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Specialisation in pollination strategies does not result in lower diversification rates in Antillean Gesneriaceae

2025· preprint· en· W4412573005 on OpenAlexaff
Marion Leménager, John L. Clark, Silvana Martén‐Rodríguez, Simon Joly

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant and Fungal Species Descriptions
Canadian institutionsEspace pour la vieUniversité de Montréal
Fundersnot available
KeywordsGesneriaceaeDiversification (marketing strategy)PollinationBiologyEcologyPollenBusinessMarketing

Abstract

fetched live from OpenAlex

Islands are typically characterised by depauperate and temporally varying pollinator communities. In such environments, plants with generalist pollination strategies are expected to have an advantage in terms of establishment and survival due to their ability to maintain more stable pollination services. This advantage could translate into higher diversification rates for generalists on islands, yet this hypothesis has rarely been tested explicitly. Here, we evaluate this idea using a clade of 80 Gesneriaceae species from the Antilles that exhibit multiple origins of generalist and specialist pollination strategies. We reconstructed the phylogeny of the group using genotyping-by-sequencing (GBS) data and Sanger sequences from five nuclear genes. We then applied state-dependent diversification models (SSE) to test whether generalist species—pollinated by bats, hummingbirds, and insects—diversified more rapidly than specialists pollinated by either hummingbirds or bats alone. Contrary to our expectations, generalist species did not exhibit higher diversification rates than specialists. Moreover, ancestral state reconstructions suggest that the most recent common ancestor of the group was likely a generalist, which reframes our understanding of pollination strategy evolution in this clade. Our findings indicate that pollination specialists are not necessarily at a disadvantage in insular ecosystems and that both generalist and specialist strategies can persist and diversify in island contexts.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score0.990

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 head0.022
GPT teacher head0.274
Teacher spread0.252 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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