DOI: 10.5061/dryad.kh18932gb
title: Data from: Differential responses of community-level functional traits to mid- and late-season experimental drought in a temperate grassland

description: Extreme precipitation events are becoming more intense and frequent due to
 climate change. This climatic shift is impacting the structure and
 dynamics of natural communities and the key ecosystem services they
 provide. Changes in species abundance under these conditions are thought
 to be mediated by functional traits, morpho-physiological characteristics
 of an organism that impact its fitness. Future environmental conditions
 may, therefore, favour different traits to those in present-day
 communities. After six years of manipulated precipitation levels,
 including drought (−50% of ambient precipitation), irrigation (+50% of
 ambient precipitation), and control (ambient precipitation), we measured
 five key functional traits (plant height, leaf dry matter content [LDMC],
 leaf thickness, specific leaf area [SLA], and leaf phosphorus
 concentration) in 586 individual vascular plants to study the effects of
 precipitation changes on community weighted functional traits.
 Additionally, we tested whether the precipitation change effects on the
 traits depend on the time of the growing season. As expected,
 reduced precipitation impacted community composition only for the late
 season timing, after the seasonal field mowing, but led to a significant
 change in all community-level plant traits between season timings. Under
 drought, communities shifted towards shorter individuals with thicker but
 small leaves and lower phosphorous content. Overall, a combination of
 community reassembly and intraspecific variation contributed to
 community-weighted differences between control and drought plots for plant
 height, SLA, and LDMC traits. Species turnover was the main driver of
 community-weighted means shifts in all traits in the late-season but SLA.
 Whereas all traits showed variations at the community level with drought,
 SLA and LDMC were the most responsive traits at the species level.
 Nevertheless, our results suggest underestimation of intraspecific
 variation due to sensitive species lower abundance under stress. No
 differences in community-weighted means of functional traits were observed
 between control and irrigated plots. Synthesis. Our findings suggest that
 functional trait composition of grassland communities may shift under
 climate change-induced drought, depending on the growing season timings .
 Trait-based attempts to predict ecosystem functioning must account for
 such temporal variation in community trait values.
description: <strong>Study site</strong> The
 experiment was conducted at the Upper Seeds field site
 (51°46'16.8"N 1°19'59.1"W, 155 m a.s.l) in Wytham
 woods, Oxfordshire, UK. The study site is a calcareous temperate grassland
 characterised by a shallow soil depth (300-500 mm depth) alkaline soils
 (Gibson & Brown, 1991), a daily average temperature ranging
 between -5 °C and 26 °C, a mean annual temperature of 11.5 ºC (2016-2021),
 daily total precipitation of 0-40 mm (2016-2021) and an annual total
 precipitation of 686 mm (2016-2021) (Table S1). According to the
 Köppen-Geiger climate classification, the study site constitutes a
 maritime temperate climate (Cfb) (Peel et al., 2007). The site is
 currently managed for maintenance by mowing twice per year, a common
 practice across most European grasslands (Török et al., 2018). The first
 mowing takes place mid-growing season (late July), and the second at the
 end of the growing season (late September). To test our
 hypotheses, we experimentally manipulated precipitation levels using the
 RainDrop (rainfall and drought platform) long-term ecological experiment
 for six years, and in July and September 2021 we determined species
 abundance and sampled the most abundant species using functional traits
 measurements. RainDrop is integrated in the DroughtNet global coordinated
 research network (https://droughtnet.weebly.com/). The RainDrop experiment
 has been running since 2016 and consists of twenty-five 5 m × 5 m
 permanent plots distributed across the study site in five randomised
 blocks (A:E) (Figure S1). Each block includes five plots that receive one
 of the following treatments during the growing season (March-September):
 drought (−50% ambient precipitation), irrigation (+50% ambient
 precipitation), two ambient control plots (no manipulation), and
 procedural control (inverted rain shelters). Rain shelters intercept 50%
 of precipitation for the drought plots. In each drought plot,
 precipitation is intercepted by gutters and collected in containers
 situated next to each rain shelter. Pipes connect these deposits to
 sprinklers that spray the water onto the adjacent irrigation plots. This
 design is based on the proposal of Yahdjian & Sala (2012) and
 Gherardi & Sala (2013), which has been applied across &gt;100
 nodes of the DroughtNet network worldwide for exploring climate
 change-induced drought and deluge effects on community composition
 (Fischer et al., 2013; Smith et al., 2024). Furthermore, specific to our
 study site, climatic projections forecast shifts in annual precipitation
 from -20% to +20% under the 8.5RCP for 2080-2099 compared to 1981-2000
 (UKCP18 Project, Met Office). Hence, the plots that experienced no
 precipitation manipulation served as ambient precipitation treatment
 (control) plots. Additionally, to control for shelter effects, each block
 has one procedural control plot. These consisted of rain shelters with
 inverted gutters that allow 100% of precipitation to pass
 through. Ongoing work at this field site has revealed
 no differences in community composition between the procedural and ambient
 controls (Jackson et al., 2024). Therefore, we did not measure traits from
 the procedural control plots, and instead sampled one additional ambient
 control plot per block. To minimise edge effects, we split the 5 m × 5 m
 plot into four quarters and marked out a 1 m × 1 m quadrat in the centre
 of the study quarter from which we took all trait and abundance
 measurements. Data were collected at two different times along the growing
 season: mid-growing season (July 2021) and late-growing season (September
 2021), just before each seasonal mowing.
 <strong>Data and metrics</strong>
 <strong>Species abundance</strong>
 We collected species abundance data to assess community
 composition dissimilarity between the precipitation treatments (H1) and
 possible differences in community-weighted means between treatments (H2).
 To do so, we quantified species percentage cover for all vascular plant
 species and bare soil percentage cover using a 1 m × 1 m quadrat (with 10
 cm grid) at each examined permanent plot. We first estimated the
 percentage cover independently for every species in each quadrat (one per
 plot), and next transformed these estimates into relative abundances that
 sum to 100%. Because the mid-season mowing removed the inflorescence from
 all grasses, species identity was difficult to know for many graminoid
 species during the late-season, which may impact the observed community
 effects. In the late season, only two of the graminoid species
 (Brachypodium pinnatum and Brachypodium sylvaticum) were identifiable at
 the species level because of their distinctive leaves. For these two
 Brachypodium species, we recorded percentage cover as described above.
 Separately, we recorded the pooled abundance of all other graminoid
 species. Although we identified graminoid species in the mid-season, to
 ensure sound comparisons between mid-season and late-season abundance data
 (NMDS analysis), we combined the mid-season abundance of non-Brachypodium
 graminoids in analyses to contrast community-weighted means among
 precipitation treatments at the different times.
 <strong>Trait measurement and community weighted
 means</strong> We quantified key functional
 traits of the grassland community to test whether CWM trait values differ
 among precipitation treatments and whether the differences are driven by
 different factors (interspecific vs. intraspecific trait variation) at the
 two contrasted growing seasons timings (H2). Trait measurement was
 performed at the same time species abundance was measured (July and
 September 2021). We measured height, specific leaf area, leaf thickness,
 leaf dry matter content and leaf phosphorus on the most abundant species
 in each quadrat, totalling 586 individual plants. We measured traits using
 a standardised protocol (Pérez-Harguindeguy et al., 2013), briefly
 summarised in Table 1. For the selected species to be representative of
 the community, we focused on species that contributed to a cumulative
 abundance of at least 80% within each quadrat, following Garnier et al.
 (2004) and Pakeman & Quested (2007). Next, in each quadrat, we
 randomly selected three mature, healthy individuals per species for trait
 measurement. Leaf traits were measured on one young but fully developed
 leaf per individual with the exception of leaf phosphorus, which require
 pooling three leaves per species per plot to obtain the 50 mg of dry
 weight required for the analysis (Esslemont et al. 2000). After pooling,
 69 samples (out of a total of 195 potential samples) reached the dry
 weight threshold and thus were eligible for analysis. Leaf phosphorus
 concentration was estimated using inductively coupled plasma mass
 spectrometry (ICP-MS) following Esslemont et al. (2000). Briefly, samples
 were digested with 1 mL of concentrated nitric acid and 0.7 mL of hydrogen
 peroxide at 50ºC overnight and diluted 25 times with MiliQ water prior
 analysis. Community-weighted means of each trait were
 calculated in each quadrat for all treatments. CWM are commonly used in
 trait-based ecology to quantify shifts in community mean trait values due
 to macro and micro-environmental selection (Garnier et al., 2004;
 Bruelheide et al., 2018; Griffin-Nolan et al., 2019; Kambach et al.,
 2023). We calculated CWM of the five assessed traits using the framework
 proposed by Lepš et al. (2011), and the trait.transform and trait.CWM
 functions from Lepš et al. (2011) and  Götzenberger et al. (2020), which
 are included in cati R package (Taudiere and Violle, 2016). This framework
 disentangles the effect of interspecific variability (species turnover)
 and the combination of species turnover and their intraspecific trait
 variability as fixed community-weighted means (fixed CWM) and specific
 community-weighted means (specific CWM), respectively. Briefly, to
 calculate the specific CWMs, we multiplied the mean trait value per
 dominant species in each treatment by each species’ relative abundance in
 the quadrat. The resulting species products were summed, and their
 abundances rescaled following de Bello et al. (2021). Our target of
 sampling species with a cumulative abundance of 80% was achieved for all
 traits in all quadrats, with the only exception of leaf phosphorus, for
 which the threshold was achieved in 25% of quadrats only (Table S2).
 Because we observed that the measured traits did not vary significantly
 across the growing season for any of the studied species, we used the same
 trait values for both growing season timings, with the exception of plant
 height. Due to the graminoids identification difficulties at the
 late-season, and because all graminoids (Agrostis capillaris,
 Arrhenatherum elatius, Brachypodium pinnatum, Brachypodium sylvaticum,
 Dactylis glomerata, Holcus lanatus, and Trisetum flavescens) showed
 similar height in comparison to other functional groups in the mid-season,
 we used the height of Brachypodium pinnatum, the most abundant
 identifiable graminoid (ca. 34%, whereas the non-Brachypodium graminoids
 showed relative abundances &lt;18% at the mid-season), as the height
 of late-season non-Brachypodium graminoids. To calculate fixed CWM, a
 single mean trait value for individual species was used for all quadrats.
 While changes in fixed averages across treatments would reflect species
 turnover, changes in specific averages reflect both between and
 within-species variability in traits (Lepš et al., 2011).
 When trying to test for differences at the species level
 (intraspecific variation), not all species presented a consistent number
 of samples, as their abundance might depend on the sensitivity to the
 treatment. We therefore restricted analysis of each species responses to
 different treatments (i.e., precipitation-driven intraspecific variation)
 to the species that had enough replication to do so, considering the
 recommended replicate number to account for natural trait variation (n= 30
 at each precipitation level, Pérez-Harguindeguy et al., 2013). These
 species consisted of three graminoids (Brachypodium pinnatum, Trisetum
 flavescens, and Arrhenatherum elatius), three legumes (Medicago lupulina,
 Trifolium repens, and Trifolium pratense), and one non-leguminous forb
 (Crepis capillaris). <strong>Statistical
 analysis</strong> <strong>H1: Do
 precipitation treatments affect community
 composition?</strong> We performed non-metric
 multidimensional scaling (NMDS) to contrast community composition between
 treatments (H1) using the vegan R package (Oksanen et al., 2020). NMDS is
 a form of dimension reduction that allows for differences in communities
 to be quantified. In its application to plant trait-based ecology, NMDS is
 based on the rank-order of species abundances and maximises the
 correlation between real-world distance and Bray Curtis distance in the
 ordination space. We assessed the significance of any differences in
 community composition using analysis of similarity (ANOSIM). To determine
 which species were responsible for any dissimilarities among treatments,
 we used similarity percentage (SIMPER). Differences between groups when
 performing multidimensional analysis (ANOSIM and SIMPER) were considered
 significant when the p-value was less than 0.05.
 <strong>H2: How do precipitation manipulations alter CWM of
 traits in different periods of a season?</strong>
 We fitted hierarchical linear mixed-effects models to test our
 expectations that both the specific and fixed CWM of each of the five
 assessed traits would differ between the three precipitation treatments in
 mid-season and late-season periods, but the pattern of the differences for
 each trait would differ between the two growing season timings (H2). We
 used the R package lme4 to account for the blocked experimental design,
 where models were fit using maximum likelihood (ML) (Bates et al., 2015).
 Precipitation treatment (three levels: drought, control, irrigated) and
 timing in the growing season (two levels: mid- vs. late-season) were fixed
 effects, and block was considered as a random effect (five levels: A:E,
 where all treatments are represented). A significant interaction effect of
 precipitation treatment and seasonal timing indicated that the impact of
 the precipitation manipulation differed between the two timings. To meet
 the assumptions of gaussian distributions in our models, we
 log-transformed plant height, while other traits’ data were not
 transformed. When interpreting the output of mixed-effects models, we
 focused on differences based on the 95% confidence intervals overlap
 rather than relying on p-values following Flechner & Tseng (2011)
 and Bates et al. (2015), who discouraged the use of p-values in
 mixed-effect models.  Finally, to explore the link
 between trait and species abundance precipitation sensitivity, we
 contrasted the observed relative abundance decrease under the drought
 treatment (as we observed significant differences, see results) with the
 position of all species along the two principal components of species
 traits variation using principal component analysis (PCA) from the
 PCAtools R package (Blighe & Lun, 2022). In addition, to further
 explore how specific CWM differ between treatments in each species, we
 fitted linear mixed-effects models, with only precipitation treatment as a
 fixed effect for individual species with enough replication across
 treatments. All analysis were performed in R (R Core
 Team, 2022, v. 4.2.1).
description: # Data from: Differential responses of community-level functional traits
 to mid- and late-season experimental drought in a temperate grassland
 [https://doi.org/10.5061/dryad.kh18932gb](https://doi.org/10.5061/dryad.kh18932gb) The dataset contains the data and scripts to reproduce the analysis performed in Fenollosa et al 2024 JEcol, which mainly consists in contrasting grassland community composition between plots subjected to three precipitation treatments in a block design, calculating specific and fixed community-weighted means for five functional traits, calculating intraspecific variability, using the functional trait data to build a PCA and calculate functional diversity. Full information of the experimental design and the data can be found in the manuscript. ## Description of the data, file structure, and code The file contains a folder with the data used and a folder with the scripts, together with a text file named "Metadata.txt" that reviews the content and purpose of each script in the context of the manuscript. In all datasets, missing values are marked as "NA". We are providing all the used data and code to reproduce the article in order of results presentation within the manuscript. In order to facilitate it we summarize here the analytic steps and the flow from raw data to all analysis included in the manuscript.  The scripts and datasets provided are as follows, sorted from raw data to results presentation within the article: 1. In order to perform community composition analysis to compare plots from different treatments, use the R script "1_Community Analysis.R" that uses "abundance_data.RData". This data file contains the raw abundances (percentage of coverage 0-100%) of the different species registered in each grassland plot. In this dataframe, one can find the factors Season (July/September), Treatment (Drought, Fertilized, Ambient, Irrigated), the block (A:E) and the species scientific name. Note that no fertilization treatment was applied even though this label was used. Fertilized plots correspond to a replicate of ambient controls. Therefore within the code, Fertilized and Ambient plots are relabelled to "Control". 2. To calculate specific and fixed CWM as described in the methods, use the R script "2_CWMFlexSpec_Calculations.R" and import data from "abundance_data.RData" described previously and "TraitsData.xslx". Use this script also to plot CWM. The Excel file "TraitsData.xslx" contains the following variables: 1. Plot: Plot number 2. Block: Block number A:E 3. Treatment: Drought, Control (Ambient and Fertilized) and Irrigated. 4. Season: July, September. 5. Species: Scientific species name found in that plot 6. Functional traits: Height (mm), Thickness (Leaf thickness, mm), SLA (Specific leaf area, mm2/mg), LDMC (leaf dry matter content, mg/g), Phos (leaf phosphorous content mg/g) Note that in this dataset some cells in plant height (column F) were coloured in orange. This was done to highlight the fact that these trait data correspond to the applied height correction. Due to the graminoids identification difficulties at the late-season, and because all graminoids (*Agrostis capillaris*, *Arrhenatherum elatius*, *Brachypodium pinnatum*, *Brachypodium sylvaticum,* *Dactylis glomerata*, *Holcus lanatus*, and *Trisetum flavescens*) showed similar height in comparison to other functional groups in the mid-season, we used the height of *Brachypodium pinnatum, *the most abundant identifiable graminoid (*ca.* 34%, whereas the non-*Brachypodium* graminoids showed relative abundances &lt;18% at the mid-season), as the height of late-season non-*Brachypodium* graminoids. Orange cells corresponds to the ones where this correction was applied. 3. To test differences between season timings and treatments in CWM, use the R script "3_StatisticsCWM.R" and import CWM from the excel file "CWM.xslx". The Excel file "CWM.xlsx", with treatment coloured acording to the manuscript colors, contains the same variables as in point 2 (plot, treatment, season, block), and then the specific and fixed CWM for each of the five functional traits, coded as:  1. P_specific.avg and P_fixed.avg: leaf phosphorous content (mg/g) specific and fixed CWM respectively.  2. LDMC_specific.avg and LDMC_fixed.avg: Same for (leaf dry matter content, (mg/g) 3. Thick_specific.avg and Thick_fixed.avg: Same for Leaf thickness (mm) 4. SLA_specific.avg and SLA_fixed.avg: Same for Specific leaf area (mm2/mg) 5. log.height_specific.avg and log.height_fixed.avg: Same for Plant height (log mm). Note that this variable was log transformed.  4. To build the PCA from Figure 3, use 4_PCASpecies.R that loads: traits.RData, abundance_data.RData and fg_summary.RData, it uses also the excel "DifferencesCommunities.xslx" which contains the differences in relative abundance between control and drought plots. 1. In "DifferencesCommunities.xslx" we provide a list of species and their dissimilarity between drought and control plots (diss), their relative abundance in control plots (RelAbC), their relative abundance in drought plots (RelAbD) and the absolute difference between those (reldif) as RelAbC- RelAbD. The coloured cells in columns diss and reldif correspond to the conditional formating using a green to red colour scale, that helps visualizing the highest and lowest values in each column.  2. In " fg_summary.RData" we include the summary of the presented trait values for each functional group (needed for September graminoids). Find more details of this within the manuscript.  5. To contrast species level traits, use "5_ConfIntPerSpecies.R" and load "Species level traits.RData" and traits.RData described previously. The script calculates confidence intervals for each species mean functional traits. 6. To calculate functional diversity, we used the fundiversity R package from Grenie and Gruison 2022 Ecography, and followed the tutorial: [https://cran.r-project.org/web/packages/fundiversity/vignettes/fundiversity.html](https://cran.r-project.org/web/packages/fundiversity/vignettes/fundiversity.html). Import mean trait data per species found in the Excel file "SpeciesMeanforFD.xsls" and use the script "Appendix_FunctionalDiversity.R". The excel file "SpeciesMeanforFD.xsls" contains: 1. Season: July, September 2. Species: Species scientific name, using underscore instead of empty spaces between words. 3. The five studied functional traits values for the previously described variables: Height (mm), Thickness (mm), SLA (Specific leaf area, mm2/mg), LDMC (leaf dry matter content, mg/g), Phos (leaf phosphorous content mg/g). ## Sharing/Access information If you have any issues with the data please contact: [erola.fenollosaromani@biology.ox.ac.uk](mailto:erola.fenollosaromani@biology.ox.ac.uk) or [erola.fenollosa@gmail.com](mailto:erola.fenollosa@gmail.com) ## Code/Software We have provided the main R code file used to process this data for the manuscript. We used R version 4.2.1 and RStudio.