The need to employ reliable and reproducible species identifications in coralline algal research
Bibliographic record
Abstract
Coralline algae perform important ecological roles in nearshore marine ecosystems globally by promoting the settlement of invertebrate larvae and enhancing biodiversity by creating habitat. However, these roles are severely threatened by global environmental changes. Most coralline algae are extremely difficult to identify, and DNA sequencing has revealed rampant inaccuracy of morpho-anatomical approaches to distinguish species, and even genera. If appropriate identification methods are not reported, or even used, we will be left with an uninterpretable body of literature where the species-specific biology of coralline algae cannot be validated. This will make it difficult to determine the impact a changing ocean may have on these ecologically important species. We reveal the magnitude of the issue in coralline algal research—both the identification methods used and the reporting of identification protocols. An analysis of 341 articles over the past decade revealed that only 7.6% used molecular methods, with over 70% not reporting any details of how species were identified. While many coralline algal taxonomists understand that the majority of species cannot be identified morphologically, this message has not disseminated to the ecological and physiological community. We provide a series of guidelines for conducting DNA-based identifications and strongly recommend the use of these methods over less informative morpho-anatomical techniques. Most importantly, the methods of identification should be adequately reported. Without following these guidelines, research on coralline algae runs the risk of collecting uninterpretable data, and conducting irreproducible science, slowing our ability to determine how these important species will respond to future ocean conditions.
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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 | 0.318 | 0.454 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".