Gelatinous matrix, an original strategy to cope with oligotrophy in Nassellaria (Radiolaria)
Bibliographic record
Abstract
Abstract Radiolaria are heterotrophic protists abundant in the world’s oceans playing important roles in biogeochemical cycles. Some species host photosynthetic algae also contributing to primary production. Such mixotrophic behaviour is believed to explain their ecological success in oligotrophic waters, notably Collodaria, exclusively mixotrophic radiolarians within a gelatinous matrix. Yet, our understanding of Radiolaria ecology is limited to direct observations, as they have so far withstood reproduction in culture and their genomes are unexplored. Sampling oligotrophic California Current communities revealed an abundant, rarely observed population of Nassellaria of the genus Phlebarachnium , characterized to live within a gelatinous matrix along with other Radiolaria. Phylogenetic reconstruction of the ribosomal DNA suggests that these distantly related lineages within Nassellaria independently developed the ability to produce a gelatinous matrix ∼150 million years ago. By matching physical samples with their genetic signature, we identified these rarely observed organisms in global metabarcoding datasets, revealing strong biogeographic affinity to oligotrophic water masses. Global ocean co-occurrence networks showed that Radiolaria with a gelatinous matrix have a distinct biogeography compared to those without the matrix. Results suggest that the gelatinous matrix is an adaptation to oligotrophic waters, but further research is needed to evaluate similarities between the gelatinous matrices across different Radiolaria groups. This strategy could increase the effective volume to weight ratio favoring prey capture and create a favorable microenvironment for symbionts, enhancing ecological success in nutrient-depleted waters. This study advances our understanding of eukaryotic diversity evolution, emphasizing specific advantages of certain adaptations, specifically when evolution occurs independently across lineages.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".