What defines mast seeding? Spatio‐temporal patterns of cone production by whitebark pine
Bibliographic record
Abstract
Summary 1. Synchronous, episodic mast seeding is common in plant populations, and is thought to increase plant fitness through economies of scale, such as satiating seed predators, attracting seed dispersers and enhancing pollination success. Although mast seeding is easy to conceptualize, it has been quantified using a number of different metrics that reflect different features of pulsed reproduction. 2. We quantified spatio‐temporal patterns of mast seeding across 36 populations of a high‐elevation tree, Pinus albicaulis , for which perceived declines in cone production are a conservation concern. We tested for trends in mean cone production through space and time, and documented patterns of mast seeding using six different metrics: coefficient of variation, lag‐1 autocorrelation, synchrony, average cone production by individual trees, and the frequency of high cone crops on absolute and relative scales. 3. Overall, we did not detect increasing or decreasing trends in cone production during our study period. Average cone production tended to increase from north‐east to south‐west. Population‐level cone production tended to alternate between high and low years, but overall the coefficient of variation was low for a mast seeding species. 4. Metrics of mast seeding were not concordant across populations. The first principal component describing mast metrics separated populations with frequent high cone crops from those with high coefficients of variation. However, the second principle component was at least somewhat correlated with all metrics of masting, suggesting some ability to separate ‘masting’ from ‘non‐masting’ populations. 5. In P. albicaulis , spatial variation in mast seeding could reflect differences in site productivity, differences in the importance of satiating generalist seed consumers versus attracting specialist seed dispersers, or recent invasion by an introduced pathogen. 6. Synthesis. Our research reinforces the conclusion that populations form a continuum of strategies between ‘masting’ versus ‘non‐masting’ extremes. However, because different features of masting do not covary in space, understanding where populations fall along this continuum will depend on the features that are most important for mast seeding in a particular context.
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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 | 0.001 | 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".