Modeling temperature-dependent survival with small datasets: insights from tropical mountain agricultural pests
Bibliographic record
Abstract
Many regions are increasingly threatened by agricultural pests but suffer from a lack of data that hampers the development of adequate population dynamics models that could contribute to pest management strategies. Here, we present a new model relating pest survival to temperature and compare its performance with two published models. We were particularly interested in their ability to simulate the deleterious effect of extreme temperatures even when adjusted to datasets that did not include extreme temperature conditions. We adjusted the models to survival data of three species of potato tuber moth (PTM), some major pests in the Tropical Andes. To evaluate model performance, we considered both goodness-of-fit and robustness. The latter consisted in evaluating their ability to predict the actual altitudinal limits of the species in the Ecuadorian Andes. We found that even though our model did not always provide the best fit to data, it predicted extreme temperature mortality and altitudinal limits accurately and better than the other two models. Our study shows that the ability to accurately represent the physiological limits of species is important to provide robust predictions of invasive pests' potential distribution, particularly in places where temperatures approach lethal extremes. The value of our model lies in its ability to simulate accurate thermal tolerance curves even with small datasets, which is useful in places where adequate pest management is urgent but data are scarce.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".