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Record W2099157623 · doi:10.1890/07-1603.1

RESTORING PISCIVOROUS FISH POPULATIONS IN THE LAURENTIAN GREAT LAKES CAUSES SEABIRD DIETARY CHANGE

2008· article· en· W2099157623 on OpenAlexaff
Craig E. Hebert, D. V. Chip Weseloh, Abde Idrissi, Michael T. Arts, Robert O’Gorman, Owen T. Gorman, Brian A. Locke, Charles P. Madenjian, Edward F. Roseman

Bibliographic record

VenueEcology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMinistry of Natural Resources and ForestryEnvironment and Climate Change Canada
Fundersnot available
KeywordsSeabirdPelagic zonePredationTrophic levelEcologyForage fishAbundance (ecology)BiologyTrophic cascadeEcosystemApex predatorFood webPiscivoreMarine ecosystemFisheryPredator

Abstract

fetched live from OpenAlex

Ecosystem change often affects the structure of aquatic communities thereby regulating how much and by what pathways energy and critical nutrients flow through food webs. The availability of energy and essential nutrients to top predators such as seabirds that rely on resources near the water's surface will be affected by changes in pelagic prey abundance. Here, we present results from analysis of a 25-year data set documenting dietary change in a predatory seabird from the Laurentian Great Lakes. We reveal significant declines in trophic position and alterations in energy and nutrient flow over time. Temporal changes in seabird diet tracked decreases in pelagic prey fish abundance. As pelagic prey abundance declined, birds consumed less aquatic prey and more terrestrial food. This pattern was consistent across all five large lake ecosystems. Declines in prey fish abundance may have primarily been the result of predation by stocked piscivorous fishes, but other lake-specific factors were likely also important. Natural resource management activities can have unintended consequences for nontarget ecosystem components. Reductions in pelagic prey abundance have reduced the capacity of the Great Lakes to support the energetic requirements of surface-feeding seabirds. In an environment characterized by increasingly limited pelagic fish resources, they are being offered a Hobsonian choice: switch to less nutritious terrestrial prey or go hungry.

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 score0.586
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 studies0.0000.000
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 head0.079
GPT teacher head0.266
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.

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

Citations106
Published2008
Admission routes1
Has abstractyes

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