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Record W4385754292 · doi:10.1126/sciadv.adf0954

Marine heatwaves threaten cryptic coral diversity and erode associations among coevolving partners

2023· article· en· W4385754292 on OpenAlexafffund
Samuel Starko, James E. Fifer, Danielle C. Claar, Sarah W. Davies, Ross Cunning, Andrew C. Baker, Julia K. Baum

Bibliographic record

VenueScience Advances · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsUniversity of Victoria
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaMitacsAdvanced Research Projects AgencyNational Science FoundationPew Charitable TrustsNational Oceanic and Atmospheric AdministrationNational Geographic Society
KeywordsBiologyCoralEcologyObligateSpecies complexSymbiosisCoral reefEvolutionary biologyAnthozoaCoral bleachingPhylogenetic treeGeneGenetics

Abstract

fetched live from OpenAlex

Climate change–amplified marine heatwaves can drive extensive mortality in foundation species. However, a paucity of longitudinal genomic datasets has impeded understanding of how these rapid selection events alter cryptic genetic structure. Heatwave impacts may be exacerbated in species that engage in obligate symbioses, where the genetics of multiple coevolving taxa may be affected. Here, we tracked the symbiotic associations of reef-building corals for 6 years through a prolonged heatwave, including known survivorship for 79 of 315 colonies. Coral genetics strongly predicted survival of the ubiquitous coral, Porites (massive growth form), with variable survival (15 to 61%) across three morphologically indistinguishable—but genetically distinct—lineages. The heatwave also disrupted strong associations between these coral lineages and their algal symbionts (family Symbiodiniaceae), with symbiotic turnover in some colonies, resulting in reduced specificity across lineages. These results highlight how heatwaves can threaten cryptic genotypes and decouple otherwise tightly coevolved relationships between hosts and symbionts.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
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
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.003
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 head0.023
GPT teacher head0.270
Teacher spread0.247 · 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

Citations52
Published2023
Admission routes2
Has abstractyes

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