Exploring the Effects of Shared Home Ranges on Human-Wildlife Interactions, Parasite Overlap, and Stress Responses in Vervet Monkeys (Chlorocebus Pygerythrus) in East Africa
Bibliographic record
Abstract
As human populations expand and encroach upon natural habitats, boundaries between human settlements and wildlife become increasingly blurred. These shared spaces influence human-wildlife interactions and elevate the risk of zoonotic disease transmission. This dissertation investigates the consequences of overlapping home ranges for human-wildlife conflict, parasite community structure, and physiological and behavioral stress responses in vervet monkeys (Chlorocebus pygerythrus). Chapter 2 compares human-wildlife conflicts (HWCs) at sites in Uganda and Kenya, analyzing how socioeconomic factors shape local responses. Respondents reported both positive and negative effects of living near a research station or conservancy, with significant variation in conflict severity and frequency. Chapter 3 examines gastrointestinal parasite communities in sympatric hosts—humans, dogs, livestock, and vervets—around Lake Nabugabo, Uganda. Findings revealed overlapping parasite taxa across species, suggesting shared transmission pathways. Chapter 4 uses a parasite removal experiment (deworming and natural reinfection) to assess how gastrointestinal parasites affect vervet monkey fecal glucocorticoid metabolites (fGC) and behaviors. Reinfection elevated fGC levels and altered behaviors, though not always in predicted ways. This interdisciplinary research integrates ecological, parasitological, and ethological approaches to understand how habitat sharing influences disease ecology and stress in wildlife. The findings underscore the complexity of human-wildlife coexistence and highlight the importance of incorporating both ecological and social dimensions into conservation and public health strategies.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".