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Record W4409370310 · doi:10.1111/fwb.70033

Minimal Introgression but Restricted Gene Flow: How Stocking and Dams Influence Wild Brook Trout (<scp><i>Salvelinus fontinalis</i></scp>) Genetics and Morphology

2025· article· en· W4409370310 on OpenAlexaff
Sozos Michaelides, Corey Pelletier, Ciara J. Frawley, Graham E. Forrester, Alan Libby, Thomas J. McGreevy

Bibliographic record

VenueFreshwater Biology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsConcordia University
FundersRhode Island Department of Environmental Management
KeywordsSalvelinusFontinalisTroutStockingIntrogressionBiologyGene flowZoologyMorphology (biology)EcologyFisheryGeneGeneticsFish <Actinopterygii>Genetic variation

Abstract

fetched live from OpenAlex

ABSTRACT Anthropogenic activities are increasingly influencing eco‐evolutionary processes across spatial, temporal, and biological scales. Freshwater ecosystems, harbouring species that are important for subsistence, recreational, and commercial fisheries, are particularly vulnerable. In North America, brook trout ( Salvelinus fontinalis ) populations have experienced widespread declines due to factors including habitat fragmentation and angling. To increase population densities and meet fishing demands, thousands of captive‐bred individuals are released annually. Stocking, however, could lead to introgression of maladaptive genes into the wild population and, in combination with the presence of dams restricting gene flow, may reduce their evolutionary potential. Here, we sampled 495 fish from 14 inland and two coastal sites in the state of Rhode Island, USA and from two hatchery strains. We used quaddRAD to develop single nucleotide polymorphism (SNP) markers and combined genetic and geometric‐morphometric analyses to evaluate intraspecific variation, the consequences of stocking, and test hypotheses, in a riverscape framework, on how dams influence gene flow. We found moderate genetic structure within streams with low levels of genetic diversity and effective population sizes but higher than the two hatchery strains. Importantly, we found no significant introgression from captive‐bred individuals. However, we did detect isolation‐by‐distance and ‐by‐resistance (due to dams) with evidence for upstream and downstream gene flow occurring mainly within catchments. Phenotypic differentiation also was apparent both between wild and hatchery brook trout and even among streams, correlating with genetic distances. Our findings highlight the importance of fine‐scale analyses in evaluating anthropogenic impacts and uncovering hidden intraspecific variation. Conservation efforts for wild brook trout populations, which harbour distinct genetic and phenotypic variation, will require careful attention, and our results will help inform management strategies across the species' range.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.847

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 science0.0000.001
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 head0.007
GPT teacher head0.225
Teacher spread0.218 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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