DOI: 10.5061/dryad.15dv41p96
title: Data from: Soil nutrient availability rather than spatial nutrient heterogeneity shapes the intraspecific response of root architectural, morphological, and mycorrhizal traits in <em>Vaccinium myrtillus</em>

description: Although rarely assessed, small-scale heterogeneity in soil nutrient
 availability is suggested to be an important factor determining
 intraspecific variation in belowground plant resource acquisition
 strategies. We tested how increased resource availability and its
 small-scale (0.25 m2) spatial heterogeneity affect the nutrient
 acquisition strategy of Vaccinium myrtillus, a dominant ericoid shrub
 species in the forest understory, by quantifying intraspecific root trait
 variation in an extensive set of root traits associated with nutrient
 acquisition. We hypothesized that increased nutrient availability would
 constrain the expression of traits that enhance nutrient acquisition,
 whereas spatial nutrient heterogeneity would promote a diversity of root
 trait values. Over three years, we applied spatially homogeneous and
 heterogeneous fertilization treatments (subplots of 6.25 m2 in ten sites
 of 1 ha) in a temperate forest in the Southern Black Forest (Germany).
 After three years, rooting units of Vaccinium myrtillus were sampled, and
 nine traits representing complementary aspects of nutrient uptake
 strategies were quantified, including aspects of root morphology,
 architecture, and mycorrhizal association. Increased nutrient availability
 induced intraspecific trait adjustments: specific root length and
 mycorrhizal colonization were reduced, whereas root diameter, tissue
 density, and branching intensity increased, suggesting a shift in plant
 investment strategies for nutrient acquisition. Heterogeneous nutrient
 availability did not increase trait variability, except for root
 phosphatase activity, indicating a more variable investment in phosphate
 extraction from organic matter. Overall, intraspecific trait variation was
 structured along the two main axes proposed by the interspecific root
 economics space. However, when additional traits tightly linked to
 nutrient acquisition were included, these formed independent axes of
 variation, suggesting that the root economics space only partially
 represents intraspecific adjustments relevant to soil resource
 acquisition. This study shows that intraspecific root traits respond to
 small-scale changes in nutrient availability and are constrained along
 similar axes as global interspecific variation, extending the validity of
 the root economics space to the intraspecific level. However, in support
 to recent claims at the interspecific level, traits strongly conceptually
 tied to resource acquisition deviate from the root economics space,
 emphasizing the need to further explore the multiple ecological dimensions
 of plant nutrient acquisition.
description: ## Metadata – Soil nutrient availability rather than spatial nutrient
 heterogeneity shapes the intraspecific response of root architectural,
 morphological, and mycorrhizal traits in *Vaccinium myrtillus* This
 submission includes one primary dataset associated with the manuscript: *
 **File:** `dataset_root_traits_Vmyrtillus.csv` * **Description:** Root
 traits for all samples collected for the submitted manuscript *
 **Dimensions:** 179 rows × 19 columns --- ## Dataset Structure ### Rows
 Each row represents a root sample of *Vaccinium myrtillus* collected in
 the field according to the methods described in the manuscript. ###
 Columns * **row_num**: row number from 1 to 179 #### Plot Information *
 **Plot**: Identifier for one of the ten plots (`CFB036`, `CFB053`,
 `CFB102`, `CFB110`, `CFB124`, `CFB137`, `CFB140`, `CFB162`, `CFB164`,
 `CFB173`) * **Subplot**: Numbered 1 to 6; each plot has 3 subplots (not
 fixed – based on *Vaccinium* cover) * **Sample number**: Sample
 identifiers from 1–180, with some additional samples (e.g., `22b`, `24b`,
 `24c`) and four samples were missing (23, 24, 87, and 73). #### Treatment
 Variables * **treatment_patch**: Patch-level treatments (`Con`, `Ca`, `K`,
 `N`, `P`) in heterogeneous subplots; `NA` for control or homogeneous
 subplots * **treatment_subplot**: Subplot-level treatment category
 (`heterogen`, `control`, `homogen`) --- ## Trait Categories ###
 Morphological Traits * **Average.diameter** *(mm)* – Mean root diameter
 based on root scans * **Median.diameter**, **Maximum.diameter** *(mm)* –
 Derived from root scan measurements * **Dry.weight** *(mg)* – Dry weight
 of root samples * **Specific.root.length** *(mm/mg)* – Total root length
 divided by dry mass * **Root.tissue.density** *(g/cm³)* – Dry weight over
 root volume **Note on NA**: Dry mass could not be determined for two
 samples (sample number 138 and 24c) due to loss during the weighing
 procedure. Consequently, traits requiring dry mass—specific root length
 (SRL) and root tissue density (RTD)—are unavailable for these samples and
 recorded as NA. ### Mycorrhizal Traits * **Hypha.occurrence** *(0–1)* –
 Proportion of root intersections colonized by intracellular hyphae *
 **Coiling.occurrence** *(0–1)* – Proportion colonized by ericoid coils *
 **DSE.occurrence** *(0–1)* – Proportion colonized by dark septate
 endophytes (DSE) - **ErM.occurrence** *(0–1)* – Combined presence of
 coils/hyphae **Note on NA**: Concerning mycorrhizal traits, three samples
 (sample numbers 20, 43, 179) were not measured due to a loss during the
 staining process. These are recorded as NA.  * **Hyphal.length** *(m/g)* –
 Length of extraradical hyphae per gram of dry soil Note on NA: For Hyphal
 length, only half of the samples were measured due to time constrain
 (except CFB137 which was fully recorded). For all plots, the samples with
 most material collected were selected per subplot and patch (2 samples in
 control, 2 in homogeneous and 5 in heterogeneous).  ### Physiological
 Trait * **Phosphatase.activity** *(µmol mg⁻¹ h⁻¹)* – Enzymatic activity
 based on 4-MUB-phosphate cleavage.\ **Note**: Results should be reported
 as *arbitrary units (a.u.)* rather than in µmol mg⁻¹ h⁻¹, as the data are
 not suitable for comparisons among species or locations. However, they are
 valid for comparing treatments within sites. **Note on NA**: Two samples
 could not be measured during the process due to a labelling issue (Sample
 numbers 75 and 96), these are reported as NA in the dataset.  ### Chemical
 Trait * **RNC (Root Nitrogen Concentration)** *(mg/g)* – Pooled per
 subplot/patch (70 values total), assigned to the first sample in each
 pooled group\ Averaged values used for multivariate trait analyses **Note
 on NA:** To ensure a sufficient amount of material (minimum 4 mg), the
 samples were pooled within subplots for homogeneous and control subplots
 (resulting in one sample per control subplot and one sample per
 homogeneous subplot) and pooled per patch within the heterogeneous subplot
 (resulting in five samples per heterogeneous subplot). A total of 70
 samples were analyzed for RNC. **Data layout note:** The dataset may list
 multiple rows per subplot (control/homogeneous) or per patch
 (heterogeneous). Within each such group, the first row carries the pooled
 RNC value that represents that row and all subsequent rows in the same
 group. All subsequent rows in that group have RNC recorded as NA (i.e.,
 they do not contain independent RNC measurements). Thus: *
 Control/homogeneous: the first row for a given *subplot* holds the pooled
 RNC; following rows with the same *subplot* have RNC = NA. -
 Heterogeneous: the first row for a given *subplot + treatment_patch* holds
 the pooled RNC; following rows with the same *subplot + treatment_patch*
 have RNC = NA. For the multivariate analysis including all traits (PCA),
 only heterogeneous subplots were used. To align trait resolution with the
 RNC measurements (patch level), all other traits were averaged to the
 patch level prior to analysis. ### Architectural Trait *
 **Branching.Intensity** *(cm⁻¹)* – Number of first-order root tips per
 length of second-order root **Notes on NA**: This method requires very
 clear scans, for this a selection of samples were analyzed for each
 treatment at the subplot and patch level. 49 samples were not used for
 this analysis and are recorded NA.  ## Structure - R codes analysis The
 code use to perform the analysis and the figures is presented in the
 following different scripts: ### 0_preparation_datasets_for_analysis.Rmd *
 This code prepares the following datasets : * dataset_all_root_2024.Rdata
 (see attached README text file with all columns and units) *
 cv_trait_all.Rdata * dataset_all_root_2024_grouped.Rdata *
 Groot_dataset.Rdata ### 1_a_lmer_models_Figure_2.Rmd * Test the effect of
 the fertilization treatments (control, homogeneous and heterogeneous) on
 all traits. * The models used are lmer models : traits~ treatment_subplot
 + (1|Plot) * The statistics will be used for the significance level in
 Figure 2 * Create table S4 and plots found in Annex Figure S4-6 ###
 1_b_response_ratio_Figure_2.Rmd * Calculate log10 response ratio for all
 traits (homogeneous over control and heterogeneous over control) * Figure
 2 - with the response ratio for all traits and both treatments with the
 statistical significance calculated based on the models found in :
 1_b_response_ratio_Figure_2 ### 2_a_lmer_models_Figure_3.Rmd * Test the
 effect of the treatments (heterogeneous versus homogeneous) on trait
 variation (CV). * CV per treatment and per plot was calculated for all
 traits in : 0_preparation_datasets_for_analysis * In particular, this
 script aims to : * extract the model estimates and significance to be used
 on Figure 3 in the article. * extract CV variation per trait for Table S5
 and CV over all traits- table S6 ### 2_b_response_ratio_Figure_3.Rmd *
 Calculate log10 response ratio for CV of all traits (CV heterogeneous over
 CV homogeneous) * Figure 3 - with the response ratio for all traits with
 the statistical significance calculated based on the models found in :
 2_b_response_ratio_Figure_3 ### 3_PCA_analysis_Correlation_Figure_4.Rmd *
 Create a correlation table - Table S6 * PCA analysis with only RES root
 traits : Figure 4A * PCA analysis with all traits measured - 1_2 axis :
 Figure 4B * PCA analysis with all traits measured - 1_3 axis : Figure 9A *
 PCA analysis with data at the interspecific level from - Figure 9B : This
 last script uses data from GRoot : Guerrero-Ramirez N, Mommer L, Freschet
 GT, Iversen CM, McCormack ML, Kattge J, Poorter H, van der Plas F,
 Bergmann J, Kuyper TW, York LM, Bruelheide H, Laughlin DC, Meier IC,
 Roumet C, Semchenko M, Sweeney CJ, van Ruijven J, Valverde-Barrantes OJ,
 Aubin I, Catford JA, Manning P, Martin A, Milla R, Minden V, Pausas JG,
 Smith SW, Soudzilovskaia NA, Ammer C, Butterfield B, Craine J, Cornelissen
 JHC, de Vries FT, Isaac ME, Kramer K, König C, Lamb EG, Onipchenko VG,
 Peñuelas J, Reich PB, Rillig MC, Sack L, Shipley B, Tedersoo L, Valladares
 F, van Bodegom P, Weigelt P, Wright JP, Weigelt A. (2021).\ *Global Root
 Traits (GRooT) Database.* *Global Ecology and Biogeography*, 30(1),
 25–37.\
 [https://doi.org/10.1111/geb.13179](https://doi.org/10.1111/geb.13179)
 This data is not included in the dataset published here and to replicate
 the code, users need to download the GRoot dataset from the related
 repository and cite accordingly
 : [https://groot-database.github.io/GRooT/](https://groot-database.github.io/GRooT/)