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Record W2564000340 · doi:10.1002/eap.1484

Assessing the ecosystem‐level consequences of a small‐scale artisanal kelp fishery within the context of climate‐change

2016· article· en· W2564000340 on OpenAlexafffund
Kira A. Krumhansl, Jordanna N. Bergman, Anne K. Salomon

Bibliographic record

VenueEcological Applications · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsTula FoundationSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaHakai Institute
KeywordsKelp forestKelpFishingFisheryContext (archaeology)Climate changeLivelihoodEcosystemEcosystem servicesThreatened speciesBiomass (ecology)Macrocystis pyriferaMarine protected areaFood securityEcologyEnvironmental scienceGeographyAgricultureBiologyHabitat

Abstract

fetched live from OpenAlex

Coastal communities worldwide rely on small-scale artisanal fisheries as a means of increasing food security and alleviating poverty. Even small-scale fishing activities, however, are prone to resource depletion and environmental degradation, which can erode livelihoods in the long run. Thus, there is a pressing need to identify viable and resilient artisanal fisheries, and generate knowledge to support management within the context of a rapidly changing climate. We examined the ecosystem-level consequences of an artisanal kelp fishery (Macrocystis pyrifera), finding small-scale harvest of this highly productive species poses minimal impacts on kelp recovery rates, survival, and biomass dynamics, and abundances of associated commercial and culturally important fish species. These results suggest that small-scale harvest poses minimal trade-offs for the other economic benefits provided by these ecosystems, and their inherent, spiritual, and cultural value to humans. However, we detected a negative impact of warmer seawater temperatures on kelp recovery rates following harvest, indicating that the viability of harvest, even at small scales, may be threatened by future increases in global ocean temperature. This suggests that negative impacts of artisanal fisheries may be more likely to arise in the context of a warming climate, further highlighting the widespread effects of global climate change on coastal fisheries and livelihoods.

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 score0.249
Threshold uncertainty score1.000

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 head0.070
GPT teacher head0.255
Teacher spread0.186 · 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

Citations36
Published2016
Admission routes2
Has abstractyes

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