DOI: 10.5061/dryad.p8cz8wb1j
title: Data from: Plant diversity loss has limited effects on belowground biomass and traits but alters community short-term root production in a species-rich grassland

description: Many biodiversity - ecosystem functioning (BEF) experiments have shown
 enhanced productivity with biodiversity, often explained by niche
 differentiation and complementarity effects. Yet, most of these results
 are based on artificial plant assemblages, with a major focus on
 aboveground productivity. Consequently, our comprehension of the BEF
 relationship in natural ecosystem and for belowground functions remains
 largely unknown. In this study, we simulated a long-term non-random
 species loss in a naturally diverse temperate meadow. We created a
 richness gradient spanning from 1 to more than 20 species per plot,
 removing the rare and sub-ordinate species from the vegetation.
  After 3 and 6 years of experimental manipulation, soil cores
 were collected to investigate the effects of species removal on
 belowground biomass, vertical distribution of roots, and root traits. We
 also used ingrowth cores to quantify the short-term root production of
 plant communities. We investigated the consistency of below and
 aboveground responses to species removal. Consistent with aboveground
 responses, species removal had little effects on root biomass and
 community root traits, except for a decrease in biomass in the uppermost
 soil layer (0-5cm) found only after 6 years. Similarly, the vertical
 distribution of root biomass and traits was largely unaffected by
 diversity loss, suggesting little evidence for vertical niche
 differentiation. Nevertheless, species loss decreased the root production
 in spring (3 months), albeit this lower short-term root production was
 diluted after a year, when monocultures of dominants almost matched the
 annual root production of controls. Synthesis: Our results from a
 non-random species removal experiment revealed minimal impact of a
 realistic diversity loss on root biomass and traits, but a decrease in
 short-term fine root production. These results challenge traditional BEF
 theory and the niche differentiation hypothesis, suggesting that dominant
 species primarily determine belowground biomass structure and
 characteristics. High diversity may only temporarily increase short-term
 root production.
description: <strong>Study site</strong> The
 study was conducted in a wet oligotrophic meadow located near České
 Budějovice, Czech Republic (48°57’10.8” N 14°35’34.8” E, 510 m a. s. l.).
 The site has a mean annual temperature of 8.3°C and a mean annual
 precipitation around 700 mm (Czech Hydrometeorological Institute,
 1992-2022). It is a particularly species-rich meadow with an average
 species richness of <em>c. </em>29 species per 0.25
 m<sup>2 </sup>(Lisner et al., 2023). The most dominant species
 is <em>Molinia caerulea </em>followed by <em>Danthonia
 decumbens</em>, and <em>Betonica officinalis.</em> A
 complete list of species can be found in the data repository attached to
 Lisner et al. (2024). Traditionally, the meadow has been extensively
 managed with a single annual mowing, at the peak of the vegetation season,
 in mid-June. No special permits were required to conduct the experiment or
 collect the data. <strong>Experimental
 design</strong> The manipulative experiment was
 established in 2016 and is described in detail in Lisner et al. (2023). A
 gradient of diversity loss was created by manually removing plant species
 based on their relative abundance in 30 1 ´ 1 m plots. Before the
 manipulation, the initial relative abundances of all species were
 determined in each plot by harvesting the aboveground biomass,
 simultaneously to the annual mowing, and hand-sorting each species
 biomass. Four levels of diversity loss were created where only the most
 abundant 1, 3, 6, and 12 species were kept (see Table S1 for targeted
 species composition). Additionally, we used two control treatments
 (naturally containing <em>ca. </em>26 species): the intact
 vegetation (C) and a disturbance control (D) to quantify the confounding
 effect of the disturbance caused by the experimental weeding. In the
 intact plots, no species was removed, while in the disturbance plots, we
 randomly removed several individuals, aiming for an amount of biomass
 equivalent to the average biomass removed in removal treatments, but
 irrespective of the species identity. These 6 treatments were replicated
 five times in Latin-square-like design resulting in 30 plots (5 times x 6
 treatments) (see Figure S1 for more details on the design). The plots were
 maintained twice a year - once in spring and once in late summer - by
 removing resprouting individuals, creeping stems from the surrounding
 vegetation, and new seedlings. After one year (<em>i.e.,</em>
 in spring 2017), the targeted species colonized most of the free space
 induced by the weeding. Consequently, the amount of biomass removed in
 each plot decreased significantly and contained only a small proportion of
 plants resprouting from belowground organs. More information on the target
 species richness, effective species richness, dominance, and removed
 biomass for each year can be found in Figure S2.
 <strong>Data collection: standing
 roots</strong> In 2019 and 2022, one soil core of
 5 cm in diameter and 40 cm deep was collected in each plot. The sampling
 was conducted at the peak of the vegetation season, in mid-June. The
 samples were collected from at least 20 cm away from the edge of the plot
 to maximally reduce the presence of roots from plants growing in the
 surrounding plots and nearby vegetation. Each core was divided into five
 sections based on layer depth: 0-5; 5-10; 10-20; 20-30; and 30-40 cm
 (Figure 1) and stored in the freezer before further processing. During the
 following months, each layer was washed over a sieve of mesh size 0.5 mm
 with water, and plant biomass excluding litter was manually sorted into
 roots and other belowground organs such as bulbs and rhizomes. Few roots
 of trees growing near the experiment could be identified and were excluded
 from the samples. All samples were oven-dried at 60ºC for 48 hours, and
 the dry weight was recorded. The same years, aboveground biomass was
 harvested at the peak of the vegetation season, in the inner 0.5 x 0.5 m
 area of each plot. The biomass was clipped close to the ground and sorted
 into species in the lab. Each species’ dry mass was weighed after being
 oven-dried at 80°C for 48 hours. For more detail, see Lisner et al.
 (2023). <strong>Data collection: ingrowth
 cores</strong> In 2022, we used ingrowth cores to
 estimate the short-term fine root production. The ingrowth cores consisted
 of plastic mesh cylinders of 3 cm in diameter, 15 cm in height, and 5 mm
 in mesh size. One ingrowth core was established in each plot to measure
 the biomass of fine roots produced in spring (from 17 March to 21 June),
 while a second one was employed to measure the root production throughout
 the entire year (from 6 December 2021 to 4 January 2023). Following the
 drilling of soil cores of appropriate size, both ingrowth cores were
 installed on opposite sides of the 0.5 x 0.5 m inner subplot (Figure 1)
 and filled with root-free fresh soil. The soil was collected from the same
 site near the experiment in autumn 2021. It underwent thorough sieving
 through a 5 mm mesh to remove gravels, stones, and unwanted plant
 material. It was then homogenized and oven-dried at 40°C for several days.
 Ingrowth cores were harvested by cutting all roots around the mesh with a
 knife before pulling the cylinder out of the ground. Post-harvest,
 ingrowth cores were washed with water, and fine roots were
 hand-sorted. <strong>Data collection: root
 traits</strong> Roots from each soil layer and
 from each ingrowth core were scanned using a flatbed scanner (Epson
 Perfection 4990 Photo) at a resolution of 600 dpi. We selected a
 representative subsample of roots from each sample (keeping a similar
 proportion of root types and diameters visually) that filled the scanned
 area without overlap. Subsamples ranged from approximately 14% to 100%
 depending on the sample size. The scanned images underwent analysis to
 measure the root length in 20 diameter classes of 0.1 mm, spanning from 0
 to 2 mm, employing RhizoVision Explorer software v2.0.3 (Seethepalli and
 York, 2020) using algorithms described by Seethepalli et al. (2021). These
 data were utilized to calculate three community root traits for every
 layer and every ingrowth core: the community specific root length (the
 total root length per unit dry mass) (as described in Freschet et al.,
 (2021)), community mean root diameter (the mean root diameter calculated
 from the root diameter distribution), and community root diameter
 variability (the variance in root diameter calculated from the root
 diameter distribution). <strong>Data
 analysis</strong> The standing root biomass
 collected in each core was expressed per ground area in
 g·m<sup>-2</sup>. When analysing the vertical distribution of
 roots, the root biomass collected in each layer was expressed per unit
 soil volume in mg·cm<sup>-3</sup>. The same was applied to the
 biomass data collected in each ingrowth core. All biomass data,
 aboveground and belowground, were log-transformed to avoid
 heteroscedasticity. We used the effective number of
 species (diversity hereafter) as a plant diversity index. It was
 calculated for each plot and each year as the reciprocal of Simpson’s
 diversity index, as: Diversity =
 1/(sum(p<sub>i</sub><sup>2</sup>) where <em>p<sub>i</sub> </em>is<em> </em>the<em> </em>relative abundance of species <em>i</em><em>, </em>calculated<em> </em>from the aboveground biomass of all species at the peak of the vegetation season, in mid-June. This index was preferred over the targeted species richness as it better captures variations in species abundances in each plot while adequately reflecting the experimental treatments (correlation between target species richness and effective number of species: <em>r</em><sub>2019 </sub>= 0.81; <em>r</em><sub>2022 </sub>= 0.90<em>; </em>Figure S2b). Before conducting the main analyses, we tested whether the disturbance caused by the repeated species removal from the plots (removed biomass bi-annually; Figure S2.d) had an impact on the different measures. We compared disturbance controls with intact vegetation controls using ANOVA models for each variable separately (total aboveground biomass (log scale), total belowground biomass (log scale), root to shoot ratio, root density in each layer (log scale), root density in ingrowth cores after 3 and 12 months (log scale), community specific root length (log scale), community mean root diameter (log scale), community root diameter variability in standing root cores and ingrowth cores). All tests showed no significant differences between the two types of controls (Figure S3 and S4), indicating that the disturbance introduced by the experimental species removal had negligible effects. Therefore, in subsequent analyses, only the intact vegetation controls were kept. Data analysis was conducted using Linear Mixed-Effects Models (LMM) in software R (ver. 4.3.0) with the <em>lme4</em> package (v1.1-26; Bates et al., 2015), and p-values were obtained with Satterthwaite's method with the <em>lmerTest</em> package (Kuznetsova et al., 2017). To test the effect of diversity on the total standing root biomass, a first LMM was fitted with diversity as the only predictor. The column and row identifiers of each plot were used as random factors to account for environmental gradients. A second LMM was fitted on the root biomass per layer with the same variables but including the interaction with soil layer, as a 5-level factor, to represent the root biomass vertical distribution. Since layers were collected from the same cores, the plot identity was included as a random factor in this model. To deepen our understanding of the effects on the different layers, individual LMM were fitted for each layer separately. Same method was used to test the effect of diversity on short-term root production in ingrowth cores, fitting an LMM with diversity and its interaction with time, as a 2-level factor (3 months and 12 months). As the interaction was not significant, it was eliminated from the final model. R code necessary for conducting the analysis can be found in Supporting Information.
description: # Data from: Plant diversity loss has limited effects on belowground
 biomass and traits but alters community short-term root production in a
 species-rich grassland
 [https://doi.org/10.5061/dryad.p8cz8wb1j](https://doi.org/10.5061/dryad.p8cz8wb1j) ## Experiment description This dataset stems from a long-term manipulative experiment conducted in a species-rich, wet oligotrophic meadow near České Budějovice, Czech Republic (48°57’10.8” N, 14°35’34.8” E; 510 m a.s.l.). Established in 2016, the experiment aimed to investigate the effects of realistic biodiversity loss on ecosystem stability. A gradient of diversity loss was created in 30 1 × 1 m plots by systematically removing plant species based on their relative abundance, resulting in four levels of reduced diversity (1, 3, 6, and 12 species remaining), alongside two control treatments: intact vegetation and a disturbance control. Data include measurements of aboveground biomass, collected during peak vegetation seasons in 2019 and 2022, and standing root biomass of the same years, sampled in five soil layers (0–5, 5–10, 10–20, 20–30, 30–40 cm) using soil cores. Short-term root production was assessed in 2022 using ingrowth cores installed for 3 and 12 months. Root traits, including community specific root length, mean root diameter, and diameter variability, were calculated using high-resolution scanned images of fine roots analyzed with RhizoVision Explorer software. The dataset provides comprehensive insights into root and aboveground biomass dynamics under realistic biodiversity loss conditions. ## Dataset description The data are divided into 4 datasets resulting for the combination of 2 core type: whole cores and ingrowth cores; and 2 data types: biomass and root traits. All datasets have the information of the Plot number (from 1 to 30), the column and row identification of the latin-square design, and the diversity treatment (1,3,6,12 species, intact control and disturbance control) and the effective number of species as the diversity index used.  ## Files and variables description #### File: ingrowth\_cores\_biomass.csv **Description:** biomass data collected in ingrowth cores after 3 and 12 months in 2022. ##### Variables * plot: plot number, ranging from 1 to 30. * treatment: diversity treatment group, with levels 1,3,6,12 species, intact control (C), and disturbance control (D). * colID: column identification in the Latin-square design  * rowID: row identification in the Latin-square design * time: Duration (in months) since the placement of the ingrowth core (3 or 12 months). * ens_simpson: Effective number of species based on Simpson's diversity index. This was the selected diversity index as described in the Methods section of the manuscript. * total_root_biomass: root biomass (g) collected from that ingrowth core. #### File: ingrowth\_cores\_traits.csv **Description:** traits data collected in ingrowth cores after 3 and 12 months in 2022. ##### Variables * plot: plot number, ranging from 1 to 30. * treatment: diversity treatment group, with levels 1,3,6,12 species, intact control (C), and disturbance control (D). * colID: column identification in the Latin-square design  * rowID: row identification in the Latin-square design * time: Duration (in months) since the placement of the ingrowth core (3 or 12 months). * ens_simpson: Effective number of species based on Simpson's diversity index. This was the selected diversity index as described in the Methods section of the manuscript. * TRL: Total Root Length (cm) of the scanned sample, calculated as the sum of root lengths across all diameter categories. * sample_TRL: estimated TRL (cm) of the entire core sample. Calculated as the TRL of the scanned sample multiplied by the proportion of the root biomass represented by the scanned sample. * SRL: Specific Root Length (m/g), calculated as the TRL (m) divided by the scanned root biomass (g). * CMD: Community Root Mean Diameter, weighted mean diameter of roots based on diameter class proportions. Calculated as: `sum(log(diamclass)*prop[i])`, being `diamclass` the diameter class and `prop` is the proportion of weight in that diameter class. * CDV: Community Root Diameter Variability, weighted variability of root diameters based on diameter class proportions. Calculated as: `sum(((log(diamclass)-CMD[i])^2)*prop[i])` being `diamclass` the diameter class,`prop` the proportion of weight in that diameter and `CMD` the CMD of that sample.  #### File: whole\_cores\_biomass.csv **Description:** standing root biomass data collected in soil cores divided in soil layers in 2019 and 2022. ##### Variables * plot: plot number, ranging from 1 to 30. * year: year of sampling (2019 or 2022). * treatment: diversity treatment group, with levels 1,3,6,12 species, intact control (C), and disturbance control (D). * colID: column identification in the Latin-square design  * rowID: row identification in the Latin-square design * ens_simpson: Effective number of species based on Simpson's diversity index. This was the selected diversity index as described in the Methods section of the manuscript. * total_aboveground_biomass: aboveground biomass sampled as g/m2. * total_belowground_biomass: total belowground biomass sampled as g/m2 * root_shoot_ratio: ratio between root biomass and aboveground (shoot) biomass. * layer_0-5: root biomass density as g/m3 in the 0 to 5 cm layer. * layer_5-10: root biomass density as g/m3 in the 5 to 10 cm layer. * layer_10-20: root biomass density as g/m3 in the 10 to 20 cm layer. * layer_20-30: root biomass density as g/m3 in the 20 to 30 cm layer. * layer_30-40: root biomass density as g/m3 in the 30 to 40 cm layer. #### File: whole\_cores\_traits.csv **Description:** root traits data collected in soil cores divided in soil layers in 2019 and 2022. ##### Variables * plot: plot number, ranging from 1 to 30. * treatment: diversity treatment group, with levels 1,3,6,12 species, intact control (C), and disturbance control (D). * colID: column identification in the Latin-square design  * rowID: row identification in the Latin-square design. * layer: soil layer (0-5,5-10,10-20,20-30,30-40 cm) from which the scanned sample was taken. * ens_simpson: Effective number of species based on Simpson's diversity index. This was the selected diversity index as described in the Methods section of the manuscript. * TRL: Total Root Length (cm) of the scanned sample, calculated as the sum of root lengths across all diameter categories. * sample_TRL: estimated TRL (cm) of the entire core sample. Calculated as the TRL of the scanned sample multiplied by the proportion of the root biomass represented by the scanned sample. * SRL: Specific Root Length (m/g), calculated as the TRL (m) divided by the scanned root biomass (g). * CMD: Community Root Mean Diameter, weighted mean diameter of roots based on diameter class proportions. Calculated as: `sum(log(diamclass)*prop[i])`, being `diamclass` the diameter class and `prop` is the proportion of weight in that diameter class. * CDV: Community Root Diameter Variability, weighted variability of root diameters based on diameter class proportions. Calculated as: `sum(((log(diamclass)-CMD[i])^2)*prop[i])` being `diamclass` the diameter class,`prop` the proportion of weight in that diameter and `CMD` the CMD of that sample.   ## Code/software All necessary code to run the analysis described in the Methods section and the Results presented is attached in the RMarkdown file: S2 - R Code.Rmd ## Access information Detailed aboveground species composition and abundance data: * [https://datadryad.org/stash/dataset/doi:10.5061/dryad.rxwdbrvj8](https://datadryad.org/stash/dataset/doi:10.5061/dryad.rxwdbrvj8).