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Record W2088269249 · doi:10.1577/m07-012.1

Adverse Effects of Alewives on Laurentian Great Lakes Fish Communities

2008· article· en· W2088269249 on OpenAlexfundaboutno aff
Charles P. Madenjian, Robert O’Gorman, David B. Bunnell, Ray L. Argyle, Edward F. Roseman, David M. Warner, Jason D. Stockwell, Martin A. Stapanian

Bibliographic record

VenueNorth American Journal of Fisheries Management · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersU.S. Geological SurveyUniversity of Wisconsin-MilwaukeeMinistry of Natural ResourcesGreat Lakes Fishery Commission
KeywordsAlewifeCoregonus clupeaformisSculpinFisherySalvelinusBiologySalmoForage fishPerchCottusPopulationTroutPredationEcologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract The alewife Alosa pseudoharengus, an invader to the Laurentian Great Lakes from the Atlantic Ocean, has been blamed for causing major disruptions of Great Lakes fish communities during the past 50 years. We reviewed the literature and examined long-term data on fish abundances in the Great Lakes to develop a new synthesis on the negative effects of alewives on Great Lakes fish communities. The results indicated that certain fish populations are substantially more vulnerable to the effects of alewives than others. More specifically, the effects of alewives on other fish populations appeared to follow a continuum—from such fishes as slimy sculpin Cottus cognatus, lake whitefish Coregonus clupeaformis, and bloater Coregonus hoyi, which were relatively unsusceptible—to Atlantic salmon Salmo salar, lake trout Salvelinus namaycush, and emerald shiner Notropis atherinoides, which were highly susceptible. Intermediate species in this continuum included yellow perch Perca flavescens, deepwater sculpin Myoxocephalus thompsonii, and burbot Lota lota. The predominant mechanism by which alewives exerted their negative effect appeared to be predation on the larvae of other fishes. The key factor in the extirpation of Atlantic salmon from Lake Ontario, however, was probably early mortality syndrome induced by a diet rich in alewives. We conclude that the degree of restoration of the native Great Lakes fish community depends in part on the degree of control of the alewife population.

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 score0.084
Threshold uncertainty score0.777

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)0.0010.000

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 head0.187
Teacher spread0.180 · 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

Citations144
Published2008
Admission routes2
Has abstractyes

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