DOI: 10.5061/dryad.w6m905r2n
title: Trait variation and plant performance: Interactive effects of diversity and spatial patterning of plants across environmental conditions

description: The diversity and spatial patterning of plants and their environmental
 determinants can influence plant‒plant interactions critical to generating
 trait variation in plants. However, how plant spatial patterns interact
 with plant diversity to shape intra- and interspecific trait variation
 and, in turn, shape plant performance remains unclear. It is also unclear
 how the availability of resources influences these relationships. We
 created experimental assemblages varying in the spatial distributions of
 the diversity of plant species under ambient watering and induced drought
 conditions, and collected data on leaf traits and plant performance (cf.
 whole plant biomass). Our analyses revealed that multitrait intra- or
 interspecific variation (quantified as the coefficient of variation and
 averaged across species and traits) was positively associated with plant
 diversity regardless of environmental conditions. However, the magnitudes
 of multitrait intraspecific variation were slightly lower for spatially
 structured than randomly distributed plants under ambient conditions.
 While multitrait interspecific variation did not vary among environmental
 conditions, multitrait intraspecific variation was 13.64% higher under
 drought than in ambient conditions. Intra- and interspecific variations in
 individual traits generally agreed with the trends observed for the
 multitrait variation, but species-specific intraspecific trait variation
 was idiosyncratic. Further analyses of this data revealed that plant
 performance was negatively associated with multitrait interspecific
 variation across environmental conditions, but plant performance was
 negatively associated with multitrait intraspecific variation under
 drought only, suggesting trait variation (quantified as cv) as an
 indicator of competitive hierarchy in this experiment. For individual
 traits, however, intra- or interspecific trait variation and plant
 performance association varied from positive and negative to neutral
 depending on traits and environmental conditions. Our results highlight
 the importance of considering the environmental condition-dependent
 interaction between diversity and spatial patterning of plants for
 understanding the causes and consequences of trait variation under present
 or future global change scenarios.
description: # Trait variation and plant performance: Interactive effects of diversity
 and spatial patterning of plants across environmental conditions Dataset
 DOI: [10.5061/dryad.w6m905r2n](https://doi.org/10.5061/dryad.w6m905r2n) ##
 Description of the data and file structure ### Files and variables
 **Description:** The dataset includes five files: (i) summarized overall
 data = plotsta_summrayF.csv, (ii) summarized data for *Portulaca
 grandiflora = plotonespP.csv* (iii) summarized data for *Trifolium repens
 = plotonespT.csv* (iv) summarized data for *Zinnia elegans
 = plotonespZ.csv* (v) raw leaf trait data = RawTraitsData.csv #### File:
 plotsta_summrayF.csv ##### Variables * plot.id: plot identifier *
 spatial_pattern: spatial distributions of plants (pos = positively
 autocorrelated, neg = negatively autocorrelated, and rand = random
 distribution) * environmental_context: environmental conditions (ambient =
 ambient watering, and drought= drought) * species_richness: number of
 species per 1 m2 plot * mix: whether the plot is a monoculture or a
 mixture  * species_composition: composition of species (P = *Portulaca
 grandiflora*, T = *Trifolium repens* , and Z = *Zinnia elegans* ) *
 BvLA_cv: interspecific variation in leaf area (values are the coefficient
 of variation) * BvSLA_cv: interspecific variation in specific leaf area
 (values are the coefficient of variation) * BvLDMC_cv: interspecific
 variation in leaf dry matter content (values are the coefficient of
 variation) * BvLT_cv: interspecific variation in leaf thickness (values
 are the coefficient of variation) * BvSPAD_cv: interspecific variation in
 leaf chlorophyll content measured by SPAD chlorophyll meter  (values are
 the coefficient of variation) * WvLA_cv: intraspecific variation in leaf
 area (values are the coefficient of variation) * WvSLA_cv: intraspecific
 variation in specific leaf area (values are the coefficient of variation)
 * WvLDMC_cv: intraspecific variation in leaf dry matter content (values
 are the coefficient of variation) * WvLT_cv: intraspecific variation in
 leaf thickness (values are the coefficient of variation) *
 WvSPAD_cv: intraspecific variation in leaf chlorophyll content measured by
 SPAD chlorophyll meter  (values are the coefficient of variation) * wmLA:
 weighted (by abundance) mean of leaf area (cm^2^) * wmSLA: weighted (by
 abundance) mean of specific leaf area (cm^2^/g) * wmLDMC: weighted (by
 abundance) mean of leaf dry matter content (dry-weight/fresh-weight ratio)
 * wmLT: weighted (by abundance) mean of leaf thickness (cm) *
 wmSPAD: weighted (by abundance) mean of leaf chlorophyll content (SPAD
 unit) * net_productivity: total biomass (g) #### File: plotonespP.csv
 ##### Variables * plot.id: plot identifier * spatial_pattern: spatial
 distributions of plants (pos = positively autocorrelated, neg = negatively
 autocorrelated, and rand = random distribution) * environmental_context:
 environmental conditions (ambient = ambient watering, and drought=
 drought) * species_richness: number of species per 1 m2 plot *
 mix: whether the plot is a monoculture or a mixture  *
 species_composition: composition of species (P = *Portulaca grandiflora*,
 T = *Trifolium repens* , and Z = *Zinnia elegans* ) * species_identity:
 species for which measurement was taken * CvLA: coefficient of variation
 in leaf area * CvSLA: coefficient of variation in specific leaf area *
 CvLDMC: coefficient of variation in leaf dry matter content *
 CvLT: coefficient of variation in leaf thickness * CvSPAD: coefficient of
 variation in leaf chlorophyll #### File: plotonespT.csv #####
 Variablesplot.id: plot identifier * spatial_pattern: spatial distributions
 of plants (pos = positively autocorrelated, neg = negatively
 autocorrelated, and rand = random distribution) * environmental_context:
 environmental conditions (ambient = ambient watering, and drought =
 drought) * species_richness: number of species per 1 m2 plot *
 mix: whether the plot is a monoculture or a mixture  *
 species_composition: composition of species (P = *Portulaca grandiflora*,
 T = *Trifolium repens* , and Z = *Zinnia elegans* ) * species_identity:
 species for which measurement was taken * CvLA: coefficient of variation
 in leaf area * CvSLA: coefficient of variation in specific leaf area *
 CvLDMC: coefficient of variation in leaf dry matter content *
 CvLT: coefficient of variation in leaf thickness * CvSPAD: coefficient of
 variation in leaf chlorophyll #### File: plotonespZ.csv ##### Variables *
 plot.id: plot identifier * spatial_pattern: spatial distributions of
 plants (pos = positively autocorrelated, neg = negatively autocorrelated,
 and rand= random distribution) * environmental_context: environmental
 conditions (ambient = ambient watering, and drought= drought) *
 species_richness: number of species per 1 m2 plot * mix: whether the plot
 is a monoculture or a mixture  * species_composition: composition of
 species (P = *Portulaca grandiflora*, T = *Trifolium repens* , and Z =
 *Zinnia elegans* ) * species_identity: species for which measurement was
 taken * CvLA: coefficient of variation in leaf area * CvSLA: coefficient
 of variation in specific leaf area * CvLDMC: coefficient of variation in
 leaf dry matter content * CvLT: coefficient of variation in leaf thickness
 * CvSPAD: coefficient of variation in leaf chlorophyll #### File:
 RawTraitsData.csv ##### Variables * plot.id: plot identifier *
 environmental_context: composition of species (P = *Portulaca
 grandiflora*, T = *Trifolium repens* , and Z = *Zinnia elegans* ) *
 species_composition: composition of species (P = *Portulaca grandiflora*,
 T = *Trifolium repens* , and Z = *Zinnia elegans* ) *
 species_identity: species for which measurement was taken * mix: whether
 the plot is a monoculture or a mixture  * species_richness :number of
 species per 1 m2 plot * leaf.id: leaf sample identifier * LA: leaf area
 (cm^2^) * SLA: specific leaf area (cm^2^/g) * LDMC: leaf dry matter
 content (dry-weight/fresh-weight ratio) * LT: leaf thickness (cm) * SPAD:
 leaf chlorophyll (SPAD unit) * spatial_pattern: spatial distributions of
 plants (pos = positively autocorrelated, neg = negatively autocorrelated,
 and rand= random distribution)