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Record W2756407990 · doi:10.1002/ecm.1278

Linking fishing pressure with ecosystem thresholds and food web stability on coral reefs

2017· article· en· W2756407990 on OpenAlexafffund
Peter Houk, Javier Cuetos‐Bueno, Alexander M. Kerr, Kevin S. McCann

Bibliographic record

VenueEcological Monographs · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsUniversity of Guelph
FundersNational Oceanic and Atmospheric AdministrationCanada First Research Excellence FundCargill Foundation
KeywordsFood webEcologyTrophic cascadeTrophic levelCoral reefIntraguild predationCoral reef fishEcosystemPopulationGuildPredationReefBiologyEnvironmental scienceFisheryHabitatPredator

Abstract

fetched live from OpenAlex

Abstract Managing fisheries for ecosystem resilience is essential, but practical guidance is limited by food‐web complexity. Processes, mechanisms, and thresholds associated with ecosystem overfishing were investigated by combining traditional concepts in fisheries biology with recent advances in food‐web modeling. Diverse coral‐reef food webs were simplified by grouping species into guilds based on the way they capture, store, and transfer energy, rather than taxonomically, as is traditionally done. Biomass fluxes between the guilds were then quantified using an allometric trophic model. The model was calibrated by linking parameters describing growth, predation, and competition with known body size and metabolic constraints, and then adjusting the base rate of parameters to match fish biomass estimates from a “pristine” coral reef system. The calibrated model was then tested by replacing equilibrium fish biomasses with observations from fished systems across the Pacific, spanning nine islands and numerous major‐reef habitats. Encouraging relationships were found between predicted algal accumulation and field observations, and between modelled and observed guild restructuring. In terms of food‐web ecology, “pristine” food webs were characterized by asynchronous population dynamics between the guilds (i.e., offsetting fluctuations), which maximized their persistence and the net accumulation of biomass within food webs. Beneficial, offsetting fluctuations were driven by the contrasting roles of density dependence, apparent competition, and predation. Fishing for predators synchronized the population fluctuations between the guilds, resulting in larger amplitudes (i.e., highs and lows), and a growing dominance of small herbivores. Continued fishing for large herbivores eventually led to an inflection point where algal biomass accumulated exponentially, revealing an ecosystem‐based fisheries benchmark. Management targets that maximized fisheries yields while controlling for algal accumulation required simultaneous exploitation across the guilds; a significant challenge because maximum yields of predators, large herbivores, and small herbivores were magnitudes of order apart. This strategy also represented a departure from modern commercial fisheries policies that place catch quotas on entire fish families taxonomically, committing systems to smaller fish, higher biomass turnover, and undesirable algal accumulation. Moving forward, the model provided a flexible and adaptable framework to consider economic and ecosystem objectives of fisheries simultaneously, ultimately balancing resilient food webs against higher fisheries productivity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.027
GPT teacher head0.222
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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

Citations35
Published2017
Admission routes2
Has abstractyes

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