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Review | Open Access

Toward managing mixed-species stands: from parametrization to prescription

Hans Pretzsch1( )Eric K. Zenner2
Chair for Forest Growth and Yield Science, Center of Life and Food Sciences Weihenstephan, Technical University of Munich, Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, Germany
Department of Forest Ecosystem Science and Management, Penn State University, University Park 16801, PA, USA
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Abstract

A better understanding and a more quantitative design of mixed-species stands will contribute to more integrative and goal-oriented research in mixed-species forests. Much recent work has indicated that the structure and growth of mixed species forests may fundamentally differ from monocultures. Here we suggest how to progress from the present accumulation of phenomenological findings to a design of mixed-species stands and advanced silvicultural prescriptions by means of modelling. First, the knowledge of mixing effects on the structure and growth at the stand, species, and individual tree level is reviewed, with a focus on those findings that are most essential for suitable modelling and silvicultural designs and the regulation of mixed stands as opposed to monocultures. Then, the key role of growth models, stand simulators, and scenario assessments for designing mixed species stands is discussed. The next section illustrates that existing forest stand growth models require some fundamental modifications to become suitable for both monocultures and mixed-species stands. We then explore how silvicultural prescriptions derived from scenario runs would need to be both quantified and simplified for transfer to forest management and demonstrated in training plots. Finally, we address the main remaining knowledge gaps that could be remedied through empirical research.

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Forest Ecosystems
Article number: 19

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Cite this article:
Pretzsch H, Zenner EK. Toward managing mixed-species stands: from parametrization to prescription. Forest Ecosystems, 2017, 4(4): 19. https://doi.org/10.1186/s40663-017-0105-z

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Received: 01 July 2017
Accepted: 15 September 2017
Published: 10 October 2017
© The Author(s) 2017.

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.