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Record W4221004585 · doi:10.1080/11956860.2022.2048533

Optimal foraging strategies in varying nutrient heterogeneity: responses of a stoloniferous clonal plant to patch pattern, size and quality

2022· article· en· W4221004585 on OpenAlexvenueno aff
Xiaona Zheng, Yang Gao, Yanan Wang, Xing Fu, Meixuan Zhao, Ying Gao

Bibliographic record

VenueEcoscience · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsForagingSpatial heterogeneityStolonSpatial ecologyNutrientBiologyScale (ratio)FertilizerEcosystemEcologyBiomass (ecology)Optimal foraging theoryEnvironmental scienceBiological systemAgronomyGeographyCartography

Abstract

fetched live from OpenAlex

Contrast and aggregation are two components of spatial heterogeneity. To precisely determine the influence of varying nutrient heterogeneity on the foraging behavior of clonal plants, we selected Potentilla anserina L. as the material and designed different patch patterns, sizes and qualities. In Experiment 1, compared with random and uniform patch patterns, aggregation increased ramet density and biomass accumulation at the patch scale, but not at the plot scale. This implies that the adaptive responses of clonal plants to nutrient heterogeneity depend on spatial scale. We further designed three levels of patch size with the same fertilizer concentration at the patch scale in Experiment 2, and four levels of patch quality with the same total amount of fertilizer at the plot scale in Experiment 3. Increased ramet density and stolon growth rate within small patches in Experiment 2 demonstrated effects of heterogeneity on plant foraging responses at the patch scale, while no difference was found in Experiment 3. This means that the influence of patch size on ramet establishment and residence time was alleviated by patch contrast at larger spatial scales. Hence, spatial scale is a key factor determining the interactions between heterogeneous ecosystem components.

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 score0.032
Threshold uncertainty score0.331

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 science0.0000.001
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 head0.026
GPT teacher head0.287
Teacher spread0.262 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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