DOI: 10.5061/dryad.95x69p8rx
title: Data associated with: Drought response of trees: Differences across Mycorrhizal type at the global scale

description: The association with mycorrhizal fungi, predominantly arbuscular
 mycorrhizal (AM) fungi or ectomycorrhizal (EM) fungi, is a critical
 strategy for trees to cope with drought stress, one of the most pervasive
 stressors affecting forest dynamics. Although experimental evidence
 suggests varying drought responses among trees with different mycorrhizal
 associations, a quantitative large-scale synthesis is still lacking. In
 this study, we compiled global datasets encompassing three types of
 drought responses (drought-induced mortality, growth reduction by severe
 droughts, and growth recovery from severe droughts) and tested the
 differences in these drought responses between the two mycorrhizal types
 using spatial autoregressive models. To elucidate the significant
 variations in drought responses, we identified key influential climatic
 factors, edaphic (soil-related) factors, and species-specific traits using
 model selection based on Akaike information criterion. Globally, EM trees
 experienced slightly greater drought-induced mortality compared to AM
 trees but showed significantly less pronounced growth reduction and
 recovery following severe droughts. The drought responses of EM trees were
 more strongly influenced by environmental stresses and size-related traits
 than those of AM trees, with higher soil pH, greater climate seasonality,
 and lower plant height significantly increasing the drought responses of
 EM trees. Despite the above-mentioned apparent differences, drought
 responses were generally more drastic at warmer and drier sites for
 species producing heavier seeds regardless of mycorrhizal type.
description: <strong><em>Drought-response dataset
 compilation</em></strong> Global datasets
 of three aspects of tree drought responses were compiled: drought-induced
 mortality, growth reduction by severe drought and growth recovery from
 severe drought. In the current study, we defined
 drought from an ecological perspective. That is, droughts are the events
 in which the water supply from precipitation falls below plant water
 demand. Therefore, the standardized precipitation evapotranspiration index
 (SPEI) is an ideal drought metric for our study. SPEI is a site-specific
 drought indicator that involves both precipitation and plant potential
 evapotranspiration (Vicente-Serrano<em> et al.</em> 2010;
 Beguería<em> et al.</em> 2022). It has been widely used in
 ecological studies and has demonstrated superior capability to capture
 drought impact at the global scale than the conventional Palmer drought
 indices (PDIs). Additionally, SPEI has exhibited better performance in
 capturing ecosystems’ responses to summer droughts than the widely used
 standardized precipitation index (SPI) (Vicente-Serrano<em> et
 al.</em> 2012). Because mortality data and growth-response data were
 all collected based on calendar years (see details below), we defined a
 drought year as a calendar year with an SPEI smaller than 0, while a
 severe drought year was a calendar year with an SPEI smaller than -1.5 in
 accordance with previous literature (Haile<em> et al.</em>
 2020; Ma<em> et al.</em>2020; Beguería<em> et
 al.</em> 2022). Data on drought-induced mortality
 were mainly based on the global dataset compiled by Greenwood<em> et
 al.</em> (2017), which included the drought-induced annual mortality
 rate for 257 species from 28 studies across the world. Additionally, we
 included other drought-induced mortality data compiled for global studies
 (Anderegg<em> et al.</em> 2016; Caudullo & Barredo
 2019; Hammond<em> et al.</em> 2022). Because our goal was to
 explore the differences between AM and EM trees, we determined the
 mycorrhizal type of each species based on the FungalRoot Database
 (Soudzilovskaia et al., 2020). For the species that had multiple
 observation records, all possible mycorrhizal types were recorded and
 combined. When the mycorrhizal type was undetermined or unknown at the
 species level, we also referred to the recommended mycorrhizal status for
 plant genera (Soudzilovskaia<em> et al.</em> 2022). All
 species that were determined to be associated with neither AM or EM fungi
 or dual AM-EM species were removed from our dataset. Additionally, because
 drought intensity strongly covaried with tree mortality
 (Greenwood<em> et al.</em> 2017), we downloaded the SPEI from
 1901 to 2018 at a 12-month timescale from SPEIbase v.2.7 (https://spei.csic.es/spei_database) (Beguería<em> et al.</em> 2022) and extracted the SPEI of the drought years within the period that mortality data were collected (Michna & Woods 2020). We removed the mortality data that were collected during a period without a drought year (i.e., SPEI &lt; 0). As a result, we obtained a dataset of 490 drought-induced mortality entries from 43 studies for 359 species (i.e., 301 AM compared with 58 EM species), which were collected from 46 different locations across the world (Figure 1a,b). See the pipeline for compilation of mortality dataset in Figure S1. Data on growth reduction by severe drought and growth recovery from severe drought were calculated using ring width data and SPEI. We first determined the mycorrhizal type for each species listed in the International Tree-Ring Data Bank (ITRDB) (https://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets/tree-ring) and downloaded the total ring width of all AM and EM species that had been documented by 3 or more studies to ensure a considerable spatial coverage of each species. Similar to the mortality dataset, mycorrhizal type was determined by the FungalRoot Database (Soudzilovskaia<em> et al.</em> 2022). Because tree growth always tends to decline with age (Gower<em> et al.</em> 1996), all ring width series were detrended to remove nonclimatic signals before calculating growth responses to drought (Yang<em> et al.</em> 2017). Among all commonly used detrending methods, the two methods based on biological growth models (i.e., the modified negative exponential curve and the modified Hugershoff curve) were chosen because they do not remove much of the climate signal or require sufficiently large samples from the same study site (Fang<em> et al.</em> 2010). Ring width detrending was performed in R version 4.1.2 using the package “dplR” (Bunn 2008, 2010; Bunn<em> et al.</em> 2020; R Core Team 2020). Both methods produced similar results, but the negative exponential curve produced some invalid, negative ring widths and was thus excluded from further analyses. For each study site, growth responses to drought were calculated using the averaged detrended ring widths of all core samples. To quantify growth responses to severe droughts, we first identified the years when severe droughts occurred at each site, defined as years with a Standardized Precipitation Evapotranspiration Index (SPEI) less than -1.5. Similar to the mortality dataset, the SPEI data were downloaded at a 12-month timescale from SPEIbase v.2.7. Given that tree growth can vary significantly across years and there is often a lag between a drought event and subsequent growth responses (Greenwood<em> et al.</em> 2017), we compared the average ring widths before and after the drought events. Specifically, we compared the average ring widths of one, three, or five year(s) before the drought with those of the one-, three-, or five-year periods afterwards. We determined the time scale that produced the largest number of significant reductions in growth across the entire dataset. Similarly, after identifying the best time scale for growth reduction quantification, we assessed the recovery phase. We compared the averaged ring widths of one, three or five year(s) after the period of growth reduction with averaged ring widths during the period of reduction. Then, wedetermined the time scale that produced the largest number of significant recoveries for the whole dataset. Because of the potentially low frequency of severe droughts at some sites that may result in small sample sizes and nonnormal data distributions, the nonparametric Wilcoxon rank-sum test (also known as the Mann-Whitney U test) was conducted using the R package “rstatix” (Kassambara 2021). To account for different frequencies of severe droughts across sites, the effect sizes of the Wilcoxon test (ranging between 0 and 1) were used. According to our analyses, the best time scale for quantifying growth reduction was three years before drought versus the year of drought, while the best time scale for quantifying growth recovery was five years after drought versus the year of drought (Table S1). In other words, most tree individuals in our dataset exhibited significant growth reduction immediately after a severe drought, whereas most individuals exhibited significant growth recovery after a time lag of four to five years. As a result, we obtained a dataset of 2876 growth-response entries for 119 species (i.e., 34 AM compared with 85 EM species) from 2446 different locations across the world (Figure 1b,c). See the pipeline for compilation of growth-performance dataset in Figure S1. <strong><em>Environmental data collection</em></strong> For climate factors, we downloaded historical monthly temperature and precipitation between 1901 and 2022 from Climate Research Union (version 4.07) (https://crudata.uea.ac.uk/cru/data/hrg/index.htm#current) (Harris<em> et al.</em> 2020) and quantified the mean annual temperature (MAT) and mean annual precipitation (MAP) for each geographic location. In addition to mean values, variations in temperature and precipitation may also be important driving factors that function in a disturbance-like manner. Therefore, we further quantified temperature and precipitation seasonality by their annual standard deviations and averaged these standard deviations across years. For edaphic (soil-related) factors, we downloaded data of soil properties, including soil pH, total carbon content (TC), total nitrogen content (TN), and total phosphorous content (TP), from the Global Soil Dataset for Use in Earth System Models (GSDE) (Shangguan<em> et al.</em> 2014). The dataset included edaphic data of eight vertical layers to the depth of 2.3 m. We used the mean value across all eight layers to represent the reference status of each soil property at each study site. See the pipeline for compilation of climatic and edaphic dataset in Figure S1. <strong><em>Trait data collection</em></strong> According to previous studies, specific leaf area (Valladares & Sánchez-Gómez 2006; Greenwood<em> et al.</em> 2017), plant size (Bennett et al., 2015; Mcdowell & Allen, 2015), rooting depth (Irvine<em> et al.</em> 2002), stomatal conductance(Medrano<em> et al.</em> 2002; Zhu<em> et al.</em> 2018) and seed mass (Lazarus<em> et al.</em> 2018) are important predictors of tree drought responses. Thus, data on these traits were collected in addition to mycorrhizal type so that we could determine the key traits that drive the drought response patterns influenced by mycorrhizal type. For the 453 tree species included in our drought response datasets, we requested public data of specific leaf area (SLA), maximum plant height, rooting depth, stomatal conductance, and seed mass from the TRY trait database (request ID 21286) (Kattge<em> et al.</em> 2020) and obtained the median values for all available traits for each study species. If a species did not have any available data but belonged to a genus containing available data for more than five different species, we extrapolated the genus-median trait value and used it as a reference value for the focal species. As a result, we obtained trait values of SLA, maximum height, rooting depth, stomatal conductance, and seed mass for 397, 349, 345, 237 and 348 species, respectively. This resulted in a total of 214 and 2337 observations with available data for all study traits for the mortality and growth-response datasets, respectively. See the pipeline for trait dataset compilation in Figure S1.
description: # Data Associated with: Trees with different mycorrhizal types show
 differential drought responses at the global scale
 [https://doi.org/10.5061/dryad.95x69p8rx](https://doi.org/10.5061/dryad.95x69p8rx) This dataset includes five data sheets that were used for data analyses. The first data sheet is named as “Data S1-Species Mycorrhiza Type Data.xlsx”. The second data sheet is named as “Data S2-Mortality Dataset.xlsx”. The third data sheet is named as “Data S3-Growth Response Data.xlsx”. The fourth is “Data S4-Trait Values and References for 453 Study Species.xlsx”. The fifth is “Data S5-Climatic and Edaphic Data.xlsx”. ## Description of the data and file structure **(1) Data S1-Species Mycorrhiza Type Data.xlsx** This contains the mycorrhizal association statuses for the 453 species used in the current study, which was based on the FungalRoot Database (Soudzilovskaia et al., 2020). “NA” indicates that focal data or information is not available. ScientificName_WFO = standardized species scientific name based on the World Flora Online database FungalRoot_Species_Mycorrhizal_Type_Original = mycorrhizal association status based on the FungalRoot dataset FungalRoot_Species_Mycorrhizal_Type = cleaned mycorrhizal association status information at the species level FungalRoot_Genus_Recommended_Type = inference of mycorrhizal association status based on genus-level recommendation Mycorrhizal_Type = mycorrhizal association status combining species- and genus-level information Mycorrhizal_Type_Proximity = the proximity of mycorrhizal association status (species- vs. genus-level) **(2) Data S2-Mortality Dataset.xlsx** This contains the drought-induced mortality data used in the current study, which was mainly based on the global studies by Greenwood et al. (2017), Anderegg et al. (2016), Caudullo and Barredo (2019) and Hammond et al. (2022). “NA” indicates that focal data or information is not available. ID = observation ID OriginalName = species name in source literature ScientificName_WFO = standardized species scientific name based on the World Flora Online database ScientificName_Authorship = author of the corresponding scientific name Family = family Annual_Mortality = annual mortality rate induced by drought Mortality_data_reference = source of the mortality data Original_reference = source of the original data that was used to quantify annual mortality rate Data_year = the time period for data collection Drought_year = the year when the drought event occurred Drought_type = drought type Longitude = longitude of the site of data collection Latitude = latitude of the site of data collection SPEI_drought_year = the standardized precipitation evapotranspiration index (SPEI) of the drought year Location = the location for data collection Geo_Source = the source of location and coordination information **(3) Data S3-Growth Response Data.xlsx** This contains contains the drought-induced growth response data used in the current study, which was quantified from the total ring width data from the International Tree-Ring Data Bank (ITRDB) ([https://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets/tree-ring](https://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets/tree-ring)). “NA” indicates that focal data or information is not available. ID = observation ID OriginalName = species name in source literature ScientificName_WFO = standardized species scientific name based on the World Flora Online database ScientificName_Authorship = author of the corresponding scientific name Family = family Reduction = growth reduction by severe drought* Recovery = growth recovery from severe drought* Longitude = longitude of the site of data collection Latitude = latitude of the site of data collection Study_Name = source of the tree-ring data \* To quantify growth responses to severe droughts, we first identified the years when severe droughts occurred at each site, defined as years with a Standardized Precipitation Evapotranspiration Index (SPEI) less than -1.5. Then, we compared the average ring widths of one, three, or five year(s) before the drought with those of the one-, three-, or five-year periods afterwards. This determined the time scale that produced the largest number of significant reductions in growth across the entire dataset. Similarly, after identifying the best time scale for growth reduction quantification, we assessed the recovery phase. We compared the averaged ring widths of one, three or five year(s) after the period of growth reduction with averaged ring widths during the period of reduction. This determined the time scale that produced the largest number of significant recoveries for the whole dataset. The nonparametric Wilcoxon rank-sum test (also known as the Mann-Whitney U test) was conducted for these comparisons. The reduction data presented in the data sheet was quantified by the averaged ring width across the three years before drought versus the ring width of the year of drought. The recovery data was quantified by the averaged ring width across the five years after drought versus that of the year of drought. **(4) Data S4-Trait Values and References for 453 Study Species.xlsx** This contains the trait values for the 453 species used in the current study, which was based on the TRY trait database (https://www.try-db.org/) (request ID 21286) (Kattge et al., 2020). “NA” indicates that focal data or information is not available. ScientificName_WFO = standardized species scientific name based on the World Flora Online database ScientificName_Authorship = author of the corresponding scientific name Genus = genus Family = family Dataset = the mortality or the growth-response dataset Mycorrhizal_Type = mycorrhizal association status combining species- and genus-level information Mycorrhizal_Type_Proximity = the proximity of mycorrhizal association status (species- vs. genus-level) SLA = specific leaf area SLA_DatasetID = dataset ID for the corresponding SLA data from the TRY trait database SLA_Ref = references for the corresponding SLA data MaxHeight = maximum height MaxHeight_DatasetID = dataset ID for the corresponding MaxHeight data from the TRY trait database MaxHeight_Ref = references for the corresponding MaxHeight data RootDepth = rooting depth RootDepth_DatasetID = dataset ID for the corresponding RootDepth data from the TRY trait database RootDepth_Ref = references for the corresponding RootDepth data StomCon = stomatal conductance StomCon_DatasetID = dataset ID for the corresponding StomCon data from the TRY trait database StomCon_Ref = references for the corresponding StomCon data SeedMass = seed dry mass SeedMass_DatasetID = dataset ID for the corresponding SeedMass data from the TRY trait database SeedMass_Ref = references for the corresponding SeedMass data **(5) Data S5-Climatic and Edaphic Data.xlsx** This contains the climatic and edaphic data for all study sites, which was based on Climate Research Union (version 4.07) (https://crudata.uea.ac.uk/cru/data/hrg/index.htm#current) (Harris et al. 2020) and the Global Soil Dataset for Use in Earth System Models (GSDE) (Shangguan et al., 2014). “NA” indicates that focal data or information is not available. Dataset = the mortality or the growth-response dataset Site/StudyName = source of the mortality or tree-ring data Longitude = longitude Latitude = latitude MAP = mean annual precipitation from 1901 to 2022 (mm) MAT = mean annual temperature from 1901 to 2022 (℃) PrepSeason = precipitation seasonality (mean annual standard deviation of precipitation from 1901 to 2022) TempSeason = temperature seasonality (mean annual standard deviation of temperature from 1901 to 2022) Annu_SPEI = mean annual standardized precipitation evapotranspiration index (SPEI) from 1901 to 2022 PHH2O = pH (0 ~ 2.3m) TC = total carbon content (% of weigth; 0 ~ 2.3m) TN = total nitrogen content (% of weigth; 0 ~ 2.3m) TP = total phosphorous content (% of weigth; 0 ~ 2.3m)