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Record W2070141430 · doi:10.2980/18-2-3396

How frequent is metapopulation structure among butterflies in grasslands? Occurrence patterns in a forest-dominated landscape in southern Sweden

2011· article· en· W2070141430 on OpenAlexvenueno aff
Thomas Ranius, Sven G. Nilsson, Markus Franzén

Bibliographic record

VenueEcoscience · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMetapopulationBiological dispersalGrasslandEcologyButterflyGeneralist and specialist speciesHabitatOccupancyGeographyExtinction (optical mineralogy)Local extinctionBiologyPopulation

Abstract

fetched live from OpenAlex

We determined the proportion of butterfly species that occur as metapopulations with grasslands as the only habitat. We counted all butterflies in 19 semi-natural grassland patches in a forest-dominated landscape in southern Sweden over a 5-y period. Seven of the 44 butterfly species observed exhibited a metapopulation structure. The other species either use grassland mainly for adult feeding but not for breeding (8 species), breed both in grassland and in surrounding habitat types (19 species), or are grassland specialists but their colonization—extinction dynamics are probably not significant, since they were present in nearly all (> 80%) patches (10 species). Occupancy was generally higher in larger patches, and tended to increase with patch connectivity. Among grassland specialists and habitat generalists, the connectivity measure tended to explain more of the variation in occupancy if the shortest dispersal paths avoiding routes over water were considered rather than a measure based on the Euclidian distance between patches. This indicates that lakes, even when they are just a few hundred metres wide, can act as barriers to dispersal for butterflies. We conclude that for many butterflies that occur in semi- natural grasslands in forest-dominated landscapes, intervening habitats are important as breeding sites or as dispersal barriers.

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 score0.309
Threshold uncertainty score0.701

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)0.0000.000

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 head0.016
GPT teacher head0.219
Teacher spread0.203 · 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

Citations10
Published2011
Admission routes1
Has abstractyes

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