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Record W3168363381 · doi:10.1111/brv.12754

Trade‐off mitigation: a conceptual framework for understanding floral adaptation in multispecies interactions

2021· article· en· W3168363381 on OpenAlexaff
Kazuharu Ohashi, Andreas Jürgens, James D. Thomson

Bibliographic record

VenueBiological reviews/Biological reviews of the Cambridge Philosophical Society · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVisitor patternTrade-offPollinatorSelection (genetic algorithm)Adaptation (eye)BiologyTraitPollinationEcologyEvolutionary biologyPollenComputer science

Abstract

fetched live from OpenAlex

Explanations of floral adaptation to diverse pollinator faunas have often invoked visitor-mediated trade-offs in which no intermediate, generalized floral phenotype is optimal for pollination success, i.e. fitness valleys are created. In such cases, plant species are expected to specialize on particular groups of flower visitors. Contrary to this expectation, it is commonly observed that flowers interact with various groups of visitors, while at the same time maintaining distinct phenotypes among ecotypes, subspecies, or congeners. This apparent paradox may be due to a gap in our understanding of how visitor-mediated trade-offs could affect floral adaptation. Here we provide a conceptual framework for analysing visitor-mediated trade-offs with the hope of stimulating empirical and theoretical studies to fill this gap. We propose two types of visitor-mediated trade-offs to address negative correlations among fitness contributions of different visitors: visitor-mediated phenotypic trade-offs (phenotypic trade-offs) and visitor-mediated opportunity trade-offs (opportunity trade-offs). Phenotypic trade-offs occur when different groups of visitors impose conflicting selection pressures on a floral trait. By contrast, opportunity trade-offs emerge only when some visitors' actions (e.g. pollen collection) remove opportunities for fitness contribution by more beneficial visitors. Previous studies have observed disruptive selection due to phenotypic trade-offs less often than expected. In addition to existing explanations, we propose that some flowers have achieved 'adaptive generalization' by evolving features to avoid or eliminate the fitness valleys that phenotypic trade-offs tend to produce. The literature suggests a variety of pathways to such 'trade-off mitigation'. Trade-off mitigation may also evolve as an adaptation to opportunity trade-offs. We argue that active exclusion, or floral specialization, can be viewed as a trade-off mitigation, occurring only when flowers cannot otherwise avoid strong opportunity trade-offs. These considerations suggest that an evolutionary strategy for trade-off mitigation is achieved often by acquiring novel combinations of traits. Thus, phenotypic diversification of flowers through convergent evolution of certain trait combinations may have been enhanced not only through adaptive specialization for particular visitors, but also through adaptive generalization for particular visitor communities. Explorations of how visitor-mediated trade-offs explain the recurrent patterns of floral phenotypes may help reconcile the long-lasting controversy on the validity of pollination syndromes.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.387
GPT teacher head0.329
Teacher spread0.058 · 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 designTheoretical or conceptual
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

Citations47
Published2021
Admission routes1
Has abstractyes

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