Identifying the tree species compositions that maximize ecosystem functioning in European forests

Lander Baeten*, Helge Bruelheide, Fons van der Plas, Stephan Kambach, Sophia Ratcliffe, Tommaso Jucker, Eric Allan, Evy Ampoorter, Luc Barbaro, Cristina C. Bastias, Jürgen Bauhus, Raquel Benavides, Damien Bonal, Olivier Bouriaud, Filippo Bussotti, Monique Carnol, Bastien Castagneyrol, Yohan Charbonnier, Ewa Chećko, David A. CoomesJonas Dahlgren, Seid Muhie Dawud, Hans De Wandeler, Timo Domisch, Leena Finér, Markus Fischer, Mariangela Fotelli, Arthur Gessler, Charlotte Grossiord, Virginie Guyot, Stephan Hättenschwiler, Hervé Jactel, Bogdan Jaroszewicz, François Xavier Joly, Julia Koricheva, Aleksi Lehtonen, Sandra Müller, Bart Muys, Diem Nguyen, Martina Pollastrini, Kalliopi Radoglou, Karsten Raulund-Rasmussen, Paloma Ruiz-Benito, Federico Selvi, Jan Stenlid, Fernando Valladares, Lars Vesterdal, Kris Verheyen, Christian Wirth, Miguel A. Zavala, Michael Scherer-Lorenzen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

74 Citations (Scopus)

Abstract

Forest ecosystem functioning generally benefits from higher tree species richness, but variation within richness levels is typically large. This is mostly due to the contrasting performances of communities with different compositions. Evidence-based understanding of composition effects on forest productivity, as well as on multiple other functions will enable forest managers to focus on the selection of species that maximize functioning, rather than on diversity per se. We used a dataset of 30 ecosystem functions measured in stands with different species richness and composition in six European forest types. First, we quantified whether the compositions that maximize annual above-ground wood production (productivity) generally also fulfil the multiple other ecosystem functions (multifunctionality). Then, we quantified the species identity effects and strength of interspecific interactions to identify the “best” and “worst” species composition for multifunctionality. Finally, we evaluated the real-world frequency of occurrence of best and worst mixtures, using harmonized data from multiple national forest inventories. The most productive tree species combinations also tended to express relatively high multifunctionality, although we found a relatively wide range of compositions with high- or low-average multifunctionality for the same level of productivity. Monocultures were distributed among the highest as well as the lowest performing compositions. The variation in functioning between compositions was generally driven by differences in the performance of the component species and, to a lesser extent, by particular interspecific interactions. Finally, we found that the most frequent species compositions in inventory data were monospecific stands and that the most common compositions showed below-average multifunctionality and productivity. Synthesis and applications. Species identity and composition effects are essential to the development of high-performing production systems, for instance in forestry and agriculture. They therefore deserve great attention in the analysis and design of functional biodiversity studies if the aim is to inform ecosystem management. A management focus on tree productivity does not necessarily trade-off against other ecosystem functions; high productivity and multifunctionality can be combined with an informed selection of tree species and species combinations.

Original languageEnglish
Pages (from-to)733-744
Number of pages12
JournalJournal of Applied Ecology
Volume56
Issue number3
DOIs
Publication statusPublished - Mar 2019
Externally publishedYes

Keywords

  • ecosystem multifunctionality
  • forest management
  • forestry
  • FunDivEUROPE
  • overyielding
  • productivity
  • species interactions
  • tree species mixtures

Fingerprint

Dive into the research topics of 'Identifying the tree species compositions that maximize ecosystem functioning in European forests'. Together they form a unique fingerprint.

Cite this