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Record W4224985594 · doi:10.1111/ecog.06259

A temperature‐driven model of phenological mismatch provides insights into the potential impacts of climate change on consumer–resource interactions

2022· article· en· W4224985594 on OpenAlexaffabout
Sébastien M. J. Portalier, Jean‐Noël Candau, Frithjof Lutscher

Bibliographic record

VenueEcography · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsNatural Resources CanadaCanadian Forest ServiceUniversity of Ottawa
Fundersnot available
KeywordsPhenologyClimate changeEcologyTrophic levelEnvironmental scienceResource (disambiguation)Atmospheric sciencesBiologyComputer science

Abstract

fetched live from OpenAlex

Anthropogenic climate change increasingly affects species phenology. Because trophic interactions often occur at specific phenological stages, changes in one species' phenology may affect others through phenological mismatch. When a consumer and a resource both exhibit a seasonal resting period, the synchrony of the end of their respective resting periods is fundamental for the persistence of their interaction. Since the consumer and its resource may react differently to changes in temperature regime, the synchrony between them could be altered. We investigate potential effects of climate change on species' synchrony. We propose a general model that determines the duration of the resting period according to temperature, and its effects on the mismatch between phenological stages of two interacting species. We illustrate our approach using the spruce budworm–balsam fir system in eastern Canada. We find that an increase in temperature advances the end of the resting period. However, the effects of a warm or cold spell during the resting period strongly vary according to the timing and the duration of the spell. Depending on how a consumer and its resource react to the same temperature shift, the mismatch between them may increase or decrease. The spruce budworm–balsam fir model predicts that an increase in temperature may increase the mismatch between the insect and the tree in southern sites, but may increase the synchrony in northern sites. This modelling approach is of prime importance to investigate potential effects of climate change on consumer–resource systems.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

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)0.0060.000

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 head0.024
GPT teacher head0.242
Teacher spread0.218 · 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 teacher head, not a consensus.

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

Citations13
Published2022
Admission routes2
Has abstractyes

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