The influence of concurrent disturbances on plant community dynamics in northern hemlock-hardwood forests
Bibliographic record
Abstract
Throughout the Upper Great Lakes region, alterations to historic disturbance regimes have influenced plant community dynamics in hemlock-hardwood forests. Several important mesic forest species, eastern hemlock (Tsuga canadensis), yellow birch (Betula alleghaniensis), eastern white pine (Pinus strobus), and Canada yew (Taxus canadensis), are in decline due to exploitive logging practices used at the turn of the 20th century and the wave of intense fires that followed. Continued regeneration and recruitment failure is attributed to contemporary forest management practices and overbrowsing by white-tailed deer (Odocoileus virginianus). Therefore, I examined the influence of two concurrent disturbances, overstory removal and herbivory, on plant community dynamics in two hemlock-hardwood forests. I measured the post-disturbance regeneration response (herbaceous and woody species) inside and outside of deer exclosures in 20 artificial canopy gaps (50 – 450 m2) and monitored survival and growth for hundreds of planted seedlings. The results of this research show that interacting disturbances can play a large role in shaping plant community composition and structure in hemlock-hardwood forests. White-tailed deer herbivory homogenized the post-disturbance plant communities across the experimental gradient of gap areas, essentially making species compositions in small gaps “look like” those in large gaps. Deer browsing also influenced probability of survival for planted Canada yew cuttings; all else being equal an individual was nearly seven times more likely to survive if protected from herbivory (P < 0.001). In contrast, the ability of sugar maple (Acer saccharum) to persist under high levels of herbivory and respond rapidly to overstory release appears to be related to the presence of stem layering(i.e., portions of below-ground prostrate stem). Layering occurred in 52% of excavated saplings (n = 100) and was significantly associated with increased post-disturbance height growth. Understory light was also important to planted seedling establishment and height growth. Higher levels of direct under-canopy light negatively impacted survival for shade-tolerant hemlock and Canada yew, while an increase in diffuse light was linked to a higher probability of survival for yellow birch and height growth for hemlock and Canada yew. Increases in white pine height growth were also significantly associated with a decrease in canopy cover.
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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 | 0.001 | 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".