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Record W4396535211 · doi:10.1111/1365-2435.14567

Temperature dependence and genetic variation in resource acquisition strategies in a model freshwater plant

2024· article· en· W4396535211 on OpenAlexafffund
Graydon J. Gillies, Amy L. Angert, Takuji Usui

Bibliographic record

VenueFunctional Ecology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsUniversity of TorontoQueen's UniversityUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyResource Acquisition Is InitializationCompetition (biology)EcologyTraitResource (disambiguation)PopulationSelection (genetic algorithm)Genetic variationEcological selectionResource allocationDemographyGeneticsGeneEconomics

Abstract

fetched live from OpenAlex

Abstract Understanding how competition varies with environmental stress is critical to anticipating species and community responses to rapid environmental change. While the stress‐gradient hypothesis predicts the strength of competition to decrease with increasing stress, our understanding of how competition varies with stress is limited by a lack of mechanistic understanding of how resource‐use traits underlying competitive dynamics respond to stress. Here, we use duckweeds in the Lemna species complex to measure how phenotypic and genetic variation in R* (a resource acquisition trait representing the minimum resource requirement for positive population growth) varies with high‐temperature stress to better understand how stress alters competitive ability for essential resources. We found that heat stress increased the R* of Lemna plants for nitrogen acquisition. Because lower R* values predict dominance in competitive dynamics where resources are limiting, this indicates that under stressful, high temperatures, plants could experience reduced competitive ability due to the higher resources required to sustain positive population growth rates. We found minimal genetic variation in R* across 11 local genotypes within the Lemna species complex, indicating that selection on resource acquisition strategies for essential resources such as nitrogen may be constrained in nature. The expression of genetic variation in R* for nitrogen was further reduced under heat stress, suggesting that the response to selection for R* could be particularly constrained under high‐temperature stress. Contrary to predictions drawn from the gleaner–opportunist trade‐off, we did not find evidence for a trade‐off in resource acquisition strategies under benign conditions or high‐temperature stress. Plants with lower R* (i.e. higher growth rates under lower nitrogen levels) were not constrained to have lower growth rates under higher nitrogen levels, possibly because the chosen genotypes have not diverged across resource acquisition strategies or because Lemna spp. has escaped this constraint. Importantly, our work indicates that high‐temperature stress could increase sensitivity to competition through an increased requirement for resources while reducing the evolutionary potential for Lemna species to respond to selection for resource traits. This study acts as a key step to understanding the mechanistic traits behind competitive dynamics in resource‐limited and stressful environments. Read the free Plain Language Summary for this article on the Journal blog.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.184
Teacher spread0.179 · 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 designSimulation or modeling
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

Citations6
Published2024
Admission routes2
Has abstractyes

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