High sensitivity of 454 pyrosequencing for detection of rare species in aquatic communities
Bibliographic record
Abstract
Summary Concerns regarding the rapid loss of endemic biodiversity, and introduction and spread of non‐indigenous species, have focused attention on the need and ability to detect species present in communities at low abundance. However, detection of rare species poses immense technical challenges, especially for morphologically cryptic species, microscopic taxa and those beneath the water surface in aquatic ecosystems. Next‐generation sequencing technology provides a robust tool to assess biodiversity, especially for detection of rare species. Here, we assess the sensitivity of 454 pyrosequencing for detection of rare species using known indicator species spiked into existing complex plankton samples. In addition, we develop universal small subunit ribosomal DNA primers for amplification of a wide range of taxa for detailed description of biodiversity in complex communities. A universality test of newly designed primers for the hypervariable V4 region of the nuclear small subunit ribosomal DNA (V4‐nSSU) using a plankton sample collected from Hamilton Harbor showed that 454 pyrosequencing based on this universal primer pair can recover a wide range of taxa, including animals, plants (algae), fungi, blue‐green algae and protists. A sensitivity test showed that 454 pyrosequencing based on newly designed universal V4‐nSSU primers was extremely sensitive for detection of very rare species. Pyrosequencing was able to recover spiked indicator species with biomass percentage as low as approximately 2·3 × 10−5% when 24 artificially assembled samples were tagged and sequenced in one PicoTiter plate (i.e. sequencing depth of an equivalent of 1/24 PicoTiter plate). In addition, spiked rare species were sometimes recovered as singletons (i.e. Operational Taxonomic Units represented by a single sequence), suggesting that at least some singletons are informative for recovering unique lineages in ‘rare biospheres’. The method established here allows biologists to better investigate the composition of aquatic communities, especially for detection of rare taxa. Despite a small‐scale pyrosequencing effort, we demonstrate the extreme sensitivity of pyrosequencing using rare species spiked into plankton samples. We propose that the method is a powerful tool for detection of rare native and/or alien species.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".