Detecting a spreading non-indigenous species using multiple methodologies
Bibliographic record
Abstract
Johansson ML, Lavigne SY, Ramcharan CW, Heath DD, MacIsaac HJ. Detecting a spreading non-indigenous species using multiple methodologies. Lake Reserv Manage. 36:432–443. Non-indigenous species (NIS) are often introduced to novel environments at very low population abundance. Detecting the presence of such an NIS can be very challenging, particularly as it spreads from the initial establishment site. This provides an opportunity to test detection limits using different approaches. This study tested the detection capability of 3 methods as zebra mussels (Dreissena polymorpha) spread from south to north through Lake Winnipeg, Manitoba, Canada. Zebra mussel veliger larvae were detected using cross-polarized light microscopy (CPLM), flow cytometry and microscopy (FlowCam), and conventional polymerase chain reaction (cPCR) analysis of environmental DNA (eDNA) on the same samples. Abundance generally declined from south to north in the lake but was lowest at Calder’s Dock (central). Although abundances could be quite low (i.e., <1 veliger/m3, Calder’s Dock) CPLM prevalence—the percentage of samples with at least one veliger—was high throughout the lake (99–100% of samples). Prevalence was lower for cPCR and FlowCam but was statistically associated with veliger abundance. Using standardized 3 mL subsamples (0.06–0.18 m3 of lake water sampled), all 3 methods had a high probability of veliger detection if large numbers of samples were processed. FlowCam was the most expensive method to process these 3 mL subsamples, while cPCR was least expensive and fastest. eDNA combined with intensive sampling is the most practical method for wide-scale monitoring programs for early detection. However, all 3 methods are complementary and could be deployed sequentially, with rapid initial sample processing using PCR, confirmation and density estimation with FlowCam, and detailed veliger counts using CPLM.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".