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RANGE-RESTRICTED, SPECIALIST BORNEAN BUTTERFLIES ARE LESS LIKELY TO RECOVER FROM ENSO-INDUCED DISTURBANCE

2006· article· en· W2084930844 on OpenAlexaff
N. A. Charrette, Daniel F. R. Cleary, Arne Ø. Mooers

Bibliographic record

VenueEcology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDisturbance (geology)EcologyLocal extinctionGeneralist and specialist speciesRange (aeronautics)Extinction (optical mineralogy)GeographyBiologyHabitatPopulationBiological dispersal

Abstract

fetched live from OpenAlex

The forest fires induced by the El Niño Southern Oscillation (ENSO) in 1997-1998 resulted in the temporary extirpation of more than 100 lowland butterfly species at a forest site in Borneo. Species with more restricted ranges were less likely to recover over the following four years. Matched-pair analyses revealed that species with lower initial abundances, restricted geographic ranges, and more specialized larvae were less likely to return. Specialization differed predictably between the (more generalist) wide-range and (more specialized) restricted-range species in our data set, and both geographic range and level of specialization were important in multivariate models. These are the first observations directly linking extent of occurrence, ecological specialization, and observed recovery following local extirpation. If recovery time exceeds the frequency of disturbance, local extirpation can lead to local extinction. Given that ENSO-induced disturbances are increasing in frequency, in severity, and in geographic scale, these results suggest that specialist species with restricted geographic ranges could be at particularly high risk of global extinction.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score1.000

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)0.0010.002

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 head0.218
Teacher spread0.206 · 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; both teacher heads agree on what is shown here.

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

Citations92
Published2006
Admission routes1
Has abstractyes

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