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Record W3201249112 · doi:10.1111/ecog.05690

Niche conservation in copepods between ocean basins

2021· article· en· W3201249112 on OpenAlexaff
Niall McGinty, Andrew D. Barton, Zoe V. Finkel, David G. Johns, Andrew J. Irwin

Bibliographic record

VenueEcography · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsDalhousie University
FundersNatural Environment Research CouncilSight Research UK
KeywordsNicheEcological nicheEcologyNiche segregationBiological dispersalBiologyEnvironmental niche modellingBiogeographyHabitatSpecies distributionPopulation

Abstract

fetched live from OpenAlex

Niche conservatism is the hypothesis that a species' niche remains stable in space and time. This concept is central to understanding phenomena ranging from the role of climate change impacts on species biogeography to community dynamics. Marine zooplankton have been used as indicators of global change in the ocean, and niche conservatism is usually assumed for these organisms due to their high dispersal capacity, which may limit the development of local adaptations. However, recent evidence suggests that several zooplankton species with a circumglobal distribution are comprised of several cryptic species complexes, but it is unknown if the niches of cryptic species complexes have remained conserved or if they have diverged. Habitat management based on niche modelling would be strengthened by better understanding patterns of niche conservation or divergence, as the usual assumption that species have fixed environmental niches may be misleading. We assess the niche differences for 15 copepod species with populations in the North Atlantic, Southern Ocean and Australia. Pairwise differences in the realised niches were computed for each species using two complementary analyses. Our analysis indicates that global‐scale niche modelling of zooplankton cannot assume niche conservatism across all lineages and future studies should look to incorporate phylogenetic information to improve future niche estimates.

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 score0.032
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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