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Record W2021357116 · doi:10.1139/x06-211

Climatic effects on caterpillar fluctuations in northern hardwood forests

2007· article· en· W2021357116 on OpenAlexvenueno aff
Lindsay V. Reynolds, Matthew P. Ayres, Thomas G. Siccama, Richard T. Holmes

Bibliographic record

VenueCanadian Journal of Forest Research · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
Fundersnot available
KeywordsCaterpillarYellow birchEcologyAbundance (ecology)Lepidoptera genitaliaBiologyDeciduousHerbivoreEnvironmental scienceHardwood

Abstract

fetched live from OpenAlex

Fluctuations in the abundance of Lepidoptera are common but inadequately understood. Here we show that caterpillar abundance in the White Mountains of New Hampshire has fluctuated by >20-fold from 1986 to 2005. We report tests of three possible causes: (i) extreme winter cold; (ii) long, warm summers; and (iii) interannual variation in tree growth, which tends to correlate with phytochemistry. Caterpillar fluctuations from summers t to t + 1 were uncorrelated or negatively correlated with minimum air temperature during the intervening winter (does not support the first cause), but were positively correlated with thermal sum during summer t (r = 0.49–0.56) (supports the second cause). There was limited interannual variation in the radial growth of two dominant tree species ( Acer saccharum Marsh. and Betula alleghaniensis Britt.) and no correlation with caterpillar fluctuations (refutes the third cause). Thermal sum might influence caterpillar fluctuations through direct effects on insect development, indirect effects on susceptibility to natural enemies, and (or) indirect effects on plant-insect interactions; the mechanisms are of particular interest because thermal sums have been increasing since local records began in 1957 (r = 0.41–0.45). In hardwoods forests of the northeastern United States, there is some broad-scale driver related to summer temperatures that generates fluctuations in caterpillar abundance, which influences herbivory as well as higher level consumers, such as insectivorous birds.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.961
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.019
GPT teacher head0.290
Teacher spread0.271 · 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

Citations43
Published2007
Admission routes1
Has abstractyes

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