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Forests 2018, 9(9), 518; https://doi.org/10.3390/f9090518

Estimation and Uncertainty of the Mixing Effects on Scots Pine—European Beech Productivity from National Forest Inventories Data

1
Department of Natural Systems and Resources, School of Forest Engineering and Natural Environment, Universidad Politécnica de Madrid, c/José Novais 10, 28040 Madrid, Spain
2
Department of Forest and Soil Sciences, BOKU University of Natural Resources and Life Sciences, Peter-Jordan-Straße 82, 1190 Wien, Austria
3
Department of Silviculture, Warsaw University of Life Sciences, Nowoursynowska 159, 02-776 Warszawa, Poland
4
Department of Biogeography and Global Change, National Museum of Natural Sciences-CSIC, C/Serrano 115bis, 28006 Madrid, Spain
5
Department of Agriculture and Forest Engineering (EAGROF), University of Lleida, Av. de l’Alcalde Rovira Roure 191, 25198 Lleida, Spain
6
Forest Sciences Centre of Catalonia (CTFC), Crta. de St. Llorenç de Morunys a Port del Comte, 25280 Solsona, Spain
7
Department of Silviculture, Institute of Forest Ecology and Silviculture, University of Agriculture in Krakow, Al. 29 Listopada 46, 31-425 Kraków, Poland
8
TUM School of Life Sciences Weihenstephan, Technische Universität München, Hans-Carl-von-Carlowitz-Platz 2, D-85354 Freising, Bavaria, Germany
9
University Grenoble Alpes, Irstea, UR LESSEM, Centre de Grenoble, F-38402 St-Martin-d’Hères, France
10
Department of Forest Dynamics and Management, Forest Research Center, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, INIA, Ctra de la Coruña km 7.5, 28040 Madrid, Spain
11
Sustainable Forest Management Research Institute, University of Valladolid & INIA Avda. Madrid, 34004 Palencia, Spain
*
Author to whom correspondence should be addressed.
Received: 31 July 2018 / Revised: 22 August 2018 / Accepted: 25 August 2018 / Published: 28 August 2018
(This article belongs to the Special Issue Competition and Facilitation in Mixed Species Forests)
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Abstract

An increasing amount of research is focusing on comparing productivity in monospecific versus mixed stands, although it is difficult to reach a general consensus as mixing effects differ both in sign (over-yielding or under-yielding) and magnitude depending on species composition as well as on site and stand conditions. While long-term experimental plots provide the best option for disentangling the mixing effects, these datasets are not available for all the existing mixtures nor do they cover large gradients of site factors. The objective of this study was to evaluate the effects and uncertainties of tree species mixing on the productivity of Scots pine–European beech stands along the gradient of site conditions in Europe, using models developed from National and Regional Forest Inventory data. We found a positive effect of pine on beech basal area growth, which was slightly greater for the more humid sites. In contrast, beech negatively affected pine basal area growth, although the effects switched to positive in the more humid sites. However, the uncertainty analysis revealed that the effect on pine at mid- and more humid sites was not-significant. Our results agree with studies developed from a European transect of temporal triplets in the same pine–beech mixtures, confirming the suitability of these datasets and methodology for evaluating mixing effects at large scale. View Full-Text
Keywords: species interactions; growth efficiency; basal area growth; monospecific and mixed stands comparison; Martonne aridity index species interactions; growth efficiency; basal area growth; monospecific and mixed stands comparison; Martonne aridity index
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Condés, S.; Sterba, H.; Aguirre, A.; Bielak, K.; Bravo-Oviedo, A.; Coll, L.; Pach, M.; Pretzsch, H.; Vallet, P.; del Río, M. Estimation and Uncertainty of the Mixing Effects on Scots Pine—European Beech Productivity from National Forest Inventories Data. Forests 2018, 9, 518.

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