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Review article2019Peer reviewedOpen access

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

Bravo, Felipe; Fabrika, Marek; Ammer, Christian; Barreiro, Susana; Bielak, Kamil; Coll, Lluis; Fonseca, Teresa; Kangur, Ahto; Lof, Magnus; Merganicova, Katarina; Pach, Maciej; Pretzsch, Hans; Stojanovic, Dejan; Schuler, Laura; Peric, Sanja; Roetzer, Thomas; del Rio, Miren; Dodan, Martina; Bravo-Oviedo, Andres

Abstract

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.

Keywords

dynamics; ecology; growth; yield; empirical; classification

Published in

Forest Systems
2019, Volume: 28, number: 1, article number: eR002
Publisher: INST NACIONAL INVESTIGACION TECHNOLOGIA AGRARIA ALIMENTARIA

    Sustainable Development Goals

    Ensure sustainable consumption and production patterns

    UKÄ Subject classification

    Forest Science

    Publication identifier

    DOI: https://doi.org/10.5424/fs/2019281-14342

    Permanent link to this page (URI)

    https://res.slu.se/id/publ/100774