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Record W2955680102 · doi:10.1002/fee.2073

Environmental <scp>DNA</scp> in lake sediment reveals biogeography of native genetic diversity

2019· review· en· W2955680102 on OpenAlexaff
Hedin Nelson-Chorney, Corey S. Davis, Mark S. Poesch, Rolf D. Vinebrooke, Chris Carli, Mark K. Taylor

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2019
Typereview
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsFisheries and Oceans CanadaUniversity of AlbertaParks Canada
Fundersnot available
KeywordsOncorhynchusTroutEcologyRainbow troutThreatened speciesEnvironmental DNAFreshwater ecosystemIntroduced speciesPopulationBiodiversityBiologyEcosystemFisheryHabitatFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Understanding the historical distributions of species is vital to the conservation and restoration of native species, yet such information is often qualitative. We show that the paleolimnological history of threatened freshwater fishes can be reconstructed using species‐diagnostic markers amplified from environmental DNA deposited in lake sediments (lake sed DNA ). This method was validated through the detection of lake sed DNA from non‐native trout (Yellowstone cutthroat trout, Oncorhynchus clarkii bouvieri ), which corroborated historical records of human‐mediated introductions. We also discovered native trout (westslope cutthroat trout, Oncorhynchus clarkii lewisi ) lake sed DNA that predated human‐mediated introductions of freshwater fishes in a watershed with high topographical relief. This unexpected result revealed that the westslope population was of native origin and requires immediate conservation protection. Our findings demonstrate that lake sed DNA can be used to determine the colonization history of freshwater fishes and the structure of ecosystems, aiding in the identification of native ranges, novel native diversity, and introductions of non‐native species.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.252
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.198
Teacher spread0.187 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreReview

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

Citations50
Published2019
Admission routes1
Has abstractyes

Explore more

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