# Continental mapping of forest ecosystem functions reveals a high but unrealised potential for forest multifunctionality

P. Fons van Der, S. Ratcliffe, P. Ruiz-Benito, Michael Scherer-Lorenzen, Kris Verheyen, C. Wirth, M.A. Zavala, E. Ampoorter, L. Baeten, Luc Barbaro, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

P. Fons van Der, S. Ratcliffe, P. Ruiz-Benito, Michael Scherer-Lorenzen, Kris Verheyen, et al. *Continental mapping of forest ecosystem functions reveals a high but unrealised potential for forest multifunctionality*. Ecology Letters, 2018, 21 (1), pp.31-42. 10.1111/ele.12868. hal-02606618

HAL Id: hal-02606618

https://hal.inrae.fr/hal-02606618

Submitted on 19 Jan 2024

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

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<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Continental mapping of forest ecosystem functions reveals a high but unrealised potential for forest multifunctionality

P. Fons van Der, S. Ratcliffe, P. Ruiz-Benito, Michael Scherer-Lorenzen, Kris Verheyen, C. Wirth, M.A. Zavala, E. Ampoorter, L. Baeten, Luc Barbaro, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

P. Fons van Der, S. Ratcliffe, P. Ruiz-Benito, Michael Scherer-Lorenzen, Kris Verheyen, et al. *Continental mapping of forest ecosystem functions reveals a high but unrealised potential for forest multifunctionality*. Ecology Letters, 2018, 21 (1), pp.31-42. 10.1111/ele.12868. hal-02606618

HAL Id: hal-02606618

https://hal.inrae.fr/hal-02606618v1

Submitted on 19 Jan 2024

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
"This is the peer reviewed version of the following article: van der Plas, F. et al. Continental mapping of forest ecosystem functions reveals a high but unrealised potential for forest multifunctionality. Ecol. Lett. 31-42 (2017), which has been published in final form at doi:10.1111/ele.12868. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."

TITLE: Continental mapping of forest ecosystem functions reveals a high but unrealized potential for forest multifunctionality

AUTHORS:

Fons van der Plas<sup>1,2,3,*</sup>, Sophia Ratcliffe<sup>3</sup>, Paloma Ruiz-Benito<sup>4,5</sup>, Michael Scherer-Lorenzen<sup>6</sup>, Kris Verheyen<sup>7</sup>, Christian Wirth<sup>3,8</sup>, Miguel A. Zavala<sup>5</sup>, Evy Ampoorter<sup>7</sup>, Lander Baeten<sup>7</sup>, Luc Barbaro<sup>9,10</sup>, Cristina C. Bastias<sup>11</sup>, Jürgen Bauhus<sup>12</sup>, Raquel Benavides<sup>6</sup>, Adam Benneter<sup>12</sup>, Damien Bonal<sup>13</sup>, Olivier Bouriaud<sup>14</sup>, Helge Bruelheide<sup>8,15</sup>, Filippo Bussotti<sup>16</sup>, Monique Carnol<sup>17</sup>, Bastien Castagneyrol<sup>9</sup>, Yohan Charbonnier<sup>9</sup>, Johannes H. C. Cornelissen<sup>18</sup>, Jonas Dahlgren<sup>19</sup>, Ewa Checko<sup>20</sup>, Andrea Coppi<sup>21</sup>, Seid Muhie Dawud<sup>22</sup>, Marc Deconchat<sup>9,10</sup>, Pallieter De Smedt<sup>7</sup>, Hans De Wandeler<sup>23</sup>, Timo Domisch<sup>24</sup>, Leena Finér<sup>24</sup>, Mariangela Fotelli<sup>25</sup>, Arthur Gessler<sup>26</sup>, André Granier<sup>13</sup>, Charlotte Grossiord<sup>27</sup>, Virginie Guyot<sup>9,10</sup>, Josephine Haase<sup>6,28</sup>, Stephan Hättenschwiler<sup>29</sup>, Hervé Jactel<sup>9</sup>, Bogdan Jaroszewicz<sup>20</sup>, François-Xavier Joly<sup>29</sup>, Tommaso Jucker<sup>30</sup>, Stephan Kambach<sup>15,31</sup>, Gerald Kaendler<sup>32</sup>, Jens Kattge<sup>8,33</sup>, Julia Koricheva<sup>34</sup>, Georges Kunstler<sup>35</sup>, Aleksi Lehtonen<sup>36</sup>, Mario Liebergesell<sup>3,8</sup>, Peter Manning<sup>1,2</sup>, Harriet Milligan<sup>34</sup>,
"This is the peer reviewed version of the following article: van der Plas, F. et al. Continental mapping of forest ecosystem functions reveals a high but unrealised potential for forest multifunctionality. Ecol. Lett. 31-42 (2017), which has been published in final form at doi:10.1111/ele.12868. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."

TITLE: Continental mapping of forest ecosystem functions reveals a high but unrealized potential for forest multifunctionality

AUTHORS:

Fons van der Plas<sup>1,2,3,*</sup>, Sophia Ratcliffe<sup>3</sup>, Paloma Ruiz-Benito<sup>4,5</sup>, Michael Scherer-Lorenzen<sup>6</sup>, Kris Verheyen<sup>7</sup>, Christian Wirth<sup>3,8</sup>, Miguel A. Zavala<sup>5</sup>, Evy Ampoorter<sup>7</sup>, Lander Baeten<sup>7</sup>, Luc Barbaro<sup>9,10</sup>, Cristina C. Bastias<sup>11</sup>, Jürgen Bauhus<sup>12</sup>, Raquel Benavides<sup>6</sup>, Adam Benneter<sup>12</sup>, Damien Bonal<sup>13</sup>, Olivier Bouriaud<sup>14</sup>, Helge Bruelheide<sup>8,15</sup>, Filippo Bussotti<sup>16</sup>, Monique Carnol<sup>17</sup>, Bastien Castagneyrol<sup>9</sup>, Yohan Charbonnier<sup>9</sup>, Johannes H. C. Cornelissen<sup>18</sup>, Jonas Dahlgren<sup>19</sup>, Ewa Checko<sup>20</sup>, Andrea Coppi<sup>21</sup>, Seid Muhie Dawud<sup>22</sup>, Marc Deconchat<sup>9,10</sup>, Pallieter De Smedt<sup>7</sup>, Hans De Wandeler<sup>23</sup>, Timo Domisch<sup>24</sup>, Leena Finér<sup>24</sup>, Mariangela Fotelli<sup>25</sup>, Arthur Gessler<sup>26</sup>, André Granier<sup>13</sup>, Charlotte Grossiord<sup>27</sup>, Virginie Guyot<sup>9,10</sup>, Josephine Haase<sup>6,28</sup>, Stephan Hättenschwiler<sup>29</sup>, Hervé Jactel<sup>9</sup>, Bogdan Jaroszewicz<sup>20</sup>, François-Xavier Joly<sup>29</sup>, Tommaso Jucker<sup>30</sup>, Stephan Kambach<sup>15,31</sup>, Gerald Kaendler<sup>32</sup>, Jens Kattge<sup>8,33</sup>, Julia Koricheva<sup>34</sup>, Georges Kunstler<sup>35</sup>, Aleksi Lehtonen<sup>36</sup>, Mario Liebergesell<sup>3,8</sup>, Peter Manning<sup>1,2</sup>, Harriet Milligan<sup>34</sup>,
23 Sandra Müller<sup>6</sup>, Bart Muys<sup>23</sup>, Diem Nguyen<sup>37</sup>, Charles Nock<sup>6</sup>, Bettina Ohse<sup>3</sup>, Alain Paquette<sup>38</sup>,

24 Josep Peñuelas<sup>39,40</sup>, Martina Pollastrini<sup>16</sup>, Kalliopi Radoglou<sup>41</sup>, Karsten Raulund-Rasmussen<sup>22</sup>,

25 Fabian Roger<sup>42</sup>, Rupert Seidl<sup>43</sup>, Federico Selvi<sup>16</sup>, Jan Stenlid<sup>37</sup>, Fernando Valladares<sup>11</sup>, Johan van

26 Keer<sup>44</sup>, Lars Vesterdal<sup>22</sup>, Markus Fischer<sup>1,2</sup>, Lars Gamfeldt<sup>42</sup>, Eric Allan<sup>1</sup>.

27

28 AUTHOR AFFILIATIONS

29 <sup>1</sup>Institute of Plant Sciences, University of Bern, Altenbergrain 21, 3013 Bern, Switzerland.

30 <sup>2</sup>Senckenberg Gesellschaft für Naturforschung, Biodiversity and Climate Research Centre, Senckenberganlage 25,
31 60325 Frankfurt, Germany.

32 <sup>3</sup>Department of Systematic Botany and Functional Biodiversity, University of Leipzig, Johannisallee 21-23, 04103
33 Leipzig, Germany.

34 <sup>4</sup>Biological and Environmental Sciences, School of Natural Sciences, University of Stirling, FK9 4LA, Stirling,
35 United Kingdom.

36 <sup>5</sup>Grupo de Ecología y Restauración Forestal, Departamento de Ciencias de la Vida, Universidad de Alcalá, Edificio
37 de Ciencias, Campus Universitario, 28805 Alcalá de Henares, Madrid, Spain.

38 <sup>6</sup>University of Freiburg, Faculty of Biology, Geobotany, Schänzlestr. 1, 79104 Freiburg, Germany.

39 <sup>7</sup>Forest & Nature Lab, Ghent University, Geraardsbergsesteenweg 267, B-9090 Melle-Gontrode, Belgium.

40 <sup>8</sup>German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5E, 04103
41 Leipzig, Germany.

42 <sup>9</sup>BIOGECO, INRA, Univ. Bordeaux, 33610 Cestas, France.

43 <sup>10</sup>Dynafor, INPT-EI Purpan, INRA, Univ. Toulouse, 31320, Auzeville, France

44 <sup>11</sup>MNCN-CSIC, Serrano 115 bis 28006 Madrid, Spain.

45 <sup>12</sup>Faculty of Environment and Natural Resources, Chair of Silviculture, University of Freiburg, Fahnenbergplatz,
46 79085 Freiburg, Germany.

47 <sup>13</sup>INRA, UMR EEF, 54280 Champenoux, France.

48 <sup>14</sup>Faculty of Forestry, Stefan cel Mare University of Suceava, Universitatii Street 13, Suceava 720229, Romania.
23 Sandra Müller<sup>6</sup>, Bart Muys<sup>23</sup>, Diem Nguyen<sup>37</sup>, Charles Nock<sup>6</sup>, Bettina Ohse<sup>3</sup>, Alain Paquette<sup>38</sup>,

24 Josep Peñuelas<sup>39,40</sup>, Martina Pollastrini<sup>16</sup>, Kalliopi Radoglou<sup>41</sup>, Karsten Raulund-Rasmussen<sup>22</sup>,

25 Fabian Roger<sup>42</sup>, Rupert Seidl<sup>43</sup>, Federico Selvi<sup>16</sup>, Jan Stenlid<sup>37</sup>, Fernando Valladares<sup>11</sup>, Johan van

26 Keer<sup>44</sup>, Lars Vesterdal<sup>22</sup>, Markus Fischer<sup>1,2</sup>, Lars Gamfeldt<sup>42</sup>, Eric Allan<sup>1</sup>.

27

28 AUTHOR AFFILIATIONS

29 <sup>1</sup>Institute of Plant Sciences, University of Bern, Altenbergrain 21, 3013 Bern, Switzerland.

30 <sup>2</sup>Senckenberg Gesellschaft für Naturforschung, Biodiversity and Climate Research Centre, Senckenberganlage 25,
31 60325 Frankfurt, Germany.

32 <sup>3</sup>Department of Systematic Botany and Functional Biodiversity, University of Leipzig, Johannisallee 21-23, 04103
33 Leipzig, Germany.

34 <sup>4</sup>Biological and Environmental Sciences, School of Natural Sciences, University of Stirling, FK9 4LA, Stirling,
35 United Kingdom.

36 <sup>5</sup>Grupo de Ecología y Restauración Forestal, Departamento de Ciencias de la Vida, Universidad de Alcalá, Edificio
37 de Ciencias, Campus Universitario, 28805 Alcalá de Henares, Madrid, Spain.

38 <sup>6</sup>University of Freiburg, Faculty of Biology, Geobotany, Schänzlestr. 1, 79104 Freiburg, Germany.

39 <sup>7</sup>Forest & Nature Lab, Ghent University, Geraardsbergsesteenweg 267, B-9090 Melle-Gontrode, Belgium.

40 <sup>8</sup>German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5E, 04103
41 Leipzig, Germany.

42 <sup>9</sup>BIOGECO, INRA, Univ. Bordeaux, 33610 Cestas, France.

43 <sup>10</sup>Dynafor, INPT-EI Purpan, INRA, Univ. Toulouse, 31320, Auzeville, France

44 <sup>11</sup>MNCN-CSIC, Serrano 115 bis 28006 Madrid, Spain.

45 <sup>12</sup>Faculty of Environment and Natural Resources, Chair of Silviculture, University of Freiburg, Fahnenbergplatz,
46 79085 Freiburg, Germany.

47 <sup>13</sup>INRA, UMR EEF, 54280 Champenoux, France.

48 <sup>14</sup>Faculty of Forestry, Stefan cel Mare University of Suceava, Universitatii Street 13, Suceava 720229, Romania.
49
15Institute of Biology / Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Am Kirchtor
50
1, 06108 Halle (Saale), Germany.

51
16Department of Agrifood Production and Environmental Sciences, Laboratory of Applied and Environmental
52
Botany, University of Firenze, P.le Cascine 28, 50144 Firenze, Italy.

53
17Laboratory of Plant and Microbial Ecology, University of Liege, Botany B22, Chemin de la Vallee 4, 4000 Liege,
54
Belgium.

55
18Systems Ecology, Department of Ecological Science, Faculty of Earth and Life Sciences, Vrije Universiteit
56
Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands.

57
19Swedish University of Agricultural Sciences, Skogsmarksgränd, 90183 Umeå, Sweden.

58
20Białowieza Geobotanical Station, Faculty of Biology, University of Warsaw, 17-230 Białowieża, Poland.

59
21University of Firenze, Department of Biology, Botanical Laboratories, Via G. La Pira 4, 50121 Firenze, Italy.

60
22Department of Geosciences and Natural Resource Management, University of Copenhagen, Rolighedsvej 23, 1958
61
Frederiksberg C, Denmark.

62
23Department of Earth and Environmental Sciences, KU Leuven, University of Leuven, Celestijnenlaan 200E Box
63
2411, BE-3001 Leuven, Belgium.

64
24Natural Resources Institute Finland (Luke), Yliopistokatu 6, FI-80100 Joensuu, Finland.

65
25Forest Research Institute of Thessaloniki, Greek Agricultural Organization-Dimitra, 57006 Vassilika,
66
Thessaloniki, Greece.

67
26Swiss Federal Research Institute WSL, Research Unit Forest Dynamics, Zuercherstr, 111, 8903 Birmensdorf,
68
Switzerland.

69
27Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.

70
28Institute for Terrestrial Ecosystems, Department of Environmental Systems Science, ETH Zurich,
71
Universitaetsstrasse 16, 8092 Zurich, Switzerland.

72
29Centre of Evolutionary and Functional Ecology (CEFE UMR 5175, CNRS – University of Montpellier –
73
University Paul-Valery Montpellier – EPHE), 1919 route de Mende, 34293 Montpellier, France.

74
30Forest Ecology and Conservation, Department of Plant Sciences, University of Cambridge, Downing Street,
75
Cambridge CB2 3EA, UK.
49
15Institute of Biology / Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Am Kirchtor
50
1, 06108 Halle (Saale), Germany.

51
16Department of Agrifood Production and Environmental Sciences, Laboratory of Applied and Environmental
52
Botany, University of Firenze, P.le Cascine 28, 50144 Firenze, Italy.

53
17Laboratory of Plant and Microbial Ecology, University of Liege, Botany B22, Chemin de la Vallee 4, 4000 Liege,
54
Belgium.

55
18Systems Ecology, Department of Ecological Science, Faculty of Earth and Life Sciences, Vrije Universiteit
56
Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands.

57
19Swedish University of Agricultural Sciences, Skogsmarksgränd, 90183 Umeå, Sweden.

58
20Białowieza Geobotanical Station, Faculty of Biology, University of Warsaw, 17-230 Białowieża, Poland.

59
21University of Firenze, Department of Biology, Botanical Laboratories, Via G. La Pira 4, 50121 Firenze, Italy.

60
22Department of Geosciences and Natural Resource Management, University of Copenhagen, Rolighedsvej 23, 1958
61
Frederiksberg C, Denmark.

62
23Department of Earth and Environmental Sciences, KU Leuven, University of Leuven, Celestijnenlaan 200E Box
63
2411, BE-3001 Leuven, Belgium.

64
24Natural Resources Institute Finland (Luke), Yliopistokatu 6, FI-80100 Joensuu, Finland.

65
25Forest Research Institute of Thessaloniki, Greek Agricultural Organization-Dimitra, 57006 Vassilika,
66
Thessaloniki, Greece.

67
26Swiss Federal Research Institute WSL, Research Unit Forest Dynamics, Zuercherstr, 111, 8903 Birmensdorf,
68
Switzerland.

69
27Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.

70
28Institute for Terrestrial Ecosystems, Department of Environmental Systems Science, ETH Zurich,
71
Universitaetsstrasse 16, 8092 Zurich, Switzerland.

72
29Centre of Evolutionary and Functional Ecology (CEFE UMR 5175, CNRS – University of Montpellier –
73
University Paul-Valery Montpellier – EPHE), 1919 route de Mende, 34293 Montpellier, France.

74
30Forest Ecology and Conservation, Department of Plant Sciences, University of Cambridge, Downing Street,
75
Cambridge CB2 3EA, UK.
76
³¹UFZ – Helmholtz Centre for Environmental Research, Department Community Ecology, 06120 Halle (Saale),
77
Germany.

78
³²Forest Research Institute Baden-Württemberg, Wonnhaldestrase 4, 79100 Freiburg, Germany.

79
³³Max Planck Institute for Biogeochemistry, Hans-Knöll-Straße 10, 07745 Jena, Germany.

80
³⁴School of Biological Sciences, Royal Holloway University of London, Egham, Surrey TW20 0EX, UK.

81
³⁵Univ. Grenoble Alpes, Irstea, UR EMGR, Centre de Grenoble, 2 rue de la Papeterie-BP 76, F-38402 Saint-Martin-d'Hères, France.

83
³⁶Natural Resources Institute Finland (Luke), Jokiniemenkuja 1, FI-01370 Vantaa, Finland.

84
³⁷Department of Forest Mycology and Plant Pathology, Swedish University of Agricultural Sciences, PO Box 7026, SE-750 07 Uppsala, Sweden.

86
³⁸Centre for Forest Research (CFR), Université du Québec à Montréal, Montréal (Québec), Canada.

87
³⁹CREAF, Cerdanyola del Vallès, 08913 Catalonia, Spain.

88
⁴⁰CSIC, Global Ecology Unit CREAF-CSIC-UB-UAB, Bellaterra, 08913 Catalonia, Spain.

89
⁴¹Democritus University of Thrace (DUTH), Department of Forestry and Management of the Environment and Natural Resources, Pantazidou 193, 68200, Nea Orestiada, Greece.

91
⁴²Department of Marine Sciences, University of Gothenburg, Carl Skottsbergs gata 22B, 41319 Göteborg, Sweden.

92
⁴³University of Natural Resources and Life Sciences (BOKU), Institute of Silviculture, Vienna, Austria.

93
⁴⁴Bormstraat 204 bus 3, 1880 Kapelle-op-den-Bos, Belgium.

94
*corresponding author: Fonsvanderplas@gmail.com; Senckenberganlage 25, D-60325 Frankfurt am Main, Germany;
95
Tel.: +49 69 7542 1820; Fax: + 49 69 7542 7904
96

97

98
# AUTHOR CONTRIBUTIONS

99
FvdP, EA, LG, MF, SR, PRB, MSL, CW, LB, RB, HB, TJ, SK, GK, CN, BO, AP and FR developed the ideas of this study at a workshop organized by EA and LG. FvdP, SR and PRB analysed the data. All authors, except FvdP, EA, MF, SKa, PM, BO, AP and FR contributed to the data collection. FvdP wrote the manuscript. All authors contributed in editing the manuscript.
76
³¹UFZ – Helmholtz Centre for Environmental Research, Department Community Ecology, 06120 Halle (Saale),
77
Germany.

78
³²Forest Research Institute Baden-Württemberg, Wonnhaldestrase 4, 79100 Freiburg, Germany.

79
³³Max Planck Institute for Biogeochemistry, Hans-Knöll-Straße 10, 07745 Jena, Germany.

80
³⁴School of Biological Sciences, Royal Holloway University of London, Egham, Surrey TW20 0EX, UK.

81
³⁵Univ. Grenoble Alpes, Irstea, UR EMGR, Centre de Grenoble, 2 rue de la Papeterie-BP 76, F-38402 Saint-Martin-d'Hères, France.

83
³⁶Natural Resources Institute Finland (Luke), Jokiniemenkuja 1, FI-01370 Vantaa, Finland.

84
³⁷Department of Forest Mycology and Plant Pathology, Swedish University of Agricultural Sciences, PO Box 7026, SE-750 07 Uppsala, Sweden.

86
³⁸Centre for Forest Research (CFR), Université du Québec à Montréal, Montréal (Québec), Canada.

87
³⁹CREAF, Cerdanyola del Vallès, 08913 Catalonia, Spain.

88
⁴⁰CSIC, Global Ecology Unit CREAF-CSIC-UB-UAB, Bellaterra, 08913 Catalonia, Spain.

89
⁴¹Democritus University of Thrace (DUTH), Department of Forestry and Management of the Environment and Natural Resources, Pantazidou 193, 68200, Nea Orestiada, Greece.

91
⁴²Department of Marine Sciences, University of Gothenburg, Carl Skottsbergs gata 22B, 41319 Göteborg, Sweden.

92
⁴³University of Natural Resources and Life Sciences (BOKU), Institute of Silviculture, Vienna, Austria.

93
⁴⁴Bormstraat 204 bus 3, 1880 Kapelle-op-den-Bos, Belgium.

94
*corresponding author: Fonsvanderplas@gmail.com; Senckenberganlage 25, D-60325 Frankfurt am Main, Germany;
95
Tel.: +49 69 7542 1820; Fax: + 49 69 7542 7904
96

97

98

AUTHOR CONTRIBUTIONS

99

FvdP, EA, LG, MF, SR, PRB, MSL, CW, LB, RB, HB, TJ, SK, GK, CN, BO, AP and FR developed the ideas of this study at a workshop organized by EA and LG. FvdP, SR and PRB analysed the data. All authors, except FvdP, EA, MF, SKa, PM, BO, AP and FR contributed to the data collection. FvdP wrote the manuscript. All authors contributed in editing the manuscript.
103

104 DATA ACCESSIBILITY STATEMENT

105 Should the manuscript be accepted, the data supporting the results will be published in Dryad

106 and the data DOI will be included at the end of the article.

107

108 **RUNNING TITLE**: continental mapping of multifunctionality

109

110 **KEYWORDS**: biodiversity, climate, ecosystem multifunctionality, ecosystem services, forest,

111 FunDivEUROPE, large-scale, phylogenetic diversity, tree communities, upscaling

112

113 **TYPE OF ARTICLE**: letter

114

115 **NUMBER OF WORDS**

116 Abstract: 150

117 Main text: 4999

118

119 **NUMBER OF REFERENCES**: 51

120 **NUMBER OF FIGURES**: 4

121 **NUMBER OF TABLES**: 1

122 **NUMBER OF TEXT BOXES**: 0
103

104 DATA ACCESSIBILITY STATEMENT

105 Should the manuscript be accepted, the data supporting the results will be published in Dryad

106 and the data DOI will be included at the end of the article.

107

108 **RUNNING TITLE**: continental mapping of multifunctionality

109

110 **KEYWORDS**: biodiversity, climate, ecosystem multifunctionality, ecosystem services, forest,

111 FunDivEUROPE, large-scale, phylogenetic diversity, tree communities, upscaling

112

113 **TYPE OF ARTICLE**: letter

114

115 **NUMBER OF WORDS**

116 Abstract: 150

117 Main text: 4999

118

119 **NUMBER OF REFERENCES**: 51

120 **NUMBER OF FIGURES**: 4

121 **NUMBER OF TABLES**: 1

122 **NUMBER OF TEXT BOXES**: 0
123 **ABSTRACT**

124 Humans require multiple services from ecosystems, but it is largely unknown whether
125 trade-offs between ecosystem functions prevent the realization of high ecosystem
126 multifunctionality across spatial scales. Here, we combined a comprehensive dataset (28
127 ecosystem functions measured on 209 forest plots) with a forest inventory dataset (105,316 plots)
128 to extrapolate and map relationships between various ecosystem multifunctionality measures
129 across Europe. These multifunctionality measures reflected different management objectives,
130 related to timber production, climate regulation and biodiversity conservation/recreation. We
131 found that trade-offs among them were rare across Europe, at both local and continental scales.
132 This suggests a high potential for "win-win" forest management strategies, where overall
133 multifunctionality is maximized. However, across sites, multifunctionality was on average 45.8-
134 49.8% below maximum levels and not necessarily highest in protected areas. Therefore, using
135 one of the most comprehensive assessments so far, our study suggests a high but largely
136 unrealized potential for management to promote multifunctional forests.
137
123 **ABSTRACT**

124 Humans require multiple services from ecosystems, but it is largely unknown whether
125 trade-offs between ecosystem functions prevent the realization of high ecosystem
126 multifunctionality across spatial scales. Here, we combined a comprehensive dataset (28
127 ecosystem functions measured on 209 forest plots) with a forest inventory dataset (105,316 plots)
128 to extrapolate and map relationships between various ecosystem multifunctionality measures
129 across Europe. These multifunctionality measures reflected different management objectives,
130 related to timber production, climate regulation and biodiversity conservation/recreation. We
131 found that trade-offs among them were rare across Europe, at both local and continental scales.
132 This suggests a high potential for "win-win" forest management strategies, where overall
133 multifunctionality is maximized. However, across sites, multifunctionality was on average 45.8-
134 49.8% below maximum levels and not necessarily highest in protected areas. Therefore, using
135 one of the most comprehensive assessments so far, our study suggests a high but largely
136 unrealized potential for management to promote multifunctional forests.
137
138 INTRODUCTION

139 One of the greatest challenges in ecology is to understand the effects of global change
140 and nature management on the multiple ecosystem functions on which humans depend (MEA
141 2005). Such an understanding would help predicting the circumstances under which trade-offs
142 between different ecosystem functions are minimal and therefore when their simultaneous
143 provisioning, i.e. ecosystem multifunctionality (Hector & Bagchi 2007; Gamfeldt et al. 2008), is
144 maximised. Previous studies have identified conditions promoting local-scale ecosystem
145 multifunctionality, e.g. through the maximization of biodiversity (Lefcheck et al. 2015).
146 However, whether such relationships also exist at large spatial scales, and how they vary in
147 space, is less clear (Isbell et al. 2017). Understanding this is essential if ecosystem-functioning
148 studies are to provide policy-relevant advice, because most policy focuses on large scales.

149 Forests provide a number of functions related to key services such as timber production,
150 climate regulation and recreation (Gamfeldt et al. 2013), and are important for the conservation
151 of many plant and animal species (FAO 2015). Understanding large-scale relationships between
152 different functions is therefore important if we are to find “win-win” management scenarios,
153 which meet different forest management objectives and promote forest multifunctionality.

154 Quantifying many ecosystem functions at large scales has so far proven challenging.
155 Studies have used exhaustive remote sensing or ground-based measurements (e.g. Prince &
156 Goward 1995; Ratcliffe et al. 2016), mechanistic models (e.g. McGuire et al. 2001), indirect
157 measures (e.g. where certain habitat types are assumed to promote certain functions; Maskell et
158 al. 2013) or a combination of these (Maes et al. 2012; Mouchet et al. 2017) to quantify single or
159 multiple functions at large spatial extents. However, for some important functions, such as
160 biological pest control or timber quality, large scale maps have not yet been developed, limiting
138 INTRODUCTION

139 One of the greatest challenges in ecology is to understand the effects of global change
140 and nature management on the multiple ecosystem functions on which humans depend (MEA
141 2005). Such an understanding would help predicting the circumstances under which trade-offs
142 between different ecosystem functions are minimal and therefore when their simultaneous
143 provisioning, i.e. ecosystem multifunctionality (Hector & Bagchi 2007; Gamfeldt et al. 2008), is
144 maximised. Previous studies have identified conditions promoting local-scale ecosystem
145 multifunctionality, e.g. through the maximization of biodiversity (Lefcheck et al. 2015).
146 However, whether such relationships also exist at large spatial scales, and how they vary in
147 space, is less clear (Isbell et al. 2017). Understanding this is essential if ecosystem-functioning
148 studies are to provide policy-relevant advice, because most policy focuses on large scales.

149 Forests provide a number of functions related to key services such as timber production,
150 climate regulation and recreation (Gamfeldt et al. 2013), and are important for the conservation
151 of many plant and animal species (FAO 2015). Understanding large-scale relationships between
152 different functions is therefore important if we are to find “win-win” management scenarios,
153 which meet different forest management objectives and promote forest multifunctionality.

154 Quantifying many ecosystem functions at large scales has so far proven challenging.
155 Studies have used exhaustive remote sensing or ground-based measurements (e.g. Prince &
156 Goward 1995; Ratcliffe et al. 2016), mechanistic models (e.g. McGuire et al. 2001), indirect
157 measures (e.g. where certain habitat types are assumed to promote certain functions; Maskell et
158 al. 2013) or a combination of these (Maes et al. 2012; Mouchet et al. 2017) to quantify single or
159 multiple functions at large spatial extents. However, for some important functions, such as
160 biological pest control or timber quality, large scale maps have not yet been developed, limiting
our understanding of ecosystem functioning synergies and trade-offs. In contrast, many local-scale studies, such as biodiversity experiments (e.g. Hector & Bagchi 2007; Zavaleta et al. 2009) or comparative studies (Lavorel et al. 2011), have accurately quantified a large number of functions. Extrapolating these small-scale observations to larger scales could increase our understanding of the drivers of ecosystem functioning trade-offs and the resulting provision of ecosystem multifunctionality.

Forests are often managed for a particular subset of functions related to certain ecosystem services (e.g. timber production, climate regulation or nature conservation) that are prioritized by a specific stakeholder group. We aimed to identify areas where functions of all these different sets are high and where trade-offs are weakest. To this end, we combined a multi-site dataset, containing accurate measures of multiple ecosystem functions, with a continental-scale inventory-based dataset with high spatial plot coverage. We extrapolated regional scale relationships between ecosystem functions and their drivers (e.g. forest community composition and climate) to larger spatial scales (Fig. S1) to map both individual ecosystem functions and ecosystem multifunctionality across Europe, in forests without recent intensive management. We then tested for potential trade-offs between sets of functions, at scales relevant for policymakers.

To do this, we developed different measures of multifunctionality corresponding to different management scenarios (Fig. 1). In these, functions related to (sustainable) timber production, climate regulation or biodiversity conservation/recreation were prioritized (Fig. 1). We also considered a scenario where all functions were valued equally. Our objectives were firstly, to identify "multifunctionality hotspots", i.e. areas with highest multifunctionality. Secondly, we investigated whether there are synergies (allowing for win-win management) or trade-offs between different multifunctionality measures at both continental and local scales, and
our understanding of ecosystem functioning synergies and trade-offs. In contrast, many local-scale studies, such as biodiversity experiments (e.g. Hector & Bagchi 2007; Zavaleta et al. 2009) or comparative studies (Lavorel et al. 2011), have accurately quantified a large number of functions. Extrapolating these small-scale observations to larger scales could increase our understanding of the drivers of ecosystem functioning trade-offs and the resulting provision of ecosystem multifunctionality.

Forests are often managed for a particular subset of functions related to certain ecosystem services (e.g. timber production, climate regulation or nature conservation) that are prioritized by a specific stakeholder group. We aimed to identify areas where functions of all these different sets are high and where trade-offs are weakest. To this end, we combined a multi-site dataset, containing accurate measures of multiple ecosystem functions, with a continental-scale inventory-based dataset with high spatial plot coverage. We extrapolated regional scale relationships between ecosystem functions and their drivers (e.g. forest community composition and climate) to larger spatial scales (Fig. S1) to map both individual ecosystem functions and ecosystem multifunctionality across Europe, in forests without recent intensive management. We then tested for potential trade-offs between sets of functions, at scales relevant for policymakers.

To do this, we developed different measures of multifunctionality corresponding to different management scenarios (Fig. 1). In these, functions related to (sustainable) timber production, climate regulation or biodiversity conservation/recreation were prioritized (Fig. 1). We also considered a scenario where all functions were valued equally. Our objectives were firstly, to identify "multifunctionality hotspots", i.e. areas with highest multifunctionality. Secondly, we investigated whether there are synergies (allowing for win-win management) or trade-offs between different multifunctionality measures at both continental and local scales, and
how these varied in space. Finally, we investigated whether forest protection status is associated
with high multifunctionality, and thus whether potential win-win policies are realized in
(protected) forests.

187

188 MATERIALS AND METHODS

189 Our approach to extrapolate ecosystem functioning relationships from regional to
190 continental scales consisted of two main steps (Fig. S1). Firstly, statistical models were fitted to a
191 comprehensive (many ecosystem functions), multi-site dataset ('fitting dataset'). Secondly, these
192 models were extrapolated to a continental-scale dataset containing forest plots distributed across
193 Western Europe ('inventory dataset'). These two datasets share variables related to climate, soils
194 and tree composition, all potential drivers of ecosystem functioning. For three ecosystem
195 functions which were independently measured in the inventory dataset, we cross-validated
196 predicted ecosystem function values. Our approach allowed testing for trade-offs and synergies
197 between individual ecosystem functions and between different multifunctionality measures, at
198 different scales: 1) using all plots (thus including both local and large-scale variation in
199 functions) and 2) within 20×20km localities.

200

201 *Fitting dataset: design*

202 As part of the EU-FP7 FunDivEUROPE project (www.fundiveurope.eu), which
203 investigates how tree species composition and diversity drive forest ecosystem functioning, 209
204 30×30 meter plots (Fig. S2) were established. The plots covered six major regions/countries,
205 representing different forest types: 28 boreal forest (Finland), 43 temperate mixed forest
206 (Poland), 38 temperate deciduous forest (Germany), 28 mountainous deciduous forest
how these varied in space. Finally, we investigated whether forest protection status is associated
with high multifunctionality, and thus whether potential win-win policies are realized in
(protected) forests.

187

188 MATERIALS AND METHODS

189 Our approach to extrapolate ecosystem functioning relationships from regional to
190 continental scales consisted of two main steps (Fig. S1). Firstly, statistical models were fitted to a
191 comprehensive (many ecosystem functions), multi-site dataset ('fitting dataset'). Secondly, these
192 models were extrapolated to a continental-scale dataset containing forest plots distributed across
193 Western Europe ('inventory dataset'). These two datasets share variables related to climate, soils
194 and tree composition, all potential drivers of ecosystem functioning. For three ecosystem
195 functions which were independently measured in the inventory dataset, we cross-validated
196 predicted ecosystem function values. Our approach allowed testing for trade-offs and synergies
197 between individual ecosystem functions and between different multifunctionality measures, at
198 different scales: 1) using all plots (thus including both local and large-scale variation in
199 functions) and 2) within 20×20km localities.

200

201 *Fitting dataset: design*

202 As part of the EU-FP7 FunDivEUROPE project (www.fundiveurope.eu), which
203 investigates how tree species composition and diversity drive forest ecosystem functioning, 209
204 30×30 meter plots (Fig. S2) were established. The plots covered six major regions/countries,
205 representing different forest types: 28 boreal forest (Finland), 43 temperate mixed forest
206 (Poland), 38 temperate deciduous forest (Germany), 28 mountainous deciduous forest
207 (Romania), 36 thermophilous deciduous forest (Italy) and 36 Mediterranean mixed forest plots
208 (Spain). These plots covered a broad climatic gradient: mean annual precipitation ranged from
209 484 to 819mm, mean annual temperature from 1.4 to 14.1°C (WorldClim; Hijmans et al. 2005)
210 and altitude from 87 to 1404m. Within regions, plots differed in the composition and diversity of
211 regionally common tree species, while site-related factors were similar. Management was either
212 at low intensity or absent (Baeten et al. 2013).

213
214 *Measurement and collation of fitting data*
215
216 In all plots, we measured 28 different ecosystem characteristics/processes ('ecosystem
functions' hereafter) linked to various ecosystem services (see overview in Fig. 1 and
217 methodology in Supplementary Material). For each plot we compiled data on tree species
218 composition (to derive measures of functional and phylogenetic diversity), stand structure, soil
219 pH, altitude and 18 climatic variables. Previous studies demonstrated that climate (Cramer et al.
220 2001), soil pH (Foy 1992), functional community composition (Diaz et al. 2004) and tree
221 diversity (van der Plas et al. 2016; Liang et al. 2016) can all drive (forest) functioning.

222 In each plot, we identified all tree stems ≥7.5cm in diameter at breast height (dbh) to
223 species level. With these data, we calculated total and average tree basal area. In addition, by
224 combining these observations with (1) published trait data (Kattge et al. 2011; Royal Botanic
225 Gardens Kew 2015; see Table S1) representing key life-history strategies (Westoby et al. 2002),
226 and (2) a phylogeny (Zanne et al. 2014), we calculated several metrics describing the functional
227 identity, functional diversity and phylogenetic diversity of the tree communities. Firstly, we
228 calculated Community Weighted Means (Garnier et al. 2004), reflecting functional identities of
229 communities, based on species values for specific leaf area (cm² g⁻¹), maximum life span (log-
207 (Romania), 36 thermophilous deciduous forest (Italy) and 36 Mediterranean mixed forest plots
208 (Spain). These plots covered a broad climatic gradient: mean annual precipitation ranged from
209 484 to 819mm, mean annual temperature from 1.4 to 14.1°C (WorldClim; Hijmans et al. 2005)
210 and altitude from 87 to 1404m. Within regions, plots differed in the composition and diversity of
211 regionally common tree species, while site-related factors were similar. Management was either
212 at low intensity or absent (Baeten et al. 2013).

213

214 Measurement and collation of fitting data

215 In all plots, we measured 28 different ecosystem characteristics/processes ('ecosystem
216 functions' hereafter) linked to various ecosystem services (see overview in Fig. 1 and
217 methodology in Supplementary Material). For each plot we compiled data on tree species
218 composition (to derive measures of functional and phylogenetic diversity), stand structure, soil
219 pH, altitude and 18 climatic variables. Previous studies demonstrated that climate (Cramer et al.
220 2001), soil pH (Foy 1992), functional community composition (Diaz et al. 2004) and tree
221 diversity (van der Plas et al. 2016; Liang et al. 2016) can all drive (forest) functioning.

222 In each plot, we identified all tree stems ≥7.5cm in diameter at breast height (dbh) to
223 species level. With these data, we calculated total and average tree basal area. In addition, by
224 combining these observations with (1) published trait data (Kattge et al. 2011; Royal Botanic
225 Gardens Kew 2015; see Table S1) representing key life-history strategies (Westoby et al. 2002),
226 and (2) a phylogeny (Zanne et al. 2014), we calculated several metrics describing the functional
227 identity, functional diversity and phylogenetic diversity of the tree communities. Firstly, we
228 calculated Community Weighted Means (Garnier et al. 2004), reflecting functional identities of
229 communities, based on species values for specific leaf area (cm² g⁻¹), maximum life span (log-
transformed; yrs), maximum height (m), wood density (g cm⁻³), seed mass (mg), conifer (proportion) and evergreen (proportion). Secondly, we calculated the functional (trait) diversity within communities as Rao's Quadratic Entropy (Botta-Dukát 2005), for each trait separately and for all traits combined. Finally, we calculated several phylogenetic diversity metrics: Phylogenetic Species Variability, Phylogenetic Species Evenness (Helmus et al. 2007), Faith's Phylogenetic Diversity (Faith 1992) and (abundance-weighted) Mean Phylogenetic Distance (Webb et al. 2002). As inventory plots differed in size, tree species richness was not investigated, and we selected functional and phylogenetic diversity metrics uncorrelated with species richness.

To represent soil conditions we used pH (methods in Supplementary Materials), as it drives many functions and was the only soil variable available for the inventory dataset. Eighteen variables (see Table S2) related to climate (worldclim data; Hijmans et al. 2005) were collated at a 30 seconds spatial resolution. Altitude data were collated from srtm.csi.cgiar.org.

Analysis of the drivers of ecosystem functioning

We used the Random Forest (Breiman 2001) algorithm to explain ecosystem function variation in the fitting dataset. Random Forest is a machine-learning algorithm, powerful for making predictions (but less suitable in explaining mechanisms) and incorporating both linear and non-linear relationships, as well as interaction effects (Strobl et al. 2007). It is relatively insensitive to multicollinearity and overfitting (Hastie et al. 2008), allowing for the inclusion of many predictors. Initially, we included the 42 predictor variables described above (see also Table S2), describing abiotic conditions, climate, stand structure, functional identity, and functional and phylogenetic diversity. Random Forests were run in R (R Core Team 2013) with the
transformed; yrs), maximum height (m), wood density (g cm⁻³), seed mass (mg), conifer (proportion) and evergreen (proportion). Secondly, we calculated the functional (trait) diversity within communities as Rao's Quadratic Entropy (Botta-Dukát 2005), for each trait separately and for all traits combined. Finally, we calculated several phylogenetic diversity metrics: Phylogenetic Species Variability, Phylogenetic Species Evenness (Helmus et al. 2007), Faith's Phylogenetic Diversity (Faith 1992) and (abundance-weighted) Mean Phylogenetic Distance (Webb et al. 2002). As inventory plots differed in size, tree species richness was not investigated, and we selected functional and phylogenetic diversity metrics uncorrelated with species richness.

To represent soil conditions we used pH (methods in Supplementary Materials), as it drives many functions and was the only soil variable available for the inventory dataset. Eighteen variables (see Table S2) related to climate (worldclim data; Hijmans et al. 2005) were collated at a 30 seconds spatial resolution. Altitude data were collated from srtm.csi.cgiar.org.

Analysis of the drivers of ecosystem functioning

We used the Random Forest (Breiman 2001) algorithm to explain ecosystem function variation in the fitting dataset. Random Forest is a machine-learning algorithm, powerful for making predictions (but less suitable in explaining mechanisms) and incorporating both linear and non-linear relationships, as well as interaction effects (Strobl et al. 2007). It is relatively insensitive to multicollinearity and overfitting (Hastie et al. 2008), allowing for the inclusion of many predictors. Initially, we included the 42 predictor variables described above (see also Table S2), describing abiotic conditions, climate, stand structure, functional identity, and functional and phylogenetic diversity. Random Forests were run in R (R Core Team 2013) with the
253 'randomForest' library (Liaw & Wiener 2012). Following Seidl et al. (2011), we iteratively
254 removed those variables not reducing the mean square error over random permutations of the
255 same variable. For final Random Forests, we identified, using the 'importance' function, the
256 degree to which the inclusion of each predictor decreases residual model variance.

257
258 Forest inventory data
259
260 We combined data from 163,451 plots of the National Forest Inventories (NFIs) of Spain
261 (59,048 plots), France (40,844), Wallonia (Belgium, 1,238), Germany (47,832), Sweden (11,212)
262 and Finland (2,456). NFIs contained data on individual trees in each plot, including species
263 identity, dbh and basal area. Furthermore, estimates of timber production (increase in tree basal
264 area per hectare per year), tree biomass and tree recruitment (tree saplings per hectare) were
265 available for many plots. To ensure that data from different NFIs were comparable to the fitting
266 dataset plots, we only included trees with dbh ≥7.5cm. Furthermore, we only included the
267 105,316 plots that were at low to mid-altitudes (<1500m), without indication of recent logging,
268 and dominated by one of the 'target' species of the fitting dataset (Baeten et al. 2013).

268 We calculated the same climate, functional identity and functional and phylogenetic
269 diversity variables for the NFI dataset as for the fitting dataset. Soil pH, calculated for the top
270 10cm of the soil at 1km² resolution, was obtained from the ESDAC database (Panagos et al.
271 2012). These variables had similar ranges as in the fitting dataset (Table S3).

272
273 Extrapolating and mapping ecosystem functions across Europe

274 We used the 'predict' function in R to predict values of each ecosystem function in
275 inventory plots, based on the Random Forests (built using the fitting dataset with independently
253 'randomForest' library (Liaw & Wiener 2012). Following Seidl et al. (2011), we iteratively
254 removed those variables not reducing the mean square error over random permutations of the
255 same variable. For final Random Forests, we identified, using the 'importance' function, the
256 degree to which the inclusion of each predictor decreases residual model variance.

257
258 Forest inventory data
259
260 We combined data from 163,451 plots of the National Forest Inventories (NFIs) of Spain
261 (59,048 plots), France (40,844), Wallonia (Belgium, 1,238), Germany (47,832), Sweden (11,212)
262 and Finland (2,456). NFIs contained data on individual trees in each plot, including species
263 identity, dbh and basal area. Furthermore, estimates of timber production (increase in tree basal
264 area per hectare per year), tree biomass and tree recruitment (tree saplings per hectare) were
265 available for many plots. To ensure that data from different NFIs were comparable to the fitting
266 dataset plots, we only included trees with dbh ≥7.5cm. Furthermore, we only included the
267 105,316 plots that were at low to mid-altitudes (<1500m), without indication of recent logging,
268 and dominated by one of the 'target' species of the fitting dataset (Baeten et al. 2013).

268 We calculated the same climate, functional identity and functional and phylogenetic
269 diversity variables for the NFI dataset as for the fitting dataset. Soil pH, calculated for the top
270 10cm of the soil at 1km² resolution, was obtained from the ESDAC database (Panagos et al.
271 2012). These variables had similar ranges as in the fitting dataset (Table S3).

272
273 Extrapolating and mapping ecosystem functions across Europe

274 We used the 'predict' function in R to predict values of each ecosystem function in
275 inventory plots, based on the Random Forests (built using the fitting dataset with independently
collected FunDivEUROPE data; Baeten et al. 2013) and the climate, functional identity,
diversity (of the most recent survey) and abiotic conditions in the inventory plots. To determine
the accuracy of our predictions, we correlated the three ecosystem functions (timber production,
tree biomass and tree recruitment) that were measured in inventory plots with the values
predicted by the Random Forests. We did the validations across all plots at continental scale
(local and large scale variation) and within (only local variation) and among (only large-scale
variation) 20×20km grid cells ('localities') containing ≥20 plots. In addition, we compared
observed correlations between ecosystem functions with extrapolated ones. We also compared
the average values for tree biomass and recruitment between fitting and inventory datasets
(productivity was not comparable as it was measured in different units). To investigate how
mapped functions changed across latitude, we fitted linear models with linear and quadratic
effects of latitude as predictors.

We used the ‘threshold-approach’ (Gamfeldt et al. 2008) to calculate ecosystem
multifunctionality for each inventory plot, based on the predicted values of individual ecosystem
functions. Ecosystem multifunctionality was measured at both local and continental scales and
defined as the number of functions exceeding a threshold. The threshold was defined as the
proportion (25%, 50% (default threshold reported in main results), 75% or 90%) of the
‘maximum’ value observed for that function, either within a 20×20km locality (local scale) or
across Europe (continental scale). The maximum was defined as the 97.5th percentile of
observed functioning across plots, thus removing extreme outliers. For a concrete example on
quantifying multifunctionality, we refer to Fig. S3. We excluded ecosystem functions that (a) had
collected FunDivEUROPE data; Baeten et al. 2013) and the climate, functional identity, diversity (of the most recent survey) and abiotic conditions in the inventory plots. To determine the accuracy of our predictions, we correlated the three ecosystem functions (timber production, tree biomass and tree recruitment) that were measured in inventory plots with the values predicted by the Random Forests. We did the validations across all plots at continental scale (local and large scale variation) and within (only local variation) and among (only large-scale variation) 20×20km grid cells ('localities') containing ≥20 plots. In addition, we compared observed correlations between ecosystem functions with extrapolated ones. We also compared the average values for tree biomass and recruitment between fitting and inventory datasets (productivity was not comparable as it was measured in different units). To investigate how mapped functions changed across latitude, we fitted linear models with linear and quadratic effects of latitude as predictors.

## *Calculating multifunctionality and quantifying trade-offs*

We used the ‘threshold-approach’ (Gamfeldt *et al.* 2008) to calculate ecosystem multifunctionality for each inventory plot, based on the predicted values of individual ecosystem functions. Ecosystem multifunctionality was measured at both local and continental scales and defined as the number of functions exceeding a threshold. The threshold was defined as the proportion (25%, 50% (default threshold reported in main results), 75% or 90%) of the ‘maximum’ value observed for that function, either within a 20×20km locality (local scale) or across Europe (continental scale). The maximum was defined as the 97.5th percentile of observed functioning across plots, thus removing extreme outliers. For a concrete example on quantifying multifunctionality, we refer to Fig. S3. We excluded ecosystem functions that (a) had
poor Ranfom Forest fit, with R² (correlation between observed and predicted) values <0.20
(default analysis; Fig. 1C) and (b), as a sensitivity analysis, also those which had a low validation
R² (see results: tree recruitment and the related function of seedling growth). As a further
sensitivity analysis, we calculated ecosystem multifunctionality using Random Forest R² values
as weights.

We also calculated multifunctionality according to various management objectives,
following Allan et al. (2015). In these measures, we gave different weightings to the various
ecosystem functions, according to their presumed importance (based on a consensus of expert
opinions of all authors) for delivering the ecosystem services required for the given objective
(Fig. 1). The equal weights measure described above corresponds with most previous studies
(e.g. Lefcheck et al. 2015). In the measures representing management objectives, functions were
weighted with loadings ranging from 0 (unimportant) to 1 (high importance). Functions directly
related to the objective received a weight of 1, i.e. timber production and quality for ‘timber
production multifunctionality’, carbon sequestration-related functions for ‘climate regulation’
and functions directly measuring biodiversity (e.g. bird/understory diversity) for ‘biodiversity
conservation/recreation’. Other functions were weighted 0.25; 0.50 or 0.75, depending on their
relevance (Fig. 1). We also quantified a ‘narrow-sense’ biodiversity conservation measure,
where only functions directly measuring biodiversity were included, with weights of 1 (Fig. 1).

Relationships between multifunctionality measures can either be caused by large-scale
climatic/biogeographical factors (e.g. temperature gradients) or local-scale factors (e.g.
management, soil conditions). Therefore, using Pearson correlations, we tested for trade-offs and
synergies, at both continental (all plots) and local scales (within localities with >10 plots). With
t-tests we investigated whether local-scale correlations, differed from zero.
poor Ranfom Forest fit, with R² (correlation between observed and predicted) values <0.20
(default analysis; Fig. 1C) and (b), as a sensitivity analysis, also those which had a low validation
R² (see results: tree recruitment and the related function of seedling growth). As a further
sensitivity analysis, we calculated ecosystem multifunctionality using Random Forest R² values
as weights.

We also calculated multifunctionality according to various management objectives,
following Allan et al. (2015). In these measures, we gave different weightings to the various
ecosystem functions, according to their presumed importance (based on a consensus of expert
opinions of all authors) for delivering the ecosystem services required for the given objective
(Fig. 1). The equal weights measure described above corresponds with most previous studies
(e.g. Lefcheck et al. 2015). In the measures representing management objectives, functions were
weighted with loadings ranging from 0 (unimportant) to 1 (high importance). Functions directly
related to the objective received a weight of 1, i.e. timber production and quality for ‘timber
production multifunctionality’, carbon sequestration-related functions for ‘climate regulation’
and functions directly measuring biodiversity (e.g. bird/understory diversity) for ‘biodiversity
conservation/recreation’. Other functions were weighted 0.25; 0.50 or 0.75, depending on their
relevance (Fig. 1). We also quantified a ‘narrow-sense’ biodiversity conservation measure,
where only functions directly measuring biodiversity were included, with weights of 1 (Fig. 1).

Relationships between multifunctionality measures can either be caused by large-scale
climatic/biogeographical factors (e.g. temperature gradients) or local-scale factors (e.g.
management, soil conditions). Therefore, using Pearson correlations, we tested for trade-offs and
synergies, at both continental (all plots) and local scales (within localities with >10 plots). With
t-tests we investigated whether local-scale correlations, differed from zero.
Several functions had high weights in multiple multifunctionality measures, reflecting their
relevance for different ecosystem services (Fig. 1B). Raw correlation coefficients between
multifunctionality measures are therefore inflated by this overlap. To remove this effect, we
calculated a null expectation for the correlation-coefficients by reshuffling ecosystem function
values, without replacement, across plots 100 times. This eliminated any correlations among
functions, while maintaining the original distribution of values. With these resampled ecosystem
functions, we again calculated the different multifunctionality measures, and the average and
95% confidence intervals of the correlations between them. We calculated correlation-
coefficients corrected for overlap in functions by subtracting expected values (in the absence of
correlations among functions) from observed ones. As a sensitivity analysis, we repeated these
analyses only including plots located within those 150 localities in which validations of both
timber production and tree biomass were adequate (both r>0.1).

In total, 11.8% of the inventory plots were within protected areas which, depending on
the NFI, indicated either that forestry activities were restricted (Germany, Sweden) or that the
plot was in a National Park or nature reserve (Finland, France, Spain, Wallonia), see
Supplementary Material for more detailed information. Within each country, we investigated, for
each measure, whether local-scale multifunctionality was higher inside versus outside protected
areas, using Welch's t-tests.

# RESULTS

*Explaining variation in ecosystem functioning*
Several functions had high weights in multiple multifunctionality measures, reflecting their
relevance for different ecosystem services (Fig. 1B). Raw correlation coefficients between
multifunctionality measures are therefore inflated by this overlap. To remove this effect, we
calculated a null expectation for the correlation-coefficients by reshuffling ecosystem function
values, without replacement, across plots 100 times. This eliminated any correlations among
functions, while maintaining the original distribution of values. With these resampled ecosystem
functions, we again calculated the different multifunctionality measures, and the average and
95% confidence intervals of the correlations between them. We calculated correlation-
coefficients corrected for overlap in functions by subtracting expected values (in the absence of
correlations among functions) from observed ones. As a sensitivity analysis, we repeated these
analyses only including plots located within those 150 localities in which validations of both
timber production and tree biomass were adequate (both r>0.1).

In total, 11.8% of the inventory plots were within protected areas which, depending on
the NFI, indicated either that forestry activities were restricted (Germany, Sweden) or that the
plot was in a National Park or nature reserve (Finland, France, Spain, Wallonia), see
Supplementary Material for more detailed information. Within each country, we investigated, for
each measure, whether local-scale multifunctionality was higher inside versus outside protected
areas, using Welch's t-tests.

# RESULTS

*Explaining variation in ecosystem functioning*
On average, across the different ecosystem functions in our fitting dataset, Random Forests explained 40.7% of the total variation. The explained variation in ecosystem functions ranged from high (timber production: 72.5%; resistance to insect herbivory: 67.6%) to low (browsing resistance: 2.4%, Fig. 1C). The single most important explanatory factor (i.e. with lowest residual variance) varied between the functions. For sixteen functions it was a climate variable, for six a functional identity variable, for two altitude, for two a functional diversity variable and soil pH and average stem diameter for one each (Fig. 1C; Table S4).

Three ecosystem functions allowed for validation of predicted values in inventory plots. For timber production and tree biomass, across all plots, predicted values correlated reasonably well with observed values, with ‘extrapolation’ R² values (correlation between predicted and observed values in inventory plots) of 0.219 and 0.280, respectively. For tree recruitment the R² was only 0.040; Fig. S4. Validations generally worked best at large spatial scales and less well at local scales. Correlations between predicted and observed values of timber production, tree biomass and tree recruitment were, respectively, 0.390; 0.472 and 0.027 *across* 20×20km localities, and on average 0.127 (range: 0-0.976); 0.124 (range: 0-0.971) and 0.091 (range: 0-0.967) *within* localities. Absolute values of tree biomass were similar between NFI observations and Random Forest predictions, but for tree recruitment the values differed (Fig. S5). For more information on model validations, see Supplementary Material (S3).

*Levels of ecosystem functioning and multifunctionality throughout Western Europe*

After removing ecosystem functions poorly explained by the Random Forests ($R^2<0.2$; see Fig. 1C), we predicted levels of 22 ecosystem functions for the inventory plots (Fig. S6). Many of the mapped functions showed clear continental trends. For example, some (e.g. timber
On average, across the different ecosystem functions in our fitting dataset, Random Forests explained 40.7% of the total variation. The explained variation in ecosystem functions ranged from high (timber production: 72.5%; resistance to insect herbivory: 67.6%) to low (browsing resistance: 2.4%, Fig. 1C). The single most important explanatory factor (i.e. with lowest residual variance) varied between the functions. For sixteen functions it was a climate variable, for six a functional identity variable, for two altitude, for two a functional diversity variable and soil pH and average stem diameter for one each (Fig. 1C; Table S4).

Three ecosystem functions allowed for validation of predicted values in inventory plots. For timber production and tree biomass, across all plots, predicted values correlated reasonably well with observed values, with ‘extrapolation’ R² values (correlation between predicted and observed values in inventory plots) of 0.219 and 0.280, respectively. For tree recruitment the R² was only 0.040; Fig. S4. Validations generally worked best at large spatial scales and less well at local scales. Correlations between predicted and observed values of timber production, tree biomass and tree recruitment were, respectively, 0.390; 0.472 and 0.027 *across* 20×20km localities, and on average 0.127 (range: 0-0.976); 0.124 (range: 0-0.971) and 0.091 (range: 0-0.967) *within* localities. Absolute values of tree biomass were similar between NFI observations and Random Forest predictions, but for tree recruitment the values differed (Fig. S5). For more information on model validations, see Supplementary Material (S3).

Levels of ecosystem functioning and multifunctionality throughout Western Europe

After removing ecosystem functions poorly explained by the Random Forests ($R^2<0.2$; see Fig. 1C), we predicted levels of 22 ecosystem functions for the inventory plots (Fig. S6). Many of the mapped functions showed clear continental trends. For example, some (e.g. timber
production) had highest levels in central Western Europe, while others had highest values in boreal (e.g. timber quality) or Mediterranean (e.g. bat diversity) regions (Fig. S6; Table S5).

Most functions tended to be highest at mid-latitudes. Consequently, most continental-scale multifunctionality measures were highest in central Western Europe (multifunctionality hotspots) and lowest in southern Europe (Fig. 2). When only diversity measures were considered (narrow-sense biodiversity conservation), multifunctionality was also high in southern/central Spain and parts of Scandinavia. These patterns were broadly similar when functions with a high proportion of explained variance were weighted more heavily (Fig. S7). As expected, local-scale multifunctionality values did not show any large-scale spatial patterns (Fig. S8). Local multifunctionality scores were on average 45.8%, 47.1%, 49.2%, 49.8% and 47.8% below their maximum possible score (i.e. all functions above the 50% threshold) in the timber production, climate regulation, broad-sense and narrow-sense biodiversity conservation and overall multifunctionality scenario, respectively, and higher than 90% of the maximum possible score in 97, 49, 49 and 11,625 plots (out of 105,316 plots) in the timber production, climate regulation, broad-sense and narrow-sense biodiversity conservation scenario, respectively, whereas it exceeded 90% and 80% of maximum overall multifunctionality in only 3 and 446 plots respectively (Fig. 2B). Importantly, while ecosystem functions varied strongly at the continental scale (with 97.5 percentile values being on average 42.8% higher than mean values), there was also substantial variation within localities, with 97.5 percentile values being on average 12.6% higher than mean values (Table S6).

Trade-offs and synergies
production) had highest levels in central Western Europe, while others had highest values in boreal (e.g. timber quality) or Mediterranean (e.g. bat diversity) regions (Fig. S6; Table S5).
Most functions tended to be highest at mid-latitudes. Consequently, most continental-scale multifunctionality measures were highest in central Western Europe (multifunctionality hotspots) and lowest in southern Europe (Fig. 2). When only diversity measures were considered (narrow-sense biodiversity conservation), multifunctionality was also high in southern/central Spain and parts of Scandinavia. These patterns were broadly similar when functions with a high proportion of explained variance were weighted more heavily (Fig. S7). As expected, local-scale multifunctionality values did not show any large-scale spatial patterns (Fig. S8). Local multifunctionality scores were on average 45.8%, 47.1%, 49.2%, 49.8% and 47.8% below their maximum possible score (i.e. all functions above the 50% threshold) in the timber production, climate regulation, broad-sense and narrow-sense biodiversity conservation and overall multifunctionality scenario, respectively, and higher than 90% of the maximum possible score in 97, 49, 49 and 11,625 plots (out of 105,316 plots) in the timber production, climate regulation, broad-sense and narrow-sense biodiversity conservation scenario, respectively, whereas it exceeded 90% and 80% of maximum overall multifunctionality in only 3 and 446 plots respectively (Fig. 2B). Importantly, while ecosystem functions varied strongly at the continental scale (with 97.5 percentile values being on average 42.8% higher than mean values), there was also substantial variation within localities, with 97.5 percentile values being on average 12.6% higher than mean values (Table S6).

Trade-offs and synergies
Pairwise correlations between individual functions were positive on average at both scales, although correlations were weaker at local ($\bar{r}$ = 0.012) than at continental scales ($\bar{r}$ = 0.021), probably due to lower variation in functioning within localities (Table S6). Moderately to strongly positive correlations ($r$>0.3; $n$ = 57 (continental-scale) and 22 (local scale)) outnumbered negative ($r$< -0.3; $n$ = 45 (continental-scale) and 14 (local-scale)) correlations (Table S7,8). At the continental scale, correlations between timber production and tree biomass were similar for observed ($r$ = 0.55) and extrapolated ($r$ = 0.65) values. However, within localities this match was weaker ($\bar{r}$ = 0.63 observed and 0.24 predicted), with fits generally best in France and central/southern Spain, and weaker in Germany and northeast Spain (Fig. S9).

As different multifunctionality variables had similar continental-scale patterns (Fig. 2), continental-scale correlations between most measures were positive (Table 1). Only correlations between narrow-sense biodiversity conservation and both timber production ($r$ = -0.13) and climate regulation multifunctionality ($r$ = 0.01) were not. These correlations became more positive at more extreme (25 and 90%) multifunctionality thresholds (Table S9-S11).

Within localities, similar patterns were found. Relationships between timber production, climate regulation and broad-sense biodiversity conservation/recreation were positive, whereas relationships between narrow-sense biodiversity conservation and other multifunctionality variables were close to zero, or negative, on average (Fig. 3, Table 1). Negative relationships largely disappeared when multifunctionality was based on 25% or 90% thresholds (Table S9-S11). Importantly, positive relationships between timber production and climate regulation multifunctionality, and to a lesser extent between timber production/climate regulation multifunctionality and broad-sense biodiversity conservation/recreation multifunctionality, were very widespread across Europe (Fig. 3).
Pairwise correlations between individual functions were positive on average at both scales, although correlations were weaker at local ($\bar{r}$ = 0.012) than at continental scales ($\bar{r}$ = 0.021), probably due to lower variation in functioning within localities (Table S6). Moderately to strongly positive correlations ($r$>0.3; $n$ = 57 (continental-scale) and 22 (local scale)) outnumbered negative ($r$< -0.3; $n$ = 45 (continental-scale) and 14 (local-scale)) correlations (Table S7,8). At the continental scale, correlations between timber production and tree biomass were similar for observed ($r$ = 0.55) and extrapolated ($r$ = 0.65) values. However, within localities this match was weaker ($\bar{r}$ = 0.63 observed and 0.24 predicted), with fits generally best in France and central/southern Spain, and weaker in Germany and northeast Spain (Fig. S9).

As different multifunctionality variables had similar continental-scale patterns (Fig. 2), continental-scale correlations between most measures were positive (Table 1). Only correlations between narrow-sense biodiversity conservation and both timber production ($r$ = -0.13) and climate regulation multifunctionality ($r$ = 0.01) were not. These correlations became more positive at more extreme (25 and 90%) multifunctionality thresholds (Table S9-S11).

Within localities, similar patterns were found. Relationships between timber production, climate regulation and broad-sense biodiversity conservation/recreation were positive, whereas relationships between narrow-sense biodiversity conservation and other multifunctionality variables were close to zero, or negative, on average (Fig. 3, Table 1). Negative relationships largely disappeared when multifunctionality was based on 25% or 90% thresholds (Table S9-S11). Importantly, positive relationships between timber production and climate regulation multifunctionality, and to a lesser extent between timber production/climate regulation multifunctionality and broad-sense biodiversity conservation/recreation multifunctionality, were very widespread across Europe (Fig. 3).
We used null models to investigate whether observed correlations between multifunctionality variables were larger than expected. Relationships between multifunctionality variables were to a large extent driven by functions contributing to multiple multifunctionality variables, as observed minus expected correlation-coefficients were often close to zero (Fig. 3, Table 1). Nevertheless, at a continental scale, relationships between timber production, climate regulation and broad-sense biodiversity conservation multifunctionality remained significantly positive (all *P*<0.05). At the local scale, relationships between timber production and climate regulation multifunctionality also remained significantly (although weakly) positive, whereas relationships between timber production and the biodiversity conservation measures became significantly, weakly, negative. In sensitivity analyses these patterns hardly changed when (i) recruitment-related functions were omitted from multifunctionality measures, (ii) ecosystem functions with a high Random Forest fit had proportionally higher loadings in multifunctionality measures, or (iii) only plots from localities with high validation R² values of Random Forests explaining timber production and tree biomass were included (Table 1). Negative relationships largely disappeared when multifunctionality was quantified based on 25% or 90% thresholds (Table S9-S11). Importantly, functional overlap-corrected correlation-coefficients between different ecosystem multifunctionality scenarios varied greatly, from positive to negative, throughout localities (Fig. 3).

*Multifunctionality inside versus outside protected areas*

Local-scale associations between values of multifunctionality and protection status differed widely between countries and scenarios (Fig. 4). In Spain and Germany, timber production and climate regulation multifunctionality were lower inside protected areas, whereas
We used null models to investigate whether observed correlations between multifunctionality variables were larger than expected. Relationships between multifunctionality variables were to a large extent driven by functions contributing to multiple multifunctionality variables, as observed minus expected correlation-coefficients were often close to zero (Fig. 3, Table 1). Nevertheless, at a continental scale, relationships between timber production, climate regulation and broad-sense biodiversity conservation multifunctionality remained significantly positive (all *P*<0.05). At the local scale, relationships between timber production and climate regulation multifunctionality also remained significantly (although weakly) positive, whereas relationships between timber production and the biodiversity conservation measures became significantly, weakly, negative. In sensitivity analyses these patterns hardly changed when (i) recruitment-related functions were omitted from multifunctionality measures, (ii) ecosystem functions with a high Random Forest fit had proportionally higher loadings in multifunctionality measures, or (iii) only plots from localities with high validation R² values of Random Forests explaining timber production and tree biomass were included (Table 1). Negative relationships largely disappeared when multifunctionality was quantified based on 25% or 90% thresholds (Table S9-S11). Importantly, functional overlap-corrected correlation-coefficients between different ecosystem multifunctionality scenarios varied greatly, from positive to negative, throughout localities (Fig. 3).

431

432 *Multifunctionality inside versus outside protected areas*

Local-scale associations between values of multifunctionality and protection status differed widely between countries and scenarios (Fig. 4). In Spain and Germany, timber production and climate regulation multifunctionality were lower inside protected areas, whereas
the opposite was observed in France. In Germany, biodiversity conservation-related
multifunctionality was highest inside protected areas, whereas in France the opposite was found.
These results were largely insensitive to the way in which multifunctionality was quantified
(Table S12).

DISCUSSION

In our study trade-offs between groups of functions were rare in European forests, at both
continental and local scales. We found synergies between individual ecosystem functions and
few trade-offs between multifunctionality measures focused on timber production, climate
regulation and biodiversity conservation/recreation. When corrected for overlap in functions
among scenarios, some relationships were weakly positive throughout most of Europe (timber
production versus climate regulation), some were weakly negative (timber production versus
biodiversity conservation/recreation) and some were close to zero (climate regulation versus
biodiversity conservation/recreation). The lack of strong trade-offs indicates that functions related
to (sustainable) timber production can go hand in hand with functions related to services such as
biodiversity conservation. Mapping local trade-offs and synergies across Europe revealed
substantial variation in these relationships, showing that strong synergies are realized in a few
environments. While biodiversity and timber production are currently maximised in some
forests, suggesting a "win-win" for conservation and commercial forestry, across plots, average
multifunctionality values were almost 50% below maximum possible levels, and the proportion
of forest plots providing high levels of 'overall multifunctionality' (where timber production,
climate regulation and biodiversity conservation are all maximized) was very small. Hence,
while forest management has the potential to realize high multifunctionality, this is currently not
the opposite was observed in France. In Germany, biodiversity conservation-related
multifunctionality was highest inside protected areas, whereas in France the opposite was found.
These results were largely insensitive to the way in which multifunctionality was quantified
(Table S12).

DISCUSSION

In our study trade-offs between groups of functions were rare in European forests, at both
continental and local scales. We found synergies between individual ecosystem functions and
few trade-offs between multifunctionality measures focused on timber production, climate
regulation and biodiversity conservation/recreation. When corrected for overlap in functions
among scenarios, some relationships were weakly positive throughout most of Europe (timber
production versus climate regulation), some were weakly negative (timber production versus
biodiversity conservation/recreation) and some were close to zero (climate regulation versus
biodiversity conservation/recreation). The lack of strong trade-offs indicates that functions related
to (sustainable) timber production can go hand in hand with functions related to services such as
biodiversity conservation. Mapping local trade-offs and synergies across Europe revealed
substantial variation in these relationships, showing that strong synergies are realized in a few
environments. While biodiversity and timber production are currently maximised in some
forests, suggesting a "win-win" for conservation and commercial forestry, across plots, average
multifunctionality values were almost 50% below maximum possible levels, and the proportion
of forest plots providing high levels of 'overall multifunctionality' (where timber production,
climate regulation and biodiversity conservation are all maximized) was very small. Hence,
while forest management has the potential to realize high multifunctionality, this is currently not
common. Most multifunctionality measures had many ecosystem functions in common, as some ecosystem functions are valued under a range of different management objectives (e.g. Chan et al. 2006; Allan et al. 2015). Relationships between different multifunctionality measures were generally much more strongly positive if not corrected for this functional overlap. While these raw correlations are statistically spurious (as the different measures partly contain the same data), they can be highly relevant for management. For instance, tree growth is important for both timber production and climate regulation, which suggests that forest management promoting tree growth will maximize both services. Our results therefore suggest many possibilities for win-win forest management strategies.

Our multifunctionality variables were intended to represent the bundle of functions needed to meet certain forest management objectives (following Allan et al. 2015). They should therefore be more useful to managers than traditional multifunctionality metrics that assume equal importance of each ecosystem function. However, they could be further improved to consider how multiple functions are related to final ecosystem services, using production functions, and then services can be valued in monetary or other units to calculate the overall benefits supplied by different management scenarios (e.g. Nelson et al. 2009; Bateman et al. 2013). Ultimately, sustainable ecosystem management needs to minimize trade-offs between ecosystem benefits for different stakeholders (Díaz et al. 2015) and our targeted multifunctionality metrics represent a step towards quantifying and mapping these trade-offs at large scales.

Other studies, performed in grasslands (e.g. Lavorel et al. 2011) or across different ecosystems or land-use types (Chan et al. 2006) have documented strong trade-offs between ecosystem functions and services, especially between productivity-related functions and those
common. Most multifunctionality measures had many ecosystem functions in common, as some ecosystem functions are valued under a range of different management objectives (e.g. Chan et al. 2006; Allan et al. 2015). Relationships between different multifunctionality measures were generally much more strongly positive if not corrected for this functional overlap. While these raw correlations are statistically spurious (as the different measures partly contain the same data), they can be highly relevant for management. For instance, tree growth is important for both timber production and climate regulation, which suggests that forest management promoting tree growth will maximize both services. Our results therefore suggest many possibilities for win-win forest management strategies.

Our multifunctionality variables were intended to represent the bundle of functions needed to meet certain forest management objectives (following Allan et al. 2015). They should therefore be more useful to managers than traditional multifunctionality metrics that assume equal importance of each ecosystem function. However, they could be further improved to consider how multiple functions are related to final ecosystem services, using production functions, and then services can be valued in monetary or other units to calculate the overall benefits supplied by different management scenarios (e.g. Nelson et al. 2009; Bateman et al. 2013). Ultimately, sustainable ecosystem management needs to minimize trade-offs between ecosystem benefits for different stakeholders (Díaz et al. 2015) and our targeted multifunctionality metrics represent a step towards quantifying and mapping these trade-offs at large scales.

Other studies, performed in grasslands (e.g. Lavorel et al. 2011) or across different ecosystems or land-use types (Chan et al. 2006) have documented strong trade-offs between ecosystem functions and services, especially between productivity-related functions and those
associated with biodiversity conservation or recreation. However, in forests, relationships between tree biomass and the biodiversity of associated taxa often show more mixed patterns (Jukes et al. 2007). For example, the positive relationship between tree productivity and bird diversity in our data could be due to the strong dependence of specialist species on forests with many old trees (Gil-Tena et al. 2007), while the trade-off between productivity and understorey biomass may be driven by light competition between trees and understorey plants. When biodiversity conservation multifunctionality was quantified using only the four direct measures of biodiversity, weakly negative relationships with timber production and climate regulation multifunctionality were found. Their approximately equal strength at continental and local scales (Table 1) suggests that the relationship was primarily driven by local-scale factors, such as stand composition. The negative response of understorey plants to tree growth is likely responsible for this trade-off, as it is difficult to maximize timber production whilst maintaining an open canopy.

We also found that protected forests were not necessarily associated with high local-scale ecosystem multifunctionality. In Spain, several multifunctionality measures were in fact lower inside protected areas. In other countries, patterns were more mixed, but overall multifunctionality was never highest inside protected areas. Importantly, associations between forest protection status and multifunctionality were unlikely to be driven by climate, as local-scale climatic variation is low within our 20×20km regions. Associations between local-scale multifunctionality and protection status seem therefore to be driven by local factors, such as tree diversity or composition. However, it is uncertain whether these observed relationships are causal, as forests were likely not designated to be protected at random. For example, they may have had low productivity and particular tree compositions before they were protected. Furthermore, services such as the conservation of forest specialist species were not quantified,
associated with biodiversity conservation or recreation. However, in forests, relationships between tree biomass and the biodiversity of associated taxa often show more mixed patterns (Jukes et al. 2007). For example, the positive relationship between tree productivity and bird diversity in our data could be due to the strong dependence of specialist species on forests with many old trees (Gil-Tena et al. 2007), while the trade-off between productivity and understorey biomass may be driven by light competition between trees and understorey plants. When biodiversity conservation multifunctionality was quantified using only the four direct measures of biodiversity, weakly negative relationships with timber production and climate regulation multifunctionality were found. Their approximately equal strength at continental and local scales (Table 1) suggests that the relationship was primarily driven by local-scale factors, such as stand composition. The negative response of understorey plants to tree growth is likely responsible for this trade-off, as it is difficult to maximize timber production whilst maintaining an open canopy.

We also found that protected forests were not necessarily associated with high local-scale ecosystem multifunctionality. In Spain, several multifunctionality measures were in fact lower inside protected areas. In other countries, patterns were more mixed, but overall multifunctionality was never highest inside protected areas. Importantly, associations between forest protection status and multifunctionality were unlikely to be driven by climate, as local-scale climatic variation is low within our 20×20km regions. Associations between local-scale multifunctionality and protection status seem therefore to be driven by local factors, such as tree diversity or composition. However, it is uncertain whether these observed relationships are causal, as forests were likely not designated to be protected at random. For example, they may have had low productivity and particular tree compositions before they were protected. Furthermore, services such as the conservation of forest specialist species were not quantified,
but these could be high inside protected areas. Many protected areas were only established relatively recently (Paillet *et al.* 2015), so protected forests may still be recovering from past management. Finally, we only investigated forests without evidence of recent logging activity, which may have reduced the contrast between protected and non-protected areas. Regardless, although our results suggest a high potential for win-win forest management scenarios, the simultaneous maximization of timber production, climate regulation and biodiversity has not yet been realized within protected areas.

Our results also provide evidence that climate drives large-scale variation in many ecosystem functions and the synergies between them. Many functions, such as tree biomass or litter production, had highest levels in central Western Europe (Fig. S6) and some synergies between multifunctionality scenarios were stronger at continental than at local scales. A strong continental-scale synergy between earthworm biomass and litter decomposition (Table S7) may have arisen because they were both strongly associated with climate (Table S4). The correlation was also present at the local scale (Table S8), suggesting additional direct links between them. While earlier studies have already shown the importance of climate for functions such as primary production and carbon sequestration (e.g. Cramer *et al.* 2001), our more comprehensive study shows that climate may be a driver of many more ecosystem functions, such as earthworm or microbial biomass. The fact that so many functions appear related to climate, especially to wet season precipitation (Table S4), may have important implications. For example, timber production multifunctionality was lower in dry climates, suggesting detrimental effects of projected future decreases in precipitation (IPCC 2014). However, while our approach is powerful in describing patterns, it is not suited to identify underlying processes. Therefore, more
but these could be high inside protected areas. Many protected areas were only established relatively recently (Paillet *et al.* 2015), so protected forests may still be recovering from past management. Finally, we only investigated forests without evidence of recent logging activity, which may have reduced the contrast between protected and non-protected areas. Regardless, although our results suggest a high potential for win-win forest management scenarios, the simultaneous maximization of timber production, climate regulation and biodiversity has not yet been realized within protected areas.

Our results also provide evidence that climate drives large-scale variation in many ecosystem functions and the synergies between them. Many functions, such as tree biomass or litter production, had highest levels in central Western Europe (Fig. S6) and some synergies between multifunctionality scenarios were stronger at continental than at local scales. A strong continental-scale synergy between earthworm biomass and litter decomposition (Table S7) may have arisen because they were both strongly associated with climate (Table S4). The correlation was also present at the local scale (Table S8), suggesting additional direct links between them. While earlier studies have already shown the importance of climate for functions such as primary production and carbon sequestration (e.g. Cramer *et al.* 2001), our more comprehensive study shows that climate may be a driver of many more ecosystem functions, such as earthworm or microbial biomass. The fact that so many functions appear related to climate, especially to wet season precipitation (Table S4), may have important implications. For example, timber production multifunctionality was lower in dry climates, suggesting detrimental effects of projected future decreases in precipitation (IPCC 2014). However, while our approach is powerful in describing patterns, it is not suited to identify underlying processes. Therefore, more
527 research on the causality of climate-ecosystem functioning relationships (e.g. De Boeck et al. 2008; Šímová & Storch 2017) is needed to predict ecosystem responses to climate change.
528
529 Extrapolations are still relatively rare in ecosystem functioning studies (but see Lee et al. 2000; Isbell et al. 2014; Manning et al. 2015), although other subtopics of ecology, such as
530
531 species distribution research (Elith & Leathwick 2009), have a much stronger tradition in this
532
533 respect. Three ecosystem functions could be validated with independent observations, which
534
535 showed that: (1) validations were generally adequate for timber production and tree biomass, but
536
537 not for tree recruitment, (2) validations worked best at large spatial scales, whereas at local
538
539 scales there was large variation in their accuracy but (3) relationships between different
540
541 multifunctionality variables were insensitive to the inclusion of localities where the validation
542
543 was less well supported. Our approach is therefore promising, but we emphasize that validations
544
545 could only be carried out for those three ecosystem functions for which independent inventory
546
547 data was available, so future validations of other functions are needed. Local-scale data related to
548
549 soil fertility or management could thus further improve the accuracy of ecosystem function
541 predictions.

542
543
544
545
546
547
548
549 Our study presents a new approach to quantify ecosystem functioning at scales relevant for policy makers. The increasing availability of large datasets on ecosystem functioning from integrated projects means our approach may become increasingly feasible for other systems and regions. A further possibility would be to combine local-scale ecosystem functioning datasets with remote sensing data to map services at large scales. Remote sensing approaches have successfully predicted some ecosystem functions, but have difficulties with other functions, such as soil processes (de Araujo et al. 2015). By combining data on forest and climate attributes with remotely sensed parameters, we could map ecosystem functions even more accurately in the
527 research on the causality of climate-ecosystem functioning relationships (e.g. De Boeck et al. 2008; Šímová & Storch 2017) is needed to predict ecosystem responses to climate change.
528
529 Extrapolations are still relatively rare in ecosystem functioning studies (but see Lee et al. 2000; Isbell et al. 2014; Manning et al. 2015), although other subtopics of ecology, such as
530
531 species distribution research (Elith & Leathwick 2009), have a much stronger tradition in this
532
533 respect. Three ecosystem functions could be validated with independent observations, which
534
535 showed that: (1) validations were generally adequate for timber production and tree biomass, but
536
537 not for tree recruitment, (2) validations worked best at large spatial scales, whereas at local
538
539 scales there was large variation in their accuracy but (3) relationships between different
540
541 multifunctionality variables were insensitive to the inclusion of localities where the validation
542
543 was less well supported. Our approach is therefore promising, but we emphasize that validations
544
545 could only be carried out for those three ecosystem functions for which independent inventory
546
547 data was available, so future validations of other functions are needed. Local-scale data related to
548
549 soil fertility or management could thus further improve the accuracy of ecosystem function
549 predictions.

542 Our study presents a new approach to quantify ecosystem functioning at scales relevant
543 for policy makers. The increasing availability of large datasets on ecosystem functioning from
544 integrated projects means our approach may become increasingly feasible for other systems and
545 regions. A further possibility would be to combine local-scale ecosystem functioning datasets
546 with remote sensing data to map services at large scales. Remote sensing approaches have
547 successfully predicted some ecosystem functions, but have difficulties with other functions, such
548 as soil processes (de Araujo et al. 2015). By combining data on forest and climate attributes with
549 remotely sensed parameters, we could map ecosystem functions even more accurately in the
future. Our study is a first step in reaching the ultimate goal of predicting how future ecosystem functioning and service provision will be altered by ongoing global trends, such as climate change (IPCC 2014), eutrophication and acidification (Galloway et al. 2008) or land-use change (Newbold et al. 2015). Future studies could combine our approach with models on climate change (e.g. IPCC scenarios), biodiversity change (e.g. Isbell et al. 2014) or management scenarios to investigate the impacts of these global trends for the future functioning and service provisioning of forests and other ecosystems.

In conclusion, our study, among most comprehensive overviews of forest ecosystem functioning to date, showed that different measures of forest multifunctionality tend not to trade-off with each other, at both local and continental scales. Within some areas there were strong synergies between different multifunctionality measures, indicating that even though they are currently uncommon, "win-win" forest management strategies are possible and could be promoted in the future. However, we also found that multifunctionality is often not higher inside than outside protected areas. Our study therefore suggests a high but unrealized potential for multifunctionality in European forests.

This paper is a joint effort of the working group 'Scaling biodiversity-ecosystem functioning relations: a synthesis based on the *FunDivEUROPE* research platforms' on the 24th-26th November 2014 in Leipzig, Germany, kindly supported by sDiv, the Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation (FZT 118). The *FunDivEUROPE* project received funding from the European Union's Seventh Programme (FP7/2007-2013) under grant agreement No. 265171.
future. Our study is a first step in reaching the ultimate goal of predicting how future ecosystem functioning and service provision will be altered by ongoing global trends, such as climate change (IPCC 2014), eutrophication and acidification (Galloway et al. 2008) or land-use change (Newbold et al. 2015). Future studies could combine our approach with models on climate change (e.g. IPCC scenarios), biodiversity change (e.g. Isbell et al. 2014) or management scenarios to investigate the impacts of these global trends for the future functioning and service provisioning of forests and other ecosystems.

In conclusion, our study, among most comprehensive overviews of forest ecosystem functioning to date, showed that different measures of forest multifunctionality tend not to trade-off with each other, at both local and continental scales. Within some areas there were strong synergies between different multifunctionality measures, indicating that even though they are currently uncommon, "win-win" forest management strategies are possible and could be promoted in the future. However, we also found that multifunctionality is often not higher inside than outside protected areas. Our study therefore suggests a high but unrealized potential for multifunctionality in European forests.

# ACKNOWLEDGEMENTS

This paper is a joint effort of the working group 'Scaling biodiversity-ecosystem functioning relations: a synthesis based on the *FunDivEUROPE* research platforms' on the 24th-26th November 2014 in Leipzig, Germany, kindly supported by sDiv, the Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation (FZT 118). The *FunDivEUROPE* project received funding from the European Union's Seventh Programme (FP7/2007-2013) under grant agreement No. 265171.
We thank the MAGRAMA for access to the Spanish Forest Inventory, the Johann Heinrich von Thünen-Institut for access to the German National Forest Inventories, the Natural Resources Institute Finland (LUKE) for making the Finnish NFI data available, the Swedish University of Agricultural Sciences for making the Swedish NFI data available, and Hugues Lecomte, from the Walloon Forest Inventory, for access to the Walloon NFI data. The study was supported by the TRY initiative on plant traits (http://www.trydb.org). The TRY initiative and database is hosted, developed and maintained at the Max Planck Institute for Biogeochemistry, Jena, Germany.

TRY is/has been supported by DIVERSITAS, IGBP, the Global Land Project, the UK Natural Environment Research Council (NERC) through its program QUEST (Quantifying and Understanding the Earth System), the French Foundation for Biodiversity Research (FRB), and GIS "Climat, Environnement et Société" France.

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We thank the MAGRAMA for access to the Spanish Forest Inventory, the Johann Heinrich von Thünen-Institut for access to the German National Forest Inventories, the Natural Resources Institute Finland (LUKE) for making the Finnish NFI data available, the Swedish University of Agricultural Sciences for making the Swedish NFI data available, and Hugues Lecomte, from the Walloon Forest Inventory, for access to the Walloon NFI data. The study was supported by the TRY initiative on plant traits (http://www.trydb.org). The TRY initiative and database is hosted, developed and maintained at the Max Planck Institute for Biogeochemistry, Jena, Germany.

TRY is/has been supported by DIVERSITAS, IGBP, the Global Land Project, the UK Natural Environment Research Council (NERC) through its program QUEST (Quantifying and Understanding the Earth System), the French Foundation for Biodiversity Research (FRB), and GIS "Climat, Environnement et Société" France.

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<figure><img src="image_3.png" /></figure>

713

Figure 1. A: Ecosystem functions included in this study, with the colours and numbers referring to the bars/circles representing them in B and C. B: Weightings used to produce five ecosystem multifunctionality measures, reflecting different management scenarios. From left to right, the ‘equal-weights’, ‘timber production’, ‘climate regulation’, the ‘broad-sense biodiversity conservation/recreation’ and the ‘strict-sense biodiversity conservation’ measure. In the equal weights measure, all ecosystem functions are valued equally. In other measures, function
<figure><img src="image_3.png" /><figcaption>Figure 1. A: Ecosystem functions included in this study, with the colours and numbers referring to the bars/circles representing them in B and C. B: Weightings used to produce five ecosystem multifunctionality measures, reflecting different management scenarios. From left to right, the ‘equal-weights’, ‘timber production’, ‘climate regulation’, the ‘broad-sense biodiversity conservation/recreation’ and the ‘strict-sense biodiversity conservation’ measure. In the equal weights measure, all ecosystem functions are valued equally. In other measures, function</figcaption></figure>
weightings reflect their importance for the management objective. Note that in the climate
regulation scenario, loadings of the decomposition variables are negative. C: Proportion of
variance of ecosystem functions explained by Random Forests. Letters above the bars indicate
which type of predictor was most important in explaining variation: C = climate-related; I =
functional identity-related; P = pH; A = altitude; D = biodiversity-related; S = stand structure
related. In further analyses, only those functions with R² values above 0.2 (dashed horizontal
line) were included.
weightings reflect their importance for the management objective. Note that in the climate
regulation scenario, loadings of the decomposition variables are negative. C: Proportion of
variance of ecosystem functions explained by Random Forests. Letters above the bars indicate
which type of predictor was most important in explaining variation: C = climate-related; I =
functional identity-related; P = pH; A = altitude; D = biodiversity-related; S = stand structure
related. In further analyses, only those functions with R² values above 0.2 (dashed horizontal
line) were included.
<figure><img src="image_4.png" /><figcaption>Figure 2. While high values of continental-scale multifunctionality (A, C-F) in central Europe across a range of scenarios indicate large scale synergies, at local scales (B) high overall multifunctionality is realized in only a few sites. Mapped levels of predicted large-scale multifunctionality are rescaled as the proportion of functions above a 50% threshold. Green values indicate relatively high functioning, while brown values indicate relatively low functioning. In A), locations of fitting dataset plot are shown in red. In B, where overall, local-scale multifunctionality is shown, the histogram indicates that in only a few plots, levels exceed 0.8.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 2. While high values of continental-scale multifunctionality (A, C-F) in central Europe across a range of scenarios indicate large scale synergies, at local scales (B) high overall multifunctionality is realized in only a few sites. Mapped levels of predicted large-scale multifunctionality are rescaled as the proportion of functions above a 50% threshold. Green values indicate relatively high functioning, while brown values indicate relatively low functioning. In A), locations of fitting dataset plot are shown in red. In B, where overall, local-scale multifunctionality is shown, the histogram indicates that in only a few plots, levels exceed 0.8.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 3. Substantial variation in the degree of local scale synergies and trade-offs exists across Europe. Observed and observed minus expected correlation coefficients between multifunctionality measures, within 20×20 km grid cells. Top: Values of all observed multifunctionality measures, except for the narrow-sense biodiversity conservation measure, correlate positively at local scales. Bottom: these correlations are largely driven by overlap in ecosystem functions, as observed minus expected correlation-coefficients are close to zero. Average correlations that deviate significantly from zero are indicated with an asterisk (*).</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 3. Substantial variation in the degree of local scale synergies and trade-offs exists across Europe. Observed and observed minus expected correlation coefficients between multifunctionality measures, within 20×20 km grid cells. Top: Values of all observed multifunctionality measures, except for the narrow-sense biodiversity conservation measure, correlate positively at local scales. Bottom: these correlations are largely driven by overlap in ecosystem functions, as observed minus expected correlation-coefficients are close to zero. Average correlations that deviate significantly from zero are indicated with an asterisk (*).</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 4. Local-scale ecosystem multifunctionality is generally not higher inside protected areas, for different multifunctionality measures and countries. Bars above zero indicate that multifunctionality is higher inside than outside protected areas, while bars below zero indicate the opposite. A: Equal-weight multifunctionality. B: timber production multifunctionality. C: climate regulation multifunctionality. D: broad-sense biodiversity conservation/recreation multifunctionality. E: narrow-sense biodiversity conservation/recreation multifunctionality.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 4. Local-scale ecosystem multifunctionality is generally not higher inside protected areas, for different multifunctionality measures and countries. Bars above zero indicate that multifunctionality is higher inside than outside protected areas, while bars below zero indicate the opposite. A: Equal-weight multifunctionality. B: timber production multifunctionality. C: climate regulation multifunctionality. D: broad-sense biodiversity conservation/recreation multifunctionality. E: narrow-sense biodiversity conservation/recreation multifunctionality.</figcaption></figure>
Table 1. Correlations between values of different multifunctionality measures at both continental and local scales and both across all plots and within countries. Here, multifunctionality was based on a 50% threshold level. Correlations were also quantified after correcting for the overlap in ecosystem functions between multifunctionality measures. This is indicated as ‘no functional overlap’ or ‘no FO’ in the table. As sensitivity analyses, correlations were also calculated based on (a) multifunctionality measures in which recruitment-related functions were excluded, (b) multifunctionality measures in which loadings of ecosystem functions was proportional to Random Forest R² values and (c) only those plots within 20x20 km grid cells with a high validation R² (>0.10) for timber production and tree biomass. Significant correlations are shown in bold. TP = timber production, CR = climate regulation, BCB = broad-sense biodiversity conservation and BCN = narrow-sense biodiversity conservation.

<table><thead><tr><th></th><th>TP-CR</th><th>TP-BCB</th><th>TP-BCN</th><th>CR-BCB</th><th>CR-BCN</th></tr></thead><tbody><tr><td>Continental scale, raw</td><td><strong>0.81</strong></td><td><strong>0.57</strong></td><td><strong>-0.13</strong></td><td><strong>0.63</strong></td><td>0.01</td></tr><tr><td>Continental scale, no FO</td><td><strong>0.06</strong></td><td><strong>0.15</strong></td><td><strong>-0.13</strong></td><td><strong>0.16</strong></td><td>0.01</td></tr><tr><td>Continental scale, no FO, no recruitment-related EFs</td><td><strong>0.07</strong></td><td><strong>0.16</strong></td><td><strong>-0.09</strong></td><td><strong>0.20</strong></td><td><strong>0.08</strong></td></tr><tr><td>Continental scale, no FO, corrected for EF R<sup>2</sup> values</td><td><strong>0.10</strong></td><td><strong>0.18</strong></td><td><strong>-0.17</strong></td><td><strong>0.12</strong></td><td><strong>-0.10</strong></td></tr><tr><td>Continental scale, no FO, only plots with high validation</td><td><strong>0.05</strong></td><td><strong>0.12</strong></td><td><strong>-0.35</strong></td><td><strong>0.11</strong></td><td><strong>-0.17</strong></td></tr><tr><td>Local scale</td><td><strong>0.79</strong></td><td><strong>0.31</strong></td><td><strong>-0.12</strong></td><td><strong>0.44</strong></td><td>-0.01</td></tr><tr><td>Local scale, Spain only</td><td><strong>0.79</strong></td><td><strong>0.32</strong></td><td><strong>-0.11</strong></td><td><strong>0.46</strong></td><td>0.02</td></tr><tr><td>Local scale, France only</td><td><strong>0.80</strong></td><td><strong>0.30</strong></td><td><strong>-0.12</strong></td><td><strong>0.42</strong></td><td><strong>-0.03</strong></td></tr><tr><td>Local scale, Wallonia only</td><td><strong>0.78</strong></td><td><strong>0.12</strong></td><td><strong>-0.31</strong></td><td><strong>0.38</strong></td><td><strong>-0.07</strong></td></tr><tr><td>Local scale, Germany only</td><td><strong>0.80</strong></td><td><strong>0.31</strong></td><td><strong>-0.16</strong></td><td><strong>0.47</strong></td><td>-0.01</td></tr><tr><td>Local scale, Sweden only</td><td><strong>0.73</strong></td><td><strong>0.30</strong></td><td>-0.03</td><td><strong>0.33</strong></td><td>-0.03</td></tr><tr><td>Local scale, Finland only</td><td><strong>0.77</strong></td><td><strong>0.34</strong></td><td>-0.08</td><td><strong>0.44</strong></td><td>-0.02</td></tr><tr><td>Local scale, no FO</td><td><strong>0.01</strong></td><td><strong>-0.08</strong></td><td><strong>-0.13</strong></td><td><strong>0.03</strong></td><td><strong>-0.01</strong></td></tr><tr><td>Local scale, no FO, Spain only</td><td><strong>0.01</strong></td><td><strong>-0.08</strong></td><td><strong>-0.11</strong></td><td><strong>0.05</strong></td><td><strong>0.02</strong></td></tr><tr><td>Local scale, no FO, France only</td><td><strong>0.01</strong></td><td><strong>-0.09</strong></td><td><strong>-0.13</strong></td><td><strong>0.01</strong></td><td><strong>-0.03</strong></td></tr><tr><td>Local scale, no FO, Wallonia only</td><td>0.00</td><td><strong>-0.26</strong></td><td><strong>-0.31</strong></td><td><strong>-0.03</strong></td><td><strong>-0.07</strong></td></tr><tr><td>Local scale, no FO, Germany only</td><td><strong>0.02</strong></td><td><strong>-0.08</strong></td><td><strong>-0.16</strong></td><td><strong>0.06</strong></td><td><strong>-0.01</strong></td></tr><tr><td>Local scale, no FO, Sweden only</td><td><strong>-0.05</strong></td><td><strong>-0.09</strong></td><td><strong>-0.03</strong></td><td><strong>-0.08</strong></td><td><strong>-0.04</strong></td></tr><tr><td>Local scale, no FO, Finland only</td><td><strong>-0.01</strong></td><td><strong>-0.05</strong></td><td><strong>-0.08</strong></td><td><strong>0.03</strong></td><td><strong>-0.02</strong></td></tr><tr><td>Local scale, no FO, no recruitment-related EFs</td><td><strong>0.03</strong></td><td><strong>-0.12</strong></td><td><strong>-0.14</strong></td><td><strong>-0.04</strong></td><td><strong>-0.02</strong></td></tr><tr><td>Local scale, no FO, corrected for EF R<sup>2</sup> values</td><td><strong>0.09</strong></td><td><strong>-0.07</strong></td><td><strong>-0.17</strong></td><td><strong>-0.04</strong></td><td><strong>-0.08</strong></td></tr><tr><td>Local scale, no FO, only plots with high validation</td><td><strong>0.10</strong></td><td><strong>-0.15</strong></td><td><strong>-0.29</strong></td><td>-0.06</td><td>-0.13</td></tr></tbody></table>

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Table 1. Correlations between values of different multifunctionality measures at both continental and local scales and both across all plots and within countries. Here, multifunctionality was based on a 50% threshold level. Correlations were also quantified after correcting for the overlap in ecosystem functions between multifunctionality measures. This is indicated as ‘no functional overlap’ or ‘no FO’ in the table. As sensitivity analyses, correlations were also calculated based on (a) multifunctionality measures in which recruitment-related functions were excluded, (b) multifunctionality measures in which loadings of ecosystem functions was proportional to Random Forest R² values and (c) only those plots within 20x20 km grid cells with a high validation R² (>0.10) for timber production and tree biomass. Significant correlations are shown in bold. TP = timber production, CR = climate regulation, BCB = broad-sense biodiversity conservation and BCN = narrow-sense biodiversity conservation.

<table><thead><tr><th></th><th>TP-CR</th><th>TP-BCB</th><th>TP-BCN</th><th>CR-BCB</th><th>CR-BCN</th></tr></thead><tbody><tr><td>Continental scale, raw</td><td><strong>0.81</strong></td><td><strong>0.57</strong></td><td><strong>-0.13</strong></td><td><strong>0.63</strong></td><td>0.01</td></tr><tr><td>Continental scale, no FO</td><td><strong>0.06</strong></td><td><strong>0.15</strong></td><td><strong>-0.13</strong></td><td><strong>0.16</strong></td><td>0.01</td></tr><tr><td>Continental scale, no FO, no recruitment-related EFs</td><td><strong>0.07</strong></td><td><strong>0.16</strong></td><td><strong>-0.09</strong></td><td><strong>0.20</strong></td><td><strong>0.08</strong></td></tr><tr><td>Continental scale, no FO, corrected for EF R² values</td><td><strong>0.10</strong></td><td><strong>0.18</strong></td><td><strong>-0.17</strong></td><td><strong>0.12</strong></td><td><strong>-0.10</strong></td></tr><tr><td>Continental scale, no FO, only plots with high validation</td><td><strong>0.05</strong></td><td><strong>0.12</strong></td><td><strong>-0.35</strong></td><td><strong>0.11</strong></td><td><strong>-0.17</strong></td></tr><tr><td>Local scale</td><td><strong>0.79</strong></td><td><strong>0.31</strong></td><td><strong>-0.12</strong></td><td><strong>0.44</strong></td><td>-0.01</td></tr><tr><td>Local scale, Spain only</td><td><strong>0.79</strong></td><td><strong>0.32</strong></td><td><strong>-0.11</strong></td><td><strong>0.46</strong></td><td>0.02</td></tr><tr><td>Local scale, France only</td><td><strong>0.80</strong></td><td><strong>0.30</strong></td><td><strong>-0.12</strong></td><td><strong>0.42</strong></td><td><strong>-0.03</strong></td></tr><tr><td>Local scale, Wallonia only</td><td><strong>0.78</strong></td><td><strong>0.12</strong></td><td><strong>-0.31</strong></td><td><strong>0.38</strong></td><td><strong>-0.07</strong></td></tr><tr><td>Local scale, Germany only</td><td><strong>0.80</strong></td><td><strong>0.31</strong></td><td><strong>-0.16</strong></td><td><strong>0.47</strong></td><td>-0.01</td></tr><tr><td>Local scale, Sweden only</td><td><strong>0.73</strong></td><td><strong>0.30</strong></td><td>-0.03</td><td><strong>0.33</strong></td><td>-0.03</td></tr><tr><td>Local scale, Finland only</td><td><strong>0.77</strong></td><td><strong>0.34</strong></td><td>-0.08</td><td><strong>0.44</strong></td><td>-0.02</td></tr><tr><td>Local scale, no FO</td><td><strong>0.01</strong></td><td><strong>-0.08</strong></td><td><strong>-0.13</strong></td><td><strong>0.03</strong></td><td><strong>-0.01</strong></td></tr><tr><td>Local scale, no FO, Spain only</td><td><strong>0.01</strong></td><td><strong>-0.08</strong></td><td><strong>-0.11</strong></td><td><strong>0.05</strong></td><td><strong>0.02</strong></td></tr><tr><td>Local scale, no FO, France only</td><td><strong>0.01</strong></td><td><strong>-0.09</strong></td><td><strong>-0.13</strong></td><td><strong>0.01</strong></td><td><strong>-0.03</strong></td></tr><tr><td>Local scale, no FO, Wallonia only</td><td>0.00</td><td><strong>-0.26</strong></td><td><strong>-0.31</strong></td><td><strong>-0.03</strong></td><td><strong>-0.07</strong></td></tr><tr><td>Local scale, no FO, Germany only</td><td><strong>0.02</strong></td><td><strong>-0.08</strong></td><td><strong>-0.16</strong></td><td><strong>0.06</strong></td><td><strong>-0.01</strong></td></tr><tr><td>Local scale, no FO, Sweden only</td><td><strong>-0.05</strong></td><td><strong>-0.09</strong></td><td><strong>-0.03</strong></td><td><strong>-0.08</strong></td><td><strong>-0.04</strong></td></tr><tr><td>Local scale, no FO, Finland only</td><td><strong>-0.01</strong></td><td><strong>-0.05</strong></td><td><strong>-0.08</strong></td><td><strong>0.03</strong></td><td><strong>-0.02</strong></td></tr><tr><td>Local scale, no FO, no recruitment-related EFs</td><td><strong>0.03</strong></td><td><strong>-0.12</strong></td><td><strong>-0.14</strong></td><td><strong>-0.04</strong></td><td><strong>-0.02</strong></td></tr><tr><td>Local scale, no FO, corrected for EF R² values</td><td><strong>0.09</strong></td><td><strong>-0.07</strong></td><td><strong>-0.17</strong></td><td><strong>-0.04</strong></td><td><strong>-0.08</strong></td></tr><tr><td>Local scale, no FO, only plots with high validation</td><td><strong>0.10</strong></td><td><strong>-0.15</strong></td><td><strong>-0.29</strong></td><td>-0.06</td><td>-0.13</td></tr></tbody></table>