Integrating Environmental DNA, Traditional Fisheries Techniques, and Species Distribution Modeling to Assess Bridle Shiner Status in Maine
Bibliographic record
Abstract
The bridle shiner (Notropis bifrenatus) is a small minnow species native to the eastern United States and southeastern Canada. The species is declining dramatically throughout most of its native range and has legal protection or concern status in thirteen states and two Canadian provinces. In Maine, the bridle shiner is listed as a Species of Special Concern and considered a Species of Greatest Conservation Need, partially because we lack a basic understanding of their status and distribution within the state. Bridle shiners have historically been found in southern and western Maine in densely vegetated, shallow habitats along the shorelines of streams and ponds. Surveys performed at sites where the shiners were once abundant have yielded very few or none of these fish. This project informed the Maine Department of Inland Fisheries & Wildlife on the status of the species in Maine and provides a foundation for future long-term monitoring of bridle shiner populations in the State. We used a combination of both direct capture techniques and environmental DNA (eDNA) to locate bridle shiners. eDNA is increasingly being used to detect rare aquatic species such as bridle shiners because it is both highly sensitive and less invasive than direct capture. We designed a single-species primer-probe assay to detect bridle shiner DNA, then surveyed 32 sites with a record of historic bridle shiner occurrence. In addition to collecting eDNA samples (2021-2022), we surveyed 29 sites using traditional seine netting techniques in 2021. In 2022, we used a preliminary habitat suitability model to select 46 locations with unknown bridle shiner presence to survey with eDNA. To refine eDNA methodology, we assessed trends in eDNA detection probability across seasons and compared DNA detection between three filter pore sizes. We rediscovered bridle shiner populations at 11 of 32 historically occupied sites and documented bridle shiners in four additional waterbodies. We determined that eDNA surveys were most effective in early or midsummer, and that larger filter pore sizes are a viable option for surveying bridle shiners. Species distribution modeling (SDM) statistically associates species occurrence data with environmental variables to evaluate habitat suitability. We used an ensemble species distribution modeling (SDM) approach to identify both the current and historic range of the bridle shiner within Maine and New Hampshire. We also investigated how local habitat characteristics influenced bridle shiner presence using generalized linear models. Both historic site surveys and ensemble SDMs suggest that there has been a substantial loss of historic bridle shiner habitat in Maine (-62%) and New Hampshire (-46%). At the landscape scale, we found significant effects of forest type, catchment position, soil composition, elevation, and slope on bridle shiners. Within a site, bridle shiners were associated with areas that had a higher proportion of complex-leaved submerged aquatic vegetation and a lower proportion of persistent emergent and floating vegetation. We determined that both eDNA and seine net surveys are viable options for monitoring bridle shiners in Maine, and that such survey strategies can be used with species distribution models to focus future surveys and to identify areas of possible conservation, reintroduction, or restoration actions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".