Understanding the climatically suitable habitat of species plays a vital role in the sustainable use and management of target species. Calligonum mongolicum Turcz., a native shrub species found in desert areas of Central Asia, is generally considered as one of the top four tree species for desertification control. However, previous works on suitable habitat simulation had focused mainly on either the national or specific geographical scales rather than entire biota scales, which have underestimated the climatic tolerance of the species. Furthermore, the uncertainty outcomes of climate change were largely ignored. With these questions, the arid regions of Central Asia were selected as our research background area. Occurrence data of C. mongolicum were obtained from various sources, such as the Global Biodiversity Information Facility, the Chinese Virtual Herbarium, and the iPlant website. The maximum entropy model (MaxEnt) was used to simulate the suitable habitat change dynamics under various climate change scenarios [5 general circulation models (GCMs) × 3 shared socioeconomic pathways (SSPs)]. The uncertainty of climate change induced by GCMs and SSPs were decomposed by the two-way ANOVA method. Our results show that hydrological-related variables are more important for the species’ habitat suitability than thermal-related variables. The climatic threshold for the core suitable habitat was 1–30 mm for precipitation of the coldest quarter, 14–401 mm for annual precipitation, −16.01–12.42 °C for mean temperature of the driest quarter, 9.48–32.63 °C for mean temperature of the wettest quarter, and −25.01–−9.77 °C for the minimum temperature of the coldest month. The size of suitable habitat was about 287.4 × 104 km2 under the current climate condition, located in China and Mongolia. Climate change has less impact on the total area size, but it has bigger impacts on the gain area and loss area sizes. The loss area is mainly located in the southeast boundaries, whereas the gain area is mainly located in Mongolia and the Qinghai-Tibet Plateau. The decomposition uncertainty of climate change indicates that GCMs could explain 14.5%, 66.4%, and 97.0% of total variation, respectively, and SSPs could explain 85.5%, 33.6%, and 3.0% of the total variation for gain, loss, and total habitat sizes, respectively. Our work clearly demonstrates that while C. mongolicum has great planting potential in Central Asia under various climate change scenarios, the sensitive areas possess large uncertainties requiring long-term climate monitoring for afforestation projects.
Chinese alfalfa (Medicago sativa) is one of the most widely planted species in China. It has considerable economic potential and plays an important role in soil and water conservation. In order to conduct scientific cultivation of Chinese alfalfa, we collected 100 occurrence records from herbarium and publications and 19 climatic variables from BIOCLIM to simulate potential suitable habitat and identified the key climatic factors of Chinese alfalfa by MaxEnt and GIS software. The result shows that the MaxEnt model performed well, with an average test AUC value of 0.86 with 10-fold cross validation. The potential distribution of Chinese alfalfa is mainly in arid and semi-arid areas of north and northwest China, about 15.2% (1.46 million km2) of China’s total land area, and the highly suitable area is Loess Hilly region and Xinjiang. The main climatic factors affecting the distribution of this species is hydrological-related factors (PDM, PS, AP, PDQ and PCQ), which explained 58.6% of the variation, and the climatic factors limiting the southern, northern, northwestern and Tibetan plateau boundaries were PDM, AMT, AP and MTCM, respectively. The climatic thresholds of the core area of Chinese alfalfa are 0.0–14.0 mm of PDM, 23.8–108.2% of PS, 3.9–15.5 °C of AMT, 14.0–664.0 mm of AP, 1.0–47.0 mm of PDQ, 2.0–51.0 mm of PCQ. The results improve our understanding of limiting climatic factors for Chinese alfalfa and suggest a priority management measures for areas with corresponding limiting climatic factor.
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