Modelling approaches for mixed forests dynamics prognosis. Research gaps and opportunities

Felipe Bravo*, Marek Fabrika, Christian Ammer, Susana Barreiro, Kamil Bielak, Lluis Coll, Teresa Fonseca, Ahto Kangur, Magnus Löf, Katarina Merganičová, Maciej Pach, Hans Pretzsch, Dejan Stojanović, Laura Schuler, Sanja Peric, Thomas Rötzer, Miren Del Río, Martina Dodan, Andrés Bravo-Oviedo

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

26 Citations (Scopus)


Aim of study: Modelling of forest growth and dynamics has focused mainly on pure stands. Mixed-forest management lacks systematic procedures to forecast the impact of silvicultural actions. The main objective of the present work is to review current knowledge and forest model developments that can be applied to mixed forests. Material and methods: Primary research literature was reviewed to determine the state of the art for modelling tree species mixtures, focusing mainly on temperate forests. Main results: The essential principles for predicting stand growth in mixed forests were identified. Forest model applicability in mixtures was analysed. Input data, main model components, output and viewers were presented. Finally, model evaluation procedures and some of the main model platforms were described. Research highlights: Responses to environmental changes and management activities in mixed forests can differ from pure stands. For greater insight into mixed-forest dynamics and ecology, forest scientists and practitioners need new theoretical frameworks, different approaches and innovative solutions for sustainable forest management in the context of environmental and social changes.

Original languageEnglish
Article numbereR002
Number of pages18
JournalForest Systems
Issue number1
Publication statusPublished - 2019


  • Classification
  • Dynamics
  • Ecology
  • Empirical
  • Growth
  • Yield


Dive into the research topics of 'Modelling approaches for mixed forests dynamics prognosis. Research gaps and opportunities'. Together they form a unique fingerprint.

Cite this