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Record W3000136958 · doi:10.1111/gcb.14991

Warming counteracts defoliation‐induced mismatch by increasing herbivore‐plant phenological synchrony

2020· article· en· W3000136958 on OpenAlexafffund
Ping Ren, Valérie Néron, Sergio Rossi, Eryuan Liang, Mathieu Bouchard, Annie Deslauriers

Bibliographic record

VenueGlobal Change Biology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsMinistère des Ressources naturelles et des ForêtsUniversité du Québec à Chicoutimi
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsHerbivorePhenologyClimate changeEcologyBiologyEnvironmental science

Abstract

fetched live from OpenAlex

Climate change is altering phenology; however, the magnitude of this change varies among taxa. Compared with phenological mismatch between plants and herbivores, synchronization due to climate has been less explored, despite its potential implications for trophic interactions. The earlier budburst induced by defoliation is a phenological strategy for plants against herbivores. Here, we tested whether warming can counteract defoliation-induced mismatch by increasing herbivore-plant phenological synchrony. We compared the larval phenology of spruce budworm and budburst in balsam fir, black spruce, and white spruce saplings subjected to defoliation in a controlled environment at temperatures of 12, 17, and 22°C. Budburst in defoliated saplings occurred 6-24 days earlier than in the controls, thus mismatching needle development from larval feeding. This mismatch decreased to only 3-7 days, however, when temperatures warmed by 5 and 10°C, leading to a resynchronization of the host with spruce budworm larvae. The increasing synchrony under warming counteracts the defoliation-induced mismatch, disrupting trophic interactions and energy flow between forest ecosystem and insect populations. Our results suggest that the predicted warming may improve food quality and provide better growth conditions for larval development, thus promoting longer or more intense insect outbreaks in the future.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 head0.044
GPT teacher head0.270
Teacher spread0.226 · 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

Citations46
Published2020
Admission routes2
Has abstractyes

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