Abstract
The distribution of tree species has traditionally been analyzed based on tree diameter (DBH) as a continuous variable. However, this approach does not usually provide information on how species are distributed across the area of interest. In this study, an inverse approach was applied to investigate tree distribution patterns in two Dinaric old-growth forest stands composed primarily of European beech, silver fir, and Norway spruce. Specifically, the variance-to-mean relationship of tree counts based on 80 plots (40 in each old-growth stand) were evaluated by using a dispersion index. Understory trees exhibited clumped and random patterns, whereas canopy trees were mostly distributed in a random manner. A regular pattern was only determined for beech and all trees in the canopy layer (two cases out of ten). The observed discrete variables were further compared with three theoretical distributions. It was found that a Poisson, binomial, and negative binomial model best fitted the observed count data, which, based on the dispersion index, exhibited a random, regular, and clumped pattern, respectively. The frequency of plots with low species presence and complete absence of species was also revealed. Consequently, the analysis and modeling of tree counts can be of practical use for species conservation purposes.
Highlights
Modeling the distribution of tree species in mixed forests has been an important task in forest ecology in the last two decades [1,2,3,4]
Understory beech trees (≤27.5 cm diameter at breast height (DBH)) had a clumped pattern in both of the studied old-growth forests
(>27.5 cm DBH) this species exhibited a random pattern in Janj and a regular pattern in Lom
Summary
Modeling the distribution of tree species in mixed forests has been an important task in forest ecology in the last two decades [1,2,3,4]. Count data that originate from different fields of study typically follow a Poisson, negative binomial, or in some cases, binomial distribution [5] These distributions have often been used to analyze and model count data in scientific fields such as parasitology [6], veterinary medicine [7], ornithology [8], and estimation of ore reserves [9]. Their application in the analysis of the distribution of tree species is not very common. A proper model can be fitted to such grouped data or to the raw data [10]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.