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Record W2819483672 · doi:10.1111/conl.12589

The area–heterogeneity tradeoff applied to spatial protection of rockfish ( <i>Sebastes</i> spp.) species richness

2018· article· en· W2819483672 on OpenAlexafffund
Alejandro Frid, Madeleine McGreer, Katie S.P. Gale, Emily Rubidge, Tristan Blaine, Mike Reid, Angeleen M. Olson, Sandie Hankewich, Ernest Mason, Dave Rolston, Ernest Tallio

Bibliographic record

VenueConservation Letters · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsTula FoundationUniversity of British ColumbiaFisheries and Oceans CanadaUniversity of Victoria
FundersFisheries and Oceans CanadaHakai InstituteTula FoundationGordon and Betty Moore Foundation
KeywordsSpecies richnessSpatial heterogeneitySebastesOverexploitationHabitatEcologyRockfishRange (aeronautics)Context (archaeology)BiodiversityBiologyFisheryGeographyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract The “area–heterogeneity tradeoff” hypothesis predicts unimodal effects of habitat heterogeneity on species richness, implying that habitats with intermediate heterogeneity may be priority for spatial protection. Alternatively, if heterogeneity effects are positive, then protecting the most heterogeneous habitats may take precedence. We tested for unimodal effects of habitat heterogeneity on the species density (area‐corrected richness) of rockfishes ( Sebastes spp.): long‐lived, benthic fishes vulnerable to overexploitation. Inconsistent with predictions, topographic structural complexity had a strong linear effect on species density; other heterogeneity measures had weaker, positive effects and the only unimodal effect (depth range) was weak. The clear implication is that, to protect the highest density of rockfish species, marine protected areas should include the most topographically complex substrates. Our results can also help refine and test species distribution models needed to inform spatial planning where in situ surveys are lacking. The area–heterogeneity tradeoff generates useful predictions for which support may be context‐dependent.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.330

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)0.0000.000

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 head0.021
GPT teacher head0.198
Teacher spread0.177 · 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.

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

Citations19
Published2018
Admission routes2
Has abstractyes

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