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Record W2597179128 · doi:10.1139/cjfr-2016-0537

Effects of phenological synchronization on caterpillar early-instar survival under a changing climate

2017· article· en· W2597179128 on OpenAlexaffvenue
Emma Despland

Bibliographic record

VenueCanadian Journal of Forest Research · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsConcordia University
Fundersnot available
KeywordsInstarBiologyPhenologyEcologyPopulationPredationOutbreakBiological dispersalHerbivoreLarvaPopulation densityDemography

Abstract

fetched live from OpenAlex

Early-instar caterpillars experience very high and often very variable mortality; if it is density dependent, it can be a key factor in outbreak dynamics. Plant physical and chemical defenses can be extremely effective against young caterpillars, even of specialists. Phenological asynchrony with host plants can lead to dispersal and mortality in the early instars and increased predation or poor nutrition in later instars. Predation on early-instar larvae (including cannibalism) can be extremely high, parasitism appears generally low, and pathogens acquired early in larval development can lead to high mortality in later stadia. Four well-studied species reveal very different roles of early-instar mortality in population dynamics. In spruce budworm and gypsy moth, early-instar mortality rates can be very high; they do not drive outbreak cycles because density dependence is weak, but can modulate cycles and contribute to outbreak size and duration. For the autumnal moth, early-instar survival depends on host plant synchrony, but may or may not be density dependent. For monarch butterflies, the relative importance of larval mortality rates in population dynamics remains unclear. Tritrophic interactions between herbivores, host plants, natural enemies, and microbes play complex and species-specific roles in early-instar ecology, leading to emergent dynamics in population fluctuations. The phenology of these relationships is often poorly understood, making their responses to climate change unpredictable.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.290
Teacher spread0.261 · 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

Citations38
Published2017
Admission routes2
Has abstractyes

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