Silicon Isotope Signatures of Radiolaria Reveal Taxon-Specific Differences in Isotope Fractionation
Bibliographic record
Abstract
The global silicon (Si) cycle plays a critical role in regulating the biological pump and the carbon cycle in the oceans. A promising tool to reconstruct past dissolved silicic acid (DSi) concentrations is the silicon isotope signature of radiolaria (δ 30 Si rad ), siliceous zooplankton that dwells at subsurface and intermediate water depths. However, to date, only a few studies on sediment δ 30 Si rad records are available. To investigate its applicability as a paleo proxy, we compare the δ 30 Si rad of different radiolarian taxa and mixed radiolarian samples from surface sediments off Peru to the DSi distribution and its δ 30 Si signatures (δ 30 Si DSi ) along the coast between the equator and 15°S. Three different radiolarian taxa were selected according to their specific habitat depths of 0–50 m ( Acrosphaera murrayana ), 50–100 m ( Dictyocoryne profunda/truncatum ), and 200–400 m ( Stylochlamydium venustum ). Additionally, samples containing a mix of species from the bulk assemblage covering habitat depths of 0 to 400 m have been analyzed for comparison. We find distinct δ 30 Si rad mean values of +0.70 ± 0.17‰ ( Acro ; 2 SD), +1.61 ± 0.20 ‰ ( Dictyo ), +1.19 ± 0.31 ‰ ( Stylo ) and +1.04 ± 0.19 ‰ (mixed radiolaria). The δ 30 Si values of all individual taxa and the mixed radiolarian samples indicate a significant ( p < 0.05) inverse relationship with DSi concentrations of their corresponding habitat depths. However, only δ 30 Si of A. murrayana are correlated to DSi concentrations under normally prevailing upwelling conditions. The δ 30 Si of Dictyocoryne sp., Stylochlamydium sp., and mixed radiolaria are significantly correlated to the lower DSi concentrations either associated with nutrient depletion or shallower habitat depths. Furthermore, we calculated the apparent Si isotope fractionation between radiolaria and DSi (Δ 30 Si ∼ 30 ε = δ 30 Si rad − δ 30 Si DSi ) and obtained values of −1.18 ± 0.17 ‰ ( Acro ), −0.05 ± 0.25 ‰ ( Dictyo ), −0.34 ± 0.27 ‰ ( Stylo ), and −0.62 ± 0.26 ‰ (mixed radiolaria). The significant differences in Δ 30 Si between the order of Nassellaria ( A. murrayana ) and Spumellaria ( Dictyocoryne sp. and Stylochlamydium sp.) may be explained by order-specific Si isotope fractionation during DSi uptake, similar to species-specific fractionation observed for diatoms. Overall, our study provides information on the taxon-specific fractionation factor between radiolaria and seawater and highlights the importance of taxonomic identification and separation to interpret down-core records.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".