The effect of landscape complexity and microclimate on the thermal tolerance of a pest insect
Bibliographic record
Abstract
Abstract Landscape changes are known to exacerbate the impacts of climate change. As such, understanding the combined effect of climate and landscape on agroecosystems is vital if we are to maintain the function of agroecosystems. This study aimed to elucidate the effects of agricultural landscape complexity on the microclimate and thermal tolerance of an aphid pest to better understand how landscape and climate may interact to affect the thermal tolerance of pest species within the context of global climate change. Meteorological data were measured at the landscape level, and cereal aphids (Sitobion avenae,Metopolophium dirhodumandRhopalosiphum padi) sampled, from contrasting landscapes (simple and complex) in winter 2013/2014 and spring 2014 in cereal fields of Brittany, France. Aphids were returned to the laboratory and the effect of landscape of origin on aphid cold tolerance (as determined byCTmin) was investigated. Results revealed that local landscape complexity significantly affected microclimate, with simple homogenous landscapes being on average warmer, but with greater temperature variation. Landscape complexity was shown to impact aphid cold tolerance, with aphids from complex landscapes being more cold tolerant than those from simple landscapes in both winter and spring, but with differences among species. This study highlights that future changes to land use could have implications for the thermal tolerance and adaptability of insects. Furthermore, not all insect species respond in a similar way to microhabitat and microclimate, which could disrupt important predator–prey relationships and the ecosystem service they provide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".