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Record W2593925537 · doi:10.1111/ele.12753

Habitat filtering not dispersal limitation shapes oceanic island floras: species assembly of the Galápagos archipelago

2017· article· en· W2593925537 on OpenAlexaff
Sofía Carvajal‐Endara, Andrew P. Hendry, Nancy C. Emery, T. Jonathan Davies

Bibliographic record

VenueEcology Letters · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsMcGill University
FundersSecretaría de Educación Superior, Ciencia, Tecnología e Innovación
KeywordsBiological dispersalArchipelagoEcologyHabitatColonisationInsular biogeographyNicheEndemismSpecies richnessBiologyGeographyColonizationPopulation

Abstract

fetched live from OpenAlex

Remote locations, such as oceanic islands, typically harbour relatively few species, some of which go on to generate endemic radiations. Species colonising these locations tend to be a non-random subset from source communities, which is thought to reflect dispersal limitation. However, non-random colonisation could also result from habitat filtering, whereby only a few continental species can become established. We evaluate the imprints of these processes on the Galápagos flora by analysing a comprehensive regional phylogeny for ~ 39 000 species alongside information on dispersal strategies and climatic suitability. We found that habitat filtering was more important than dispersal limitation in determining species composition. This finding may help explain why adaptive radiation is common on oceanic archipelagoes - because colonising species can be relatively poor dispersers with specific niche requirements. We suggest that the standard assumption that plant communities in remote locations are primarily shaped by dispersal limitation deserves reconsideration.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score0.999

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 studies0.0010.000
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 head0.044
GPT teacher head0.214
Teacher spread0.171 · 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

Citations123
Published2017
Admission routes1
Has abstractyes

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