Integrating temperature-dependent life table data into Insect Life Cycle Model for predicting the potential distribution of Scapsipedus icipe Hugel & Tanga
Bibliographic record
Abstract
Scapsipedus icipe Hugel and Tanga (Orthoptera: Gryllidae) is a newly described edible cricket species. Although, there is substantial interest in mass production of S. icipe for human food and animal feed, no information exists on the impact of temperature on their bionomics. Temperature-dependent development, survival, reproductive and life table parameters of S. icipe was generated and integrated into advanced Insect Life Cycle Modeling software to describe relative S. icipe population increase and spatial spread based on nine constant temperature conditions. We examined model predictions and implications for S. icipe potential distribution in Africa under current and future climate. These regions where entomophagy is widely practiced have distinctly different climates. Our results showed that S. icipe eggs were unable to hatch at 10 and 40°C, while emerged nymphs failed to complete development at 15°C. The developmental time of S. icipe was observed to decrease with increased in temperature. The lowest developmental threshold temperatures estimated using linear regressions was 14.3, 12.67 and 19.12°C and the thermal constants for development were 185.2, 1111.1- and 40.7-degree days (DD) for egg, nymph and pre-adult stages, respectively. The highest total fecundity (3416 individuals/female/generation), intrinsic rate of natural increase (0.075 days), net reproductive rate (1330.8 female/female/generation) and shortest doubling time (9.2 days) was recorded at 30°C. The regions predicted to be suitable by the model suggest that S. icipe is tolerant to a wider range of climatic conditions. Our findings provide for the first-time important information on the impact of temperature on the biology, establishment and spread of S. icipe across the Africa continent. The prospect of edible S. icipe production to become a new sector in food and feed industry is discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".