Plant community response to novel silvicultural treatments in Great Lakes northern hardwoods
Bibliographic record
Abstract
The objective of this dissertation is to assess plant community response across a range of silvicultural disturbances and test ecological hypotheses to better inform ecologists and forest managers. To provide context for the utility of revising silvicultural systems, I review natural disturbance regimes and historical practices that have shaped contemporary Great Lakes northern hardwood forests (Chapter 2). Further, I identify important ways to expand the silvicultural toolbox and better emulate natural disturbance regimes. Building on this theoretical underpinning, I investigate the initial regeneration and plant community response to two novel silvicultural experiments: the Northern Hardwood Experiment for Enhancing Diversity (NHSEED) near Alberta, Michigan, and a strip clearcut experiment near Mountain Iron, Michigan. Three themes emerged from the findings in this dissertation. First, seedlings and saplings receive few benefits from reduced canopy cover if they cannot overcome additional limitations. For example, yellow birch (Betula alleghaniensis Britt.) seedling density was better predicted by conspecific overstory basal area and litter depth variation than silvicultural treatments (Chapter 3), and sugar maple recruitment into the sapling size class in clearcut strips may be limited by deer browse (Chapter 5). Second, silvicultural disturbances tend to favor low-mass fruit, long-lived fruit, or vegetative reproduction, except for sugar maple which relies on robust advance regeneration to benefit from overstory disturbances (Chapters 3, 4 and 5). Third, the relationship between disturbance severity and diversity is not conclusive. Initial responses to silvicultural disturbances did not follow the intermediate disturbance hypothesis, which proposes that diversity is maximized at intermediate levels of disturbance intensity or frequency (Chapter 4). Moreover, taxonomic and phylogenetic diversity do not always respond similarly to disturbances (Chapter 4), suggesting that both indices should be incorporated into informed management decisions. Integrating these findings into management planning may allow better predictions to silvicultural disturbances now and in the future.
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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 | 0.000 |
| 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".