Characteristics of Human-Wildlife Conflicts in Kenya: Examples of Tsavo and Maasai Mara Regions
Why this work is in the frame
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Bibliographic record
Abstract
Human-wildlife conflict (HWC) is a widespread and persistent challenge to conservation. However, relatively few studies have thus far examined long-term monitoring data to quantify how the type, and severity of HWC varies across species, seasons, years and ecosystems. Here, we examine human-wildlife conflicts in Tsavo and Maasai Mara, two premier wildlife conservation areas in Kenya. Using Kenya Wildlife Service (KWS) data (2001-2016), we show that both the type and severity of conflicts vary among species such that the African elephant (Loxodonta africana), is the leading conflict species in both the Tsavo (64.3%, n= 30664) and Mara (47.0%, n=12487) ecosystems. The next four most notorious conflict animals, in decreasing order, are nonhuman primates (Tsavo 11.4%, n=3502; Mara 11.8%, n=1473), African buffalo (Syncerus caffer, Tsavo 5.5%, n=1676; Mara 11.3%, n=1410), lion (Panthera leo,Tsavo 3.6%, n=1107; Mara 3.3%, n=416) and spotted hyena (Crocuta crocuta, Tsavo 2.4%, n=744; Mara 5.8%, n=729). We group the observed conflict incidences (n= 43,151) into four major conflict types, including crop raiding, the most common conflict type, followed by human and livestock attacks and property damage. The severity of conflicts also varies markedly seasonally and inter-annually. Crop raiding peaks in May-July, during and at the end of the wet season when crops are maturing but is lowest in November during the late dry season and beginning of the early rains. Attacks on humans and livestock increased more than other conflict types in both Tsavo (from 2001) and Mara (from 2013). Relatively fewer people in Mara (7.2%, n=901) than in Tsavo (38.2%, n = 11714) felt threatened by wildlife, suggesting that the Maasai people are more tolerant of wildlife. Minimizing HWC is tightly linked to successfully resolving the broader conservation challenges, including enhancing ecosystem connectivity, community engagement and conservation benefits to communities.
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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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it