A comprehensive review of spatial distribution modeling of plant species in mountainous environments: Implications for biodiversity conservation and climate change assessment

Abstract

Species Distribution Modelling (SDM) techniques, developed in the 1980s, have gained significant attention in recent years. These techniques are increasingly recognized as powerful tools to support forest management strategies in the context of climate change. This study presents a comprehensive literature review of SDM techniques in mountainous environments, utilizing remote sensing techniques and data. Forty-one published papers were reviewed, covering 25 years (1997–2022). The review explores various SDM techniques, the use of remotely sensed data, accuracy assessments, environmental variables, and the limitations and challenges of species distribution modeling in mountainous environments across different spatial scales. The study revealed that the most widely used SDM techniques were Maximum Entropy (MaxEnt), Random Forest (RF), and Generalized Linear Models (GLMs), with recent studies emphasizing machine learning. We describe different modeling algorithms, including presence-only and presence/absence modeling algorithms, machine-learning algorithms, distance-based algorithms, and regression-based algorithms. This study presents the first global literature review of SDM techniques in mountainous environments, emphasizing the necessity of considering the uncertainties associated with climate change scenarios. This study also argues the strengths and limitations of SDM techniques in mountainous environments. Despite limitations of SDM techqniues, the study found an increasing trend in their application in mountainous environments. Finally, this review aims to provide a valuable resource for forest managers, researchers, practitioners, and policymakers employed in forest conservation in mountainous environments around the globe.