DOI: 10.5061/dryad.p5hqbzkw5
title: Environmental conditions modulate warming effects on plant litter decomposition globally

description: Empirical studies worldwide show that warming has variable effects on
 plant litter decomposition, leaving the overall impact of climate change
 on decomposition uncertain. We conducted a meta-analysis of 109
 experimental warming studies across seven continents, using natural and
 standardised plant material, to assess the overarching effect of warming
 on litter decomposition and identify potential moderating factors. We
 determined that at least 5.2 degrees of warming is required for a
 significant increase in decomposition. Overall, warming did not have a
 significant effect on decomposition at a global scale. However, we found
 that warming reduced decomposition in warmer, low-moisture areas, while it
 slightly increased decomposition in colder regions, although this increase
 was not significant. This is particularly relevant given the past
 decade's global warming trend at higher latitudes where a large
 proportion of terrestrial carbon is stored. Future changes in vegetation
 towards plants with lower litter quality, which we show were likely to be
 more sensitive to warming, could increase carbon release and reduce the
 amount of organic matter building up in the soil. Our findings emphasize
 the connection among warming responses, macro-environment and litter
 characteristics which can refine predictions of warming’s consequences on
 key ecosystem processes and its contextual dependence.
description: <strong>Literature data on natural plant litter
 decomposition</strong> We conducted an extensive
 literature survey for peer-reviewed publications in the ISI Web of Science
 database (http://apps.webofknowledge.com/) on September 1st 2023. We used
 (warming OR heat* OR OTC OR open top chamber*) AND (litter* OR litter bag)
 AND (decomposition OR mass loss) as search criteria, which returned 1184
 studies (Figure S1). We considered terrestrial field studies that compared
 litter decomposition (mass loss and decomposition rate) under
  experimentally increased temperatures (methods found in our search were
 open-top chambers, heating cables, infrared heaters, sunlit
 controlled-environment chambers, UVB filter films, open-topped polythene
 tents, and closed-top chambers) and ambient conditions. From the 60
 studies that met our criteria, we extracted mean values, sample sizes and
 measures of variation (i.e., standard errors or standard deviations) for
 decomposition (i.e., decomposition rates, absolute and relative mass loss,
 remaining mass of plant material). We contacted the corresponding authors
 to obtain access to the raw data for studies that did not report them and
 had to exclude 8 studies due to insufficient reporting. This resulted in
 52 studies used for the meta-analysis (Table S1). Whenever warming was
 applied in factorial combination with one or more additional treatments
 (e.g., warming and plant species removal), we extracted the parameters of
 interest for the warming treatment only, together with the ambient
 control. If the litter was incubated at different time steps, each time
 step was used as an independent data point. We thus extracted a total of
 523 paired (ambient vs warmed) data points from the 52 studies, either
 directly from the text or tables or from figures using the software
 WebPlotDigitizer (v. 4.6, Rohatgi 2021). When decomposition was reported
 as remaining mass of plant material, the latter was transformed into mass
 loss. <br>We extracted coordinates of each study
 location (Figure 1A), the incubation duration of the litter (from 14 days
 to 4.9 years, standardised to days), the mesh size of the litter bags
 (from 0.02 to 5 mm), the position of incubation (i.e., if litter bags were
 put on the soil surface or buried below ground), the plant species and
 thus the plant functional type (i.e., forb, nonvascular, graminoid, woody
 species), and the plant organ type (i.e., leaf, shoot or root). For 32
 studies, we also extracted litter C:N ratio reported by the researchers
 (ranging from 12 to 201). All reported values were from single species,
 with the only exception being two studies on root decomposition, which
 included a mixture of grass species. Yet, as all the species in these
 samples were indeed within the graminoid functional type, we included
 these studies in the meta-analysis. For each study, we extracted the
 duration of the warming experiment prior to incubation start (from first
 year to 23 years). The warming method was classified as Heating cables
 (number of studies n=11), Infrared heaters (n=17), and Open-top chambers
 (n=19), with ‘Other methods’ including Sunlit controlled-environment
 chambers (n=1), UVB filter films (n=1), Open-topped polythene tents (n=2)
 and Closed-top chambers (n=1).
 <strong>Standardised plant litter data from open-top
 chamber warming experiments</strong> Following
 the standard Tea Bag Index protocol (Keuskamp et al., 2013), green
 (Camellia sinensis; EAN no.: 8 722700 055525) and rooibos (Aspalathus
 linearis; EAN no.: 8 722700 188438, Lipton, Unilever) tea bags with woven
 nylon mesh (0.257 mm), were buried at a depth of 8 cm and at a distance of
 at least 15 cm from each other in open-top chambers (OTC) and controls at
 57 locations (Figure 1, Table S2). The incubations covered one growing
 season (82 ± 18 days; mean ± SD), that is, from May/June 2016 to
 August/September 2016 in the northern hemisphere and from January 2017 to
 March 2017 in the southern hemisphere. For two sites in Japan (i.e., JPN_1
 and JPN_3, Table S2), tea bags were incubated from July to October 2012.
 Retrieved bags were cleaned of adhering soil and roots. The mass of the
 remaining tea was determined after drying it in an oven at 60-70 °C for at
 least 48 h. To align with the literature data, we calculated treatment
 means of mass loss, sample sizes and standard deviations for each
 experiment/GPS location. <strong>Explanatory
 macro-environmental drivers</strong> We obtained
 map-based environmental data based on the geographical locations of the
 study sites to identify macro-environmental factors that may influence the
 response of decomposition to warming. We used 48 environmental layers
 reflecting major gradients in climate, soil, vegetation, and topographic
 variables as covariates in our analysis (Table S3). Due
 to the occurrence of many confounding variables, we summarised the
 macro-environmental variation across studies with a Principal Component
 Analysis (PCA; Table S3). The first principal component (PC 1) was
 strongly positively correlated with temperature-associated variables and
 negatively correlated with soil organic carbon (SOC) and explained 26.9 %
 of the total variance (Figure 3A, Table S3). The second component (PC 2)
 correlated positively with precipitation-associated variables and
 explained 18.1% of the total variance (Figure 3A, Table S3). The third PC
 axis was not considered as it described negligible amounts of the
 variation (4.2%). In our dataset, the range of annual mean temperature was
 -12 to 28 °C, annual precipitation was 78 to 2100 mm, and soil saturated
 water content was 42 to 81 %.  We created four
 ‘macro-environmental classes’ based on the origin of the PC1 and PC2
 variables as a separation line. These four ‘macro-environmental classes’
 were described as following: (1) high temperatures and high precipitation
 (number of effect sizes k=156), (2) high temperatures and low
 precipitation (k=170), (3) low temperatures and high precipitation
 (k=156), and (4) low temperatures and low precipitation (k=155) (Figure
 S2, Table S4). <strong>Explanatory
 micro-environmental drivers altered by experimental
 warming</strong> For both datasets (i.e., natural
 and standardised plant litter), we collected available data on the actual
 degree of warming, i.e., the mean absolute temperature difference between
 the warmed and ambient control, as well as soil moisture in warming and
 control treatments when available. The degree of warming included air or
 soil temperature measures, depending on whether the litter was incubated
 on the soil surface or below ground, respectively. We calculated relative
 change in soil moisture with warming according to:<br>"relative
 change in soil moisture  = " ("Mc" /"MW"
 -1)×100<br>where MC and MW are soil moisture in control and warming
 treatment, respectively. Positive and negative values indicate drier and
 wetter conditions under warming than under ambient conditions,
 respectively. <strong>Litter
 quality</strong> We focused on three different,
 frequently used characterisations of litter qualities: the C:N ratio
 before decomposition, reported in the initial studies, the decomposability
 measured as decomposition rate under ambient condition (i.e., standardised
 to mass loss in % d-1) (Cornelissen et al. 2004; Freschet et al. 2012),
 and plant functional type (Dorrepaal et al. 2005). We categorised the
 plant species into four different plant functional types (sensu Chapin et
 al. 1996), forbs (number of studies n=7), graminoids (i.e., grasses and
 sedges, n=28), woody species (i.e., shrubs and needle-leaved and
 broad-leaved trees, n=27), and nonvascular (i.e. mosses, n=4; lichens,
 n=1). For graminoids and woody species, we were able to further specify
 litter type into aboveground (i.e., shoots and leaves of graminoids, n=25;
 broadleaves and needles of woody species, n=25) and below ground plant
 organs (i.e., roots of graminoids, n=6; and root of woody species,
 n=2). <strong>Data
 analysis</strong> In order to evaluate the
 relative effect of experimental warming on decomposition, we used
 Hedges' g, which is a standardised mean difference (SMD) calculated
 by dividing the difference between the mean mass loss in the warming
 treatment (x ̅1) and control (x ̅2) by the pooled standard deviation
 (Hedges 1981). <br>Hedge's g =(〖(x ̅〗_1-x
 ̅_2))/(√((n_1-1)*s_1^2  + (n_2-1)*s_2^2) / (n_1+n_2-2))<br>where n1
 and n2 are sample size, and s12 and s22 are the sample variance of the
 warming treatment and the control, respectively. Therefore, a SMD larger
 than one indicates that warming enhanced decomposition, while a SMD lower
 than one indicate that warming decreased decomposition. By using the SMD
 as a measure of effect size, we were able to synthesize data measured on
 different scales or units (e.g. mass loss vs. decomposition rate), while
 still accounting for the precision (variance) of the measurement. We used
 the escalc() function in the R package METAFOR (Viechtbauer 2010) to
 derive SMDs and corresponding 95% confidence intervals (CI) from all
 paired (i.e., warmed/control) mean values, sample sizes and standard
 deviations for decomposition. We used multivariate linear mixed-effects
 models fitted through the rma.mv() function (METAFOR package) to calculate
 the pooled average SMD across all studies. In our multivariate linear
 mixed-effects models, we used a sampling error covariance matrix to
 account for the correlation between sampling errors within studies from
 which multiple effect sizes were extracted. To test whether decomposition
 was significantly affected by warming, we considered a pooled average
 effect size estimate to be significantly different from zero if the 95% CI
 around the mean did not include zero. Effects were considered significant
 at α=0.05. <br>To account for spatial autocorrelation between the
 study locations, we used a random effect consisting of longitude and
 latitude (in decimal degrees, with negative values for West and South)
 based on their great-circle distance (WGS84 ellipsoid method). In
 multivariate linear mixed-effects models, total heterogeneity (QT) in
 effect sizes can be partitioned into heterogeneity explained by the model
 structure (QM) and unexplained heterogeneity (QE). We used the Test of
 Moderators (QM test) to determine whether there were any significant
 effects of various moderators  on decomposition SMD (Koricheva et al.
 2013). We tested first for differences between the
 natural and the standardised plant litter dataset by using the data type
 (i.e., natural litter or standardised plant litter) as a moderator in the
 multivariate linear mixed-effects model. Because the effect of warming on
 decomposition of natural and standardised plant litter did not
 significantly differ (moderators' test: QM (df = 2) = 2.7, p = 0.26),
 we combined   the natural and standardised plant litter dataset in the
 following analyses and added the dataset type to the random-effects
 structure of our models to account for the different origins of our
 datasets (random = ~ LON + LAT | dataset type). To test
 the impact of macro-environment on the warming effect on decomposition, we
 first used multivariate linear mixed effects models (n=48) to explore
 whether the macro-environmental factors individually had a significant
 effect on the decomposition SMD (Table S6). However, as most environmental
 factors were confounded, we combined the macro-environmental factors to
 the underlying gradients using a Principal Component Analysis (PCA) on the
 scaled environmental variables using the R package FACTOMINER (v.2.4; Lê
 et al. 2008). We then used the four ‘macro-environmental classes’ created
 based on the origin of the PC1 and PC2 variables as a separation line, as
 moderator in the following multivariate linear mixed effects models to
 test whether the four environmental classes differed in their warming
 effect on decomposition. We used this factor ‘class’ as interacting
 moderator in the model totest for interactions in the macro-environment
 and the natural and standardised plant litter dataset. <br>We tested
 the impact of experimental induced changes in micro-environment, i.e.,
 degree of warming and warming-induced changes in soil moisture and their
 interaction by testing them as moderators in the same multivariate linear
 mixed effects model (METAFOR package). We included the
 ‘macro-environmental class’ as interacting moderator to the model to test
 whether experimental induced changes in micro-environment differ between
 the four macro-environmental classes. To investigate whether experimental
 warming affected temperatures and soil moisture, we used a one-sample
 t-test to test whether the absolute difference between the warming
 treatment and the ambient control differed significantly from
 zero. To test for differences in the warming effect
 between the different warming methods used in the different studies and
 experiments (Table S1, 2), we used ‘warming method’ as moderator in
 another multivariate linear mixed effects model. In this model, the
 macro-environmental class was not integrated because the warming methods
 were not evenly distributed across the four macro-environmental classes
 (e.g., more OTC studies in higher latitudes). To test for differences in
 the warming methods in their effect on micro-environment, we used linear
 mixed-effects models (R package LMERTEST, v. 3.1-3; Kuznetsova et al.,
 2017) to test the overall effect of the categorical independent variable
 ‘warming method’ on the continuous dependent variables ‘degree of warming’
 and ‘warming-induced changes in soil moisture’, respectively. We used
 Tukey HSD post-hoc tests (R packages MULTCOMP, v. 1.4-19; Hothorn et al.
 2008, and EMMEANS, v. 1.7.5; Lenth 2019) to check for significant
 differences between the warming methods in degree of warming and
 warming-induced changes in soil moisture, respectively. We further tested
 with a linear regression for correlations between warming-induced changes
 in soil moisture and the degree of warming. To test for
 differences in litter quality, described as C:N ratio or ambient
 decomposability, between plant functional types in different
 macro-environments, we used linear mixed-effects models (R package
 LMERTEST) to test the overall effect of the categorical independent
 variables ‘plant functional type’ (including plant organ types) and
 ‘macro-environmental class’ and their interactions on the continuous
 dependent variables ‘C:N ratio’ or ‘ambient decomposability’ (different
 model for each). We then used Tukey HSD post-hoc tests (R packages
 MULTCOMP, v. 1.4-19; and EMMEANS, v. 1.7.5) to check for significant
 differences between the plant functional types and four
 macro-environmental classes in C:N ratio and ambient decomposability,
 respectively. To test our hypothesis that lower litter
 quality is associated with a stronger positive warming effect on
 decomposition, we used multivariate linear mixed-effects models (METAFOR
 package) with the three applied proxies for litter quality ‘C:N ratio’,
 ‘ambient decomposability’ and ‘plant functional type’ (different model for
 each of the proxies) as moderators and ‘macro-environmental class’ again
 as an interactive factor. In addition, we tested the
 site-specific drivers related to environmental conditions (absolute
 latitude and, altitude), experimental setup (duration of warming before
 the experiment, mesh size) as individual moderators fitting separate
 multivariate linear mixed-effects models (Table S5). <br>For each
 model, we tested the assumptions of normality and homogeneity of variance
 of the residuals. Whenever necessary, data were log-transformed (C:N
 ratio) or rank-transformed (warming-induced changes in soil moisture),
 with the latter case resulting in a non-parametric regression. Graphical
 displays were produced using the R packages GGPLOT2 (v. 3.3.6, Wickham
 (2016)) and ORCHARD (v.2.0, Nakagawa et al. 2021). We used R version 4.2.3
 (R Core Team 2023) for all analyses.
description: # Environmental conditions modulate warming effects on plant litter
 decomposition globally
 [https://doi.org/10.5061/dryad.p5hqbzkw5](https://doi.org/10.5061/dryad.p5hqbzkw5) #### title: "README\_EcolLet\_Schwieger\_et\_al" author: "Sarah Schwieger" date: "2024-11-08" output: html\_document This dataset and R codes correspond to the manuscript: "Macro-environment strongly interacts with warming in a global analysis of decomposition" of Schwieger et al. ## Description of the data and file structure * "df1_meta_analysis_data.csv": Data containing the extracted metadata of the literature data on natural plant litter decomposition and the standardised plant litter data from the open-top chamber warming experiments. The file contains the extracted variables reported from the studies as described in the "Data collection" section (see Methods), as well as the map-based data described in the "Explanatory macro-environmental drivers" section and Supplementary Table S3 (see Methods). * This file is used to calculate effect sizes, run multivariate linear mixed effects models and generate figures. * "df2_environmental_data.csv": data on the obtained map-based environmental data based on the geographical locations of the study sites. More information is provided in the "Explanatory macro-environmental drivers" section and Supplementary Table S3 (see Methods). * This file is used to perform the Principal Component Analysis (PCA) described in the Data analysis section of the Methods. * "df3_pca_variables_explained.csv": data on the coordinates of the map-based variables calculated from the PCA. * This file is used to plot the PCA variables. * "Schwieger_et_al__Rcode.rmd": R markdown code to reproduce the calculation of effect sizes, multivariate linear mixed effects models, PCA and figures of the manuscript. ## Table Column Explanations df1\_meta\_analysis\_data | Column | Description | | ---------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Nr | Sequential identifier for each entry in the dataset. | | Reference | Citation or reference associated with the dataset entry. | | LAT | Latitude of the location where data was collected (in decimal degrees). | | LON | Longitude of the location where data was collected (in decimal degrees). | | altitude | Altitude of the location where data was collected (in meters). | | MAT | Mean Annual Temperature at the site of data collection (in °C). | | MAP | Mean Annual Precipitation at the site of data collection (in mm). | | ecosystem\_type | Type of ecosystem at the data collection site (e.g., forest, grassland). | | species | Species involved in the experiment or observation. | | Litter.form | Form of plant litter used in the experiment (e.g., leaf, root, shoot). | | data.type | Type of data collected in the experiment (e.g., decomposition rate, carbon content). | | data.type2 | Additional classification of data type for the experiment (if applicable). | | PFT | Plant Functional Type of the species involved (e.g., shrub, grass). | | litter.type\_2 | Additional classification of litter type used in the experiment (if applicable). | | mesh\_size | Size of the mesh used for litter bags in the experiment (in mm). | | incubation\_d | Duration of the incubation period (in days). | | warming\_amount | Amount of warming applied in the experiment (in °C, if applicable). | | soil\_moisture\_change | Change in soil moisture levels due to experimental conditions (if applicable). | | method | Methodology used for data collection or experiment. | | start\_incubation | Start date of the incubation period (format: YYYY-MM-DD). | | end\_incubation | End date of the incubation period (format: YYYY-MM-DD). | | warming\_est | Estimated warming effect for the experiment site. | | duration\_warming | Duration for which warming was applied in the experiment (in days). | | climate | Climate classification of the study area (e.g., temperate, tundra). | | location | Geographic location or description of the study area. | | CN | Carbon-to-nitrogen ratio of the plant material. | | unit\_decomposition | Unit of measurement for decomposition data (e.g., % mass loss). | | AMB\_N | Number of observations under ambient conditions. | | AMB\_mean | Mean decomposition or other measurement under ambient conditions. | | AMB\_sd | Standard deviation of measurements under ambient conditions. | | WARM\_N | Number of observations under warming conditions. | | WARM\_mean | Mean decomposition or other measurement under warming conditions. | | WARM\_sd | Standard deviation of measurements under warming conditions. | | Dim.1 | Dimension 1 or the first component in the principal component analysis | | Dim.2 | Dimension 2 or the second component in the principal component analysis | | Country | Country where the study or data collection was conducted. | | Code | Abbreviation for each country. | | observation | Identifier or index for each observation within the dataset. | | yi | Effect size or response variable in a meta-analysis or statistical model (i.e., Standardised Mean Difference). | | vi | Variance of the effect size (yi) in the meta-analysis, indicating the precision of the effect estimate. | | weights | Weights assigned to each observation in the analysis, often based on variance (vi) for meta-analyses. | | class | Classification variable for the 4 marco-environmental classes cold and dry (cold-dry), cold and wet (cold-wet), warm and dry (warm-dry), and warm and wet (warm-wet). | ## Additional Column Explanations for df1 and df2 | Column Name | Description | | -------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- | | `ID` | Unique identifier for each data entry. | | `Aridity_Index` | Aridity index, indicating the dryness of the environment (dimensionless). | | `PET` | Potential Evapotranspiration, measuring potential water loss through evaporation and transpiration. | | `cover_Barren` | Percentage cover of barren land (e.g., rock, sand, ice). | | `cover_Cultivated` | Percentage cover of cultivated or managed vegetation. | | `cover_Deciduous_Broadleaf_Trees` | Percentage cover of deciduous broadleaf trees. | | `cover_Evergreen_Broadleaf_Trees` | Percentage cover of evergreen broadleaf trees. | | `cover_Evergreen_Deciduous_Needleleaf_Trees` | Percentage cover of evergreen or deciduous needleleaf trees. | | `cover_Herbaceous` | Percentage cover of herbaceous vegetation (non-woody plants). | | `cover_Regularly_Flooded` | Percentage cover of areas that are regularly flooded. | | `cover_Shrubs` | Percentage cover of shrub vegetation. | | `AspectCosine` | Cosine of the aspect angle, indicating slope orientation. | | `AspectSine` | Sine of the aspect angle, indicating slope orientation. | | `eastness` | East-facing component of aspect (cosine transformed). | | `elevation` | Elevation of the site above sea level (in meters). | | `northness` | North-facing component of aspect (cosine transformed). | | `global.biomass` | Global above ground biomass density in Mg ha-1. | | `EVI` | Enhanced Vegetation Index, an index that measures vegetation greenness and is less sensitive to canopy saturation than NDVI. | | `FPAR` | Fraction of Photosynthetically Active Radiation, measuring vegetation productivity. | | `GPP` | Gross Primary Productivity, the total amount of carbon dioxide that plants convert to biomass through photosynthesis. | | `LAI` | Leaf Area Index, a measure of the leaf area per unit ground area, often used to estimate vegetation density. | | `NDVI` | Normalized Difference Vegetation Index, an index that measures vegetation health based on light absorption and reflection. | | `NPP` | Net Primary Productivity, the rate at which plants produce useful biomass, calculated as GPP minus plant respiration. | | `bulk_density_5_cm` | Bulk density of soil at 5 cm depth (g/cm³). | | `CEC` | Cation Exchange Capacity, a measure of the soil’s ability to hold and exchange positively charged ions (cations), related to soil fertility. | | `H2O_capacity_5_cm` | Water holding capacity of soil at 5 cm depth (%). | | `SOC_content_5_cm` | Soil Organic Carbon content at 5 cm depth (%). | | `SOC_density_5_cm` | Soil Organic Carbon density at 5 cm depth (g/cm³). | | `SOC_stock_0_5_cm` | Total Soil Organic Carbon stock in the 0-5 cm soil layer (g/m²). | | `saturated_H2O_content_5_cm` | Saturated water content in soil at 5 cm depth (%). | | `soil_ph_H2O_5_cm` | Soil pH measured in H₂O at 5 cm depth. | | `Annual_Mean_Temperature` | Average annual temperature (°C). | | `Annual_Precipitation` | Total annual precipitation (mm). | | `Isothermality` | Ratio of the mean diurnal temperature range to the annual temperature range (%). | | `Max_Temperature_of_Warmest_Month` | Maximum temperature of the warmest month (°C). | | `Mean_Diurnal_Range` | Mean of monthly temperature ranges (°C). | | `Mean_Temperature_of_Coldest_Quarter` | Mean temperature of the coldest quarter (°C). | | `Mean_Temperature_of_Driest_Quarter` | Mean temperature of the driest quarter (°C). | | `Mean_Temperature_of_Warmest_Quarter` | Mean temperature of the warmest quarter (°C). | | `Mean_Temperature_of_Wettest_Quarter` | Mean temperature of the wettest quarter (°C). | | `Min_Temperature_of_Coldest_Month` | Minimum temperature of the coldest month (°C). | | `Precipitation_Seasonality` | Coefficient of variation for monthly precipitation, indicating seasonality. | | `Precipitation_of_Coldest_Quarter` | Total precipitation during the coldest quarter (mm). | | `Precipitation_of_Driest_Month` | Precipitation in the driest month (mm). | | `Precipitation_of_Driest_Quarter` | Total precipitation during the driest quarter (mm). | | `Precipitation_of_Warmest_Quarter` | Total precipitation during the warmest quarter (mm). | | `Precipitation_of_Wettest_Month` | Precipitation in the wettest month (mm). | | `Precipitation_of_Wettest_Quarter` | Total precipitation during the wettest quarter (mm). | | `Temperature_Annual_Range` | Difference between the maximum and minimum annual temperature (°C). | | `Temperature_Seasonality` | Standard deviation of monthly temperatures, indicating seasonality (°C). | | `annual_mean_solar_radiation` | Average solar radiation received annually (W/m²). | | `total_nitrogen_5_cm` | Total nitrogen content in soil at 5 cm depth (%). | | `total_nitrogen_15_cm` | Total nitrogen content in soil at 15 cm depth (%). | | `total_nitrogen_30_cm` | Total nitrogen content in soil at 30 cm depth (%). | | `mean_temp_incub_period` | Mean temperature during the incubation period (°C). | | `sum_prec_incub_period` | Total precipitation during the incubation period (mm). | ## Table Column Explanations df3\_pca\_variables\_explained ## Column Explanations (Dimensional and Grouping Variables) | Column | Description | | -------- | ----------------------------------------------------------------------------------------------------------------- | | variable | Name of the map-dervied environmental factor being analysed. | | Dim.1 | First dimension or principal component in multivariate analysis, representing a major variance factor. | | Dim.2 | Second dimension or principal component in multivariate analysis, representing another variance factor. | | Dim.3 | Third dimension or principal component in multivariate analysis, representing an additional variance factor. | | Dim.4 | Fourth dimension or principal component in multivariate analysis, representing an added variance factor. | | Dim.5 | Fifth dimension or principal component in multivariate analysis, representing a further variance factor. | | group | Category or group to which the variable or observation belongs, i.e., temperature, soil, precipitation, and other | ## Notes on Missing Values * `NA` indicates that the data for that specific entry is not available or was not recorded. ## Code/Software We used R version 4.2.3 (R Core Team 2023) for all analyses. We used the *escalc() and rma.mv()* functions in the R package metafor (v.4.0-0; Viechtbauer 2010). We combined the macro-environmental factors in a Principal Component Analysis (PCA) using the R package FactoMineR (v.2.4; Lê *et al.* 2008). We used linear mixed-effects models using the R package lmerTest (v. 3.1-3; Kuznetsova et al., 2017). We used Tukey HSD post-hoc tests from the R packages multcomp (v. 1.4-19; Hothorn *et al.* 2008) and emmeans (v. 1.7.5; Lenth 2019). Graphical displays were produced using the R packages ggplot2 (v. 3.3.6, Wickham *et al.* 2016) and orchaRd (v.2.0, Nakagawa *et al.* 2021). We extracted data points from the 52 studies, either directly from the text or tables or from figures using the software WebPlotDigitizer (v. 4.6, Rohatgi 2021). 1. **Hothorn T, Bretz F, Westfall P**. **2008**. 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