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Record W2985284926 · doi:10.1093/plankt/fbz043

Herbivory alters thermal responses of algae

2019· article· en· W2985284926 on OpenAlexafffund
Michelle Tseng, Evgeniya Yangel, Yi Lin Zhou

Bibliographic record

VenueJournal of Plankton Research · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundCanada Foundation for Innovation
KeywordsDaphnia pulexHerbivoreBiologyAlgaeDaphniaPredationEcologyScenedesmusBotanyCrustacean

Abstract

fetched live from OpenAlex

Abstract The temperature-size rule (TSR) describes the widespread pattern in which organisms grown at higher temperatures mature at smaller adult sizes, or exhibit smaller cell sizes in the case of microbes. Although the TSR has been shown in a wide range of taxa, most TSR studies have been conducted in the absence of species interactions such as competitors, parasites or predators. Given that these interactions are ubiquitous in nature, here we examine how the presence of a live herbivore (Daphnia pulex) affects the response of a cosmopolitan green algae (Scenedesmus obliquus) to the thermal environment. In the absence of direct herbivory, algae exhibited the characteristic TSR, exhibiting smaller cells, and smaller colonies at higher temperatures. However, in the presence of Daphnia herbivory, we saw no evidence of the TSR. Rather, both cell and colony size were uniform across the three rearing temperatures. These results suggest that Daphnia consume larger-sized algae at cooler temperatures, and smaller-sized algae at higher temperatures. Overall this study demonstrates that species interactions such as herbivory can alter the response of primary producers to the thermal environment, and suggests that the TSR may be readily modified in the natural world, where predators, herbivores and competitors abound.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0030.000

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 head0.058
GPT teacher head0.334
Teacher spread0.276 · 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 designBench or experimental
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

Citations4
Published2019
Admission routes2
Has abstractyes

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