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Record W2953513889 · doi:10.1139/cjfas-2018-0357

An invader in salmonid rearing habitat: current and future distributions of smallmouth bass (<i>Micropterus dolomieu</i>) in the Columbia River Basin

2019· article· en· W2953513889 on OpenAlexvenueno aff
Erika S. Rubenson, Julian D. Olden

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsnot available
FundersRocky Mountain Research StationNational Science FoundationWashington Department of Fish and WildlifeIdaho Department of Fish and GameCalifornia Department of Fish and WildlifeUniversity of WashingtonU.S. Geological SurveyMinistry of Fisheries, New ZealandMassachusetts Department of Fish and Game
KeywordsMicropterusHabitatBass (fish)Invasive speciesEcologyFisheryRange (aeronautics)Introduced speciesFreshwater ecosystemCritical habitatEnvironmental scienceClimate changeSpecies distributionEcosystemBiologyEndangered species

Abstract

fetched live from OpenAlex

Invasive species and climate change are leading threats to freshwater ecosystems. In the Columbia River Basin (CRB), nonnative fishes are a critical consideration in salmon recovery, yet managers lament a lack of distribution information. Combining a species distribution model (SDM) with environmental DNA (eDNA), we locate range boundary regions of nonnative smallmouth bass (Micropterus dolomieu) and evaluate its overlap with native salmonids. A combination of thermal, hydrological, and geomorphic variables predict that smallmouth bass is distributed across ∼18 000 river kilometres and overlaps with 3%–62% of rearing habitat of salmonids (species-dependent) in the CRB. Under a moderate climate change scenario, smallmouth bass is predicted to expand its range by two-thirds (totaling ∼30 000 river kilometres) by 2080. Basin-wide models were sufficiently accurate to identify upstream invasion extents to within 15 km of the eDNA-based boundary, and including eDNA data improved model performance at critical range boundary regions without sacrificing broadscale model performance. Our study highlights how eDNA approaches can supplement large geospatial data sets to result in more accurate SDM predictions, guiding nonnative species management.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.200
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations44
Published2019
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicEnvironmental DNA in Biodiversity StudiesFrench-language works237,207