Impact of temperature and relative humidity on the eye-spotted bud moth, Spilonota ocellana (Lepidoptera: Tortricidae): a climate change perspective
Bibliographic record
Abstract
Global climate change models predict an increase in the frequency, severity and duration of extreme weather events.Weather extremes are important for poikilothermic species limited by their capacity to withstand conditions beyond their optimum for survival and development.To understand insect population dynamics, and forecast outbreaks in agro-ecosystems, we need a better understanding of the biology of insect pests of concern.In this study, I explored physiological responses of Spilonota ocellana (Denis and Schiffermüller) in the context of spring frost and summer drought, by focusing on the most vulnerable life stages.I determined that S. ocellana spring larval instars are susceptible to temperatures above their mean supercooling point (SCP) which ranged from -9.1 ± 0.2 °C (4 th instar) to -7.9 ± 0.2 °C (6 th instar).While supercooling point increased with instar, the median LLT of -7.3 ± 0.4 °C across all instars demonstrates that a hard spring frost would be necessary to cause larval mortality.Exposure to low humidity resulted in lower egg hatch; this effect was exacerbated at higher temperatures.Furthermore, I discovered that exposure to low humidity during the latter half of egg development resulted in reduced survival and faster development rates; similar effects were also observed during a period of hot and dry conditions in an apple orchard.This study provides information on the impacts of extreme weather events on survival and development within and between life stages of S. ocellana, which could have the potential to alter population abundance, phenology, and thus management of this pest.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".