Citation: Mirhashemi, H.; Heydari, M.; Karami, O.; Ahmadi, K.; Mosavi, A. Modeling Climate Change Effects on the Distribution of Oak Forests with Machine Learning. Forests 2023, 14, 469. https:// doi.org/10.3390/f14030469 Academic Editors: Antonio Gazol and Ester González-de-Andrés Received: 16 January 2023 Revised: 14 February 2023 Accepted: 20 February 2023 Published: 24 February 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Article Modeling Climate Change Effects on the Distribution of Oak Forests with Machine Learning Hengameh Mirhashemi 1 , Mehdi Heydari 1, *, Omid Karami 2 , Kourosh Ahmadi 3 and Amir Mosavi 4,5,6 1 Department of Forest Science, Faculty of Agriculture, Ilam University, Ilam 516-69315, Iran 2 General Department of Natural Resources and Watershed Management of Ilam Province, Ilam 516-69315, Iran 3 Department of Forestry, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Tehran 111-14115, Iran 4 German Research Center for Artificial Intelligence, 26129 Oldenburg, Germany 5 John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary 6 Institute of the Information Society, University of Public Service, 1083 Budapest, Hungary * Correspondence: m.heidari@ilam.ac.ir Abstract: The present study models the effect of climate change on the distribution of Persian oak (Quercus brantii Lindl.) in the Zagros forests, located in the west of Iran. The modeling is conducted under the current and future climatic conditions by fitting the machine learning method of the Bayesian additive regression tree (BART). For the anticipation of the potential habitats for the Persian oak, two general circulation models (GCMs) of CCSM4 and HADGEM2-ES under the representative concentration pathways (RCPs) of 2.6 and 8.5 for 2050 and 2070 are used. The mean temperature (MT) of the wettest quarter (bio8), solar radiation, slope and precipitation of the wettest month (bio13) are respectively reported as the most important variables in the modeling. The results indicate that the suitable habitat of Persian oak will significantly decrease in the future under both climate change scenarios as much as 75.06% by 2070. The proposed study brings insight into the current condition and further projects the future conditions of the local forests for proper management and protection of endangered ecosystems. Keywords: species distribution; climate change; Bayesian; machine learning; artificial intelligence; deep learning; mathematics; forest; big data; data science 1. Introduction Forest ecosystems have been globally affected by climate change, geomorphic features and human impact [1,2]. Climate change is widely introduced as a main threat to diversity, species survival and ecosystem stability in most biomes [3,4]. In fact, climate change has been introduced as one of the main causes of the emergence of new species, change in a species range, species extinction, destruction of biodiversity, loss of ecosystem resilience and disturbance regimes [4–6]. A species’ distribution is an essential spatial feature influenced by the environment and human impact. On a large scale, the climate has been suggested as a fundamental factor determining the distribution of species worldwide [7]. Various plant species in different geographical regions of the world are affected by climate change in different ways [8]. Due to the fact that climate change has a major role in species distribution [9], predicting the distribution of plant species under climate change scenarios is important for ecosystem sustainability and management [10,11]. Recently, several studies have predicted the potential effects of climate change on plant species distribution [12–20], and generally, these studies show that climate change causes changes in the range of plant species. Species distribution models (SDMs) are widely used to predict species response to climate change [9,21–24]. SDMs were found to be reliable models to anticipate current and future habitats as demonstrated by, e.g., Guisan and Thuiller (2005), Joseph et al. (2009) and Levin et al. (2013) [25–27]. Such models are also used to demonstrate the relationship Forests 2023, 14, 469. https://doi.org/10.3390/f14030469 https://www.mdpi.com/journal/forests