# Global decoupling of functional and phylogenetic diversity in plant communities

Georg Hähn, Gabriella Damasceno, Esteban Alvarez-Davila, Isabelle Aubin, Marijn Bauters, Erwin Bergmeier, Idoia Biurrun, Anne Bjorkman, Gianmaria Bonari, Zoltán Botta-Dukát, et al.

► To cite this version:

Georg Hähn, Gabriella Damasceno, Esteban Alvarez-Davila, Isabelle Aubin, Marijn Bauters, et al. Global decoupling of functional and phylogenetic diversity in plant communities. Nature Ecology & Evolution, 2025, 9, pp.237-248. 10.1038/s41559-024-02589-0. hal-04820079

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Submitted on 10 Nov 2025

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Distributed under a Creative Commons Attribution 4.0 International License
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Global decoupling of functional and phylogenetic diversity in plant
communities

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Georg J. A. Hähn<sup>1,2,3,*</sup>, Francesco Maria Sabatini<sup>3,4</sup>, Gabriella Damasceno<sup>2,1</sup>, Esteban Alvarez-Davila<sup>5</sup>, Isabelle Aubin<sup>6</sup>, Marijn Bauters<sup>7</sup>, Erwin Bergmeier<sup>8</sup>, Idoia Biurrun<sup>9</sup>, Anne D. Bjorkman<sup>10,11</sup>, Gianmaria Bonari<sup>12</sup>, Zoltán Botta-Dukát<sup>13</sup>, Juan A. Campos<sup>9</sup>, Andraž Čarni<sup>14,15</sup>, Milan Chytrý<sup>16</sup>, Renata Ćušterevska<sup>17</sup>, André Luís de Gasper<sup>18</sup>, Michele De Sanctis<sup>19</sup>, Jürgen Dengler<sup>20</sup>, Jiri Dolezal<sup>21</sup>, Mohamed A. El-Sheikh<sup>22</sup>, Manfred Finckh<sup>23</sup>, Antonio Galán-de-Mera<sup>24</sup>, Emmanuel Garbolino<sup>25</sup>, Hamid Gholizadeh<sup>12</sup>, Valentin Golub<sup>26</sup>, Sylvia Haider<sup>27</sup>, Mohamed Z. Hatim<sup>28</sup>, Bruno Hérault<sup>29,30</sup>, Jürgen Homeier<sup>31</sup>, Ute Jandt<sup>1,2</sup>, Florian Jansen<sup>32</sup>, Anke Jentsch-Beierkuhnlein<sup>33</sup>, Jens Kattge<sup>34,2</sup>, Michael Kessler<sup>35</sup>, Larisa Khanina<sup>36</sup>, Holger Kreft<sup>37</sup>, Filip Küzmič<sup>38</sup>, Jonathan Lenoir<sup>39</sup>, Jesper Erenskjold Moeslund<sup>40</sup>, Ladislav Mucina<sup>41,42</sup>, Alireza Naqinezhad<sup>43</sup>, Jalil Noroozi<sup>44</sup>, Aaron Pérez-Haase<sup>45</sup>, Oliver L. Phillips<sup>46</sup>, Valério D. Pillar<sup>47</sup>, Gonzalo Rivas-Torres<sup>48</sup>, Eszter Ruprecht<sup>49</sup>, Brody Sandel<sup>50</sup>, Marco Schmidt<sup>51</sup>, Ute Schmiedel<sup>52</sup>, Stefan Schnitzer<sup>53</sup>, Franziska Schrodt<sup>54</sup>, Urban Šilc<sup>55</sup>, Ben Sparrow<sup>56</sup>, Maria Sporbert<sup>1</sup>, Zvjezdana Stančić<sup>57</sup>, Ben Strohbach<sup>58</sup>, Jens-Christian Svenning<sup>59</sup>, Cindy Q. Tang<sup>60</sup>, Zhiyao Tang<sup>61</sup>, Alexander Christian Vibrans<sup>62</sup>, Cyrille Violle<sup>63</sup>, Donald Waller<sup>64</sup>, Desalegn Wana<sup>65</sup>, Hua-Feng Wang<sup>66</sup>, Timothy Whitfeld<sup>67</sup>, Georg Zizka<sup>68</sup>, Helge Bruelheide<sup>1,2</sup>

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* Corresponding author

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¹ Martin Luther University Halle-Wittenberg, Institute of Biology / Geobotany and Botanical Garden, Am Kirchtor 1, 06108 Halle, Germany

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² German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstrasse 4, Leipzig, 04103, Germany

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³ University of Bologna, Department of Biological, Geological and Environmental Sciences, Via Irnerio 42, Bologna, 40126, Italy

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⁴ Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, 165 00 Praha, Czech Republic

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⁵ Universidad Nacional Abierta y a Distancia (Colombia), Escuela ECAPMA, Street 14 Sur # 14-23, Bogotá, Postal Code 111511, Colombia

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⁶ Canadian Forest Service, Great Lakes Forestry Centre, 1219 Queen St. East, Sault Ste Marie, ON, P6A 2E5, Canada

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⁷ Ghent University, Department of Environment, Coupure Links 653, 9000 Gent, Belgium
32 ⁸ University of Göttingen, Department of Vegetation & Phytodiversity Analysis, Untere Karspüle 2, Göttingen, 37073, Germany

34 ⁹ University of the Basque Country UPV/EHU, Department of Plant Biology and Ecology, Apdo. 644, 48080, Bilbao, Spain

36 ¹⁰ University of Gothenburg, Biological & Environmental Sciences, Box 463, 40530, Gothenburg, Sweden

38 ¹¹ Gothenburg Global Biodiversity Centre, Box 463, 40530, Gothenburg, Sweden

39 ¹² University of Siena, Department of Life Sciences, Via P.A. Mattioli 4, 53100, Siena, Italy

40 ¹³ Centre for Ecological Research, Institute of Ecology and Botany, Alkotmány 2-4, Vácrátót, 2163, Hungary

42 ¹⁴ Research Centre of the Slovenian Academy of Sciences and Arts, Jovan Hadži Institute of Biology, Novi trg 2, SI 1000 Ljubljana, Slovenia

44 ¹⁵ University of Nova Gorica, School for Viticulture and Enology, Vipavska cesta 13, 5000 Nova Gorica, Slovenia

46 ¹⁶ Masaryk University, Faculty of Science, Department of Botany and Zoology, Kotlářská 2, 611 37 Brno, Czech Republic

48 ¹⁷ University of Ss. Cyril and Methodius, Faculty of Natural Sciences and Mathematics, Institute of Biology, Arhimedova Str. 3, 1000, Skopje, North Macedonia

50 ¹⁸ Universidade Regional de Blumenau, Rua Antonio da Veiga, 140, 89030903, Blumenau, Santa Catarina, Brazil

52 ¹⁹ Sapienza University of Rome, Department of Environmental Biology, P.le Aldo Moro 5, Rome, 00185, Italy

54 ²⁰ Zurich University of Applied Sciences (ZHAW), Wädenswil, Switzerland

55 ²¹ Czech Academy of Science, Institute of Botany, Dukelská 135, 379 01 Trebon, Czechia

56 ²² King Saud University, College of Science, Botany and Microbiology Department, P.O. Box 2455, Riyadh 11451, Saudi Arabia

58 ²³ University of Hamburg, Institute of Plant Science and Microbiology, Ohnhorststr., 18, Hamburg, 22609, Germany

60 ²⁴ Universidad San Pablo-CEU, CEU Universities, Botany Lab, Carretera de Boadilla Km 5,300, 28660- Boadilla del Monte, Madrid, Spain

62 ²⁵ MINES Paris PSL - ISIGE, 35 rue Saint-Honoré, 77300 Fontainebleau, France

63 ²⁶ Samara Federal Research Scientific Center, Institute of Ecology of the Volga River Basin,
64 Komzina 10 Togliatti, 445003, Russia

65 ²⁷ Leuphana University of Lüneburg, Faculty of Sustainability, Institute of Ecology,

66 Universitätsallee 1, 21335 Lüneburg, Germany

67 ²⁸ Wageningen University, Plant Ecology and Nature Conservation Group, Environmental

68 Sciences Department, P.O. Box Postbus 47, Droevendaalsesteeg 3, 6700 AA, Wageningen,

69 The Netherlands

70 ²⁹ CIRAD, UPR Forêts et Sociétés, Campus de Baillarguet, F-34398 Montpellier, France

71 ³⁰ University Montpellier, CIRAD, Forêts et Sociétés, Campus de Baillarguet, F-34398

72 Montpellier, France

73 ³¹ HAWK Goettingen, Resource Management, Daimlerstraße 2, 37075 Goettingen, Germany

74 ³² University of Rostock, Justus-von-Liebig-Weg 6, 18059 Rostock, Germany

75 ³³ University of Bayreuth, Bayreuth Center of Ecology and Environmental Research,

76 Department of Disturbance Ecology, Bayreuth, Germany

77 ³⁴ Max Planck Institute for Biogeochemistry, Hans Knöll Str. 10, 07745 Jena, Germany

78 ³⁵ University of Zurich, Systematic and Evolutionary Botany, Zollikerstrasse 107, CH-8008

79 Zurich, Switzerland

80 ³⁶ IMPB RAS, Branch of the M.V. Keldysh IAM RAS, 1 Prof. Vitkevicha Str., Pushchino,

81 142290, Russia

82 ³⁷ University of Göttingen, Department of Biodiversity, Macroecology & Biogeography,

83 Büsgenweg 1, 37077 Göttingen, Germany

84 ³⁸ Research Centre of the Slovenian Academy of Sciences and Arts, Jovan Hadži Institute of

85 Biology, Novi trg 2, 1000, Ljubljana, Slovenia

86 ³⁹ UMR CNRS 7058 Ecologie et Dynamique des Systèmes Anthropisés (EDYSAN), Université

87 de Picardie Jules Verne, 1 rue des Louvels, 80000 Amiens, France

88 ⁴⁰ Aarhus University, Department of Ecoscience, C. F. Møllers Allé 6-8, DK-8000, Aarhus C,

89 Denmark

90 ⁴¹ Murdoch University, Harry Butler Institute, 90 South Street, Murdoch 6150, Perth,

91 Western Australia, Australia

92 ⁴² Stellenbosch University, Department of Geography & Environmental Studies, Private Bag

93 X1, 7602 Matieland, Stellenbosch, South Africa

94 ⁴³ University of Mazandaran, Pasdaran, Campus of the University, Babolsar, 47416-74765,

95 Iran
96 ⁴⁴ University of Vienna, Department of Botany and Biodiversity Research, Rennweg 14,
97 Vienna, 1220, Austria

98 ⁴⁵ Universitat de Barcelona, Facultat de Biologia, Institut de Recerca de la Biodiversitat
99 (IRBio), Av. Diagonal 643, Barcelona, 08036, Spain

100 ⁴⁶ University of Leeds, Leeds LS2 9JT, United Kingdom

101 ⁴⁷ Universidade Federal do Rio Grande do Sul, Department of Ecology, Department of
102 Ecology, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, 91501-970, BRAZIL

103 ⁴⁸ Universidad San Francisco de Quito USFQ, Estación de Biodiversidad Tiputini, Colegio de
104 Ciencias Biológicas y Ambientales, Diego de Robles sn e Interoceanica, Quito, Ecuador

105 ⁴⁹ Babeş-Bolyai University, Faculty of Biology and Geology, Hungarian Department of Biology
106 and Ecology, Republicii street 42., 400015 Cluj-Napoca, Romania

107 ⁵⁰ Santa Clara University, Department of Biology, 500 El Camino Real, Santa Clara CA, 95053,
108 USA

109 ⁵¹ Palmengarten der Stadt Frankfurt am Main, Wissenschaft, Siesmayerstraße 61, 60323
110 Frankfurt am Main, Germany

111 ⁵² University of Hamburg, Institute of Plant Science and Microbiology, Ohnhorststraße 18,
112 22609 Hamburg, Germany

113 ⁵³ Marquette University, PO Box 1881, Milwaukee WI 53202

114 ⁵⁴ University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom

115 ⁵⁵ ZRC SAZU, Institute of Biology, Novi trg 2, Ljubljana, Slovenia

116 ⁵⁶ University of Adelaide, TERN, The School of Biological Sciences, Waite Campus, PMB 1,
117 Glen Osmond, SA, 5064, Australia

118 ⁵⁷ University of Zagreb, Faculty of Geotechnical Engineering, Hallerova aleja 7, HR-42000
119 Varaždin, Croatia

120 ⁵⁸ Namibia University of Science and Technology, Biodiversity Research Center, Faculty of
121 Health, Natural Resources and Applied Sciences, 13 Jackson Kaujeua street, Windhoek,
122 Namibia

123 ⁵⁹ Aarhus University, Department of Biology, Center for Ecological Dynamics in a Novel
124 Biosphere (ECONOVO), Ny Munkegade 114, DK-8000 Aarhus C, Denmark

125 ⁶⁰ Yunnan University, Institute of Ecology and Geobotany, College of Ecology and
126 Environmental Science, Building Shixun, Chenggong Campus, Dongwaihuan South Road,
127 University Town, Chenggong New District, Kunming, Yunnan 650504, China
128 ⁶¹ Peking University, Department of Ecology, College of Urban and Environmental Sciences,

129 Yiheyuan Road 5, Haidian, Beijing 100871, China

130 ⁶² Universidade Regional de Blumenau (FURB), Rua São Paulo, 3250, Blumenau-Santa

131 Catarina Zipcode 89030-000, Brazil

132 ⁶³ CEFE, Univ Montpellier, CNRS, EPHE, IRD, Campus CNRS, 1919 route de Mende, 34293

133 Montpellier, France

134 ⁶⁴ University of Wisconsin - Madison, Botany, 2150 West Lawn Ave, Madison WI, 53711, USA

135 ⁶⁵ Addis Ababa University, Department of Geography & Environmental Studies, Bole street,

136 Addis Ababa, P.O. Box 150178, Ethiopia

137 ⁶⁶ Hainan University, Sanya Nanfan Research Institute, Sanya, 572500, Hainan, China

138 ⁶⁷ University of Minnesota, Bell Musuem, 1479 Gortner Avenue, St. Paul, MN 55108, USA

139 ⁶⁸ Senckenberg Research Institute and Natural History Museum Frankfurt and Goethe

140 University, Department Botany and Molecular Evolution, Senckenberganlage 25, 60325

141 Frankfurt/Main, Germany

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## Summary

Plant communities are composed of species that differ both in functional traits and evolutionary histories¹⁻³. Understanding how species' traits and histories interact with environmental conditions of a site to drive the assembly of ecological communities is the basis for predicting the impacts of climate change on biodiversity and ecosystem functioning⁴⁻⁶. As species' functional traits partly result from their individual evolutionary history, the functional diversity (FD) of communities is expected, on average, to covary positively with their phylogenetic diversity (PD)⁷⁻⁹. However, this expectation has only been tested at local scales, for specific growth forms or specific habitat types, e.g. grasslands⁷,¹⁰. Here, using the global sPlot database, we compared the standardized effect sizes of FD and PD across 1,782,777 vegetation plots¹¹. In contrast to our expectations, the correlation between FD and PD was weak, not positive but negative, and these two facets of diversity were more often decoupled than coupled. While PD reflected recent climatic conditions and vegetation type, being higher in forests, FD depended on recent and past climatic conditions. The independent nature of functional and phylogenetic diversity makes it crucial to consider both aspects of diversity when analyzing ecosystem functioning or prioritizing conservation efforts.

## Introduction

Climate change and biodiversity loss are two of the most pressing environmental issues of our time, with rising temperatures and shifting precipitation patterns increasingly becoming one of the main drivers of the extinction of many plant species¹². These changes have significant implications for ecosystems and human societies alike, with impacts ranging from altered agricultural yields to increased risk of natural disasters¹³,¹⁴. In order to understand and mitigate the effects of climate change and biodiversity loss, it is crucial to understand how plant species are assembled into communities and how these communities respond to changing environmental and climatic conditions⁴,⁵. This means that we need to understand the underlying mechanisms of plant community assembly, and how these mechanisms are mediated by the interactions between species' functional traits, evolutionary histories and environmental conditions⁶.
Community assembly is the net result of several processes that partially reinforce or oppose each other. On the one hand, environmental filtering results in a phenotypic clustering of traits in a community¹,². On the other hand, biotic interactions, and in particular competitive exclusion, result in trait divergence, increasing the trait differences among resident species³,¹⁵. While species functional traits clearly play a big role in community assembly, they are also the result of the evolution of a species in its environment. In other words, functional traits have a phylogenetic component. This means that species that are closer on the evolutionary tree are more likely to share similar traits compared to species that are less closely related. Depending on the velocity of evolution, specific traits can be more or less conserved on the phylogenetic tree¹⁶,¹⁷. If the distances in trait values between the resident species in a community can be reflected by the distances in the phylogeny, we consider this trait to be phylogenetically conserved⁶,⁷,¹⁸.

If the species in a community share similar traits, the community is said to show phenotypic clustering, which is equivalent to having a low functional diversity (FD). Phenotypic clustering can be the result of two patterns, either of a combination of phylogenetic clustering with trait conservatism (Fig. 1, bottom left) or a combination of phylogenetic dispersion with trait convergence (Fig. 1, bottom right)⁶,⁸,¹⁶. In the former case, there is a positive covariation between phylogenetic and functional distances, which is why we call the resulting communities coupled. In the latter case, the phylogenetic and functional distances are inversely related, and thus, we call the resulting communities decoupled.

In contrast, if species in a community have dissimilar traits, the community has a high phenotypic dispersion, which is equivalent to a high FD. High FD can either happen in combination with phylogenetic dispersion (Fig. 1, top right) or phylogenetic clustering (Fig. 1, top left). Again, in the former case phylogenetic and functional diversities are coupled, while they are inversely related, and therefore decoupled, in the latter case⁸,¹⁹. Many local studies found a prevalence of coupled communities with positive relationships of functional and phylogenetic diversity (FD, PD)⁹,²⁰,²¹, but negative relationships¹⁰,²² and unclear patterns also have been encountered²³. However, it is not yet known under which conditions communities express coupled or decoupled functional and phylogenetic diversities.

By calculating functional and phylogenetic diversity for 1,782,777 vegetation plots from sPlot¹¹, the global vegetation plot database, we tested whether patterns of coupling or
decoupling 1) dominate at the global level, 2) have regional patterns, 3) differ between forest and non-forest ecosystems, and 4) correlates with current and past climatic gradients. We hypothesized an overall coupled pattern of functional and phylogenetic diversity, since phylogenetic diversity has often been found to reflect functional trait diversity, including phylogenetically conserved traits which are not easily measurable in plants, such as herbivore and pathogen resistance<sup>7,16,24</sup>. We expected higher phylogenetic diversity in forests than in non-forest ecosystems due to the co-occurrence of woody and non-woody plant species, given that the herbaceous habit has evolved from the ancestral woody state multiple times and in different lineages<sup>25-28</sup>. Since phylogenetic and functional diversity metrics are correlated with species richness, we used null models to calculate standardized effect sizes and quantify how much phylogenetic and functional diversity differed from random expectations, before comparing them<sup>29</sup>.
<figure><img src="image_2.png" /><figcaption>Figure 1: Conceptual figure of the relationship between functional and phylogenetic diversity after Ref. 7 & 8. If functional diversity is proportional to community phylogenetic diversity, we consider the community to be coupled (diagonal). The extremes are the results either of phylogenetic clustering in combination with trait convergence (bottom left) or phylogenetic overdispersion in combination with trait divergence (top right). Decoupled communities can be either observed if a community shows phylogenetic overdispersion in combination with trait convergence (bottom right) or if it shows phylogenetic clustering with trait divergence (top left).</figcaption></figure>

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# Results

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## The relationship of functional and phylogenetic diversity

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For three selected traits (specific leaf area, specific root length and plant height) we modelled the relationship between functional diversity (SES.FDQ) and phylogenetic diversity (SES.PDQ).

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Both indices were calculated using Rao's quadratic entropy and transformed to standardized effect sizes, based on a null model that accounts for uneven species richness across plots. We did this because both functional and phylogenetic diversity are tightly related to species richness. Out of 1,782,777 vegetation plots, approximately 37.45% showed trait and phylogenetic coupling as SES.FDQ and SES.PDQ were equally high or low. Approximately 34.29% of the vegetation plots tended to be higher in SES.FDQ than in SES.PDQ and 28.26% to be higher SES.PDQ than SES.FDQ, suggesting that decoupled plant communities are twice as common than coupled ones (Fig. 2A). Additionally, we found that most coupled plant communities (95%) show low functional and phylogenetic diversity with values close to zero, whereas the decoupled communities were further spread to the extremes of either SES.FDQ or SES.PDQ.

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We did not find any clear geographical pattern on the global scale (Fig. 2B). Decoupled communities with high SES.FDQ and low SES.PDQ, (see Methods for definition of high and low values of SES.FDQ and SES.PDQ) occurred in the western USA and locally across Europe, while communities with low SES.FDQ and high SES.PDQ were found close to the Arctic circle in Scandinavia and Siberia, and in New-Zealand and Japan. Coupled communities with high values of both diversity indices were encountered in the eastern USA, Central-Europe as well as in New-Zealand and Japan.

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Overall, we found a negative relationship between SES.FDQ and SES.PDQ. Accounting for the spatial structure of the data, our general additive model explained 5.8% of the deviance in SES.FDQ (Fig. 2A). Modelling the raw values of FDQ against the raw values of PDQ, i.e., not accounting for the effect of species richness, also returned a negative relationship with 18.5% of deviance explained. The explained deviance increased to 36.2% when the distance matrix of phylogenetic distances was square root-transformed, accounting for the non-linearity of trait evolution. For standardized effect size the explained deviance increased to 6.2% (Fig. S 1).

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<figure><img src="image_3.png" /><figcaption>Figure 2: The relationship of standardized effect size of quadratic functional (SES.FDQ) and phylogenetic diversity (SES.PDQ). SES.FDQ is based on three functional traits: specific leaf area,</figcaption></figure>

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plant height and specific root length. A SES.FDQ as a function of SES.PDQ with the linear regression slope (blue) after accounting for spatial autocorrelation with a general additive model (5.8% explained deviance). Additionally, the line of coupling with the 1:1 relationship (black) and the confidence interval (grey, see Methods). 37.45% observations are lying within the confidence interval, where the 2.5% quantile and 97.5% quantile within this confidence interval are marked with the dashed lines, indicating coupling is observed with relatively low values of SES.FDQ and SES.PDQ. 34.29% and 28.26% of all observations show decoupling, with either FD > PD or FD < PD, respectively. B Mean standardized effect sizes of functional (SES.FDQ) and phylogenetic diversity (SES.PDQ) per raster cell (863.8 km²). The observed residuals and the extracted smooth on spline from the General Additive Model (GAM) can be found in Fig. S 2.

## The environmental drivers of functional and phylogenetic diversity

We used Boosted Regression Trees (BRT) to select the environmental variables that best explain either SES.FDQ or SES.PDQ. The BRTs suggested climate variables to be most relevant for shaping patterns of SES.FDQ. Temperature of the coldest quarter and coldest month (both reflected by PC2 in a principal component analysis based on 19 bioclimatic variables) had the highest relative influence followed by the climate variability after the Last Glacial Maximum (LGM) and vegetation type (i.e., forest or non-forest) on SES.FDQ (Fig. 3). Furthermore, annual temperature range (PC3), precipitation seasonality (PC5) and the plot size had more relative influence than expected by chance. Partial dependence plots suggested a negative relationship between SES.FDQ and climate variability after the LGM as well as annual temperature and precipitation seasonality (Fig. S 3). Temperature of the coldest quarter and month, plot size and non-forest vegetation were positively correlated with SES.FDQ.

Regarding phylogenetic diversity, SES.PDQ was especially related to the vegetation type, being higher in forest compared to non-forest ecosystems, and increased with annual precipitation (PC1; Fig. S 4).
<figure><img src="image_4.png" /><figcaption>Figure 3: Results of the Boosted Regression Trees for SES.FDQ and SES.PDQ. An explanatory variable was considered relevant in the model when its relative influence was greater than 10%, indicated by the dashed line, which is the expected influence of a variable if all ten predictors had an equal relative importance.</figcaption></figure>

When modelling the log ratio of SES.FDQ and SES.PDQ, as log(SES.FDQ/SES.PDQ), the BRTs showed that the relationship was largely driven by SES.PDQ, with the ranking of influential variables mostly equivalent to that observed for SES.PDQ (Fig. 5, S 6).

Only those variables with relative influence greater than 10% in the BRTs were used as predictors in the general additive models (GAM) with SES.FDQ or SES.PDQ as response variables and accounting for spatial autocorrelation. The model for SES.FDQ explained 5.3% of the deviance and suggested a positive relationship with climate variability after the LGM, temperature of the coldest quarter and month (PC2) and plot size (Fig. 4). A negative relationship was shown for annual temperature range and precipitation seasonality (PC3, PC5). SES.FDQ tended to be higher in non-forest vegetation types than in forests.

Annual precipitation (PC1), vegetation type and the geographical smoother spline explained 39.8% of the deviance of the SES.PDQ model. SES.PDQ increased with increasing precipitation and was higher in forests than in non-forest ecosystems (Fig. 5).
<figure><img src="image_5.png" /></figure>

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Figure 4: Drivers of standardized effect size of functional diversity (SES.FD<sub>Q</sub>). SES.FD<sub>Q</sub> as a function of **A** climate variability after the last glacial maximum, **B** temperature of the coldest quarter and month (PC2), **C** annual temperature range (PC3), **D** precipitation seasonality, **E** plot size, and **F** vegetation type based on the dominant species. The general additive model (GAM) explained 5.3% of the deviance. The solid line shows the regression obtained from the GAM. The density hexagons show the regression by plotting the residuals of the model without the explanatory variable of interest. **G** The smooth term of SES.FD<sub>Q</sub> in the space indicates lower and higher than expected functional diversity with blue and red colour, respectively.

<figure><img src="image_12.png" /></figure>

Figure 5: Drivers of standardized effect size of phylogenetic diversity (SES.PD<sub>Q</sub>). SES.PD<sub>Q</sub> as a function of **A** PC1 - annual precipitation, and **B** vegetation type based on the dominant species. The general additive model (GAM) explained 39.8% of the deviance. The solid line shows the regression obtained from the GAM. The density hexagons show the regression by plotting the residuals of the model without the explanatory variable of interest. **C** The smooth term of SES.PD<sub>Q</sub> in the space indicates lower and higher than expected functional diversity with blue and red colour, respectively.

Discussion

Plant communities differ in their functional and phylogenetic composition. Here, we modelled
the relationship between functional and phylogenetic diversity in plant communities across
the globe and made inferences about the drivers of these diversity facets. We showed that
values of functional and phylogenetic diversity tend to be decoupled, suggesting global
patterns of community assembly are primarily driven by either functional or phylogenetic
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diversity, rarely acting together. Functional diversity is driven by both recent climate conditions, as well as by past climatic events. In line with our initial hypothesis, we found high phylogenetic diversity in forest communities.

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Contrary to our hypothesis, we found a negative but weak relationship between functional diversity (FD) and phylogenetic diversity (PD) at the global scale. AS PD is often considered to be a proxy for capturing unmeasured patterns of species functional traits, we did expect a positive relationship between FD and PD³⁰, as postulated also by theoretical studies⁹. The negative correlation observed at the global scale shows functional and phylogenetic diversity were more often decoupled than coupled in plant communities, with communities either having high phylogenetic or functional diversity is in line with recent results in grassland communities¹⁰. Additionally, varying trait patterns on the phylogeny have been observed on small spatial scales, including both trait clustering and overdispersion⁷,¹⁶. This indicates that phylogenetically related species exhibit greater differences in their trait values, while phylogenetically distant species tend to share more similar trait values. It is likely that co-occurring species with similar traits differ in other, not easily measurable traits, e.g., herbivory resistance, which are captured by phylogeny but less so by functional traits. Vice versa, phylogenetically close species tend to vary in their functional traits. These patterns might be explained by the trade-off between limiting similarity and niche partitioning in community assembling processes³¹⁻³³. In phylogenetically closely related communities, niche partitioning seems to be the main acting mechanism, causing higher FD. In contrast, limiting similarity seems to be the driving process in communities with high PD. In communities with intermediate values of PD, both processes seem to be equally important, thus resulting in coupled communities. However, our findings do not exclude the existence of coupled functional and phylogenetic diversities. In fact, we show that coupling does occur, particularly in plant communities with relatively low FD and PD. Moreover, the predominance of decoupling points to different geographical or local drivers affecting the functional and phylogenetic relationship among co-occurring species.

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We found no clear spatial patterns of functional and phylogenetic diversity, as vegetation plots with coupling and decoupling of FD and PD were observed in geographically close plant communities, indicating that local scale factors are the drivers of diversity within specific regions. Previous studies have reported geographical patterns of functional diversity based

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on climatic conditions, such as precipitation gradients³⁴. Similarly, phylogenetic diversity can differ along latitudinal gradients, decreasing northwards³⁵,³⁶. However, those patterns are often observed at smaller spatial scales and studying specific species or species groups rather than complete plant communities. This means that regional diversity patterns do not translate into global patterns, because local geographical conditions are likely to be blurred by broader environmental conditions at the global scale.

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Functional diversity was not well explained by current climatic conditions, climate variability after the Last Glacial Maximum (LGM), plot size, and the vegetation type. Surprisingly, we found a positive relationship between plot size and FD. Plot size is known to be positively related to species richness³⁷. Having controlled for the effect of species richness on FD through the calculation of standardized effect sizes, however, we did not expect to see this pattern. Rather than an effect of species richness *per se*, we believe that this positive relationship depends on the different plot sizes commonly used by ecologists when sampling different vegetation types. Forests, for instance, are normally sampled in larger plots compared to grasslands and other open habitats. This suggests the predictor "plot size" carries information on the vegetation type of the plot, which complements the simple forest\non-forest classification we used. It is likely that using a finer classification of vegetation types, as it was done in a recent Europe-wide analysis on climate-trait relationship³⁸, would both decrease the predictive power of plot size, and increase the overall explanatory power of our model. However, even though we were able to reveal some tendencies between SES.FDQ and environmental conditions, we were not able to show that SES.FDQ is strongly driven by those conditions at the global scale. This is in line with studies suggesting that the functional composition of local communities depends mostly on local factors, such as land use history, soil properties and microclimatic conditions²¹,³⁹.

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Phylogenetic diversity (SES.PDQ) was consistently higher in forests compared to non-forest ecosystems, which points to the complex evolutionary structure of the different layers of forest communities. Forests are normally characterized by the presence of different structural layers, with woody and non-woody species occupying different layers. Interestingly, this stratification was connected to increased phylogenetic diversity but not to higher functional diversity. We interpret this pattern as the result of the evolutionary history of trees, which significantly differs from that of the understory vegetation. Many tree species belong to

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families that are mostly woody, meaning they are more phylogenetically distant to other plant families which contributes to the high phylogenetic diversity found in forest ecosystems²⁵⁻²⁷.
This is particularly true for conifers, which represent a clade of woody species separating from today's angiosperms as early as 300 Mya¹⁸, and now most commonly found in forests.
Furthermore, ferns and lycopods, which are typical components of the herb layer in temperate forests and typically occur as epiphytes in tropical forests, represent very distinct evolutionary histories compared to trees, contributing to the increased phylogenetic distance between co-occurring species that we observed in forests⁴⁰,⁴¹. Additionally, more stable microclimatic conditions under a closed canopy could have led to more favorable conditions for different species across distinct families⁴²,⁴³. Overall, our findings suggest that while forest ecosystems display high PD, the functional diversity of plant species within these ecosystems may be more limited due to convergence in functional traits across different layers.

Our work represents the first example of the relationship between functional diversity and phylogenetic diversity on the global scale, but it does not come without limitations. Even since sPlot is one of the biggest databases of harmonized global vegetation plots, it is not balanced at the global distribution of vegetation observations. This limitation leads to uneven coverage of plots across Earth's biomes and vegetation types, resulting in different coverages of plant families with more or less conserved traits occurring in specific regions. As species abundance data is not well standardized in sPlot, the use of presence-absence data was the robust choice, which might limit the comparison to other studies on functional and phylogenetic diversity. However, abundance data might not reveal the complete community structure by being influenced by single hyper-abundant species. Furthermore, the selection of functional traits might lead to differences in the observed relationship of functional and phylogenetic diversity. However, we tested the behavior of individual traits, revealing that individual functional traits can effectively capture the overall pattern of the relationship between FD, calculated based on multiple traits, and PD. We observed a consistent negative correlation between FD and PD across different traits, indicating that the specific trait chosen to calculate FD does not significantly impact this relationship (Fig. S 7, Tab. S 1). However, when including PD as an explanatory variable for future studies, it is important to consider the relationship between traits and phylogeny and the potential non-linearity of trait evolution. Additionally, our analysis revealed that none of the potential traits exhibited a strong phylogenetic signal
across all families. Moreover, it appeared that certain families tend to possess more
conserved traits compared to others. Consequently, depending on the sampled community
and plant species, different patterns may emerge in the relationship between FD and PD.

Our findings on the relationship of SES.FDQ and SES.PDQ, imply that ecological communities
can exhibit a wide range of combinations of functional and phylogenetic diversity. Considering
the diverse and context-dependent nature of FD and PD, will help us to better understand
and protect the intricate dynamics and complexities of ecological communities.

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# Methods

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## Species community data

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The vegetation plot database sPlot¹¹ (www.idiv.de/splot) is a harmonized collection of national- and regional-scale vegetation-plot datasets. sPlot provides geo-referenced information on the presence and abundance of all vascular plants co-occurring in a sampling area, i.e., vegetation plot. The database version sPlot 3.0 contains 1,977,637 vegetation plot records from 160 datasets collected between 1873 and 2019, across six continents and most biomes, including 76,912 vascular plant species (for version 2.1, see ref. 11). The size of a plot varies according to the type of vegetation being sampled; plot size ranged from 1 m² in grasslands to 250,000 m² in forest ecosystems. Vegetation type was classified as forest and non-forest based on tree layer cover and the growth form of dominant species¹¹. Vegetation plot records were included in the study if the cumulative coverage of species for which both trait and phylogenetic information was available accounted for at least 50% of the relative vegetation cover in that plot (see below).

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In addition, the global, open-access dataset of vegetation plots sPlotOpen⁴⁴, which is an environmentally balanced subset of the sPlot vegetation database was used for validating the trait selection when calculating functional diversity.

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## Functional diversity

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Plant functional traits were available from the gap-filled version of the TRY database⁴⁵⁻⁴⁸. Functional diversity was calculated as Rao's quadratic entropy (FDQ) for all vegetation plots in sPlot 3.0. The calculation of Rao's quadratic entropy⁴⁹ is based on a Gower distance matrix calculated for the species present in each vegetation plot. We based this calculation on three functional traits selected to represent different axes in the plant economic spectrum and having good data coverage in the TRY database, i.e., specific leaf area (SLA), specific root length (SRL) and plant height. Additional, FDQ was calculated for eight functional traits (SLA, SRL, seed mass, plant height, leaf phosphorus and nitrogen content, leaf dry matter content, chromosome n) single and combined based on sPlotOpen to evaluate the influence of the trait selection on the relationship of functional and phylogenetic diversity.
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Functional traits can be conserved on the phylogeny. This was tested with two evolutionary models, i.e., Blomberg's *K* and Pagel's *λ*, where the latter is known to be more robust against incomplete resolved phylogenies or suboptimal branch lengths⁵⁰,⁵¹. Pagel's *λ* and Blomberg's *K* were calculated using the function *phylosig* from the R-package *picante*⁵². In contrast to other tests for phylogenetic signals both can be used to compare values across different phylogenies⁵⁰, which needs to be done as a global plant phylogeny is simply too large for an appropriate calculation of phylogenetic signals. Therefore, the phylogenetic signal for each trait was calculated within each family. All eight functional traits showed either no or low phylogenetic signals either for Pagel's *λ* and Blomberg's *K* (Fig. S 7 B & C).

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## Phylogenetic diversity

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For all species present in sPlot, a phylogenetic tree was built using the function *phylo.maker* from the R-package *V.PhyloMaker*⁵³. The phylogenetic backbone of the package is the combination of GenBank taxa with a backbone provided by the Open Tree of Life, version 9.1 (GBOTB), for seed plants⁵⁴ and the clade of pteridophytes⁵⁵. Missing genera were inserted to the half point of the family tree. This approach was evaluated by ref. 56, who showed that phylogenetic indices based on the calculated tree were highly correlated with indices based on the "PhytoPhylo megaphylogeny" (updated phylogenetic tree from ref. 55). Species that could not be inserted by the *phylo.maker* were bound to the half of the terminal level of a sister species if only one species was available in this genus, or to the most recent ancestor (MRCA) if the genus included more than one species. This additional binding was done with the *bind.node* function from the R-package *phytools*⁵⁷.

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The computed phylogenetic tree for sPlot contained 160 families with 68,052 of 76,912 species (88%) present within the database. Additional 3,802 species were included, with 3,348 being bound to the node of the most recent ancestor (MRCA) of already present sister species and 454 species to the half of the terminal level on the family node. The final phylogenetic tree contained 71,854 species on 32,395 nodes. A total of 31,727 species in the phylogeny also had traits in the TRY database. These species were used to calculate functional and phylogenetic diversity. Vegetation plot records were only included in the analysis if the species in that record for which both traits and phylogenetic information was available for at

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least 50% of the total relative cover. In total, 1,782,777 out of 1,977,637 plot records remained.

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Phylogenetic diversity was calculated as Rao's quadratic entropy (PDQ) with the function *raoD* from the R-package *picante*<sup>52</sup> based on the cophenetic distance of all *n* species in the phylogeny, pruned to contain only the species in that plot. To account for the non-linearity of evolutionary histories, we also calculated PDQ based on the square root-transformed cophenetic distance<sup>58</sup>. All indices were calculated based on the species present in the phylogeny and with known traits.

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Standardized effect size

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Functional and phylogenetic diversity indices are known to depend on species richness⁵⁹⁻⁶¹. Especially for functional diversity, a higher number of species in a community is more likely to return higher functional diversity values than communities with fewer species⁶⁰. Therefore, the standardised effect size of each diversity index was calculated for every vegetation plot record⁶², fixing the number of species of the plot record and drawing species randomly from all species present in the whole dataset. This is equivalent to shuffling traits across species. The mean index value and its standard deviation were calculated from 499 draws of randomly selected species present in a given vegetation plot. Vegetation plots only containing one species or for which trait and phylogenetic information was not available were excluded from functional or phylogenetic diversity calculations. Species richness of the remaining vegetation records ranged from two species to 412 species. Standardized effect sizes (SES) were obtained by subtracting the mean index of the randomized data from the observed index and dividing the result by the standard deviation of the index of the randomized data.

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Definition of coupling and decoupling

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To measure the percentage of coupled and decoupled communities a confidence interval was defined. We randomly drew one million values from a uniform distribution with the minimum and maximum of observed standardized effect sizes of Rao's quadratic entropy based on functional traits (SES.FDQ) as explanatory variable. We created a correlated response variable by adding an error from a normal distribution, obtained from the mean and the standard deviation of the observed SES.FDQ. We fitted a linear model and extracted the intercept and
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the confidence interval. Communities with an observed value of SES.FDQ were considered
coupled if the standardized effect sizes of Rao's quadratic entropy based on phylogenetic
distance (SES.PDQ) fell within this interval.

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Additionally, we calculated the log ratio between SES.FDQ and SES.PDQ as
log(SES.FDQ/SES.PDQ) after scaling the values between 0.001 and 1. Positive and negative
values define the deviation with higher and lower SES.FDQ than SES.PDQ, respectively, from a
perfect coupled community.

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Explanatory variables

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Current climate conditions (1981-2010) were represented by the 19 bioclimatic variables
from CHELSA v.2.1<sup>63,64</sup>. A principal component analyses (PCA) was performed to reduce data
dimensionality. In the following analyses, we only used the first five PCA axes, collectively
accounting for 92.3% of explained variation. We interpreted the axes based on the highest
loadings of the corresponding climate variable as follows: annual precipitation for PC1; mean
daily air temperature of the coldest quarter and mean daily minimum air temperature of the
coldest month for PC2; annual air temperature range for PC3; isothermality for PC4; and
precipitation seasonality for PC5 (Tab. S 2, Fig. S 8).

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Mean air temperature variability after the Last Glacial Maximum (LGM) was derived from the
open-access StableClim v1.1. dataset, containing estimates from 21,000 years ago at 2.5°
spatial resolution⁶⁵. Climate variability represents rapid global warming during the last
deglaciation during the Bølling-Allerød transition⁶⁶ on land and sea. The mean temperature
variability between 21,000 B.P. and 100 A.D. was used as indices for the climate variability
after the LGM.

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All climate variables were extracted for each plot with the *extract* function from the R-package *raster*<sup>67</sup>.

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Not all vegetation plot records were complete in terms of the sampled functional groups.
Records from tropical forest plots often contained either only tree data, or tree and shrub
data. As the exclusion of those plots would have substantially reduced the spatial coverage of
our model, we added the nominal predictor variable called 'plants recorded' to our models
to partially control for this source of bias as in ref. 35. The variable 'plants recorded' has four

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values: all vascular plants, only dominant species, all woody plants, only trees. Additional
predictor variables were available from the vegetation plot database sPlot: plot size,
vegetation type (forest vs. non-forest) and a purely biogeographical classification (biome).

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In total, we prepared ten explanatory variables, five related to the current climate conditions, one to past climate variability, three to plot record characteristics, and one to the biogeographical classification.

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## Statistical modelling

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A generalised additive model (GAM) was used to model the relationship between functional and phylogenetic diversity. A GAM is a generalised linear model in which the linear response can depend on unknown smooth functions of the explanatory variables. To account for the spatial structure of the data, the spatial coordinates were included as smooth spherical splines. All GAMs included a basis penalty smoother spline on the sphere (bs = "sos"), applied to the geographic coordinates of every plot, thus taking spatial autocorrelation into account. The explanatory variable were included as linear predictors without any smooth function. The model was performed using the function *gam* from the R-package *mgcv*<sup>68-73</sup>, defined as following:

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```
gam( SES.FDₐ ~ SES.PDₐ + s(Longitude, Latitude, bs = "sos"), family = "gaussian", method = "REML")
```

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SES.FDQ is the standardized effect size of Rao's quadratic entropy based on the three selected functional plant traits and SES.PDQ is the standardized effect size of Rao's quadratic entropy based on the phylogenetic distances of species present in the community.

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To model the relationship between either functional or phylogenetic diversity and the set of the ten explanatory variables described above, we used a two-step approach. In the first step, we used Boosted Regression Trees to select relevant explanatory variables and quantify their relative influence. In the second step, we fitted GAMs using functional and phylogenetic diversity as response variables, and the predictors selected in the first step as explanatory variables. We did this because fitting a full GAM algorithm with all predictors would lead to convergence issues, due to the huge number of data points.
569 Boosted regression trees (BRTs) are a machine-learning technique used in regression and classification having few prior assumptions and being robust against overfitting and collinearity. They are known to uncover nonlinear relationships as well as interactions among predictors. The parameters of the BRT were set as follows: a tree complexity of five and a bag fraction of 0.5. The learning rate was set to 0.01 with a maximum number of 20,000 trees.
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573 The BRTs were calculated using the *gbm.step* routine from the *dismo* package<sup>74</sup>. An explanatory variable was considered relevant in the model if its relative influence was greater than 10%, which is the expected influence of a variable if all the 10 predictors had an equal relative importance.
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The variables that were considered as relevant from the BRTs were then used in a second set of GAMs, having as response variable either functional diversity (SES.FDₐ) or phylogenetic diversity (SES.PDₐ) and as explanatory variables those that turned out to be relevant in the corresponding BRT. The spatial coordinates were included as smooth spherical splines in the model as explained above.
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The prediction of each explanatory variable was performed using the *prediction* function from
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the R-package *marginaleffects*<sup>75</sup> by predicting the explanatory variable based on the
sequence between the minimum and maximum of the variable in the original data and the
GAM model. The plotted regressions were obtained by extracting the residuals from a GAM
without the explanatory variable of interest.

Functional and phylogenetic variables were plotted as mean for each grid cell with a size of
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863.8 km². The spatial smooth within the GAM was plotted at the same resolution based on
the following model (example based on SES.FDₐ):

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`gam(SES.FD_a ~ 1 + s(Longitude, Latitude, bs = "sos"), family = "gaussian", method = "REML")`

592 All analyses were performed in R 4.1.3<sup>76</sup>.
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690

691
692 **Data availability**

693 The vegetation-plot raw data contained in the sPlot database are available upon request by
694 submitting a project proposal to sPlot's Steering Committee. The proposals should follow the
695 Governance and Data Property Rules of the sPlot Working Group available on the sPlot
696 website (www.idiv.de/splot).

697 **Code availability**

698 All R scripts used for this study can be found in our GitHub repository at
699 https://github.com/georghaehn/Haehn-et-al-2023-FD-PD-coupling.

700 **Method references**

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## Acknowledgements

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The authors are thankful for the efforts of thousands of vegetation scientists sampling and digitalizing vegetation data and making them available in regional, national, or international databases. We appreciate the support of the German Research Foundation for funding sPlot as one of the iDiv research platforms (DFG FZT 118, 202548816). F.M.S. gratefully acknowledges financial support from the Rita Levi Montalcini (2019) programme, funded by the Italian Ministry of University and Research (MUR). J.-C.S. considers this work a contribution to Center for Ecological Dynamics in a Novel Biosphere (ECONOVO), funded by Danish National Research Foundation (grant DNRF173) and his VILLUM Investigator project "Biodiversity Dynamics in a Changing World", funded by VILLUM FONDEN (grant 16549). V.D.P. received support from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brazil, grant 313315/2022-1). I.B. and J.A.C. were funded by the Basque Government (IT1487-22). A.D.B was supported by the Knut and Alice Wallenberg Foundation (WAF KAW 2019.0202) and the Swedish Foundation for Strategic Research (FFL21-0194).

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## Author contributions

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G.J.A.H, F.M.S. and H.B. conceived the idea. G.J.A.H. performed the analysis with substantial input from F.M.S, G.D. and H.B. G.J.A.H. drafted the first version of the manuscript with support by F.M.S, G.D., M.S. and H.B. E.A.-D., I.A., M.B., E.B., I.B., A.D.B., G.B., Z.B.-D., J.A.C.,
A.Č., M.C., R.Ć., A.L.G, M.D.S., Jü.D., J.D, M.E.-S., M.F., A.G.-d.-M., E.G., H.G., V.G., S.H., M.H.,
B.H., J.H., U.J., F.J., A.J.-B., J.K., M.K., L.K., H.K., F.K., J.L., J.E.M., L.M., A.N., J.N., A.P.-H., O.P.,
V.D.P., G.R., E.R., B.S., M.Sch., U.S., S.S., F.S., U.Š., B.S., M.S., Z.S., B.St., J.-C.S., C.T., Z.T., A.C.V.,
C.V., D.W, De.W., H.-F.W., T.W., and G.Z. provided parts of the data. All co-authors edited the
manuscript and provided suggestions on how to improve the analyses.

The authors declare no competing interests.

Supplementary Information is available for this paper at: Suppl. S1 & S2.

Correspondence and request for materials should be addressed to Georg J. A. Hähn
(georg.haehn@idiv.de).