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Widespread slow growth of acquisitive tree species

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Abstract

Trees are an important carbon sink as they accumulate biomass through photosynthesis1. Identifying tree species that grow fast is therefore commonly considered to be essential for effective climate change mitigation through forest planting. Although species characteristics are key information for plantation design and forest management, field studies often fail to detect clear relationships between species functional traits and tree growth2. Here, by consolidating four independent datasets and classifying the acquisitive and conservative species based on their functional trait values, we show that acquisitive tree species, which are supposedly fast-growing species, generally grow slowly in field conditions. This discrepancy between the current paradigm and field observations is explained by the interactions with environmental conditions that influence growth. Acquisitive species require moist mild climates and fertile soils, conditions that are generally not met in the field. By contrast, conservative species, which are supposedly slow-growing species, show generally higher realized growth due to their ability to tolerate unfavourable environmental conditions. In general, conservative tree species grow more steadily than acquisitive tree species in non-tropical forests. We recommend planting acquisitive tree species in areas where they can realize their fast-growing potential. In other regions, where environmental stress is higher, conservative tree species have a larger potential to fix carbon in their biomass.

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Fig. 1: The negative relationship between the leaf Amax of tree species and tree growth.
Fig. 2: The influence of site productivity on growth–trait relationships.
Fig. 3: Growth rates according to species strategy and resource supply.
Fig. 4: The effective ability of acquisitive tree species and conservative tree species to grow fast.

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Data availability

The datasets generated in this study (EAN, TDN, SBD, TED) have been deposited in the https://entrepot.recherche.data.gouv.fr database (https://doi.org/10.57745/3OIGHB; Etalab Open License 2.0, compatible CC-BY 2.0). Data supporting Figs. 1–4 and Table 1 are provided in the Article and its Supplementary Information. There is no restriction on data availability. Source data are provided with this paper.

Code availability

Data were analysed using code developed by authors (R language, v.9.4 and v.4.2.3) and common statistical methods: random forest (randomForest R package, v.4.7-1.1), linear models (olsrr R package, v.0.5.3), mixed models (lme4 R package, v.1.1-32), Kruskal–Wallis test (R core). All analyses are fully described in the Methods. The main R procedures that were used have been deposited in the https://entrepot.recherche.data.gouv.fr database (https://doi.org/10.57745/3OIGHB). Complementary information is available from the authors on request.

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Acknowledgements

The EAN common gardens were installed thanks to the financial support of the REINFFORCE project (INTERREG Atlantic Area; FCT grant number PD/BD/52405/2013 and 2008-1/005). Some of the EAN common gardens were then supported by European funding (LIFE IP CLIMAZ), national funding (Forestry Commission of the United Kingdom) or regional funding (Nouvelle Aquitaine region (France), Euskadi region (Spain), Sustainable Forest Management Research Institute (iuFOR; Spain)). The TDN common gardens were installed and monitored thanks to the financial support of different national agencies: the German Research Foundation (grants Ei 862/29-1, Ei 862/31-1, project no. 439223434) and the German Centre for Integrative Biodiversity Research (iDiv; grants DFG—FZT 118, 202548816); the BIOTREE experiment has been established by the Max-Planck-Institute for Biogeochemistry Jena, Germany, and its data collection in 2019 was supported by a grant to M.S.-L. by the German Research Foundation (DFG project number 439223434); the USA National Science Foundations (grants DEB-1234162, DEB−1831944, DEB-2106014, DEB-2044406); the USA NSERC-Discovery (grant RGPIN-2018-05201); and the Walloon Forest Service of Belgium. Other common gardens were supported by different funding, such as the AnaEE infrastructure (grant ANR-11-INBS-0001AnaEE-Services; France), the SoilSolution project (grant 41007-00210400; Finland), the Department of Geosciences and Natural Resource Management, University of Copenhagen and the Silva Nova project (grant NNF20OC0059948; Denmark). D.L.G. was supported by the EU Horizon project EXCELLENTIA (grant number 101087262) at Mendel University in Brno during the manuscript preparation phase. This study was funded by the CARTON project (grant ANR-19-CE32-0006), supported by the French Agence Nationale de la Recherche (ANR). We thank the landowners of the sites where the common gardens were installed; the colleagues who contributed to the installation of some of these common gardens: M. H. Almeida, M. Ferreira, S. Jorge, A. Nordin and P. Pastuszka; the numerous colleagues without whom it would have been impossible to install and maintain common gardens, and acquire data in the field, in particular, M. Belluau, S. Benham, O. Bouchez, V. Bouttier, T. Bouvet, M. Bustos, C. Chesseron, N. Cheval, R. Deblir, J. L. Denou, E. Diz, A. Don, C. Garbe, C. Gire, J. Haase, K. Hahner, B. Issenhuth, B. James, B. Bilde Jørgensen, F. Khalfallah, A. Kokko, B. Laffitte, J. Lakey, N. Laurent, D. Lesieur, P. Lhoir, F. Lyrou, D. Mackensen, T. Maxwell, M. El-Mazlouzi, M. Mörsdorf, S. Müller, C. Nock, M. Oram, A. Pazos, M. Pietrzak, F. Plume, O. Power, J. Pullen, A. Quintairos, J. Quosh, A. Reichard, L. Richardson, S. Stöckli, S. Thunot, S. R. Tziaferidis, J. Urgoiti, G. Xanthopoulos and all of the CNPF staff; the following colleagues, for their help during the measurement campaigns of functional traits: M. Aimaiti, A. Bosc, R. Burlett, N. Devert, J. C. Domec, T. Guzman, L. Jordan-Meille, F. Lagane, C. Lambrot, A. Loches, S. Milin and L. Wingate; A. Bourdin, P. Donoso, A. Fayolle, C. Lusk and A. Mirabel for providing data; G. Augusto-Sciama, M. Desailly, L. Fan, N. Fanin, F. Gosselin, R. Lemaire-Patin, C. Nguyen, A. Nys, E. Paturle, B. Ringeval and J. P. Wigneron for their help during data handling, data analyses and preparation of the manuscript; and our colleague, William ‘Bill’ Mason, who passed away before the publication of this study. Bill had a deep knowledge of forestry and substantially contributed to this work by questioning results, and discussing them in a very kind and constructive way.

Author information

Authors and Affiliations

  1. INRAE, Bordeaux Sciences Agro, UMR 1391 ISPA, Villenave d’Ornon, France

    L. Augusto, R. Borelle, L. Bon, M. R. Bakker & M. Charru

  2. Latvia University of Life Sciences and Technologies, Jelgava, Latvia

    A. Boča

  3. Institut Européen de la Forêt Cultivée (IEFC), Cestas, France

    C. Orazio

  4. NEIKER, Basque Institute for Agricultural Research and Development, Department of Forest Sciences, Bizkaia, Spain

    A. Arias-González & N. Gartzia-Bengoetxea

  5. Helmholtz Centre for Environmental Research-UFZ, Halle, Germany

    H. Auge

  6. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

    H. Auge & N. Eisenhauer

  7. INRAE, UEFP, Cestas, France

    F. Bernier

  8. Granja Modelo, HAZI, Arkaute, Spain

    A. Cantero

  9. Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA

    J. Cavender-Bares & R. A. Montgomery

  10. Forest Research Centre, School of Agriculture, University of Lisbon, Lisbon, Portugal

    A. H. Correia

  11. Research Centre AgroFoodNature, HOGENT University of Applied Sciences and Arts, Ghent, Belgium

    A. De Schrijver

  12. Sustainable Forest Management Research Institute (iuFOR), University of Valladolid, Palencia, Spain

    J. J. Diez-Casero & J. Martín-García

  13. Institute of Biology, Leipzig University, Leipzig, Germany

    N. Eisenhauer

  14. Forest Research Institute, Hellenic Agricultural Organization Dimitra, Thessaloniki, Greece

    M. N. Fotelli & G. Spyroglou

  15. INRAE, UEVT, Antibes Juan-les-Pins, France

    G. Gâteblé

  16. Department of Forest Protection and Wildlife Management, Mendel University in Brno, Brno, Czech Republic

    D. L. Godbold

  17. Institute of Forest Ecology, Department of Ecosystem Management, Climate and Biodiversity, BOKU University, Vienna, Austria

    D. L. Godbold

  18. SRAAC, Azores Regional Ministry for Environment and Climate Change, Angra do Heroísmo, Azores, Portugal

    M. Gomes-Caetano-Ferreira

  19. Swedish University of Agricultural Sciences, Umeå, Sweden

    M. J. Gundale & M. Larsson

  20. INRAE, University of Bordeaux, BIOGECO, Cestas, France

    H. Jactel & C. Meredieu

  21. Department of Biological Sciences, Royal Holloway University of London, Egham, UK

    J. Koricheva

  22. Department of Agricultural, Food and Forest Sciences, University of Palermo, Palermo, Italy

    V. A. Laudicina

  23. INRAE, BEF, Nancy, France

    A. Legout

  24. Department of Plant Production and Forest Resources, University of Valladolid, Palencia, Spain

    J. Martín-García

  25. Forest Research, Northern Research Station, Roslin, UK

    W. L. Mason

  26. CNR-IBE, Consiglio Nazionale delle Ricerche, Istituto per la BioEconomia, Sassari, Italy

    S. Mereu

  27. ONF, UMR 0588 BioForA, Orléans, France

    B. Musch

  28. Department of Earth & Environmental Sciences, KU Leuven, Leuven, Belgium

    B. Muys

  29. Leuven Plant Institute, KU Leuven, Leuven, Belgium

    B. Muys

  30. Institut pour le Développement Forestier (IDF), Paris, France

    E. Paillassa

  31. Centre for Forest Research, Université du Québec à Montréal, Montreal, Quebec, Canada

    A. Paquette

  32. Smithsonian Environmental Research Center, Edgewater, MD, USA

    J. D. Parker

  33. Ontario Ministry of Natural Resources and Forestry, Sault Ste. Marie, Ontario, Canada

    W. C. Parker

  34. Earth and Life Institute, UCLouvain–Université Catholique de Louvain, Louvain-la-Neuve, Belgium

    Q. Ponette

  35. Forest Research, Alice Holt Lodge, Farnham, UK

    C. Reynolds & E. I. Vanguelova

  36. AGACAL-Centro de Investigación Forestal de Lourizán, Pontevedra, Spain

    M. J. Rozados-Lorenzo & F. J. Silva-Pando

  37. Institute of Forest Science (ICIFOR-INIA), CSIC, Madrid, Spain

    R. Ruiz-Peinado

  38. GAN-NIK, Pamplona, Spain

    X. Santesteban-Insausti

  39. Geobotany, Faculty of Biology, University of Freiburg, Freiburg, Germany

    M. Scherer-Lorenzen

  40. Natural Resources Institute Finland (Luke), Helsinki, Finland

    A. Smolander

  41. DROTRH, Ponta Delgada, Portugal

    E. B. Teixeira-Barcelos

  42. Forest & Nature Lab, Department of Environment, Ghent University, Melle-Gontrode, Belgium

    K. Verheyen

  43. Department of Geosciences and Natural Resource Management, University of Copenhagen, Frederiksberg, Denmark

    L. Vesterdal

Authors
  1. L. Augusto
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  2. R. Borelle
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  14. A. De Schrijver
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  17. M. N. Fotelli
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  20. M. Gomes-Caetano-Ferreira
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  23. J. Koricheva
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  24. M. Larsson
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  25. V. A. Laudicina
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  26. A. Legout
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  27. J. Martín-García
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  28. W. L. Mason
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  29. C. Meredieu
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  30. S. Mereu
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  31. R. A. Montgomery
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  32. B. Musch
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  33. B. Muys
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  39. C. Reynolds
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  40. M. J. Rozados-Lorenzo
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  51. M. Charru
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Contributions

L.A. initiated the study and L.A. and M.C. designed it. L.A., M.C. and L.B. collected and curated data, with particular support from R.B., C.O. and N.G.-B. (EAN data) and A.B. (SBD data). Trait data were collected and consolidated by L.A., R.B., M.C. and M.R.B.; N.G.-B. and A.A.-G. provided soil data from the EAN sites. A.A.-G., N.G.-B., H.A., F.B., A.C., J.C.-B., A.H.C., A.D.S., J.J.D.-C., N.E., M.N.F., G.G., D.L.G., M.G.-C.-F., M.J.G., H.J., J.K., M.L., V.A.L., A.L., J.M.-G., W.L.M., C.M., S.M., R.A.M., B. Musch, B. Muys, E.P., A.P., J.D.P., W.C.P., Q.P., C.R., M.J.R.-L., R.R.-P., X.S.-I., M.S.-L., F.J.S.-P., A.S., G.S., E.B.T.-B., E.I.V., K.V. and L.V. provided data about tree growth from their respective common gardens. L.A., M.C. and R.B. made the first analysis and interpretation of data. L.A. wrote the first version of the manuscript, with the contribution of M.C. and R.B. Then L.A. revised the successive following versions of the manuscript with contributions from all of the authors, particularly M.C., R.B., M.R.B., M.J.G., B. Muys, C.M. and C.O.

Corresponding authors

Correspondence to L. Augusto or M. Charru.

Ethics declarations

Competing interests

R.B. is employed by a company that works with landowners to implement projects of reforestation or afforestation worldwide. F.J.S.-P. and M.J.R.-L. occasionally advise foresters or landowners. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks Giuliano Locoselli, Stuart Wright and Pieter Zuidema for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Effect of specific leaf area (SLA) on seedling growth under favourable conditions.

Data were collected from 10 independent publications that reported seedling growth representing 263 species-experiments combinations and 212 distinct plant species (a), different forest biomes (b), plant types (c), and forested continents (d). A subset of values representing the tree species shared by the 10 publications about seedling growth and by the present study about trees is shown (e). Data are standardized to enable comparisons among sites (see Methods). Linear regressions were fitted by class of values. The SLA trait was chosen because it was studied in all the 10 publications (Supplementary Note 1) and because SLA is a key trait in the plant economics spectrum (ref. 24).

Extended Data Fig. 2

Context, rationale and hypothesis of the present study.

Extended Data Fig. 3 Effect of functional traits on tree growth.

Data were standardized to enable comparisons among sites (see Methods). Linear regressions were fitted (level of confidence of the error band = 0.95) by latitudinal class (limit values = 23° and 45°). P values of correlations are symbolized as follows: *** (P < 0.001), ** (P < 0.010), * (P < 0.050), (*) (P < 0.100), ns (P ≥ 0.100). For the scope of readability, a regression line and its data points are not presented together when several lines are shown in a given panel.

Extended Data Fig. 4 Effect of fine root traits on tree growth in the European Atlantic Network.

Fine roots are roots with a diameter ≤ 2 mm. Data are standardized to enable comparisons among sites (see Methods). For panels from a to h, the statistics of the regressions (level of confidence of the error band = 0.95) were: t = −1.3, +3.6, +9.6, −2.6, +0.7, −1.3, −5.2 and −3.6; df = 617 and n = 619 in all cases. Specific root length (SRL) results are presented in Extended Data Fig. 3. Original units: fine root content in carbon and nutrients (mg g−1), fine root dry matter content (g g−1), and fine root length density (cm−root cm−3−soil).

Extended Data Fig. 5 Relationships between photosynthetic capacity and growth at different latitudes.

Tree growth was quantified based on several metrics and values were standardized (see Methods). A linear regression was fitted (level of confidence of the error band = 0.95). Results are presented by latitude class: high-latitudes sites: |latitude| ≥ 45° (a); intermediate sites: 23° <|latitude| <45° (b); tropical sites: |latitude| ≤ 23° (c). For panels a, b, and c, the statistics of the regressions were respectively: t = −3.79, −2.83, and +0.88; df = 499, 434, and 135; n = 501, 436, and 137.

Extended Data Fig. 6 Examples of relationships between a functional trait and tree species growth.

Each panel presents the relationship between the Specific Leaf Area (SLA) value and the growth rate value of different tree species growing in a given common garden. Scatter plots present three sites of the European Atlantic Network (a, b, c) and three sites of the Tree Diversity Network (d, e, f). These sites are also identified in Fig. 2b. For panels from a-f, the statistics of the regressions (level of confidence of the error band = 0.95) were respectively: t = −2.13, −0.55, +1.85, −2.05, +1.33, and +3.40; df = 17, 18, 21, 10, 8, and 10; n = 19, 20, 23, 12, 10, and 12.

Extended Data Fig. 7 Influence of soil and climate on growth-trait relationships in the European Atlantic Network.

Linear regressions between growth rate and trait value were fitted for each site of the European Atlantic Network (see panels abc of Extended Data Fig. 6 for three examples of SLA-growth relationships). The correlation values (r) were then regressed to site productivity (i.e. the mean value of growth per site; Fig. 2). The fitted linear regression between site productivity and r values was finally used to draw the graph: for instance, the correlation between growth and trait value was systematically negative for wood density (WD; fitted linear regression in Fig. 2a), but switched from being negative at low productivity sites to positive at high productivity sites for SRL (Fig. 2d). Functional traits: Max height = tree species maximum height (m); Amax = maximum photosynthetic capacity (µmol g−1 s−1); SLA = specific leaf area (mm2 mg−1); SRL = specific root length (m g−1); WD = wood density (mg cm-3); Leaf N, Leaf P, and Root P = organ content in nitrogen or phosphorus (mg g−1).

Extended Data Table 1 Site drivers of tree growth in the European Atlantic Network (EAN)
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Extended Data Table 2 Influence of functional traits on tree growth in the European Atlantic Network (EAN)
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Extended Data Table 3 Distribution of trait values between acquisitive species and conservative species
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Augusto, L., Borelle, R., Boča, A. et al. Widespread slow growth of acquisitive tree species. Nature (2025). https://doi.org/10.1038/s41586-025-08692-x

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