Theoretical and experimental investigations of the larval life history of Anopheles gambiae
Bibliographic record
Abstract
This work addresses how the larval environment of Anopheles gambiae sensu stricto affects its growth, development, and survival. This mosquito is a major vector of human malaria in Africa, and how its larval habitat affects its fitness is relevant to epidemiology of this devastating disease. While my experimental work was species-specific, I applied general theory to understanding An. gambiae's responses to its environment. I pursued two avenues of enquiry, looking at how larval development and survival are influenced by 1) variable food availability and 2) multiple different variables acting simultaneously. I began with a theoretical investigation of how variable food affects optimal age and size at emergence under a trade-off between growth and mortality. As a consequence of risk-sensitivity, variable food produced a small delay in emergence and a shallower L-shaped age-size relationship across food levels but did not qualitatively affect predictions for optimal development. I then investigated experimentally how An. gambiae responded to increases or decreases in food availability during the larval period and compared the results to predictions from theory. While results were qualitatively consistent with predictions, age at maturity diverged substantially. To examine how multiple variables interact to affect development, I conducted a factorial experiment manipulating food availability, water depth, and temperature. I detected effects of each of these factors upon age and size at emergence. Furthermore, many of these effects depended on the levels of other factors. In the context of an L-shaped relationship between age and size, some of these interaction-effects could be seen as an outcome of energy budgets of individuals. To directly assess effects on energy, I measured lipid and glycogen of newly-emerged mosquitoes from the experiment. These values suggested that treatments decoupled body size from levels of metabolic reserves. Finally, larval diving activity was recorded during the experiment. Larvae dived more frequently under low food, providing novel evidence that An. gambiae dives to forage. Theory developed in the context of constant environments proved useful for understanding development under changing conditions. Reducing environmental variables to their effects on energy budgets may help in elucidating how mosquitoes respond to the variety of environments in nature.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".