SILVICULTURE, SEEDS, AND DEER: ASSESSING GROUND-LAYER DIVERSITY ALONG EXPERIMENTAL DISTURBANCE GRADIENTS IN A MANAGED NORTHERN HARDWOOD FOREST
Bibliographic record
Abstract
The objective of this dissertation research is to examine drivers of ground-layer diversity along experimental gradients of overstory and understory disturbance in a managed northern hardwood forest. My research was conducted at the Northern Hardwood Silviculture Experiment to Enhance Diversity (NHSEED) project site in Alberta, Michigan. In the first chapter, I examine the effect of canopy removal, soil disturbance, deer exclusion, and manual seed addition on the diversity, composition, and heterogeneity of tree regeneration. The primary conclusion drawn from this chapter is that although greater canopy openness and soil disturbance may enhance species richness and heterogeneity, the highest species richness across all treatment combinations was achieved through a combination of deer exclusion and seed addition. Importantly, deer exclusion alone did not enhance richness or compositional heterogeneity, underlining the bottleneck effect of limited propagule availability. Manual seed addition successfully altered species composition, increasing species richness and compositional heterogeneity. Addressing propagule limitation and deer browse is essential for enhancing tree species diversity alongside selecting appropriate regeneration and site preparation methods. In the second chapter, I explore whether the implementation of artificial tip-up mounds could improve tree regeneration diversity in managed northern hardwood forests. The results demonstrated that these mounds can create distinct seedling communities and reduce the dominance of competitive maple regeneration in forests regenerated with selection systems. Implementing artificial tip-up mounds may be beneficial for promoting tree species diversity and supporting natural regeneration over time. Lastly, in the third chapter I assess the effect of canopy removal, soil disturbance, and deer browse on the herbaceous layer and explore the applicability of theoretical frameworks to predict diversity-disturbance relationships. The key findings that emerged from this chapter reveal effects of disturbance on diversity are mediated by effects of disturbance on ground-layer productivity, which could be predicted by a unimodal response to canopy openness. Furthermore, deer herbivory is a critical component of the understory disturbance regime and can alter ground-layer responses to disturbance and competition. In the absence of deer, diversity and richness were primarily linked to overstory and ground disturbance, showing either a positive or unimodal response. In the presence of deer, however, increased diversity was associated with higher productivity of the ground layer, indicating greater resilience of browse sensitive plants when resources were more abundant. In addition, it is important to carefully assess changes in diversity as quantitative increases do not necessarily reflect compositional quality as measured by floristic quality indexes.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".