Improving species distribution models for dominant trees in climate data-poor forests using high-resolution remote sensing

Abstract

Species distribution models (SDMs) are useful for predictive and explanatory purposes, allowing biologists to identify how human and environmental factors influence distributions of plants and animals. Lack of high-resolution climatic variables is one of the challenges for accurately predicting distributions of organisms at local or landscape scales. This study used SDMs to predict four dominant tree species distributions in northern Iran temperate forests (400km2 area) using high-resolution Sentinel-2 data (20 m) and topographic variables. We divided the explanatory variables into four datasets with increasing complexity of Sentinel-2 data and modelled distributions using four statistical and machine learning algorithms: random forest (RF), generalized boosting model (GBM), generalized linear model (GLM), and generalized additive model (GAM). Our results suggested differences in the predictive performance of the four algorithms. We found the most complex dataset, including topographic variables, Sentinel-2 bands, and vegetation and soil indices, gave the best fit for the four tree species, improving the accuracy of models for the different species between 5 and 16%. We then selected the most complex dataset to produce an ensemble model of the modeling algorithms where evaluation criteria were varied for tree species. Our result showed that the performance of SDMs improved using different satellite remote sensing data including raw bands, topographic variables and indices in the Hyrcanian forest, northern Iran. Elevation was a more significant variable than Sentinel bands and Sentinel vegetation indices variables for predicting the tree species distributions. With the Hyrcanian Forest included in this study region of northern Iran recently declared a UNESCO World Heritage site, a key result of our improved species distribution maps for these four dominant tree species is to support conservation management of forest biodiversity in this region.