Recruitment Success for Mast Year Cohorts of Sugar Maple (Acer saccharum) Over Three Decades of Heavy Deer Browsing
Bibliographic record
Abstract
We examined whether particular years of mast seed production in sugar maple (Acer saccharum) are associated with increased likelihood of cohort recruitment success into the sapling stage over three decades of heavy browsing pressure from white-tailed deer in a mature woodland population in southeastern Ontario, Canada. The population was sampled in 2014 for seedling and sapling stages (≤6 cm in stem diameter) to obtain an age frequency distribution spanning about 80 y and including survivors of seed cohorts produced in two known mast years at the study site (2013 and 1984) and in other mast years known to have occurred within the broader region (but not confirmed for the study site). The age frequency distribution is roughly bimodal with zero to very few individuals recorded for ages 9 through 29 y, corresponding with the known time period (early 1980's to late 2000's) of regional overabundance for white-tailed deer in eastern Ontario and at the study site in particular. The 1 y old seedlings (from the 2013 mast year) and the survivors of putative mast year cohorts from 2006 and 2000, however, are especially conspicuous, with less striking recruitment success indicated for the older confirmed mast year cohort from 1984 (which had more years of accumulated impact from mortality risks). Our results suggest seed masting in sugar maple can bolster cohort recruitment success that otherwise would virtually (or completely) fail when severe impact from deer browsing is combined with other typical early life-stage mortality risks, e.g., from drought, neighborhood competition, and persistent overhead canopy shade.
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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 | 0.000 | 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) | 0.001 | 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".