Insights into climatic adaptability of Acer truncatum and Quercus mongolica in typical sandy lands of China using the Maximum Entropy (MaxEnt) model

doi:10.1016/j.regsus.2026.100391

Abstract

Abstract:

Climate change poses an escalating threat to forest sustainability in transitional zones between semi-arid and semi-humid regions, primarily by intensifying tree mortality and hindering ecological recovery. In northern China, Acer truncatum Bunge and Quercus mongolica Fisch. ex Ledeb., two native cold- and drought-tolerant temperate tree species, are widely used for afforestation. However, their future climatic suitability under continuous warming remains unclear. Using the Maximum Entropy model, we assessed the current and projected suitable habitats of these two species across China under three climate scenarios, including Shared Socioeconomic Pathway (SSP)126, SSP245, and SSP585, with a particular focus on the four major sandy lands in northern China: Hulun Buir Sandy Land, Hunshandak Sandy Land, Horqin Sandy Land, and Mu Us Sandy Land. Occurrence records and environmental variables were integrated to quantify habitat suitability for the two species and their spatial distribution dynamics under ongoing climate change. Model outputs were used to identify shifts in suitable habitat areas, expansion-contraction patterns, and changes in habitat stability across scenarios. The results show that the distribution of A. truncatum is primarily shaped by precipitation of wettest month (bio13), elevation, and temperature seasonality, whereas Q. mongolica is mainly influenced by bio13, annual mean temperature, and precipitation of driest month. At present, the suitable habitat area is 9.30×10⁵ km² for A. truncatum and 9.90×10⁵ km² for Q. mongolica, accounting for 9.69% and 10.31% of China’s land area, respectively. Future projections indicate that both species will retain stable core distributions, with potential expansion toward northeastern, southern, and southwestern China. The suitable range for A. truncatum is expected to expand, with newly suitable areas accounting for 2.59%-4.69% of China’s land area, whereas Q. mongolica may see new suitable areas accounting for 2.25%-4.50% of the total land area, despite projected contractions along its southern margins. Within the four major sandy lands, both species were predicted to shift from peripheral areas to interior zones. Notably, potential suitable habitats for Q. mongolica may expand to the Hulun Buir Sandy Land under SSP245 and SSP585 scenarios by 2090. These findings highlight the climatic resilience and ecological potential of both species and provide scientific support for climate-informed afforestation and restoration strategies in water-limited areas.

Key words: Climate change, Potential suitable habitat, Species distribution, Modeling (SDM), Maximum Entropy (MaxEnt) model, Sandy lands

SHI Han, PENG Xi, GUO Jingjing, ZHAO Ruxia, HAO Guangyou. Insights into climatic adaptability of Acer truncatum and Quercus mongolica in typical sandy lands of China using the Maximum Entropy (MaxEnt) model[J]. Regional Sustainability, 2026, 7(5): 100391.

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A. truncatum			Q. mongolica
Variable	Description	Contribution (%)	Variable	Description	Contribution (%)
bio3	Isothermality	2.00	bio1	Annual mean temperature	23.30
bio4	Temperature seasonality	23.50	bio2	Mean diurnal range	7.70
bio5	Max temperature of warmest month	4.00	bio3	Isothermality	6.30
bio13	Precipitation of wettest month	28.50	bio7	Temperature annual range	2.90
bio14	Precipitation of driest month	2.10	bio8	Mean temperature of wettest quarter	1.30
bio15	Precipitation seasonality	13.50	bio13	Precipitation of wettest month	34.20
elev	Elevation	25.90	bio14	Precipitation of driest month	18.60
t_texture	Topsoil texture (0-30 cm)	0.60	bio15	Precipitation seasonality	1.80
			elev	Elevation	2.10
			t_texture	Topsoil texture (0-30 cm)	1.90

Climate scenario	Year	Potential suitable habitat area of A. truncatum (×10⁵ km²)
		In China			In the four major sandy lands
		Gain area	Loss area	Stable area	Gain area	Loss area	Stable area
SSP126	2050	2.99	0.82	8.45	0.57	0.09	0.36
SSP126	2090	2.49	1.67	7.59	0.67	0.01	0.44
SSP245	2050	2.25	0.79	8.47	0.58	0.01	0.44
SSP245	2090	4.28	0.26	9.01	0.81	0.00	0.45
SSP585	2050	3.07	0.31	8.95	0.79	0.00	0.45
SSP585	2090	4.50	0.45	8.82	0.71	0.01	0.44
SSP126	2050	2.78	2.01	7.90	0.40	0.47	0.64
SSP126	2090	3.52	1.75	8.15	0.13	0.04	1.07
SSP245	2050	2.97	2.64	7.26	0.18	0.16	0.95
SSP245	2090	2.16	4.10	5.80	0.37	0.25	0.85
SSP585	2050	5.84	2.29	7.61	0.91	0.00	1.11
SSP585	2090	4.23	6.48	3.42	0.77	0.49	0.62

Variable	Description	Unit
bio1	Annual mean temperature	°C
bio2	Mean diurnal range	°C
bio3	Isothermality	-
bio4	Temperature seasonality	°C
bio5	Max temperature of warmest month	°C
bio6	Min temperature of coldest month	°C
bio7	Temperature annual range	°C
bio8	Mean temperature of wettest quarter	°C
bio9	Mean temperature of driest quarter	°C
bio10	Mean temperature of warmest quarter	°C
bio11	Mean temperature of coldest quarter	°C
bio12	Annual precipitation	mm
bio13	Precipitation of wettest month	mm
bio14	Precipitation of driest month	mm
bio15	Precipitation seasonality	-
bio16	Precipitation of wettest quarter	mm
bio17	Precipitation of driest quarter	mm
bio18	Precipitation of warmest quarter	mm
bio19	Precipitation of coldest quarter	mm
elev	Elevation	m
t_texture	Topsoil texture (0-30 cm)	-

A. truncatum		Q. mongolica
Variable	VIF	Variable	VIF
bio3	1.76	bio1	9.55
bio4	1.98	bio2	9.98
bio5	1.54	bio3	8.36
bio13	2.94	bio7	3.31
bio14	4.50	bio8	8.89
bio15	5.18	bio13	4.22
		bio14	4.52
		bio15	6.92

Species	Period	Scenario	AUC
A. truncatum	Current (1970-2000)		0.902
	Future (2050)	SSP126	0.906
		SSP245	0.907
		SSP585	0.905
	Future (2090)	SSP126	0.907
		SSP245	0.905
		SSP585	0.907
Q. mongolica	Current (1970-2000)		0.910
	Future (2050)	SSP126	0.911
		SSP245	0.909
		SSP585	0.905
	Future (2090)	SSP126	0.903
		SSP245	0.909
		SSP585	0.909