Study on rare and endangered plants under climate: maxent modeling for identifying hot spots in northwest China

ABSTRACT

Background:

Climate warming has caused substantial changes in temporal and spatial environmental patterns. The study on hot spots of rare and endangered plants in Northwest China under predicted climate change can provide a scientific reference for the restoration and reconstruction of those degraded habitats, as well as the improvement of the protection system in Northwest China.

Results:

Based on MaxEnt algorithm, 813 effective distribution records and 11 environmental factor variables of rare and endangered plants in Northwest China, this study identified the changes of biodiversity hotspots of rare and endangered plants in Northwest China under predicted climate change. The results showed that: (1) the prediction accuracy of MaxEnt model is high, the area under the curve (AUC) is 0.876, and the total suitable area for potential geographical distribution of rare and endangered plants in Northwest China is 137.96×104km², mainly including Western and Southwestern Xinjiang, Southern Gansu, parts of Eastern and Southern Qinghai Province, and Southern Shaanxi Province; (2) altitude, temperature and precipitation are the main environmental factors affecting the hot spots of rare and endangered plants in Northwest China; (3) under four climate change scenarios in the future, with the increase of emission scenarios from low to high forcing, Xinjiang would have the most obvious loss of hot spots of rare and endangered plants in Northwest China, and the most obvious increase of which would occur in Qinghai and Gansu provinces.

Conclusion:

Under the climate change scenario in the future, with the emission scenario increasing from low forcing to high forcing, the most obvious loss of the hot spots of rare and endangered plants in Northwest China happens in Xinjiang Province, and that of the most obvious increase occurs in Qinghai and Gansu provinces.

Keywords:
Northwest China; rare and endangered plants; MaxEnt model; hot spots; climate change