Linear extension and calcification rates in a cold‐water, crustose coralline alga are modulated by temperature, light, and salinity
Bibliographic record
Abstract
Abstract Long‐lived crustose coralline algae are important ecosystem engineers and environmental archives in regions where observations of climate variability are sparse. Clathromorphum compactum is a cold‐water alga that precipitates calcite that serve as archives of change at annual to sub‐annual resolution. Understanding how environmental variability impacts the growth of this species is imperative for application in paleoclimate research, and for evaluating its vulnerability to change. Here, we present the results of the first, to‐our‐knowledge, controlled laboratory experiment isolating the effects of light, temperature, and salinity on calcification rates of C. compactum. Algal calcification rates were modulated by a combination of light exposure, salinity, and temperature, where temperature and salinity were positively correlated, and light level was negatively correlated with calcification rate. Linear extension of the skeleton also varied with treatment conditions, with the epithallial and perithallial layers of skeleton responding differently. Epithallial extension increased with salinity, while perithallial extension was governed only by a positive parabolic relationship with temperature. These results suggest that C. compactum growth will be impacted by environmental changes predicted for the Arctic over the coming decades. While increased temperature in the region may facilitate calcification in the algae, reductions in salinity associated with increased sea ice melt, and potentially increased light levels, may counteract this effect. The negative impact of increased light levels on algal calcification observed may not reflect the true impact of light availability on growth associated with a lengthening of the growing season (not evaluated in this study) accompanying reductions in annual sea‐ice.
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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) | 0.001 | 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".