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Record W3000216592 · doi:10.1111/1365-2435.13528

Plasticity and habitat choice match colour to function in an ambush bug

2020· article· en· W3000216592 on OpenAlexaff
Julia A. Boyle, Denon Start

Bibliographic record

VenueFunctional Ecology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiologyPhenotypic plasticityTraitHabitatPredationMatching (statistics)EcologyComputer science

Abstract

fetched live from OpenAlex

Abstract Individuals aim to maximize their fitness by matching their own phenotype to the optimum phenotype in their environment. Individuals can achieve matching through several mechanisms including habitat choice and adaptive plasticity. A key trait of interest to biologists is colour, with background matching reciprocally camouflaging predators and prey. However, the multiple mechanisms matching an individual's colour to their background, and its consequences for function (e.g. species interactions), are rarely explored simultaneously. Here we investigate colour variation in ambush bugs, Phymata americana , that feed on insects visiting white and yellow flowers. We conducted surveys of wild populations to establish phenotype–environment matching and its effects on prey capture, then performed habitat choice and plasticity (colour change) trials to test for the mechanisms underlying putative patterns of habitat matching. Ambush bugs matched their background—yellower ambush bugs were found on yellow flowers and whiter ambush bugs on white flowers, and matching increased prey capture. This pattern was seemingly driven by a combination of plasticity and habitat choice. Our study highlights how organisms can optimize trait values through a combination of plasticity and habitat choice with tangible effects on individual performance. We suggest that multiple mechanisms interactively shape phenotypes, optimizing function and fitness in the wild. A free Plain Language Summary can be found within the Supporting Information of this article.

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 score0.064
Threshold uncertainty score0.974

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 head0.056
GPT teacher head0.211
Teacher spread0.155 · 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

Citations26
Published2020
Admission routes1
Has abstractyes

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