Host-seeking efficiency can explain population dynamics of the tsetse fly Glossina morsitans morsitans in response to host density decline
Bibliographic record
Abstract
Females of all blood-feeding arthropod vectors must find and feed on a host in order to produce offspring. For tsetse-vectors of the trypanosomes that cause human and animal African trypanosomiasis-the problem is more extreme, since both sexes feed solely on blood. Host location is thus essential both for survival and reproduction. Host population density should therefore be an important driver of population dynamics for haematophagous insects, and particularly for tsetse, but the role of host density is poorly understood. We investigate the issue using data on changes in numbers of tsetse (Glossina morsitans morsitans Westwood) caught during a host elimination experiment in Zimbabwe in the 1960s. During the experiment, numbers of flies caught declined by 95%. We aimed to assess whether models including starvation-dependent mortality could explain observed changes in tsetse numbers as host density declined. An ordinary differential equation model, including starvation-dependent mortality, captured the initial dynamics of the observed tsetse population. However, whereas small numbers of tsetse were caught throughout the host elimination exercise, the modelled population went extinct. Results of a spatially explicit agent-based model suggest that this discrepancy could be explained by immigration of tsetse into the experimental plot. Variation in host density, as a result of natural and anthropogenic factors, may influence tsetse population dynamics in space and time. This has implications for Trypanosoma brucei rhodesiense transmission. Increased tsetse mortality as a consequence of low host density may decrease trypanosome transmission, but hungrier flies may be more inclined to bite humans, thereby increasing the risk of transmission to humans. Our model provides a way of exploring the role of host density on tsetse population dynamics and could be incorporated into models of trypanosome transmission dynamics to better understand how spatio-temporal variation in host density impacts trypanosome prevalence in mammalian hosts.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".