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Downloaded from the Worldclim-Global Climate Database ( Accessed on 11 October 2020) Article The Potential Distribution of Juniperus rigida Sieb. et Zucc. Vary Diversely in China under the Stringent and High GHG Emission Scenarios Combined Bioclimatic, Soil, and Topographic Factors Zhenjiang Lv 1,2 and Dengwu Li 1,2,* 1 College of Forestry, Northwest A & F University, Yangling, Xianyang 712100, China; lv.fl[email protected] 2 Shaanxi Key Laboratory of Economic Plant Resources Development and Utilization, Yangling, Xianyang 712100, China * Correspondence: [email protected] Abstract: Global warming poses an enormous threat to particular species with shifts to their suitable habitat. Juniperus rigida Sieb. et Zucc., an endemic species to East Asia and a pioneer species in the Loess plateau region, is endangered because of the shrinking and scattered habitat threatened by climate change. For the sake of analyzing the impact of climate warming on its possible habitat, we herein projected the current and future potential habitats of J. rigida in China and comparatively analyzed the ecological habitat changes in three main distribution regions. There were 110 specimen records of J. rigida collected across China and 22 environmental datasets, including bioclimatic variables and soil and topographical factors, selected by the Pearson Correlation Coefficient. The Citation: Lv, Z.; Li, D. The Potential MaxEnt model based on specimen presence and environmental factors was used for projecting the Distribution of Juniperus rigida Sieb. et potential habitats of J. rigida in China in the 2050s and the 2070s of RCP 2.6 and RCP 8.5 scenarios. The Zucc. Vary Diversely in China under results indicated an excellence model performance with the average value of the area under curve the Stringent and High GHG (AUC) is 0.928. The mean temperature of the driest quarter (MTDq) and the temperature annual Emission Scenarios Combined range (TAR) provided important contributions to the potential distribution of J. rigida. There were Bioclimatic, Soil, and Topographic three main distribution areas in China, the Xinjiang region, the Loess-Inner Mongolian Plateau region, Forests 2021 12 Factors. , , 1140. and the Changbai Mountain region. The distribution increased overall in area under RCP 2.6 and https://doi.org/10.3390/f12091140 decreased for RCP 8.5. The mean altitude of the core distribution shifted upward in general under both scenarios. The Loess–Inner Mongolian Plateau region is the biggest distribution, encompassing Academic Editor: × 4 2 × 4 2 Nadezhda Tchebakova ca. 61.39 10 km (86.87 10 km in China). The region threatened most by climate change is located in the Changbai Mountain distribution, with the centroid of the cord suitable habitat Received: 16 July 2021 migrating southwest about 227.47 and 260.32 km under RCP 2.6 and RCP 8.5 by the 2070s. In Accepted: 20 August 2021 summary, these findings provided a well-grounded understanding of the effect of climate change Published: 24 August 2021 on ecological distribution and furnished theory evidence for the protection, management, and sustainable use of J. rigida. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in Keywords: potential distribution; climate change; Juniperus rigida published maps and institutional affil- iations. 1. Introduction Greenhouse gas (GHG) emissions have led to global warming, which has now become Copyright: © 2021 by the authors. a global issue [1]. It was projected that, by the end of the 21st century (2081–2100), relative Licensee MDPI, Basel, Switzerland. to 1986–2005 with the reason of increasing GHG emission during the industrial age, the This article is an open access article global average surface temperature is likely to increase by 0.3–1.7 ◦C under the RCP 2.6 distributed under the terms and scenario, 1.1–2.6 ◦C under the RCP 4.5 scenario, and could be 1.4–3.1 ◦C under the RCP 6.0 conditions of the Creative Commons scenario and 2.6–4.8 ◦C under the RCP 8.5 scenario [2]. Changing climatic conditions have Attribution (CC BY) license (https:// had a profound impact on the distribution range of species, placing the global ecological creativecommons.org/licenses/by/ environment under inconceivable pressure [3,4], determining the viability and conservation 4.0/). Forests 2021, 12, 1140. https://doi.org/10.3390/f12091140 https://www.mdpi.com/journal/forests Forests 2021, 12, 1140 2 of 15 potential of various species [5], and altering species’ dominance, survival, migration, and community structure [6]. Consequently, some species will either shift their distributions or be extinguished as a result of further climate change in the future [7]. The ecological niche was defined by Grinnell [8] in 1917 and Elton [9] in 1927, respec- tively. Hutchinson [10] proposed the hypervolume niche and defined it as the set of all variables that allow species to exist indefinitely in 1957. There are numerous established models, such as Bioclim [11], DOMAIN [12], GARP [13], and MaxEnt [14], to simulate eco- logical requirements and distribution areas of species based on niche theory. Among these models, MaxEnt has been widely used owing to its advantages over other approaches, for instance, stable operation result, high simulation precision, and so on [15]. These features stand out in cases involving incomplete known geographical distribution data and the relevant environment variables that affect distributions [16,17]. The Juniperus rigida Sieb. et Zucc., Cupressaceae plant with the characteristics of har- diness, drought resistance, and soil and water conservation, is endemic to East Asia. The species is mainly distributed in cold temperate regions of the northern hemisphere, North and Northeastern China, Korea, and Japan. It is the key protected plant and almost endan- gered species now in China [18]. It is climate warming and its biological characteristics that shrink and fragment its distributions [19]. Therefore, it is meaningful to figure out the impact of climate change on its distribution in terms of formulating effective conservation strategies for J. rigida. Here, we used MaxEnt to project the potential distribution of Juniperus rigida Sieb. et Zucc. under current and future climate scenarios and to analyze the change of potential distribution in three main distributions across China. The specific objectives are (1) to project the potential distribution areas of J. rigida across China, (2) to assess the important variables affecting the distribution of J. rigida, and (3) to analyze the rules of the area change and the centroid shift in the three main distributions. 2. Materials and Methods 2.1. Collection and Processing of Species Distribution Data The current occurrence records of Juniperus rigida Sieb. et Zucc. throughout China derived from the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/, accessed on 20 November 2020), the Chinese Virtual Herbarium databases (CVH, http:// v5.cvh.org.cn/, accessed on 28 November 2020) and Specimen Resources Sharing Platform for Education (http://mnh.scu.edu.cn/, accessed on 5 December 2020). We obtained 557 occurrence records. The duplicates and those that lacked exact geo-coordinate record points were not taken into account. Finally, there remained 110 occurrence points that could carry out the MaxEnt model program and develop the distribution map integrated the topographic information with the ArcGIS 10.2 (Esri, Redlands, CA, USA) (Figure1). Those records were divided into three regions, which will be emphasized. 2.2. Collection and Selection Climate, Soil, and Topographical Data The current period (1950–2000) bioclimatic variables with 30 arc seconds (approxi- mately 1 km2) spatial resolution were downloaded from the WorldClim-Global Climate Database (http://worldclim.org/, accessed on 11 October 2020). They represented annual or seasonality trends and extreme or limiting environmental factors [20] and were the most crucial variables affecting potential species distribution [21]. Future period (2050s and 2070s) bioclimatic variables with the same geographical accuracy and variable names with current bioclimatic variables were extrapolated by a global circulation model. There were four representative concentration pathways (RCPs) established in the 5th report of the IPCC (Intergovernmental Panel on Climate Change) [22], which means the future climate change was diverse and uncertain. RCP 2.6 was a lower greenhouse gas emission scenario and was paid widespread attention because it could achieve the goal of no more than 2 ◦C of global warming by 2100 relative to pre-industrial levels [23,24]. Holding the increase in the global average temperature below 2 ◦C above pre- Forests 2021, 12, 1140 3 of 15 industrial levels and striving to limit the increase to 1.5 ◦C above pre-industrial levels is the common goal for all countries in the world by reducing greenhouse gas emissions [25]. RCP 8.5 was a robust GHG emission scenario without additional efforts to constrain emissions, which posed a high risk of abrupt and irreversible regional-scale change in the composition, structure, and function of ecosystems [22]. Therefore, we chose RCP 2.6 and RCP 8.5 under BCC-CSM1-1 climate change modeling, an efficient global climate tool for the simulation Forests 2021, 12, x FOR PEER REVIEWof future climatic conditions that performs perfectly for the simulation of precipitation3 of and 16 temperature in the Chinese region [26,27]. There were two periods for model projection; the 2050s and 2070s represent 2041–2060 and 2061–2080, respectively. Figure 1. DistributionDistribution records of J. rigida in China. 2.2. CollectionInsolation and duration Selection and Climat relativee, Soil, humidity and Topographical were provided Data by the China Meteorological Data Service Center (CMDC, http://data.cma.cn/en, accessed on 25 October 2020). These The current period (1950–2000) bioclimatic variables with 30 arc seconds (approxi- data were observed by 613 data and basic ground meteorological observation stations in mately 1 km2) spatial resolution were downloaded from the WorldClim-Global Climate China, and the mean value of each observation station was calculated during 1951~2000 to Database (http://worldclim.org/, accessed on 11 October 2020).
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