Landscape variation in tree species richness in northern Iran forests.

Abstract

Mapping landscape variation in tree species richness (SR) is essential to the long term management and conservation of forest ecosystems. The current study examines the prospect of mapping field assessments of SR in a high-elevation, deciduous forest in northern Iran as a function of 16 biophysical variables representative of the area’s unique physiography, including topography and coastal placement, biophysical environment, and forests. Basic to this study is the development of moderate-resolution biophysical surfaces and associated plot-estimates for 202 permanent sampling plots. The biophysical variables include: (i) three topographic variables generated directly from the area’s digital terrain model; (ii) four ecophysiologically-relevant variables derived from process models or from first principles; and (iii) seven variables of Landsat-8-acquired surface reflectance and two, of surface radiance. With symbolic regression, it was shown that only four of the 16 variables were needed to explain 85% of observed plot-level variation in SR (i.e., wind velocity, surface reflectance of blue light, and topographic wetness indices representative of soil water content), yielding mean-absolute and root-mean-squared error of 0.50 and 0.78, respectively. Overall, localised calculations of wind velocity and surface reflectance of blue light explained about 63% of observed variation in SR, with wind velocity accounting for 51% of that variation. The remaining 22% was explained by linear combinations of soil-water-related topographic indices and associated thresholds. In general, SR and diversity tended to be greatest for plots dominated by Carpinus betulus (involving ≥ 33% of all trees in a plot), than by Fagus orientalis (median difference of one species). This study provides a significant step towards describing landscape variation in SR as a function of modelled and satellite-based information and symbolic regression. Methods in this study are sufficiently general to be applicable to the characterisation of SR in other forested regions of the world, providing plot-scale data are available for model generation.