Comparing thermal tolerance across contrasting landscapes: first steps towards understanding how landscape management could modify ectotherm thermal tolerance
Bibliographic record
Abstract
Abstract Insects are highly dependent on ambient temperatures to ensure their biological functions. Their persistence in the environment and their resistance to unfavourable temperatures are governed by their physiological thermal tolerance. Global change extends beyond climatic conditions to encompass modifications to the landscape. Studies of climate change and landscape composition effects on ecosystem services, such as biological control, are commonly performed independently. Moreover, coarse scales are not always relevant when assessing climate change's impacts on ectotherms. We aimed to better understand the ecological relationships that may exist between microclimatic variation and insect thermal tolerance across a landscape composition gradient. To determine how landscape composition may impact insect thermal tolerance, parasitic wasps (Hymenoptera: Braconidae: Aphidiinae) of aphids were sampled along a landscape gradient from ‘closed’ to ‘open’ habitats. Sampling was performed during the winter 2013/14 and spring 2014 in cereal fields of Brittany, France. Meteorological data were recorded along this gradient. First, our results show an influence of landscape composition on local microclimate. Additionally, parasitoids from open landscapes had a higher tolerance to low temperatures, leading to higher physiological costs, compared with parasitoids from closed landscapes. This trend was stronger in winter than in spring. These results have numerous implications in the context of climate change, suggesting that targeted landscape management practices could create sheltered microclimatic areas that reduce the physiological costs of thermal tolerance and promote the persistence of biological control agents.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".