Effects of Deer Browsing on Nutrient Cycling and Regeneration Dynamics in a Carolinian Old-Growth Forest of Ontario
Bibliographic record
Abstract
Old growth forests are increasingly rare but important carbon sinks which harbour rich biodiversity. Chronic browsing by the white-tailed deer (Odocoileus virginianus) is a major problem in northern temperate forests where deer numbers have increased in recent decades driven by stricter hunting rules and reduced predation necessitating local monitoring of vegetation responses. The objective of this study was to determine the effects of deer exclusion on tree regeneration dynamics and soil nutrient cycling in an old growth Carolinian forest. This was done using exclusion fencing and tip-up mounds at McMaster Forest Nature Preserve and the Sheelah Dunn Dooley Nature Sanctuary in Hamilton Ontario. Tree regeneration was surveyed from thirty 1m x 1m quadrats within exclusion plots and another thirty quadrats from deer browsed areas adjacent to the exclusion plots. Soil samples were taken from each quadrat to analyze browsing impacts on nitrate, phosphate and soil organic matter. Red oak (Quercus rubra) was planted at the top and base of tip-up mounds of varying heights and widths and monitored for deer access and browsing activity. Results show a significantly higher density of woody plants within exclosures compared to non-exclosures (p=0.0089) and twice more abundance of highly palatable species within the exclosures. However, species richness (p > 0.05) and diversity (p > 0.05) were minimally impacted by deer browsing showing a resilient old growth forest. Soil nitrate was consistently higher in the non-exclosures while phosphate was consistently higher within deer exclosures. Finally, more seedlings survived at the top of mounds than the bases showing the potential of tip-up mounds to be a natural method of deer exclusion and a critical avenue for restoring over-browsed forests.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".