Environmental DNA as a tool to supplement sampling for fish community monitoring
Bibliographic record
Abstract
Environmental DNA (eDNA), the genetic material shed by organisms into the environment, is a promising tool for fisheries monitoring due to its non-lethal, and efficient sampling. My thesis evaluates the use of eDNA as a complementary approach to gillnet surveys within the Coordinated Aquatic Monitoring Program (CAMP) in Manitoba. The primary objectives of my thesis were to assess (1) whether seasonal variation and site differences influence eDNA detectability of target species, and (2) whether eDNA concentration is associated with traditional measures of relative abundance, including Catch Per Unit Effort (CPUE) and Biomass Per Unit Effort (BPUE). Water samples were collected at various time points from Lac du Bonnet and Pointe du Bois, Winnipeg River system, and analyzed with species-specific assays for five fishes: Walleye (Sander vitreus), Spottail Shiner (Hudsonius hudsonius), Yellow Perch, (Perca flavescens), Trout-perch (Percopsis omiscomaycus), and Burbot (Lota lota). Seasonal eDNA sampling detected all species on both waterbodies during spring and fall, except for Burbot, which was not detected at one of the three sites in the fall. eDNA and gillnetting results showed consistent detections for four of the five species, with Burbot detected more often by eDNA than gillnets. While most species showed weak association between eDNA and CPUE/BPUE, Burbot displayed a significant positive relationship. These findings demonstrate that eDNA has the potential to expand detection of underrepresented species and enhance monitoring programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.002 | 0.001 |
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".