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Research article - Peer-reviewed, 2022

Species Mixing Proportion and Aridity Influence in the Height-Diameter Relationship for Different Species Mixtures in Mediterranean Forests

Rodriguez de Prado, Diego; Riofrio, Jose; Aldea, Jorge; McDermott, James; Bravo, Felipe; Herrero de Aza, Celia

Abstract

Estimating tree height is essential for modelling and managing both pure and mixed forest stands. Although height-diameter (H-D) relationships have been traditionally fitted for pure stands, attention must be paid when analyzing this relationship behavior in stands composed of more than one species. The present context of global change makes also necessary to analyze how this relationship is influenced by climate conditions. This study tends to cope these gaps, by fitting new H-D models for 13 different Mediterranean species in mixed forest stands under different mixing proportions along an aridity gradient in Spain. Using Spanish National Forest Inventory data, a total of 14 height-diameter equations were initially fitted in order to select the best base models for each pair species-mixture. Then, the best models were expanded including species proportion by area (m(i)) and the De Martonne Aridity Index (M). A general trend was found for coniferous species, with taller trees for the same diameter size in pure than in mixed stands, being this trend inverse for broadleaved species. Regarding aridity influence on H-D relationships, humid conditions seem to beneficiate tree height for almost all the analyzed species and species mixtures. These results may have a relevant importance for Mediterranean coppice stands, suggesting that introducing conifers in broadleaves forests could enhance height for coppice species. However, this practice only should be carried out in places with a low probability of drought. Models presented in our study can be used to predict height both in different pure and mixed forests at different spatio-temporal scales to take better sustainable management decisions under future climate change scenarios.

Keywords

mixed forests performance; species mixing proportions; climate-smart forestry; height-diameter relationship; adaptive silviculture; national forest inventory data; NLMM; programming; machine learning

Published in

Forests
2022, volume: 13, number: 1, article number: 119
Publisher: MDPI

Authors' information

Rodriguez de Prado, Diego
Universidad de Valladolid
Riofrio, Jose
University of British Columbia
Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre
McDermott, James
National University of Ireland Galway (NUI Galway)
Bravo, Felipe
Universidad de Valladolid
Herrero de Aza, Celia
Universidad de Valladolid

Sustainable Development Goals

SDG15 Life on land
SDG13 Climate action

UKÄ Subject classification

Forest Science

Publication Identifiers

DOI: https://doi.org/10.3390/f13010119

URI (permanent link to this page)

https://res.slu.se/id/publ/115895