Bleaching of an intertidal coralline alga: untangling the effects of light, temperature, and desiccation
Bibliographic record
Abstract
MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 416:57-67 (2010) - DOI: https://doi.org/10.3354/meps08782 Bleaching of an intertidal coralline alga: untangling the effects of light, temperature, and desiccation Patrick T. Martone1,2,*, Michael Alyono1, Shira Stites1 1Hopkins Marine Station of Stanford University, 120 Ocean View Blvd, Pacific Grove, California 93950, USA 2Present address: Department of Botany, University of British Columbia, 6270 University Blvd, Vancouver, British Columbia V6T 1Z4, Canada *Email: pmartone@interchange.ubc.ca ABSTRACT: 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 15°C. 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. KEY WORDS: Calliarthron tuberculosum · Coralline algae · Desiccation · Ecomechanics · Light · Physiology · Pigmentation · Temperature · Zonation Full text in pdf format PreviousNextCite this article as: Martone PT, Alyono M, Stites S (2010) Bleaching of an intertidal coralline alga: untangling the effects of light, temperature, and desiccation. Mar Ecol Prog Ser 416:57-67. https://doi.org/10.3354/meps08782 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 416. Online publication date: October 14, 2010 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2010 Inter-Research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".