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Record W4206442810 · doi:10.1098/rsbl.2021.0468

Habitat choice promotes and constrains phenotypic plasticity

2022· article· en· W4206442810 on OpenAlexafffund
Andy J. Turko, Giulia S. Rossi

Bibliographic record

VenueBiology Letters · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsThe Scarborough HospitalUniversity of TorontoMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhenotypic plasticityBiologyHabitatEcologyPlasticityPreferenceLocal adaptationEconomics

Abstract

fetched live from OpenAlex

Habitat choice can either speed up or slow rates of phenotypic evolution, depending on which trait is measured. We suggest that habitat choice plays an analogous, and generally overlooked, role in shaping patterns of phenotypic plasticity. Using our work with an amphibious fish, we discuss two case studies that demonstrate how habitat choice can both promote and constrain expression of plasticity. First, habitat choice during the dry season accentuates adaptive metabolic plasticity and minimizes maladaptive changes to muscle, ultimately increasing survival time out of water. Second, a trade-off between water- and air-breathing drives matching habitat choice, resulting in positive feedback that reinforces respiratory specialization and environmental preference. Overall, these case studies demonstrate that we must consider the interactions between plasticity and habitat choice to fully understand how animals survive in the face of environmental change. Without considering both processes simultaneously, the performance of animals in challenging conditions can be either under- or over-estimated. Finally, because habitat choice shapes the frequency and predictability of environmental changes that animals experience, feedback between habitat choice and expressions of phenotypic plasticity may be an important factor that influences how plasticity evolves.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.940
Threshold uncertainty score1.000

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)0.0010.000

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 head0.015
GPT teacher head0.215
Teacher spread0.200 · 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.

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

Citations18
Published2022
Admission routes2
Has abstractyes

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