Interacting global change drivers suppress a foundation tree species

Dynamics of the distribution of invasive alien plants (Asteraceae) in China under climate change

Climate change and biological invasions pose significant threats to the conservation of biodiversity and the provision of ecosystem services. With the rapid development of international trade and economy, China has become one of the countries most seriously affected by invasive alien plants (IAPs), especially the Asteraceae IAPs. For this end, we selected occurrence data of 31 Asteraceae IAPs and 33 predictor variables to explore the distribution pattern under current climate using MaxEnt model. Based on future climate data, the changes in distribution dynamics of Asteraceae IAPs were predicted under two time periods (2041-2060 and 2081-2100) and three climate change scenarios (SSP126, SSP245 and SSP585). The results indicated that the potential distribution of IAPs was mainly in the southeast of China under current climate. Climatic variables, including precipitation of coldest quarter (BIO19), temperature annual range (BIO07) and annual precipitation (BIO12) were the main factors affecting the potential distribution. Besides, human footprint (HFP), population (POP) and soil moisture (SM) also had a great contribution for shaping the distribution pattern. With climate change, the potential distribution of IAPs would shift to the northwest and expand. It would also accelerate the expansion of most Asteraceae IAPs in China. The results of our study can help to understand the dynamics change of distributions of Asteraceae IAPs under climate change in advance so that early strategies can be developed to reduce the risk and influence of biological invasions.

Savanna fire regimes depend on grass trait diversity

Grasses fuel most fires on Earth and strongly influence local fire behaviour through traits that determine how flammable they are. Therefore, grass communities that differ in their species and trait compositions give rise to significant spatial variation in savanna fire regimes across the world, which cannot be otherwise explained. Likewise, fire regimes are continuously modified by alterations to savanna grass community traits, through species introductions and climatic changes. However, current representation of grassy fuels in global fire models misses important variation and therefore limits predictive power. The inclusion of grass trait diversity in models, using remotely sensed trait proxies, for example, will greatly improve our ability to understand and project savanna fires and their roles in the Earth system.