Deforestation, Predator-Prey Systems & Environmental Policy in British Columbia's Great Bear Rainforest
Bibliographic record
Abstract
<br> This is a master's thesis completed at Toulouse School of Economics in September 2023. The collection contains the manuscript, all codes, as well as a novel panel data set on yearly forest loss in BC. Any questions should be addressed to peter.kamal@t-online.de ABSTRACT: The human impact on food webs cannot be understated. One instance of this complex problem is the effect of deforestation on predator-prey systems: Logging and the resulting habitat destruction can trigger negative feedback loops all throughout the trophic cascade. As viable predator populations are a crucial part of ecosystem stability, it is imperative for modern conservation science to understand both the extent of human interference in forest ecosystems and its consequences on predator populations. In this thesis, I therefore set out to answer three key questions: (1) Does modern economic forest protection policy actually curb deforestation? (2) When it does not, what are the effects of continued deforestation on the plant-herbivore-predator trophic cascade? (3) Can ecological theory provide alternative policy recommendations? To do so, I first perform an econometric impact evaluation of a recent and highly appraised forest protection policy in British Columbia’s Great Bear Rainforest. I am able to show that despite the policy’s lofty goals, it did not significantly reduce deforestation levels. Motivated by this result, I then construct an agent-based-model of the wolves and deer that make up one of the major trophic cascades in this ecosystem. I am able to show detrimental effects of logging on population levels and stability. Further, I can demonstrate that a block protection policy - as opposed to controlled logging scattered throughout the forest - provides a qualitative remedy to these effects. These results do not only inform ecological theory on perturbed predator prey-systems, but also provide policy recommendations for the Great Bear Rainforest and other comparable ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.028 | 0.013 |
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; both teacher heads agree on what is shown here.
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".