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Contrasting effects of climate warming on host-parasitoid interactions: insights from Rocky Mountain aspen leaf miners and their parasitoids

2023· preprint· en· W4386723296 on OpenAlexaff
Alisha A. Shah, Emily Hamant, Juan G. Rubalcaba, Beau Larkin, Andrew A. Forbes, Arthur Woods

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsMcGill University
Fundersnot available
KeywordsParasitoidMicroclimateEcologyHost (biology)BiologyClimate changeHymenopteraGlobal warming

Abstract

fetched live from OpenAlex

Because temperature has pervasive effects on biological rates, climate warming may alter the outcomes of interactions between insect hosts and their parasitoids, which, for many host species, constitute the single largest source of mortality. Many studies report that climate warming is depressing the performance of parasitoids more than that of hosts. We examined this consensus by assessing the thermal ecology of a host-parasitoid interaction in the Rocky Mountains using wild populations of the aspen leaf miner (Phyllocnistis populiella) and a set of eulophid wasps that attack them. Host and wasp development rates were differentially sensitive to temperature. In addition, upper thermal limits in adult wasps were lower than those of host caterpillars, and wasps preferred low temperatures in choice experiments. When coupled to simulations of leaf microclimates in aspen canopies, these observations suggest, contrary to expectations, that climate warming is likely to benefit parasitoids at the expense of hosts.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.245
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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