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Record W2783093892 · doi:10.1111/geb.12712

Regional forcing explains local species diversity and turnover on tropical islands

2018· article· en· W2783093892 on OpenAlexaff
Thomas Ibanez, Gunnar Keppel, Cláudia Baider, Chris Birkinshaw, Heike Culmsee, Susan Cordell, F. B. Vincent Florens, Janet Franklin, Christian P. Giardina, Thomas W. Gillespie, Melinda J. Laidlaw, Creighton M. Litton, Tara G. Martin, Rebecca Ostertag, Narayanaswamy Parthasarathy, Richard Randrianaivo, Miramasoandro Randrianjanahary, L. Rasingam, Fidy Ratovoson, Ludovic Reza, Lawren Sack, Shin‐ichiro Aiba, Edward L. Webb, Timothy J. S. Whitfeld, Runguo Zang, Philippe Birnbaum

Bibliographic record

VenueGlobal Ecology and Biogeography · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of British Columbia
FundersAlexander von Humboldt-Stiftung
KeywordsArchipelagoEcologyOrdinationInsular biogeographyGeographySpecies diversityBiogeographyTropicsBeta diversityGamma diversityTropical climateBiologyBiodiversityAlpha diversity

Abstract

fetched live from OpenAlex

Abstract Aim To determine the role of regional forcing on plot‐level species diversity and composition, and to quantify the relative importance of biogeographical and climatic factors in explaining woody plant diversity and composition at the local‐, island‐ and archipelago‐scale. Location Forty‐one tropical islands of the Indo‐Pacific region from Madagascar to Hawai‘i Island. Methods We analysed the diversity and composition of tropical woody plant communities located across 113 plots, 41 islands and 19 archipelagos. We used generalized linear mixed‐effects models and generalized dissimilarity models to determine the role of regional forcing at the island and archipelago scale and to assess the relative importance of biogeographical (area and isolation of islands or archipelagos, geographical distance between plots) and climatic factors in explaining differences in local diversity and composition (species turnover). Analyses were conducted at different geographical scales (local, island and archipelago) and taxonomic levels (species, genus and family). Results Variation in local (plot‐level) diversity (as species density, the number of species per 100 woody plants) was primarily explained by island and archipelago identity. Maximum species density was positively correlated with the area of an island (or archipelago) and negatively correlated with the isolation of an archipelago. Local climatic variability was also a significant predictor of species density, but less important than regional forcing. Climate variables explained < 20% of the variation in species turnover across all plots. The importance of geographical distance between plots relative to climate in driving species turnover decreased from the species to family level, and from the regional to island level. Main conclusions Regional forcing was the key driver of local diversity and composition on islands. Island area and archipelago isolation are likely driving local diversity through their effects on the pool of island species. Geographical distance between plots is the main factor explaining species turnover, while at higher taxonomic levels, climatic factors and niche conservatism are the main drivers.

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 score0.011
Threshold uncertainty score0.845

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 studies0.0010.002
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 head0.010
GPT teacher head0.208
Teacher spread0.198 · 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

Citations68
Published2018
Admission routes1
Has abstractyes

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