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Record W2001655346 · doi:10.3354/meps08782

Bleaching of an intertidal coralline alga: untangling the effects of light, temperature, and desiccation

2010· article· en· W2001655346 on OpenAlexaff
PT Martone, M Alyono, S Stites

Bibliographic record

VenueMarine Ecology Progress Series · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIntertidal zoneDesiccationFrondIntertidal ecologyRocky shoreEcologyBiologyTide poolOceanographyBotanyGeology

Abstract

fetched live from OpenAlex

Intertidal macroalgae must tolerate temperature, light, and desiccation stresses when the tide recedes, and differences in physiological tolerance to these environmental stresses contribute directly to zonation patterns and community structure along the shore. When low tides occur on particularly hot sunny days, seaweeds may sustain physiological damage, lose pigment, and 'bleach.' Because bleaching events often occur when temperature, light, and desiccation stresses coincide, their precise cause is not understood. We conducted fully factorial laboratory manipulations to explore the individual and interactive effects of temperature, light, and desiccation on acute pigment loss in the intertidal coralline Calliarthron tuberculosum (Postels & Ruprecht) E. Y. Dawson. Findings suggest that desiccation is the most significant contributor to bleaching; desiccating fronds even bleached in the dark at 15C. Susceptibility to desiccation may explain why mid-intertidal C. tuberculosum fronds are rarely found outside tidepools. Light and temperature had only marginal effects on pigment loss, although stresses interacted with increasing significance through time. When combined, temperature, light, and desiccation stresses were capable of inducing, on average, 50% pigment loss in C. tuberculosum within 24 min of emersion. These physiological data could be used in conjunction with environmental datasets to generate 'ecomechanical' models to predict future bleaching events and their ecological consequences under hypothetical climate change scenarios.

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 score0.100
Threshold uncertainty score0.984

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 head0.003
GPT teacher head0.193
Teacher spread0.190 · 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

Citations82
Published2010
Admission routes1
Has abstractyes

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