Akademik Veri Yönetim Sistemi
Ecological Modelling, cilt.513, 2026 (SCI-Expanded, Scopus)
This systematic review presents a comprehensive synthesis of species distribution modeling (SDM) applied to forest ecosystems in the context of climate change. Our analysis covered 50 papers each from four consecutive five-year periods dating from 2003 to 2022. We thus assessed 200 articles from the Web of Science database and meticulously categorized them according to 16 categories to discern patterns and developments in SDM research. The classification scheme included key factors such as continent of study, number of species modeled, grain size, type of species data, and the range of analytical and statistical models utilized. Our analysis explored the evolution of SDM practices and their methodological diversity. This assessment evaluated various modeling approaches, as influenced by advances in technology and data availability. By focusing on distinctive parameters such as ancillary non-climate data, choice of climate model institutions, and climate change scenarios, the review paints a detailed picture of methodologies shaping our understanding of species distribution under climatic alterations. Our results reveal three major insights: (1) a marked rise in the use of machine learning techniques, especially Random Forest and MaxEnt; (2) a persistent dominance of presence-only or presence–absence data, which limits ecological inference in many studies; and (3) limited integration of multi-scenario climate projections, constraining uncertainty representation in model outputs. The findings highlight not only the methodological intricacies and regional focuses of SDM studies, but also the interdisciplinary nature of addressing biodiversity changes under climate dynamics. The revealed trends underscore the increasing complexity of SDMs and stress the importance of adopting multifaceted research strategies. This comprehensive review aims to inform future research directions, review enhancements to the predictive capacity of SDMs, and improve our understanding of biodiversity responses to ongoing and future climate change.