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Record W2990760890 · doi:10.1101/841833

Temperature-related biodiversity change across temperate marine and terrestrial systems

2019· preprint· en· W2990760890 on OpenAlexaff
Laura H. Antão, Amanda E. Bates, Shane A. Blowes, Conor Waldock, Sarah R. Supp, Anne E. Magurran, María Dornelas, Aafke M. Schipper

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsMemorial University of Newfoundland
FundersPlanbureau voor de LeefomgevingJane ja Aatos Erkon SäätiöUniversity of St AndrewsInternational Seafood Sustainability FoundationWellcome TrustLeverhulme TrustJohn Templeton Foundation
KeywordsBiodiversitySpecies richnessClimate changeTemperate climateAbundance (ecology)EcologyGeographyGlobal warmingLatitudeEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Abstract Climate change is reshaping global biodiversity as species respond to changing temperatures. However, the net effects of climate-driven species redistribution on local assemblage diversity remain unknown. Here, we relate trends in species richness and abundance from 21,500 terrestrial and marine assemblage time series across temperate regions (23.5-60.0°) to changes in air or sea surface temperature. We find a strong coupling between biodiversity and temperature changes in the marine realm, which is conditional on the baseline climate. We detect increases in species richness with increasing temperature that is twice as pronounced in warmer locations, while abundance declines with warming in the warmest marine locations. In contrast, we did not detect systematic temperature-related richness or abundance trends on land, despite a greater magnitude of warming. We also found no evidence for an interaction between biodiversity change and latitude, further emphasizing the importance of baseline climate in structuring assemblages. As the world is committed to further warming, significant challenges remain in maintaining local biodiversity amongst the non-uniform inflow and outflow of “climate migrants” across distinct regions, especially in the ocean.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.023
GPT teacher head0.220
Teacher spread0.197 · 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 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

Citations33
Published2019
Admission routes1
Has abstractyes

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