Variation of tsetse fly abundance in relation to habitat and host presence in the Maasai Steppe, Tanzania
Bibliographic record
Abstract
Human activities modify ecosystem structure and function and can also alter the vital rates of vectors and thus the risk of infection with vector-borne diseases. In the Maasai Steppe ecosystem of northern Tanzania, local communities depend on livestock and suitable pasture that is shared with wildlife, which can increase tsetse abundance and the risk of trypanosomiasis. We monitored the monthly tsetse fly abundance adjacent to Tarangire National Park in 2014-2015 using geo-referenced, baited epsilon traps. We examined the effect of habitat types and vegetation greenness (NDVI) on the relative abundance of tsetse fly species. Host availability (livestock and wildlife) was also recorded within 100×100 m of each trap site. The highest tsetse abundance was found in the ecotone between Acacia-Commiphora woodland and grassland, and the lowest in riverine woodland. Glossina swynnertoni was the most abundant species (68%) trapped throughout the entire study, while G. pallidipes was the least common (4%). Relative species abundance was negatively associated with NDVI, with greatest abundance observed in the dry season. The relationship with the abundance of wildlife and livestock was more complex, as we found positive and negative associations depending on the host and fly species. While habitat is important for tsetse distribution, hosts also play a critical role in affecting fly abundance and, potentially, trypanosomiasis risk.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".