1. Fine root traits vary greatly with environmental changes, but the understanding of root trait variation and its drivers is limited over broad geographical scales, especially for ectomycorrhizal (ECM)-dominated conifers in alpine forests. Herein, the covariation patterns of and environmental controls for fine root traits among ECM-dominated conifers were examined to test whether and how climate and soil nutrients differentially affect fine root trait variations. 2. Eight traits of first-and second-order roots were measured, that is, root diameter (RD), specific root length (SRL), branching intensity (BRI), root tissue density (RTD), mycorrhizal colonization rate (MCR) and concentrations of carbon (C), nitrogen (N) and phosphorus (P), across 76 alpine coniferous populations on the eastern Tibetan Plateau, China. 3. Our results showed that variations of the fine root traits fell into two major dimensions: the first dimension (32.39% of the total variance) was mainly represented by RD and SRL, potentially conveying a trade-off between root life span and efficiency of resource foraging; the second dimension (23.70% of the variance) represented coordinated variation for root nutrients (i.e. N and P) and RTD, which depicts the conservation-acquisition trade-off in resource uptake, that is, root economic spectrum. Variations in RD and SRL were mainly driven by climatic variables, characterized by a significant increase in RD and a decrease in SRL with increasing mean annual precipitation. In contrast, variations in fine root nutrients (i.e. N and P) and RTD were primarily driven by soil fertility, showing a significant increase in root N and P concentrations but a decrease in RTD with increasing soil resource levels. 4. Synthesis. Our study clearly shows two distinct dimensions of the variation of fine root traits in ECM-dominated alpine coniferous forests, providing further evidence of the inherent multidimensionality of root traits. Moreover, our findings highlight different roles of climatic and soil variables in driving the variation of fine root traits, potentially leading to the multidimensionality of root traits. This | 2545

The outbreak of coronavirus disease 2019 (COVID-19) has caused tremendous loss to human life and economic decline in China and worldwide. It has significantly reduced gross domestic product (GDP), power generation, industrial activity and transport volume; thus, it has reduced fossil-related and cement-induced carbon dioxide (CO 2 ) emissions in China. Due to time delays in obtaining activity data, traditional emissions inventories generally involve a 2–3-year lag. However, a timely assessment of COVID-19's impact on provincial CO 2 emission reductions is crucial for accurately understanding the reduction and its implications for mitigation measures; furthermore, this information can provide constraints for modeling studies. Here, we used national and provincial GDP data and the China Emission Accounts and Datasets (CEADs) inventory to estimate the emission reductions in the first quarter (Q1) of 2020. We find a reduction of 257.7 Mt. CO 2 (11.0%) over Q1 2019. The secondary industry contributed 186.8 Mt. CO 2 (72.5%) to the total reduction, largely due to lower coal consumption and cement production. At the provincial level, Hubei contributed the most to the reductions (40.6 Mt) due to a notable decrease of 48.2% in the secondary industry. Moreover, transportation significantly contributed (65.1 Mt), with a change of −22.3% in freight transport and −59.1% in passenger transport compared with Q1 2019. We used a point, line and area sources (PLAS) method to test the GDP method, producing a close estimate (reduction of 10.6%). One policy implication is a change in people's working style and communication methods, realized by working from home and holding teleconferences, to reduce traffic emissions. Moreover, GDP is found to have potential merit in estimating emission changes when detailed energy activity data are unavailable. We provide provincial data that can serve as spatial disaggregation constraints for modeling studies and further support for both the carbon cycle community and policy makers.