A Common Terminology to Unify Research and Conservation of Coralline Algae and the Habitats They Create
Bibliographic record
Abstract
ABSTRACT Linguistic uncertainty is a prime source of uncertainty pervading ecology and conservation. Coralline algae are a widespread and diverse group of calcifying red macroalgae that underpin coastal ecosystem function and service provision. Recent increasing interest in coralline algae in the scientific literature has revealed a diverse but confusing terminology at organism to habitat scales. Coralline algal research and conservation are international and multidisciplinary, so there are geographic and disciplinary imbalances in research and conservation efforts. To reach consensus and reduce uncertainty, we propose a unified terminology. We review trends in cultural and scientific use of coralline algal terms and propose a system based on six morphologies: (1) attached, (2) free‐living geniculate, (3) encrusting and free‐living nongeniculate coralline algae, the latter either being (4) nucleated or (5) non‐nucleated thalli or (6) fragments. We take inspiration from other coastal systems that have achieved consensus through umbrella terms, such as ‘coral’ and ‘kelp’, to accelerate global progress in coralline algal research and conservation. We characterise 14 coralline algae–dominated habitat global types, falling within seven functional groups, four biomes and four realms: (1) freshwater coralline streams; (2) coralline tide pools; (3) intertidal coralline rims and (4) turf; (5) coralline sea caves; (6) coral–algal reefs; (7) algal ridges; (8) coralligenous reefs; subtidal (9) carbonate crusts, (10) coralline barrens and (11) turf; and (12) articulith, (13) maerl and (14) rhodolith beds, which fall into the coralline algal bed functional group. We hope this unified terminology promotes data comparison, enables cross‐boundary and cross‐sector sharing of best practices, develops capacity for meta‐analyses and improves conservation strategies.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".