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Record W2113925151 · doi:10.1139/cjfas-2012-0493

Fragmentation and patch size shape genetic structure of brook trout populations

2013· article· en· W2113925151 on OpenAlexvenueno aff
Andrew R. Whiteley, Jason A. Coombs, Mark Hudy, Zachary L. Robinson, Amanda R. Colton, Keith H. Nislow, Benjamin H. Letcher

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSalvelinusTroutBiologyGenetic diversityHabitat fragmentationEcologyFontinalisEffective population sizeExtinction (optical mineralogy)Genetic structureHabitatPopulationFragmentation (computing)ZoologyDemographyFisheryFish <Actinopterygii>

Abstract

fetched live from OpenAlex

We tested the relative influence of habitat patch size and connectivity on genetic structure and effective population size in eight brook trout (Salvelinus fontinalis) habitat patches in a watershed in Virginia, USA. Variation at eight microsatellite loci in 2229 young-of-the-year brook trout for two successive cohorts (2010 and 2011) was examined. Genetic differentiation across all populations was pronounced. Overall [Formula: see text] was 0.397 (95% CI: 0.322–0.525) and overall F ST was 0.124 (95% CI: 0.096–0.159). Above-barrier patch size had a strong positive relationship with genetic diversity, [Formula: see text], and genetic differentiation. Our analysis is consistent with greater extinction risk in smaller above-barrier patches. Larger above-barrier patches contained greater genetic diversity but reduced [Formula: see text] relative to adjacent below-barrier patches. The primary effect of barriers may be to reduce available above-barrier spawning habitat, even for larger above-barrier patches. Below-barrier patches also showed evidence of reduced genetic diversity and lack of connectivity. Genetic monitoring focused at gaining a broader understanding of the relationships here will be necessary to fully evaluate local extinction risks.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.012
GPT teacher head0.204
Teacher spread0.192 · 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
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

Citations132
Published2013
Admission routes1
Has abstractyes

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