DOI: 10.5061/dryad.2z34tmpv1
title: Data from: Measuring leaf and root functional traits uncovers multidimensionality of plant responses to arbuscular mycorrhizal fungi

description: Premise of the study While many studies have measured the aboveground
 responses of plants to mycorrhizal fungi at a single time point, little is
 known about how plants respond belowground or across time to mycorrhizal
 symbiosis. By measuring belowground responses as well as growth over time
 in many plant species, we create a more complete picture of how
 mycorrhizal fungi benefit their hosts. Methods We grew 26 prairie plant
 species with and without mycorrhizal fungi and measured fourteen
 functional traits measuring above and belowground tissue quality and
 quantity responses and changes in resource allocation. We used
 function-value trait (FVT) modeling to characterize changes in species
 growth rate when colonized. Key results While aboveground biomass
 responses were positive, the response of traits belowground were much more
 variable. Changes in aboveground biomass accounted for 60.8% of the
 variation in mycorrhizal responses, supporting the use of aboveground
 biomass response as the primary response trait. Responses belowground were
 not associated with aboveground responses and accounted for 18.3% of the
 variation. Growth responses over time were highly variable across species.
 Interestingly, none of the measured responses were phylogenetically
 conserved. Conclusions Mycorrhizal fungi increase plant growth in most
 scenarios, but the effects of these fungi belowground and across time are
 more complicated. This study highlights how differences in plant
 allocation priorities might affect how they utilize the benefits from
 mycorrhizal fungi. Identifying and characterizing these differences is a
 key step to understanding the effects of mycorrhizal mutualisms on whole
 plant physiology. 
description: <strong>Source material and
 planting</strong> To understand the effects of
 mycorrhizal fungi on plant traits across the plant kingdom, we inoculated
 26 prairie plant species (Table 1) with a mix of AM fungi containing
 <em>Claroideoglomus claroideum</em> (=<em> Glomus
 claroideum</em>), <em>Funneliformus mosseae</em> (=
 <em>Glomus mosseae</em>), <em>Cetraspora
 pellucida</em>, <em>Claroideoglomus lamellosum, Acaulospora
 spinosa, Racocetra fulgida</em> and <em>Entrophospora
 infrequens </em>(MycoBloom LLC, Lawrence, Kansas, USA). The plant
 species we chose for this experiment represent most of the functional and
 phylogenetic diversity present in the Midwest prairie ecosystem. The
 mycorrhizal inoculum was initially cultured from Midwest prairie soils and
 contains species that provide diverse benefits to hosts (Koziol and Bever,
 2016). Prior to inoculation, we performed spore extractions on the
 inoculum to confirm spore counts and viability. For
 each focal species, we scarified and cold stratified seeds for a
 month,then moved these seeds to the greenhouse for an additional month so
 that plants could germinate and grow robust enough to withstand
 transplantation into test pots. We filled each test pot with 1:1
 sterilized soil/sand medium and maintained soil moisture at consistent
 levels with drip irrigation. Right before transplanting seedlings, we
 added mycorrhizal inoculum to each inoculated treatment pot. We chose the
 healthiest seedlings from each species and randomly planted two seedlings
 in each test pot to account for potential mortality due to transplantation
 and after two weeks of growth, the smallest of the two seedlings was
 thinned. We used two different sets of plants to measure root and leaf
 traits. To collect root trait data, we grew plants for 4 weeks and
 harvested the plants before the root systems were too large to accurately
 measure root traits. Plants that were used for measuring leaf traits were
 grown for 8 weeks so that we could track differentiation in the growth
 over time for each species in control and inoculated soils. Data from both
 sets of plants were used to calculate trait responses to mycorrhizal fungi
 and for the principal component analysis. For each treatment group of each
 species, we had four replicate pots, for a total of 16 plants per
 species. <strong>Measuring plant functional
 traits</strong> We measured fourteen focal
 functional traits in this experiment to represent five types of plant
 tissue response to mycorrhizal fungi: 1) change in quantity of aboveground
 tissues, 2) change in quality of aboveground tissues, 3) change in
 quantity of belowground tissues, 4) change in quality of belowground
 tissues, and 5) change in proportion of biomass allocation to plant
 tissues. We measured dried aboveground biomass (AGB), height or longest
 leaf in species without stems (H), and leaf area (LA) to assess
 aboveground tissue quantity responses to mycorrhizal fungi. To assess
 aboveground tissue quality responses to mycorrhizal fungi, we measured
 specific leaf area (SLA; ​​leaf area/leaf dry mass), leaf dry matter
 content (LDMC; dry leaf mass/fresh leaf mass), and leaf lifespan (LL;
 calculated as the proportion of living leaves at final time point to total
 number of leaves throughout growing period). We used SLA and LDMC as
 predictors of species’ placement on the leaf economic spectrum (Wright et
 al. 2004). We used both SLA and LDMC because, while SLA was initially
 described as a key element of this spectrum, the trait varies across
 different nutrient and light environments. Leaf dry matter content depends
 less on environmental conditions, so it has also been proposed as a better
 predictor variable for this spectrum (Hodgson et al. 2011). By measuring
 LL, we were able to capture the degree of photosynthetic organ turnover,
 which is also associated with the leaf economic spectrum (Edwards et al.
 2014). To assess belowground tissue quantity response, we measured
 belowground dried biomass (BGB), root length (RL), root system volume
 (RV), and average root diameter (RD). These four metrics capture the
 biomass allocation belowground as well as the size of individual roots. We
 measured specific root length (SRL; total root length/root dried mass),
 root tissue density (RTD; root dry mass/root volume), and root dry matter
 content (RDMC; dry root mass/fresh root mass) to assess species
 belowground tissue quality responses. The root traits RTD and SRL
 represent the tradeoffs on two axes of the proposed root economic spectrum
 (Bergmann et al. 2020). We used RDMC as a directly analogous trait to
 aboveground tissue quality trait LDMC. Finally, we used RSR to assess the
 relative investment to roots and shoots in each plant (Qi et al. 2019).
 Throughout the growing period, we also measured plant height measurements
 (or longest leaf for plants without clearly defined stems) to track plant
 aboveground growth over time. To measure changes in
 root traits, we grew inoculated and non-inoculated seedlings with a 1:1
 sand-soil mix in 164 mL pots (cone-tainers, Stuewe and Sons, Tangent,
 Oregon, USA) for 4 weeks. The roots were placed on a 31×21cm water-filled
 tray and light scanned using an Expression 10000XL Pro scanner (Seiko
 Epson Corporation, Nagano, Japan). Root length and average root diameter
 was measured using WinRhizo software 2019a (Regent Instruments, Québec
 City, Quebec). We measured root volume by measuring water displacement in
 a graduated cylinder, which is more accurate for roots with heterogeneous
 diameter classes than measurements with WinRhizo (Rose, 2017). We oven
 dried and weighed the root and shoot tissues of these plants to calculate
 root to shoot ratio (RSR). We used the dried root biomass, total root
 length, and volume of the roots to calculate specific root length (SRL)
 and root tissue density (RTD). To assess aboveground traits responses to
 mycorrhizal fungi, inoculated and non-inoculated seedlings were grown for
 8 weeks in 656 mL pots (Deepots, Stuewe and Sons, Tangent, Oregon, USA).
 We weighed and scanned all leaves during harvest and used ImageJ (v. 1.53)
 to calculate the total leaf surface area for each plant. The aboveground
 biomass of the plants was oven-dried and weighed to measure dried biomass.
 We collected a subsample of roots from inoculated and non-inoculated
 plants and stained them to test the efficacy of the mycorrhizal
 inoculation treatments.  <strong>Data
 analysis</strong> We extracted phylogenetic
 relationships from a larger, published phylogeny of seed plants (Smith
 & Brown, 2018). This phylogeny was constructed using both genetic
 data from GenBank and phylogenetic data from the Open Tree of Life, in
 order to create an inclusive, dated phylogeny of seed plants. Some species
 in our data set were not represented in this phylogeny, so we chose a
 closely related species to represent these species instead. We used
 the <em>multi2di</em> function in the <em>ape</em>
 package in R (v. 5.7.1) to convert all polytomies in this tree to
 dichotomies (Paradis and Schliep, 2019). In order to
 understand how plant functional traits are affected by mycorrhizal fungi,
 we assessed the correlations between the traits for inoculated and
 non-inoculated plants separately. Mycorrhizal and non-mycorrhizal traits
 were log or square root transformed when necessary to reduce skew. To test
 whether our chosen functional traits exhibited phylogenetic signal across
 plant species, we used the <em>phylosig</em> function in the R
 package <em>phytools</em> (v. 1.5.1) to assess Pagel’s λ and
 Blomberg’s <em>K </em>(Revell, 2012). Because most of the
 traits exhibited no phylogenetic signal (Appendix S1), we chose to not use
 phylogenetic comparative methods in subsequent analyses. 
 To test the effect of mycorrhizal fungi on these plant traits, we
 created a separate set of variables that were calculated as the natural
 logarithm of a species trait mean when grown with mycorrhizal fungi
 divided by the mean when grown in sterile conditions. With these metrics,
 positive values indicate that the trait mean increases when the species is
 grown with mycorrhizal fungi, compared to sterile conditions. One goal of
 this study was to determine whether there were coordinated responses to
 mycorrhizal fungi, so we ran a standard PCA with the fourteen response
 traits. The PCA was based on a variance-covariance matrix, because the
 traits for this analysis were already transformed to equivalent scales
 (log-response ratios), meaning that plant functional traits that have
 larger degrees of change between mycorrhizal and non-mycorrhizal
 treatments are more strongly weighted in the PCA. Due to high plant
 mortality, primarily in the control treatments, there were unequal sample
 sizes across the fourteen traits measured. To account for this, we
 multiplied each value in the response trait dataframe by the square root
 of the smallest sample size used to calculate each trait. This method
 preserves the direction of response but allows for data points with more
 confidence to be weighted stronger in the analyses. Ultimately, the
 weighted PCA and unweighted PCA yielded similar results, so we present the
 results of the weighted PCA. We used either plant
 height or longest leaf in plants without stems to create function-value
 trait (FVT) models. In short, this method used time series data on plant
 growth to plot an average logistic growth curve for each species, and we
 calculated the response ratio of the average curve parameters to describe
 changes in growth over time. We dropped all individuals that did not fit
 logistic growth models from further analyses. We were able to fit logistic
 growth curves to 23 species when plants were grown with mycorrhizal fungi,
 compared to 13 species when grown in sterile conditions. For comparison of
 growth parameters, we could only include species that had logistic growth
 in both the inoculated and control groups. The species that were not
 included in this analysis were generally dropped because the growth of the
 control plants did not fit a logistic model due to lack of growth.We used
 the remaining species to calculate how response to mycorrhizal fungi
 changed over time by calculating maximum growth rate, time until growth
 curve inflection, and asymptote. Changes in growth rate indicate that
 mycorrhizal fungi change the rate at which the plant is able to create
 aboveground tissue. Decreases in inflection point when plants are
 colonized by mycorrhizal fungi indicate that plants reach their maximum
 growth rate earlier than when they were colonized by mycorrhizal fungi,
 whereas increases in inflection point indicate that plants are able to
 grow exponentially for a longer length of time. Changes in asymptote are
 directly analogous to changes in final size.
description: # Measuring leaf and root functional traits uncovers multidimensionality
 of plant responses to arbuscular mycorrhizal fungi
 [https://doi.org/10.5061/dryad.2z34tmpv1](https://doi.org/10.5061/dryad.2z34tmpv1) The data and code presented here contains all of the necessary information to complete analyses in the paper "Measuring leaf and root functional traits uncovers multidimensionality of plant responses to arbuscular mycorrhizal fungi" published in American Journal of Botany. The includes the following five parts: 1. Calculating Plant Functional Traits 2. Calculating Response Ratios 3. Calculating Phylogenetic Signal 4. Creating Trait PCAs 5. Conducting Function-Value Trait Analysis ## Description of the data and file structure The following code files are included in this submission: * *CalculatePFTs.R*: Calculates the plant traits used in this analysis, using the raw data file* RawData.csv*. This creates a data frame with only the focal traits called* RawData1.csv*, which is used in* ResponseRatios.R* * *ResponseRatios.R:* Calculates the response ratios of each focal trait, using the file *RawData1.csv*. This creates a new data frame with the response ratios named *TraitsMeans.csv.* It also creates *Response_boxplots.tiff*, which is Figure 1 in the paper. * *Phylosig.R:* Calculates Pagel’s λ and Blomberg’s *K * for each response ratio. Requires the files *species_list.csv*, *supertree.tre*, *TraitsMeans.csv*, and *my_ultra.tre*. * *PCA.R*: Creates traditional trait PCAs and weighted PCAs (see methods for more information) Requires *TraitsMeans.csv* and *weights.csv. * and creates the files *responsetrait_loadings.csv* and *PCA.tiff,* which is Figure 2 in the paper. * *FVT.R:* Conducts FVT analyses on change in height for each species and treatment (see methods for more information). Requires *Height.csv * and creates output folders *height_logmodel_plots* and *height-expomodel_plots,* and it saves this data to the dataframe *height_log.csv.* This data was used for Figure 3 in the paper. The following data files are included in this submission: * *RawData.csv*: .csv file containing all the traits collected for each individual plant. Contains the following columns (see method for additional information about measurements): * Species: Plant species name * ID: Plant species ID * Inoculation: Inoculation treatment—Mycorrhizal (M), Non-mycorrhizal (C) * Block: Five blocks (A,B,C,D,E) for replicates. Block A was dropped due to shading effects that made this block significantly different than the other 4. * Final_Leaves: Number of leaves at final harvest; counted from leaf scans * Final_Height: Height of stem (or longest leaf) at final harvest (cm) * Leaf_area: Leaf area at final harvest (cm^2); measured by scanning leaves before drying and calculating area using ImageJ * LFW: Biomass (g) of leaf tissue at harvest * SFW: Biomass (g) of stem tissue at harvest; NA for species without stems * RFW: Biomass (g) of root tissue at harvest * Subsample_RW: Fresh root weight taken from plants for root staining check; NA for species without subsample * RV: Root volume (mL^3) measured using water displacement method * Dead_Leaves: Total number of dead leaves by harvest; tracked throughout growth period and check using leaf scars and counts of dead leaves in pot * Sequencing_subset: Fresh root weight taken from plants for potential sequencing; NA for species without subsample * LDW: Dried Biomass (g) of leaf tissue * SDW: Dried Biomass (g) of shoot tissue * RDW: Dried Biomass (g) of root tissue * RootLength: Root length calculated using WinRhizo root scans (m) * RootDiam: Average root diameter calculated using WinRhizo root scans (mm) * RDW_adj: Root dry weight, after including Subsample_RW and Sequencing_subset fresh weight * *species_list.csv*: Contains the species names that were used in this analysis and the synonymic names needed for the tree data files * Species: Species names in experiment * New.names: Species names that are in tree. If the original species name was not in the tree, this value will be the closest related species available. * *supertree.tre*: The super tree file obtained from Smith & Brown, 2018 * *my_ultra.tre*: dichotomous tree file containing all species used in the analysis - *weights.csv*: File containing the number of replicates for each trait and species, which was used to create a weighted PCA based on certainty of each species trait measurement. * Species: plant species ID * Inoculation: inoculation treatment—Mycorrhizal (M), Non-mycorrhizal (C) * H: height of stem or longest leaf (cm) * LA: total leaf area at end of experiment (cm^2) * AGB: total aboveground biomass at the end of experiment (g) * LDMC: leaf dry matter content (g dry weight/g fresh weight) * SLA: specific leaf area (cm^2/g) * LL: leaf lifespan, measured as the number of living leaves/total leaves at the end of experiment * BGB: total belowground biomass at the end of experiment (g) * RL: root length calculated using WinRhizo root scans (m) * RV: root volume (mL^3) measured using water displacement method * RD: average root diameter calculated using WinRhizo root scans (mm) * RDMC: root dry matter content (g dry weight/g fresh weight) * SRL: specific root length (m/g) * RTD: root tissue density (g/mL^3) * RSR: root:shoot ratio * *Height.csv*: .csv file containing height measurements over time for all individuals used in this analysis * Species: species ID * Inoculation: inoculation treatment—Mycorrhizal (M), Non-mycorrhizal (C) * Block: species block (2-5) * ID: plant identifier (species+inoculation+block) * Height: height of stem or longest leaf (cm) * Days_Since_Transplant: number of days since transplant (where transplant=0)