Cone characteristics and insect predation levels vary across years in mast seeding white spruce
Bibliographic record
Abstract
Populations of many tree species exhibit synchronous and highly temporally variable seed crops across years. This is called mast seeding, and there are two predominant hypotheses for this pattern of reproduction: pollination efficiency and seed-predator satiation. Mast seeding studies typically involve records of population-level reproduction, with less information on the characteristics of reproductive structures. Here, we use data across 6 years (2012–2017), spanning a range of population-level cone conditions, to characterize (i) white spruce (Picea glauca (Moench) Voss) cone lengths and seeds per cone, and (ii) levels of seed predation. We quantified population-level cone production and collected 1399 cones from a total of 38 trees in the Huron Mountains, Michigan, USA. Linear mixed models showed that mean and minimum cone lengths varied significantly across years; both being longest during the greatest cone production year. Larger cones had more seeds and the slopes of the relationships as well as the intercepts varied significantly across years. Generalized linear mixed models and Akaike’s information criterion model selection showed that cones with insect predation damage was greatest when population-level reproduction was the lowest, with a mean proportion of cones damaged 0.82 in that year. Our findings show that white spruce cone characteristics and losses to insect seed predation vary temporally, and follow expectations based on mast seeding hypotheses.
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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 | 0.000 |
| 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".