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Record W2181111840 · doi:10.1579/0044-7447-32.3.240

Use of Rehabilitation Experiments to Understand the Recovery Dynamics of Acid-stressed Fish Populations

2003· article· en· W2181111840 on OpenAlexaffabout
Ed Snucins, John M. Gunn

Bibliographic record

VenueAMBIO · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsLaurentian University
Fundersnot available
KeywordsMicropterusTroutBass (fish)StockingSalvelinusFisheryEcologyBiologyPopulationForage fishIntroduced speciesWater qualityEnvironmental scienceFish <Actinopterygii>

Abstract

fetched live from OpenAlex

We used rehabilitation experiments involving the stocking of 2 native sportfish, lake trout (Salvelinus namaycush) and smallmouth bass (Micropterus dolomieu), in combination with recent fish community surveys, to study the recovery dynamics of fish populations in acid-stressed lakes near Sudbury and Killarney, Ontario, Canada. Population recovery rates differed between the 2 species. Introduced lake trout did poorly in species-rich lakes and exhibited slower growth, lower survival and delayed recruitment. Smallmouth bass, in contrast, readily colonized species-rich lakes. The biomass of natural smallmouth bass recruits increased to reference lake levels within 5 years following water quality recovery and spawning by stocked fish, whereas the biomass of natural lake trout recruits remained well below reference levels 5-15 years after water quality recovery and spawning by adults occurred. We document introductions by anglers of smallmouth bass into acid-damaged lake trout lakes, including some lakes that did not contain bass prior to acidification. This range expansion of a warm-water species (bass) that can alter food-web structure and reduce the growth of a cold-water species (trout), illustrates the potential for the combination of climate warming and species introductions to greatly alter the biological recovery endpoints in acid-stressed lakes.

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 score0.106
Threshold uncertainty score0.268

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 studies0.0000.000
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 head0.038
GPT teacher head0.260
Teacher spread0.222 · 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

Citations22
Published2003
Admission routes2
Has abstractyes

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Same venueAMBIOSame topicFish Ecology and Management StudiesFrench-language works237,207