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Record W2151589195 · doi:10.1093/icesjms/fsr062

The trophic-level-based ecosystem modelling approach: theoretical overview and practical uses

2011· article· en· W2151589195 on OpenAlexafffund
Didier Gascuel, Sylvie Guénette, Daniel Pauly

Bibliographic record

VenueICES Journal of Marine Science · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British Columbia
FundersSixth Framework ProgrammeUniversity of British ColumbiaPew Charitable Trusts
KeywordsTrophic levelEcosystemBiomass (ecology)FishingEnvironmental scienceFood webMarine ecosystemEcosystem-based managementEcologyEcosystem modelPredationFisheryBiology

Abstract

fetched live from OpenAlex

Abstract Gascuel, D., Guénette, S., and Pauly, D. 2011. The trophic-level-based ecosystem modelling approach: theoretical overview and practical uses. – ICES Journal of Marine Science, 68: 1403–1416. A trophic-level (TL)-based ecosystem modelling approach is presented, where ecosystem functioning is modelled as a flow of biomass up the foodweb through predation and ontogenetic processes. The approach, based on simple equations derived from fluid dynamics, provides insights into ecosystem functioning and the impact of fishing. A virtual ecosystem is simulated and the model shown to be capable of mimicking the effects of various exploitation patterns on ecosystem biomass expected from the theory. It provides the theoretical basis to explain complex patterns, such as cascading effects, maximum sustainable ecosystem yield, and fishing down the foodweb. The utility of the TL-based approach as a practical tool for determining fishing impacts in specific ecosystems is illustrated using the Guinean ecosystem as a case study, showing how current fishing effort levels led to full exploitation of higher TLs, confirming and generalizing previous single-species assessment results. Finally, catch trophic spectrum analysis is presented to show that it provides reliable biomass estimates when catches per TL and primary production are known.

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)0.003
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.115
GPT teacher head0.299
Teacher spread0.184 · 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 designSimulation or modeling
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

Citations88
Published2011
Admission routes2
Has abstractyes

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