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Impact of extreme and fluctuating temperatures on aphid–parasitoid dynamics

2013· article· en· W2062777990 on OpenAlexafffund
Jordan A. Bannerman, Bernard D. Roitberg

Bibliographic record

VenueOikos · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsParasitoidAutocorrelationEcologyBiologyAphidRange (aeronautics)Host (biology)Environmental scienceMathematicsStatisticsMaterials scienceAgronomy

Abstract

fetched live from OpenAlex

Climate change is predicted to increase the occurrence of extreme temperature events. We constructed a stage‐structured Leslie matrix model with intra‐generational dynamics to explore the impact of extreme and fluctuating temperatures on host–parasitoid dynamics. We varied three temperature parameters to generate a range of temperature regimes that varied in their daily maximum temperatures, the number of warmer‐than‐average days and the autocorrelation of those warmer‐than‐average days. All three temperature parameters influenced host–parasitoid dynamics. Increasing the frequency of warmer than average days and the degree of autocorrelation only exerted significant impacts on host– parasitoid dynamics when daily maximum temperatures were sufficient to produce temperature‐dependent mortality. The effects of increasing autocorrelation of daily temperatures were dependent on the maximum daily temperatures and the frequency of warmer than average days. When daily maximum temperatures were severely warm, but the frequency of those severely warm days was low, increasing autocorrelation increased the probability that the aphids and parasitoids will persist, but when the frequency of severely warm days is increased, increasing autocorrelation decreases the probability that the populations will persist. These temperature phenomena exert significant effects on host–parasitoid dynamics in addition to those effects produced by changes in mean temperatures and warrant further investigation at the community level.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score0.950

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)0.0010.000

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 head0.012
GPT teacher head0.230
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.

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

Citations37
Published2013
Admission routes2
Has abstractyes

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