Received: 18 February 2022 | Revised: 7 December 2022 | Accepted: 4 January 2023 DOI: 10.1111/geb.13644 RESEARCH ARTICLE Traits of dominant plant species drive normalized difference vegetation index in grasslands globally Thore Engel1,2,3,4,5 | Helge Bruelheide4,6 | Daniela Hoss4,5 | 4,6,7,8 9,10 Francesco M. Sabatini | Jan Altman | Mohammed A. S. Arfin-­Khan11 | 12 10 13 14 Erwin Bergmeier | Tomáš Černý | Milan Chytrý | Matteo Dainese | 4,15,16 17,18 19 20 Jürgen Dengler | Jiri Dolezal | Richard Field | Felícia M. Fischer | 21 4,6 22 23 Dries Huygens | Ute Jandt | Florian Jansen | Anke Jentsch | 24 4,25 26 27,28 Dirk N. Karger | Jens Kattge | Jonathan Lenoir | Frederic Lens | Jaqueline Loos29 | Ülo Niinemets30,31 | Gerhard E. Overbeck32 | 33 34,35 36 Wim A. Ozinga | Josep Penuelas | Gwendolyn Peyre | Oliver Phillips37 | Peter B. Reich38,39,40 | Christine Römermann4,41 | Brody Sandel42 | 43 19 5 Marco Schmidt | Franziska Schrodt | Eduardo Velez-­Martin | 44 5 Cyrille Violle | Valério Pillar 1 Institute of Biodiversity, Friedrich Schiller University Jena, Jena, Germany 2 Institute of Computer Science, Martin Luther University Halle-­Wittenberg, Halle (Saale), Germany 3 Department of Ecosystem Services, Helmholtz Centre for Environmental Research –­UFZ, Leipzig, Germany 4 German Centre for Integrative Biodiversity Research (iDiv) Halle-­Jena-­Leipzig, Leipzig, Germany 5 Department of Ecology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil 6 Institute of Biology/Geobotany and Botanical Garden, Martin-­Luther University Halle-­Wittenberg, Halle (Saale), Germany 7 BIOME Lab, Department of Biological, Geological and Environmental Sciences (BiGeA), Alma Mater Studiorum University of Bologna, Bologna, Italy 8 Czech University of Life Sciences Prague, Faculty of Forestry and Wood Sciences, Praha, Czech Republic 9 Czech Academy of Sciences, Institute of Botany, Průhonice, Czech Republic 10 Faculty of Forestry and Wood Science, Department of Forest Ecology, Czech University of Life Sciences, Suchdol, Czech Republic 11 Department of Forestry and Environmental Science, Shahjalal University of Science and Technology, Sylhet, Bangladesh 12 Vegetation and Phytodiversity Analysis, University of Göttingen, Göttingen, Germany 13 Department of Botany and Zoology, Faculty of Science, Masaryk University, Brno, Czech Republic 14 Eurac Research, Institute for Alpine Environment, Bozen/Bolzano, Italy 15 Vegetation Ecology Group, Institute of Natural Resource Sciences (IUNR), Zurich University of Applied Sciences (ZHAW), Wädenswil, Switzerland 16 Plant Ecology, Bayreuth Center of Ecology and Environmental Researcher (BayCEER), University of Bayreuth, Bayreuth, Germany 17 Department of Functional Ecology, Institute of Botany, The Czech Academy of Sciences, Trebon, Czech Republic 18 Faculty of Science, Department of Botany, University of South Bohemia, Ceske Budejovice, Czech Republic 19 School of Geography, University of Nottingham, Nottingham, UK 20 Centro de Investigaciones sobre Desertificación (CSIC-­UV-­GV), Valencia, Spain 21 Isotope Bioscience Laboratory, Ghent University, Ghent, Belgium 22 Faculty of Agricultural and Environmental Sciences, University of Rostock, Rostock, Germany 23 Disturbance Ecology, University of Bayreuth, Bayreuth, Germany 24 Biodiversity and Conservation Biology, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2023 The Authors. Global Ecology and Biogeography published by John Wiley & Sons Ltd. Global Ecol Biogeogr. 2023;32:695–706.  wileyonlinelibrary.com/journal/geb | 695 | 14668238, 2023, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/geb.13644 by JOSEP Penuelas - Readcube (Labtiva Inc.) , Wiley Online Library on [12/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 696 ENGEL et al. 25 Functional Biogeography, Max Planck Institute for Biogeochemistry, Jena, Germany 26 UMR CNRS 7058, Ecologie et Dynamique des Systèmes Anthropisés (EDYSAN), Université de Picardie Jules Verne, Amiens, France 27 Naturalis Biodiversity Center, Functional Traits, Leiden, The Netherlands 28 Institute of Biology Leiden, Plant Sciences, Leiden University, Leiden, The Netherlands 29 Institute of Ecology, Leuphana University, Lüneburg, Germany 30 Crop Science and Plant Biology, Estonian University of Life Sciences, Tartu, Estonia 31 Estonian Academy of Sciences, Tallinn, Estonia 32 Department of Botany, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil 33 Team Vegetation, Forest and Landscape Ecology, Wageningen University & Research, Wageningen, The Netherlands 34 CSIC, Global Ecology Unit CREAF-­C SIC-­UAB, Bellaterra, Spain 35 CREAF, Bellaterra, Spain 36 Civil and Environmental Engineering, University of the Andes, Bogotá, Colombia 37 School of Geography, University of Leeds, Leeds, UK 38 Forest Resources, University of Minnesota, Minnesota, St. Paul, USA 39 Hawkesbury Institute for the Environment, Western Sydney University, New South Wales, Penrith, Australia 40 Institute for Global Change Biology, School for Environment and Sustainability, University of Michigan, Michigan, Ann Arbor, USA 41 Plant Biodiversity, Institute of Ecology & Evolution, Friedrich Schiller University Jena, Jena, Germany 42 Department of Biology, Santa Clara University, California, Santa Clara, USA 43 Palmengarten der Stadt Frankfurt am Main, Wissenschaftlicher Dienst, Frankfurt am Main, Germany 44 CEFE, Univ Montpellier, CNRS, EPHE, IRD, Campus du CNRS, Montpellier, France Correspondence Thore Engel, Institute of Biodiversity, Abstract Friedrich Schiller University Jena, Aim: Theoretical, experimental and observational studies have shown that Dornburger Straße 159, 07743 Jena, Germany. biodiversity–­ecosystem functioning (BEF) relationships are influenced by functional Email: thore.engel@idiv.de community structure through two mutually non-­exclusive mechanisms: (1) the Funding information dominance effect (which relates to the traits of the dominant species); and (2) the Deutsche Forschungsgemeinschaft, niche partitioning effect [which relates to functional diversity (FD)]. Although both Grant/Award Number: FZT 118 mechanisms have been studied in plant communities and experiments at small spatial Handling Editor: Jason Pither extents, it remains unclear whether evidence from small-­extent case studies translates into a generalizable macroecological pattern. Here, we evaluate dominance and niche partitioning effects simultaneously in grassland systems world-­wide. Location: Two thousand nine hundred and forty-­one grassland plots globally. Time period: 2000–­2014. Major taxa studied: Vascular plants. Methods: We obtained plot-­based data on functional community structure from the global vegetation plot database “sPlot”, which combines species composition with plant trait data from the “TRY” database. We used data on the community-­weighted mean (CWM) and FD for 18 ecologically relevant plant traits. As an indicator of primary productivity, we extracted the satellite-­derived normalized difference vegetation index (NDVI) from MODIS. Using generalized additive models and deviation partitioning, we estimated the contributions of trait CWM and FD to the variation in annual maximum NDVI, while controlling for climatic variables and spatial structure. Results: Grassland communities dominated by relatively tall species with acquisitive traits had higher NDVI values, suggesting the prevalence of dominance effects for BEF relationships. We found no support for niche partitioning for the functional traits analysed, because NDVI remained unaffected by FD. Most of the predictive power of traits was shared by climatic predictors and spatial coordinates. This highlights | 14668238, 2023, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/geb.13644 by JOSEP Penuelas - Readcube (Labtiva Inc.) , Wiley Online Library on [12/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License ENGEL et al. 697 the importance of community assembly processes for BEF relationships in natural communities. Main conclusions: Our analysis provides empirical evidence that plant functional community structure and global patterns in primary productivity are linked through the resource economics and size traits of the dominant species. This is an important test of the hypotheses underlying BEF relationships at the global scale. KEYWORDS biodiversity–­ecosystem functioning, biodiversity, community-­weighted mean, ecosystem functioning, functional diversity, sPlot, traits, vegetation 1 | I NTRO D U C TI O N scales, whereas more recent approaches have specifically included or manipulated environmental variation, such as climate and nutrient The question of how biodiversity influences ecosystem functions, supply (Chollet et al., 2014; Craven et al., 2016; Roscher et al., 2013; such as biomass production and biogeochemical cycling, has been Zhou et al., 2017). Overall, experimental and observational studies discussed for decades (Cardinale et al., 2011; Hooper et al., 2005; tend to find dominance effects more frequently and with stronger Schulze & Mooney, 1993; Tilman et al., 1996; van der Plas, 2019). evidence than niche partitioning effects (Chollet et al., 2014; Kröber The biodiversity–­ecosystem functioning (BEF) debate has become et al., 2015; Lavorel, 2013; Mokany et al., 2008; Mouillot et al., 2011; even more relevant recently, given that the ongoing global biodiver- van der Plas, 2019). In particular, plant traits related to the trade-­off sity crisis (Ceballos et al., 2015; Díaz et al., 2019; McGill et al., 2015) between resource acquisition and conservation show strong domi- requires us to understand how changes in the diversity and compo- nance effects on productivity (Díaz et al., 2004; Grigulis et al., 2013; sition of biological communities (especially plants) will affect ecosys- Lavorel, 2013). Recent studies, however, have shown that both tem processes and services (Pillar et al., 2018). niche partitioning and dominance effects contribute to ecosys- Over recent decades, the focus of BEF research has shifted tem multifunctionality (i.e., providing multiple functions; Hector from considering species richness alone to investigating how plant & Bagchi, 2007; Mouillot et al., 2011; Valencia et al., 2015) and to functional community structure (i.e., the distribution of trait values biomass production outside the peak productivity season (Chollet measured in a given community; Garnier et al., 2016) affects ecosys- et al., 2014). Despite these advances, there continues to be a lack tem processes (Cadotte et al., 2011, 2013; Dias et al., 2013; Fischer of studies and syntheses focusing on large spatial scales (but see et al., 2016; Grigulis et al., 2013; Kröber et al., 2015). Such trait-­ Cornwell et al., 2008), and we know little about how dominance and based approaches have offered two main hypotheses for a mech- niche partitioning effects interplay with climatic factors to shape anistic explanation of BEF relationships. The dominance hypothesis biomass production along larger biogeographical gradients. (also known as the mass ratio hypothesis) states that ecosystem pro- Remote sensing observations might allow this knowledge gap cesses are determined primarily by the traits of the dominant species to be closed (Franklin et al., 2017; Polley et al., 2020; Rocchini in a community (Grime, 1998). It suggests that ecosystem function- et al., 2021). Remote Earth observation products have become ing is linked to the community-­weighted mean (CWM) of relevant widely available and allow the assessment of ecosystem function- traits (Garnier et al., 2004). The niche partitioning hypothesis, in ality and biodiversity (Lausch et al., 2016; Schmidtlein et al., 2012). contrast, refers to the variation of a trait value within a community. Satellite-­derived normalized difference vegetation index (NDVI), It suggests that a higher range of trait values [i.e., functional diver- for instance, can be used to measure surface greenness over wide sity] reflects niche differences, allowing for enhanced use of biotope extents as a proxy of primary productivity (Paruelo et al., 1997; space, better resource use efficiency and, ultimately, enhanced eco- Running, 1990) and other ecosystem processes (Ustin et al., 2004), system functioning (Petchey & Gaston, 2002). including the stability of productivity (White et al., 2020). As such, Dominance and niche partitioning effects are not mutually NDVI has been used in ecological studies to evaluate ecosys- ­exclusive, and disentangling them has proved difficult. For exam- tem responses to environmental changes (reviewed by Pettorelli ple, it is challenging to manipulate functional community structure et al., 2005), to measure ecosystem stability (De Keersmaecker experimentally such that functional diversity and CWM vary inde- et al., 2014) and to study the effect of plant functional community pendently of each other (Dias et al., 2013; Ricotta & Moretti, 2011). structure on resilience (Spasojevic et al., 2016). Yet, no study to date Furthermore, in natural assemblages, both plant traits and ecosys- has linked NDVI to plant functional community structure over large tem functioning are strongly influenced by environmental drivers, extents to disentangle the relative contributions of the dominance such as climate, and exhibit strong spatial autocorrelation. Traditional and niche partitioning effects. experimental approaches have tried to keep abiotic factors constant Here, we investigate the BEF relationship in grassland systems and have therefore been restricted to small spatial and temporal at the global scale. We rely on the world's most comprehensive | 14668238, 2023, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/geb.13644 by JOSEP Penuelas - Readcube (Labtiva Inc.) , Wiley Online Library on [12/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 698 ENGEL et al. repository of plant community data, sPlot (Bruelheide et al., 2019), the following criteria: (1) the plot was marked as “grassland” by the coupled with the plant trait database TRY (Kattge et al., 2011), to vegetation survey that provided the sPlot entry; and (2) ≥90% of the quantify plant functional community structure in grassland commu- plot was covered by species that were not trees or shrubs or that had nities around the globe. We used maximum annual NDVI as a proxy a plant height >2 m (according to the TRY database). We excluded for primary productivity and modelled it using plant functional com- plots that were labelled as “forest”, “shrubland” or “wetland”, ob- munity data and climatic variables. Specifically, we were interested in servations before the year 2000 (launch of the MODIS programme) the role of CWM (as a proxy for dominance) and functional ­diversity and plots with high location uncertainty (>100 m) and imprecise GPS (as a proxy for niche partitioning) for predicting the biogeographical coordinates (less than four decimal places in decimal degrees). We patterns of NDVI. In addition, we discerned the unique and shared used the land cover map GlobCover 2009 to identify and exclude contributions of our functional and climatic predictors using a devi- plots on heterogeneous MODIS pixels that contained non-­grassland ance partitioning approach. land cover types. In total, 2941 grassland plots fulfilled the selection criteria and were included in the analysis (Figure 1). The median size plot size was 78.5 m2, with an interquartile range from 20 to 80 m2. 2 | M ATE R I A L S A N D M E TH O DS Species richness ranged from 1 to 105 species (median: 22). 2.1 | Datasets 2.3 | Annual maximum NDVI We based this study on the integration of data compiled from three global initiatives: (1) the vegetation plot database sPlot, v.2.1 We used annual maximum NDVI as a proxy for primary productivity (Bruelheide et al., 2019), combined with (2) the TRY database of plant at the selected grassland plots. This value corresponds to the yearly functional traits (Kattge et al., 2011) and (3) MODIS satellite data maximum photosynthetic activity of the vegetation and is consid- providing NDVI time series (Didan, 2015). Additionally, we used the ered an indicator for ecosystem productivity (Pettorelli et al., 2005). land cover map GlobCover 2009 (Arino et al., 2012) and temperature We obtained the annual maximum NDVI values from the MODIS and precipitation data from the CHELSA project (Karger et al., 2016). product MOD13Q1, which has global coverage, a spatial resolu- tion of 250 m and a temporal resolution of 16 days (Didan, 2015). Furthermore, owing to the high image frequency of MODIS, its NDVI 2.2 | Filtering of sPlot product has a relatively low susceptibility to cloud cover, which is why we chose it over Landsat. For each of the selected vegetation The sPlot database, v.2.1, contains information on plant community plots, we retrieved NDVI time series covering the period 2000–­2016 composition in >1.1 million vegetation plots contributed by >100 using Google Earth Engine (Google Earth Engine Team, 2015) and vegetation plot databases. In line with a long tradition of BEF research averaged the maximum annual NDVI values of the year when the in grassland ecosystems (Fraser et al., 2015; Hooper et al., 2005; vegetation plot was sampled and the following year. This averag- Tilman et al., 1996), we chose grasslands as a model system for this ing across two consecutive years was done to reduce the effect of analysis. We screened the database for vegetation plots in grass- anomalies and noise in the NDVI values and bearing in mind that the land communities. Any selected plot had to satisfy at least one of growing season in the Southern Hemisphere starts in one calendar F I G U R E 1 Locations of included vegetation plots coloured by annual maximum normalized difference vegetation index (NDVI). | 14668238, 2023, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/geb.13644 by JOSEP Penuelas - Readcube (Labtiva Inc.) , Wiley Online Library on [12/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License ENGEL et al. 699 year and ends in the next one. In cases where an NDVI pixel con- and, among them, those associated with symbionts such as mycor- tained more than one vegetation plot, surveys from different years rhiza or nitrogen-­fixing bacteria, are underrepresented in our trait were treated as independent observations. Whenever there were selection, although these traits have long been known to be related multiple vegetation plots per year and NDVI pixel, one plot was se- strongly to productivity in grasslands (Cadotte et al., 2009; Díaz & lected at random. Cabido, 2001; Weigelt et al., 2021). For more information on the selected traits and their ecological relevance, see the Supporting Information (Appendix S1). Species mean traits were retrieved from 2.4 | Functional community structure TRY (Kattge et al., 2011) and gap-­filled using Bayesian hierarchical probabilistic matrix factorization (Schrodt et al., 2015). The gap-­ We selected 18 ecologically relevant plant traits to characterize the filled trait data were available for 88.7% of all species occurrences in functional community structure at all selected plots (Figure 2). The sPlot (Bruelheide et al., 2018). We ln-­transformed all trait values for same set of traits has already been used by Bruelheide et al. (2018) downstream analysis. and Testolin et al. (2021) to analyse global trait–­environment as- We calculated the CWM for all traits using the following sociations. These traits represent different ecological trade-­offs equation: and plant strategies, such as the leaf economics spectrum (Wright ∑nk et al., 2004), plant size and reproduction (Moles & Westoby, 2006). CWMl,k = pi,k ti,l (1) i=1 Although our trait selection was ultimately driven by data availabil- ity, we note that most of the selected traits relate to the leaf eco- where nk is the number of species in plot k, pi,k is the relative cover of nomics spectrum of a plant (i.e., to its carbon gain strategy; Wright species i in plot k, and ti,l is the mean value of species i for trait l. The et al., 2004). We considered these traits as “effect traits” because of CWM is an abundance-­weighted trait mean value, which is most af- their role for biomass production (Suding et al., 2008). We acknowl- fected by the dominant species in the community. Accordingly, CWMs edge that other important aspects of plant physiology, particularly are often linked to the mass ratio effect and dominance hypothesis those related to C3/C4 photosynthetic pathways, belowground traits (Garnier et al., 2004). F I G U R E 2 Variables factor map on the first two principal components analysis (PCA) axes for community-­weighted mean (CWM) traits. Arrows indicate the strength and direction of correlation between the trait CWM and the axes. We interpret CWM axis 1 as a resource economics spectrum (acquisitive vs. conservative) axis and CWM axis 2 as a plant size axis. Traits are as follows: specific leaf area (SLA), leaf P concentration (Leaf.P), leaf N concentration (Leaf.N), leaf area (Leaf.A), number of seeds of the reproductive unit (Rel.seed.num), leaf fresh mass (Leaf.fr.mass), stem conduit density (Stem.cond.dens), leaf δ15N (dN15), plant height (Height), dispersal unit length (Disp.u.l), wood vessel length (Vessel.l), seed length (Seed.l), leaf C per dry mass (Leaf.Cpmass), seed mass (Seed.m), leaf N/P ratio (NpP), leaf N per area (Leaf. NpA), leaf dry matter content (LDMC) and stem specific density (Stem.Dens). | 14668238, 2023, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/geb.13644 by JOSEP Penuelas - Readcube (Labtiva Inc.) , Wiley Online Library on [12/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 700 ENGEL et al. To reduce the functional trait space described by the 18 CWM (Laliberté & Legendre, 2010). Here, we interpret associations be- variables, we performed a principal components analysis (PCA) tween FD and NDVI as evidence for niche partitioning effects. on the CWM values of the selected sPlot communities using the R package FactoMineR (Lê et al., 2008). The first two PCA axes of the CWM space captured 27.42% (CWM axis 1) and 20.88% (CWM 2.5 | Data analysis axis 2) of the total variance in the data (Figure 2). Communities that had negative scores on CWM axis 1 were dominated by species We modelled annual maximum NDVI using a number of nested gen- with “acquisitive” traits, such as high specific leaf area and high leaf eralized additive models (GAMs; Wood, 2017) with subsets of pre- concentrations of nitrogen and phosphorus. Positive values on this dictors relating to traits (CWM axis 1, CWM axis 2 and FD), climate axis were accompanied by a dominance of species with “conserva- (temperature and precipitation) and space (latitude and longitude) tive” traits, such as high leaf dry matter content and stem density (Table 1). We chose the GAM framework because it allowed us to (Supporting Information Appendix S2). This axis corresponds to the include the spatial coordinates as smooth spherical splines, which resource economics spectrum described by many studies (i.e., leaf accounts for the spatial structure of the NDVI response variable and economics spectrum, fast–­slow spectrum; Díaz et al., 2016; Reich spatial autocorrelation in the residuals. All other predictors were in- et al., 1997; Wright et al., 2004). The CWM axis 2 was related to cluded as simple linear terms. As climatic variables, we included mean overall plant size: communities with high scores on this axis were annual temperature (Temp) and the precipitation of the driest quar- characterized by relatively tall species, with both large and heavy ter (Prec). With the response variable NDVI ranging between zero leaves and seeds. This corresponds to a plant size spectrum that is and one, we specified the models using the beta-­regression family commonly found in plants (Díaz et al., 2016). We used the two axes and a logit link function, using the mgcv R package (Wood, 2017). All as composite variables characterizing the community position along linear predictors were scaled and centred before entering the model. the resource economics and plant size spectrum. In the subsequent After fitting all models, we used a deviance partitioning ap- analysis, we interpreted associations between the CWM axes and proach to disentangle the predictive effects of climate, traits and NDVI as evidence for dominance effects. space on NDVI. To obtain the unique fraction explained by a set of To quantify functional diversity across all 18 traits, we calcu- predictors, we calculated the difference in model deviance of nested lated functional dispersion (hereafter, FD) using the R package FD models with and without the given set of predictors and expressed (Laliberté et al., 2014) and taking Gower's distance as the dissimilar- it as a percentage of the total deviation of the full model. Shared ity measure (Podani, 1999). FD has the advantage of accommodating fractions between two predictor sets were calculated by subtracting multiple traits in addition to species abundances. Furthermore, it is their unique contributions from the joint model deviance. Taking the independent of species richness and relatively unaffected by outliers same approach, we also partitioned the contribution of traits further TA B L E 1 Fitted models predicting Deviance annual maximum normalized difference Model name Predictors ΔAIC explained (%) vegetation index (NDVI) Full CWM axis 1+ CWM axis 2+ 0 85.97 FD + Prec + Temp + s(Lat, Lon) Traits and space CWM axis 1+ CWM axis 2+ FD + s(Lat, 16 85.90 Lon) Climate and space Prec + Temp + s(Lat, Lon) 42 85.74 Only space s(Lat, Lon) 64 85.63 Climate and traits CWM axis 1+ CWM axis 2+ 2383 63.52 FD + Prec + Temp Only traits CWM axis 1+ CWM axis 2+ FD 3133 51.95 Only climate Prec + Temp 3257 49.77 CWM1 and FD CWM axis 1+ FD 3353 47.85 Only CWM CWM axis 1+ CWM axis 2 3521 44.53 Only CWM1 CWM axis 1 3648 41.83 CWM2 and FD CWM axis 2+ FD 4467 21.30 Only FD FD 4617 16.77 Only CWM2 CWM axis 2 5035 3.04 Abbreviations: ΔAIC, difference in Akaike information criterion; CWM, community-­weighted mean; FD, functional dispersion; Lat, latitude; Lon, longitude; Prec, precipitation of the driest quarter; Temp, mean annual temperature. | 14668238, 2023, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/geb.13644 by JOSEP Penuelas - Readcube (Labtiva Inc.) , Wiley Online Library on [12/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License ENGEL et al. 701 into the joint and unique effects of the two CWM axes and FD. All (Figure 4b). For the pairwise correlations between all model vari- analyses were run in R v.3.5.3 (R Core Team, 2019). ables, see the Supporting Information (Appendix S4). 3 | R E S U LT S 4 | DISCUSSION The full model explained 86% of the deviance in the NDVI and had A large body of research revolves around the question of how traits an adjusted R 2 of 0.87 (Table 1). All linear predictors except FD were contribute to ecosystem functioning. Using data from grasslands all significant (Figure 3a). Temperature and precipitation had positive over the globe, we found strong support for a dominance effect for effects on annual maximum NDVI. CWM axis 1 had a negative effect peak season productivity (i.e., dominant plant species contribute on annual maximum NDVI (i.e., communities dominated by species disproportionately to ecosystem productivity). At the same time, with acquisitive traits tended to have higher NDVI than those domi- our findings failed to provide evidence for the niche partitioning hy- nated by species with conservative traits), and CWM axis 2 (i.e., plant pothesis. For the large spatial extent and the traits covered by our size) had a positive effect (Figure 3a). The model coefficients and sig- study, we did not find that higher trait diversity resulted in enhanced nificance were similar across the different nested models, and the ef- biomass production. fect of FD was significant only when no spatial term was included in Our results show that grasslands dominated by species with ac- the model (Supporting Information Appendix S3). All models includ- quisitive traits have higher productivity than those dominated by ing the spatial term showed by far the best fit (Table 1). Generally, species with conservative traits. This is in line with existing evidence the spatial term was highest in mid-­ to high latitudes of Eurasia and from observational studies of smaller extents and experiments that lowest in parts of Africa and Australia (Figure 3b). The importance of point out the role of dominant species for determining ecosystem-­ space was also reflected by the “space only” model, which explained level primary productivity through their resource economics traits nearly as much deviance as the full model. The largest fraction of the (Lavorel, 2013; Mokany et al., 2008). Acquisitive species with high explained model deviance was shared between the three variable specific leaf area and leaf nutrient concentrations generally exhibit sets of climate, traits and space (38.16%), followed by the unique rapid carbon and nitrogen turnover, which entails high ecosystem-­ fraction of space (22.44%), the shared fractions of space and traits level rates of, for instance, biomass accumulation, decomposition (13.52%) and space and climate (11.50%). The unique and shared and evapotranspiration (Reich, 2014). Our study suggests that this fractions of climate and traits were relatively small, but traits had a relationship also holds at a larger spatial scale, because we found this larger unique importance than climate. Most of the unique fraction resource axis to be an important predictor of NDVI, which is a proxy of traits was linked to the CWM (especially axis 1) and not to FD for primary productivity. Traits related to overall plant, leaf and seed (Figure 4a). Notably, even the “only traits” and “only climate” models size were also positively associated with productivity. This shows that showed relatively high predictive power, with R 2 values of 0.54 and the NDVI signal is not captured fully by leaf economics traits alone 0.55, respectively. Most of total deviance explained by traits was but also requires a second trait dimension as an estimation of stand associated with the unique fraction of CWM axis 1, whereas FD and biomass (Chave et al., 2005). This axis is captured by plant height and CWM axis 2 made smaller contributions to the trait effect. There seed size, which are allometrically linked (Moles & Westoby, 2006; also was a sizeable fraction shared between CWM axis 1 and FD Rees & Venable, 2007; Thompson & Rabinowitz, 1989). F I G U R E 3 Estimates of the full model. (a) Standardized effect sizes of linear predictors (mean and confidence intervals). Negative values are red; positive values are blue. (b) Estimated smooth spatial term at the plot locations. Abbreviations: CWM, community-­weighted mean; FD, functional dispersion. | 14668238, 2023, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/geb.13644 by JOSEP Penuelas - Readcube (Labtiva Inc.) , Wiley Online Library on [12/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 702 ENGEL et al. F I G U R E 4 Venn diagram partitioning total explained model deviance (i.e., 85.99%) among sets of predictors. (a) Partitioning total deviance among climate (mean annual temperature and precipitation of the driest quarter), traits (i.e., CWM axis 1, CWM axis 2 and FD) and space (i.e., smooth term using spatial coordinates). (b) Partitioning the total fraction of traits [i.e., yellow circle in (a)] between CWM axis 1, CWM axis 2 and FD. Values are expressed as percentages. Although ecological theory and many experiments show that et al., 2019). Possibly, the role of functional diversity might be more niche partitioning can enhance ecosystem functions (Hooper substantial when quantifying productivity as an integrated measure et al., 2005; Petchey & Gaston, 2002; van der Plas, 2019), we found over the whole vegetative season, rather than a snapshot at the peak no association between functional diversity and grassland produc- of the vegetation season (Chi et al., 2017). tivity at the global scale, at least for the traits we used. Overall, the Although grassland productivity ultimately depends on plants, recent literature suggests that the effect of plant functional diversity our models highlighted the existence of important confounding on ecosystem processes is varied and subtle, especially in naturally variables, namely climate and other unmeasured factors that are assembled communities (van der Plas, 2019). Although a majority of spatially structured. It is well known that precipitation (La Pierre studies report positive relationships (Cardinale et al., 2011; Mouillot et al., 2011; Sala et al., 1988) and temperature (Briggs & Knapp, 1995) et al., 2011; Petchey & Gaston, 2006; van der Plas, 2019), it is not un- are important determinants of grassland productivity and often out- common to find negative (Thompson et al., 2005) or no associations weigh the pure effect of biodiversity (van der Plas, 2019). Although (Chollet et al., 2014) between functional diversity and ecosystem substantial, the deviance explained by climate was mostly shared functions. The effect of functional diversity is also known to vary with plant traits, highlighting the importance of climate as an en- among traits (Petchey et al., 2004). Although we covered the major vironmental filter shaping the functional structure of vegetation plant trait axes identified by Díaz et al. (2016), we cannot exclude assemblages (Bruelheide et al., 2018). Indeed, the resource econom- the possibility that some other traits would lead to a stronger as- ics axis described by CWM axis 1 is known to be linked to drought-­ sociation between functional diversity and NDVI. For example, we tolerance strategies (Reich, 2014). Both climate and vegetation are did not have data on nitrogen use (e.g., N fixation) strategies and spatially structured and therefore expected to be collinear (Currie photosynthetic pathways that are known to play a role for comple- et al., 2020). In fact, the largest fraction of the explained model mentarity in grasslands (Cadotte et al., 2009). The selection of the deviance was shared between space, climate and traits, following functional traits affecting an ecosystem function remains difficult, Tobler's well-­known first law of geography: “everything is related to because different traits might play different roles across communi- everything else, but near things are more related than distant things” ties or biomes. A new methodology has recently been published to (Tobler, 1970). Nonetheless, even when we included the smooth select those traits that contribute to functional community assembly spatial term in the model, the effect of the functional structure (i.e., (Pillar et al., 2021). Whether this technique can prove useful also for CWM) of vegetation assemblages remained significant, which sug- selecting those traits related to a specific ecosystem function, how- gests that the relationship between plant traits and productivity ever, remains to be tested. To test the sensitivity of our results to the goes beyond the mere spatial covariation in abiotic conditions and choice of the functional diversity metric, we also performed a sup- local species pools. plementary analysis using a functional diversity measure based on There is no doubt that the approach adopted in this study has Rao's quadratic entropy (Rao, 1982) rather than FD. Overall, those some limitations. Not only does the use of trait databases lead to a results were qualitatively consistent with the findings presented very rough approximation of functional community structure (e.g., here in the main text, but the effect of functional diversity was even neglecting intraspecific and site-­specific trait variation) but also smaller (Supporting Information Appendix S5). the vegetation plot database had vast gaps in its global coverage, Another possible explanation of our results relates to the use of which was partly attributable to the strict filtering criteria applied annual maximum NDVI as a response variable. This value represents here. South America and Africa are particularly underrepresented the high-­productivity season, when resource turnover is expected to in our compilation. Nevertheless, our analysis has an unprecedented be at its maximum (Pettorelli et al., 2005). Previous work shows that spatial coverage, and spans more than nine of the ten sPlot biomes niche partitioning effects in plant communities might be stronger (sensu Bruelheide et al., 2018). Furthermore, by explicitly including outside the high-­productivity season (Chollet et al., 2014; Doležal geographical coordinates in our models, we mitigated as much as | 14668238, 2023, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/geb.13644 by JOSEP Penuelas - Readcube (Labtiva Inc.) , Wiley Online Library on [12/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License ENGEL et al. 703 possible the bias deriving from the uneven distribution of plots, were funded under this scheme. We thank Ludmilla F​igu​eir​ besides partly controlling for other spatially structured drivers of edo and the iDiv Data and Code Unit for assistance with the cura- productivity, such as soil fertility and management. Moreover, there tion and archiving of the dataset. The study was supported by was a substantial scale mismatch between the vegetation surveys the TRY initiative on plant traits (http://www.try-­db.org). T.E. was and the NDVI measure. Even though the MODIS images have a pixel financially supported by the European Commission through the size of 250 m, which is roughly two orders of magnitude coarser than programme Erasmus Mundus Master Course, International Master a typical grassland plot, this was the best possible compromise in in Applied Ecology (EMMC-­IMAE; FPA 532524-­1-­FR-­2012-­ERA the trade-­off between temporal coverage and spatial resolution. MUNDUS-­EMMC). D.H. was s​upp​ort​ed by th​e C​oor​dina- The fact that we limited our analysis only to vegetation plots lo- tion of Superior Level Staff Improvement (CAPES). V.P. was sup- cated within MODIS pixels representing uniform land cover classes, ported by the National Council of Scientific and Technological Deve however, is expected to mitigate the negative consequences of this lopment (CNPq), Brazil (grants 307689/2014-­0 and 431193/2016-­9). scale mismatch. Furthermore, NDVI, like other vegetation indices, is C.V. was supported by the European Research Council (ERC) known to saturate in highly productive systems, which means that it Starting Grant Project “ecophysiological and biophysical constraints might not correctly discern productivity differences in those areas on domestication in crop plants” (grant ERC-­StG-­2014-­639706-­ (e.g., the tropics). However, this mostly concerns forests with high CONSTRAINTS). G.E.O. was funded by CNPq grant 310345/2018-­9. leaf area index values and should not be a major issue for our sites in J.A. was supported by research grants LTAUSA19137 (programme open grasslands (Haboudane et al., 2004). Finally, although the se- INTER-­E XCELLENCE, subprogramme INTER-­ACTION) provided by lected functional traits account for two of the main trait dimensions Czech Ministry of Education, Youth and Sports, 20-­05840Y of the in plants (leaf economics and size), data coverage was limited for Czech Science Foundation and long-­term research development other important aspects, such as photosynthetic pathways and as- project no. RVO 67985939 of the Czech Academy of Sciences (CAS). sociation with nitrogen-­fixing bacteria. We agree that it would have J.D. was supported by research grants MŠMT Inter-­E xcellence been very desirable to have more specific traits, example, on C3/C4 (LTAUSA18007) and Czech Science Foundation (GA 21-­26883 S). photosynthetic pathways or bacterial associations. More impor- J.P. was funded by the Spanish government grant PID2019-­ tantly, we missed belowground traits in general. We cannot exclude, 110521GB-­I 00, Catalan government grant SGR-­2017-­1005 and therefore, that such traits could increase the relative contribution of European Research Council grant ERC-­SyG-­2013-­610028. M.C. was niche partitioning mechanisms to ecosystem productivity patterns. funded by the Czech Science Foundation (project no. 19-­28491X). In conclusion, our analysis provides empirical evidence that plant P.B.R. acknowledges National Science Foundation (NSF) Long-­Term functional community structure and global patterns in primary pro- Ecological Research (LTER) grants DEB-­1234162 and DEB-­1831944, ductivity are mostly linked through the resource economics and size Long-­Term Research in Environmental Biology (LTREB) grants DEB-­ traits of the dominant species. Over large extents, the dominance 1242531 and DEB-­1753859, and Biological Integration Institutes hypothesis received substantially more support than the niche par- grant NSF-­DBI-­2021898. T.C. was funded by the Czech Science titioning hypothesis, at least based on the functional traits included Foundation (GA17-­07378 S). Open Access funding enabled and in the present study. This is consistent with the hypothesis that ­organized by Projekt DEAL. productivity mostly relates to the functional profile of dominant species, rather than to the functional diversity of a community. Our C O N F L I C T O F I N T E R E S T S TAT E M E N T results also provide an empirical justification for focusing on dom- The authors have no conflicts of interest to declare. inant plant species when running Earth system models simulating the response of vegetation to changing conditions. Identifying the DATA AVA I L A B I L I T Y S TAT E M E N T dominant traits and species and assessing their susceptibility to en- The data that support the findings of this study are openly avail- vironmental change then becomes crucial to improve our ability to able in the iDiv Data Repository at: https://doi.org/10.25829/​ foresee, and possibly mitigate negative consequences on the provi- idiv.3534-­q4ja39. sioning of plant biomass by grasslands. ORCID AC K N O​W L E​D G E​M E N T S Thore Engel https://orcid.org/0000-0002-9245-4397 sPlot has been initiated by sDiv, the Synthesis Centre of the Helge Bruelheide https://orcid.org/0000-0003-3135-0356 German Centre for Integrative Biodiversity Research (iDiv) Daniela Hoss https://orcid.org/0000-0002-6766-3904 Halle-­Jena-­Leipzig, funded by the German Research Foundation Francesco M. Sabatini https://orcid.org/0000-0002-7202-7697 (FZT 118) and is now a platform of iDiv. We are grateful to thou- Jan Altman https://orcid.org/0000-0003-4879-5773 sands of vegetation scientists who sampled vegetation plots Mohammed A. S. Arfin-­Khan https://orcid. in the field or digitized them into regional, national or interna- org/0000-0001-6275-7023 tional databases. We appreciate the support of the German Erwin Bergmeier https://orcid.org/0000-0002-6118-4611 Research Foundation for funding sPlot as one of the iDiv research Tomáš Černý https://orcid.org/0000-0003-2637-808X platforms (DFG FZT 118, 202548816). T.E., D.H. and F.M.S. Milan Chytrý https://orcid.org/0000-0002-8122-3075 | 14668238, 2023, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/geb.13644 by JOSEP Penuelas - Readcube (Labtiva Inc.) , Wiley Online Library on [12/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 704 ENGEL et al. Cardinale, B. J., Matulich, K. L., Hooper, D. U., Byrnes, J. E., Duffy, E., Matteo Dainese https://orcid.org/0000-0001-7052-5572 Gamfeldt, L., Balvanera, P., O'Connor, M. I., & Gonzalez, A. (2011). 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