Abstract
Description of the subject. This study evaluates the application of Boosted Regression Trees (BRT) for predicting beech dominant height in the Hyrcanian forests of Iran, inscribed as a UNESCO’s World Heritage due to its remarkable biodiversity. Objectives. It is widely accepted that tree growth can be influenced by a wide variety of factors such as climate, topography, soil conditions and competition for resources. The early dominant height of trees modelling studies used the multiple linear regression. The development of more advanced non-parametric and machine learning methods provided opportunities to overcome the nonlinear relationships in forest ecosystems. Method. In this study, boosted regression trees was evaluated to model the dominant height of Fagus orientalis as the most important tree species in the Hyrcanian forest, Iran. Dominant height was related to soil and topographical variables, which are available for 190 sample plots covering all importance environmental gradients in the research area. Results. The results indicated BRT were found to outperform for modelling beech dominant height. This technique showed that phosphorus, percentage nitrogen, magnesium and percentage sand were among the most important variables. Conclusions. This study demonstrates the ability of BRT to accurately model the dominant height of oriental beech in relation to environmental predictors, and encourages its use in forest ecology.
Highlights
Northern forests of Iran, called Hyrcanian or Caspian forests, cover a relatively narrow strip in the north of Iran, which are among the most important and valuable ecosystems inscribed in United Nations Educational, Scientific, and Cultural Organization (UNESCO) World Heritage List
This study evaluates the application of Boosted Regression Trees (BRT) for predicting beech dominant height in the Hyrcanian forests of Iran, inscribed as a UNESCO’s World Heritage due to its remarkable biodiversity
We evaluated the dominant height of oriental beech, which is one of the most abundant species in the Hyrcanian forests of Iran, using a boosted regression tree model and edaphic and topographic variables
Summary
Northern forests of Iran, called Hyrcanian or Caspian forests, cover a relatively narrow strip in the north of Iran, which are among the most important and valuable ecosystems inscribed in United Nations Educational, Scientific, and Cultural Organization (UNESCO) World Heritage List. Covering an area of about 1.85 million ha, these forests account for 15% of the total Iranian forests and 1.1% of the country’s area These forests range from sea level up to an altitude of 2,800 m and comprise various forest types, harboring approximately 80 woody species (trees and shrubs). Hyrcanian forests, along with similar North American and East Asian forest communities, are nowadays seen as remnants of contiguous Tertiary deciduous belt (Sagheb Talebi et al, 2014), and one of the world’s oldest extant forests. Today, these forests are regularly harvested, but their management is rarely based on assessments of growth, standing biomass or specific target forest composition and must, in places, be considered unsustainable
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