MétaCan
Menu
Back to cohort
Record W2255327451 · doi:10.1098/rsbl.2015.0798

Predators modify the evolutionary response of prey to temperature change

2015· article· en· W2255327451 on OpenAlexafffund
Michelle Tseng, Mary I. O’Connor

Bibliographic record

VenueBiology Letters · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyDaphnia pulexPredationDaphniaEvolutionary ecologyEcologyAdaptation (eye)Natural selectionEnvironmental changeContext (archaeology)PopulationPhenotypic plasticityExperimental evolutionEcosystemEvolutionary physiologyPredatorClimate changeCrustacean

Abstract

fetched live from OpenAlex

As climate regimes shift in many ecosystems worldwide, evolution may be a critical process allowing persistence in rapidly changing environments. Organisms regularly interact with other species, yet whether climate-mediated evolution can occur in the context of species interactions is not well understood. We tested whether a species interaction could modify evolutionary responses to temperature. We demonstrate that predation pressure by Dipteran larvae (Chaoborus americanus) modified the evolutionary response of a freshwater crustacean (Daphnia pulex) to its thermal environment over approximately seven generations in laboratory conditions. Daphnia kept at 21°C evolved higher population growth rates than those kept at 18°C, but only in those populations that were also reared with predators. Furthermore, predator-mediated selection resulted in the evolution of elevated Daphnia thermal plasticity. This laboratory natural selection experiment demonstrates that biotic interactions can modify evolutionary adaptation to temperature. Understanding the interplay between multiple selective forces can improve predictions of ecological and evolutionary responses of organisms to rapid environmental change.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.198

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)0.0000.000

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 head0.025
GPT teacher head0.243
Teacher spread0.218 · 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

Citations48
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueBiology LettersSame topicAquatic Ecosystems and Phytoplankton DynamicsFrench-language works237,207