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Record W4318323537 · doi:10.1002/eap.2814

Managing aquatic habitat structure for resilient trophic interactions

2023· article· en· W4318323537 on OpenAlexafffund
Charlotte Ward, Tyler D. Tunney, Kevin S. McCann

Bibliographic record

VenueEcological Applications · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans CanadaUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHabitatEcologyTrophic levelBiomass (ecology)PredationEcosystemContext (archaeology)Trophic cascadeAbiotic componentEnvironmental scienceFood webAquatic ecosystemBiology

Abstract

fetched live from OpenAlex

Structural habitat (the three-dimensional arrangement of physical matter, abiotic and biotic, at a location) is a foundational element for the resilience and maintenance of biodiversity, yet anthropogenic development is driving the global simplification of aquatic environments. Resource managers regularly seek to conserve aquatic food webs by increasing structural habitat complexity with expected benefits to fisheries; however, the global effectiveness of such actions is unclear. Our synthesis and theoretical analyses found that the response of a consumer-resource interaction (predatory sportfish and forage fish prey) to the addition of prey refuge habitat differed among systems with low and high rates of biomass transfer from resource to consumer (i.e., biomass potential); stabilization was not the rule. Greater prey refuge habitat availability tended to stabilize systems characterized by high biomass potential while simultaneously increasing consumer densities. In contrast, increasing prey refuge habitat availability in systems characterized by low biomass potential tended to mute energy transfer and moved consumer densities toward local extinction. Importantly, biomass potential and prey refuge can have antagonistic effects on stability and relative consumer densities, and it is therefore important to consider the local conditions of a system when using habitat manipulation as a management measure. Further development of our context-dependent perspective to whole food webs, and across different environments, may help to guide structural habitat management to better restore and protect aquatic ecosystems.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.015
GPT teacher head0.263
Teacher spread0.248 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations17
Published2023
Admission routes2
Has abstractyes

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