Molecular analyses of protists in long-term observation programmes—current status and future perspectives
Bibliographic record
Abstract
Protists (microbial eukaryotes) are diverse, major components of marine ecosystems, and are fundamental to ecosystem services. In the last 10 years, molecular studies have highlighted substantial novel diversity in marine systems including sequences with no taxonomic context. At the same time, many known protists remain without a DNA identity. Since the majority of pelagic protists are too small to identify by light microscopy, most are neither comprehensively or regularly taken into account, particularly in Long-term Ecological Research Sites. This potentially undermines the quality of research and the accuracy of predictions about biological species shifts in a changing environment. The ICES Working Group for Phytoplankton and Microbial Ecology conducted a questionnaire survey in 2013–2014 on methods and identification of protists using molecular methods plus a literature review of protist molecular diversity studies. The results revealed an increased use of high-throughput sequencing methods and a recognition that sequence data enhance the overall datasets on protist species composition. However, we found only a few long-term molecular studies and noticed a lack of integration between microscopic and molecular methods. Here, we discuss and put forward recommendations to improve and make molecular methods more accessible to Long-term Ecological Research Site investigators.
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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 | 0.023 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".