Prior reproduction and weather affect berry crops in central Ontario, Canada
Bibliographic record
Abstract
Abstract Populations of many perennial plants intermittently produce large seed crops—a phenomenon referred to as mast seeding or masting. Masting may be a response to spatially correlated environmental conditions (the Moran effect), an adaptive reproductive strategy reflecting economies of scale, or a consequence of the internal resource budgets of individual plants. Fruit production by endozoochorous plants representing eight genera varied synchronously over much of central Ontario, Canada, 1998–2009. We tested for effects of weather and prior reproduction on fruit production by comparing AICc values among regression models fit to time series of fruit production scores and partitioning contributions by different predictors to multiple R2 into independent and joint contributions. Fruit production by mountain ash (Sorbus spp.), juneberry (Amelanchier spp.), dogwoods (Cornus spp.), nannyberry (Viburnum lentago), and possibly cherries (Prunus spp.) was inversely related to production in the previous year. These effects were independent of weather conditions, suggesting that intrinsic factors such as internal resource budgets or an adaptive strategy of variable reproductive output influenced fruit production. To our knowledge, this is the first evidence of masting in members of the genera Cornus, Viburnum, and Amelanchier, and in members of Prunus and Sorbus in North America. All species produced fewer fruits when weather conditions were dry, so the Moran effect could have synchronized fruit production both within and among species. Patterns and causes of variation in berry crops have implications for ecosystem dynamics, particularly in boreal and subArctic environments where berry crops are important wildlife foods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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 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".