in mast species shows little inter-annual variability
Bibliographic record
Abstract
Abstract Patterns of crop production in mast species do not track crop-year climate, but instead are regulated by climate cues in prior-years. Whether the pattern of year-to-year seed mass variation is coupled in time with mast seeding, maintaining seed mass-number trade-offs, and coherently driven by similar climate cues as other seed traits (e.g. seed germination) remains unknown. Using ca. 6,000 long-term seed inventory data over the years 1955-2015 in conifers, this retrospective study revealed the temporal patterns of mast species’ seed mass and its associated trait, seed germination. To pinpoint their ecological drivers, pairwise correlation analysis was performed between each trait and seasonal climates in crop year and four prior-years. Using climate variables key to each trait, regression models were constructed to project trait values. Findings showed minor seed mass variation among years, which rejects the generality of seed mass-number trade-offs in many plant species. This result reasonably arises as the economies of scale (compensating benefits) theory are often used to account for mast seeding but not for seed mass. Moreover, final germination fraction also varied little over time, but exhibited an increasing tendency. In addition, we found that temperature-based climate variables drive seed mass, number, and germination variation, but these variables in different seasons of crop year or prior-years did not have equal influences on trait variability. Finally, regression models showed that the number of frost-free days and evapotranspiration are crucial to the three traits and climate in autumn is a critical season, followed by summer and winter. This study holds considerable promise for explaining reproductive strategies of taxonomic groups with mast seeding characteristics in allocating reproductive resources to different life-history traits using ecological signals.
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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 | 0.001 | 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 | 0.000 |
| 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".