Bibliographic record
Abstract
One central question in ecology is why some areas have many species and others have few. Many explanations have been proposed and often the forces that drive species richness are context-dependent. These forces are divided into two general categories: biotic drivers and abiotic drivers. Biotic drivers are most commonly described in terms as top-down and bottom-up effects while abiotic drivers are commonly described in terms of climate and habitat disturbance. The objective of this thesis is to determine how these drivers affect species richness in terrestrial ecosystems. To test this I examine an insect herbivore assemblage in a disturbed forest fragment landscape in southern Canada. I use geographic information systems techniques to determine the impact of a natural episodic disturbance (i.e. an abiotic natural driver) and a chronic human disturbance (i.e. an abiotic human driver) on forest habitat quality (Chapter 1) and on a forest-dwelling caterpillar assemblage (Chapter 2). I show that ice storms result in a heterogeneous pattern of spatial damage across a forest landscape, differing depending on the type of coarse woody debris examined. These different types of coarse woody debris provide habitat for a diversity of taxa. In contrast with natural disturbance, I found that human-based disturbance do not have a positive impact on caterpillar assemblages. Pursuant to this, I explore the concept of habitat quality from the perspective of host plant identity (Chapter 3) and host plant quality (Chapter 4). I found that caterpillar assemblages have strong host plant preferences and that these preferences may depend on quadrat-scale foliar qualities (i.e. a biotic bottom-up driver) and parasitoid densities (i.e. a biotic top-down driver) at different times in the growing season. This thesis adds to a growing body of literature aimed to better understand the drivers of insect species richness across disturbed landscapes. In addition, this thesis develops several management-specific tools for measuring forest disturbance and provides valuable insight into how the selection of different tree species for planting initiatives can have important impacts on forest communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".