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Water–energy balance and the geographic pattern of species richness of western Palearctic butterflies

2003· article· en· W2064760278 on OpenAlexaboutno aff
Bradford A. Hawkins, Eric E. Porter

Bibliographic record

VenueEcological Entomology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSpecies richnessButterflyBody size and species richnessEcologyMacroecologyRange (aeronautics)Spatial analysisSpatial ecologySpecies diversitySpatial variabilitySpatial heterogeneityBiologyGeographyStatisticsMathematicsRemote sensing

Abstract

fetched live from OpenAlex

Abstract. 1. Using two sources of data to estimate butterfly species richness, the potential influences of 11 environmental variables on the richness gradient of butterflies in western/central Europe and northern Africa were examined with multiple regression and spatial autocorrelation analysis. A measure of water–energy balance, actual evapotranspiration, explained 79% of the variance in butterfly species richness using data derived from range maps, and 72% of the variance using data derived from grid‐based distribution maps. All other variables explained less than 4% of the variance in the regression models and differed depending on the data source. 2. The spatial analysis indicated that actual evapotranspiration successfully removed most of the spatial autocorrelation in both richness data sets at all spatial scales, confirming the ability of the model to account for the spatial pattern in butterfly richness. 3. Plant species richness, a rarely tested variable hypothesised to be an important determinant of herbivore diversity, was weakly associated with butterfly richness, suggesting that it has little or no direct influence on butterfly richness. 4. A historical variable, the length of time that areas have been exposed for recolonisation after the retreat of the ice sheet following the last ice age, was also not associated with richness patterns, indicating that butterfly richness is in equilibrium with contemporary climate. 5. It was not possible to confirm a result reported for Canadian butterflies that land cover diversity is a strong predictor of butterfly richness, possibly because of methodological differences in the studies, differences in the range of climates found in Canada and the western Palearctic, or because of the highly modified landscape characteristic of Europe. 6. Water–energy balance offers a parsimonious explanation for the butterfly richness gradient in this region, operating partially indirectly via effects on plant productivity and partially directly via physiological effects on butterflies, and this conclusion is robust to differences in the types of distribution maps used to estimate richness patterns.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.970

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 studies0.0000.002
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 head0.008
GPT teacher head0.203
Teacher spread0.195 · 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

Citations136
Published2003
Admission routes1
Has abstractyes

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