Exploring multiple dimensions of conservation success: Long‐term wildlife trends, anti‐poaching efforts and revenue sharing in Kibale National Park, Uganda
Bibliographic record
Abstract
Abstract Parks are essential for protecting biodiversity and finding ways to improve park effectiveness is an important topic. We contributed to this debate by examining spatial and temporal changes in illegal activities in Kibale National Park, Uganda between 2006 and 2016 and used existing data to evaluate how the changes were correlated with the living conditions of people in neighboring communities, as well as patrolling effort. We explore the effectiveness of conservation strategies implemented in Kibale, by quantifying changes in the abundance of nine animal species over two to five decades. While uncertainty in such animal survey data are inherently large and it is hard to generalize across a 795‐km2 area that encompasses diverse habitat types, data suggest an increase in animal abundance in the National Park. An increase in patrolling effort by park guards over the decade was correlated with a decline in the number of traps and snares found, which suggests patrolling helped limit resource extraction from the park. The park’s edge was extensively used for illegal forest product extraction, while the setting of snares occurred more often deeper in the forest. Perhaps counter‐intuitively, increased community wealth or park‐related employment in a village next to the park were positively correlated with increased illegal forest product extraction. Overall, our results suggest that the portfolio of conservation strategies used over the last two to five decades were effective for protecting the park and its animals, although understanding the impact of these efforts on local human populations and how to mitigate any losses and suffering they sustain remains an important area of research and action. It is evident that complex social, political and economic drivers impact conservation success and more interdisciplinary studies are required to quantify and qualify these dimensions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".