Comparison of <scp>DNA</scp> Extraction Methods for Detecting the Sea Otter ( <scp> <i>Enhydra lutris</i> </scp> ) in Marine Sediments
Bibliographic record
Abstract
ABSTRACT Environmental DNA (eDNA) preserved in sediments (sed‐eDNA) holds promise for improving our understanding of historical species occurrences and contemporary biomonitoring. However, uneven DNA distribution, DNA fragmentation, and polymerase chain reaction (PCR) inhibition present challenges to species detection. Herein, we evaluate the efficacy of sed‐eDNA methods to detect the presence of a patchily distributed marine mammal in a coastal marine ecosystem. We developed a targeted quantitative PCR (qPCR)‐based assay for the detection of sea otter ( Enhydra lutris ) DNA. This assay successfully amplified target DNA from aquaria occupied by sea otters and from two of eight seawater samples from areas where sea otters are often present. Additionally, we conducted an experiment to examine the utility of our assay in detecting sea otter DNA in sediment. We compared four sed‐eDNA extraction techniques and two DNA cleaning protocols in surface sediment samples taken from areas with varying sea otter occupancy. To test for DNA extraction efficiency, we used fish and chloroplast as endogenous controls. DNA quantity varied between the different extraction protocols and between the different DNA sample types. Sea otter DNA was detected at lower yields than expected, considering the presence of sea otters at the sampled sites. Furthermore, sediment cleaning protocols further reduced sed‐eDNA yield. Among the extraction methods tested, the Qiagen Powersoil Pro kit was most effective, yielding higher rates of target species detection with smaller input sediment amounts and no need for cleaning to remove PCR inhibitors. The present study lays the groundwork for large‐scale monitoring of marine mammals using sed‐eDNA and advances the use of sed‐eDNA detection as a valuable tool for reconstructing the temporal and spatial patterns of marine mammal presence. Importantly, we identify the need for a better understanding of the effects of marine sediment composition, mammal eDNA shedding rates, and DNA fragment size on detecting target sed‐eDNA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".