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Record W7019832995

Integrating Environmental DNA, Traditional Fisheries Techniques, and Species Distribution Modeling to Assess Bridle Shiner Status in Maine

2023· article· en· W7019832995 on OpenAlexaboutno aff

Bibliographic record

VenueDigitalCommons (California Polytechnic State University) · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMinnowNotropisNettingHabitatIntroduced speciesInvasive speciesRange (aeronautics)
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.033
GPT teacher head0.204
Teacher spread0.171 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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