Environmental DNA barcoding as a method of amphibian species detection compared to conventional monitoring techniques in southern Ontario vernal pools
Bibliographic record
Abstract
Ongoing monitoring is vital for the conservation of amphibian species and is conducted through conventional auditory and visual surveys. A molecular method, termed environmental DNA (eDNA) barcoding, may offer a more sensitive method of species detection that negates the need for direct species observation. The research aims of this thesis were to conduct a comparative analysis of eDNA barcoding versus conventional (audio/visual) species detection methods for six amphibian species in southern Ontario. I hypothesized that eDNA barcoding would offer equal or greater species presence detections compared to the conventional methods. Conventional surveys and eDNA collections were conducted in three vernal pools from April-July 2019 in collaboration with rare Charitable Research Reserve (Cambridge, ON). Conventional methods included collection of daily audio files from acoustic song meters and weekly/biweekly visual encounter surveys. Audio data was analyzed using Kaleidoscope Pro. Alongside conventional surveys, duplicate water samples containing eDNA were collected at multiple sampling locations around three vernal pools. After water collection, eDNA was concentrated by filtration, extracted, and quality controlled. eDNA samples were processed using optimized eDNA barcoding assays using quantitative PCR. Comparative analysis between conventional methods and eDNA barcoding contradicts a one-size-fits-all model of amphibian monitoring. eDNA barcoding offered a reliable and effective method of species detection for five of the target amphibians especially for obligate vernal pool breeding species, however this method failed to accurately detect the spring peeper despite detections by passive acoustic surveys. I propose using eDNA barcoding alongside a conventional method of species detection to optimize detections across a spatiotemporal scale, however, this should be catered to the target species of interest. Future studies could implement a multi-year study as well as a comparison of eDNA barcoding to metabarcoding for Ontario amphibian species. eDNA barcoding offers a new method of species detection that could aid in ongoing amphibian monitoring and therefore conservation efforts of the declining taxa.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".