Next‐Generation Sequencing to Identify Lacustrine Haptophytes in the Canadian Prairies: Significance for Temperature Proxy Applications
Bibliographic record
Abstract
Abstract The Great Plains of North America often experience prolonged droughts that have major economic and environmental impacts. Temperature reconstructions are thus crucial to help decipher the mechanisms responsible for drought occurrences. Long‐chain alkenones (LCAs), lipids produced by three major phylogenetic groups (Groups I, II, and III) of haptophyte algae within the order Isochrysidales, are increasingly used for temperature reconstructions in lacustrine settings. However, to select the most appropriate calibration of the LCA‐based temperature proxy, it is first essential to identify the LCA‐producing haptophyte species present. Here we used next‐generation sequencing to target the 18S rRNA haptophyte gene from sediments with distinct LCA profiles to identify the LCA‐producer(s) from five Canadian prairie lakes. In total, 374 operational taxonomic units (OTUs) were identified across the studied samples, of which 234 fell within the Phylum Haptophyta. Among the most abundant OTUs, three were characterized as LCA‐producers, one falling within the Group I haptophytes and two within the Group II haptophytes. The OTU from Group I haptophytes was associated with a single, highly specific LCA profile, whereas Group II OTUs showed higher variability in LCA distributions. Our study revealed that most of the LCA‐producing OTUs thriving in the Canadian lakes are included within the genus Isochrysis, which helps guide selection of the most appropriate calibration for down‐core temperature reconstructions. Our findings also suggest that the temperature dependency is likely consistent within different taxa from Group I and Group II haptophytes, but that other environmental parameters may influence the accuracy of the calibration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".