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The Niviventer fulvescens species complex (NFSC), a group of abundant and taxonomically ambiguous rodent taxa, is distributed from Southeast Asia to south-eastern China. We combined molecular and morphological datasets to clarify the species composition and variation of the NFSC. Our phylogenetic analyses, using molecular data, recovered eight genetic lineages in the NFSC, including a novel, distinct lineage from Jilong, Tibet, China, which is described as a new species, N. fengi sp. nov. The species status of N. fengi is supported by a species delimitation analysis, and it is morphologically distinguished from other members of the NFSC by its greyish dorsal fur, soft hairs covering the whole body and a hairy tail. NFSC species bearing well-developed spines are found at lower elevations. A comprehensive taxonomic revision of the NFSC within China is provided, represented by five species: N. cremoriventer, N. fulvescens, N. huang, N. mekongis comb. nov. and N. fengi. A further study of this species complex, including samples from Southeast Asia, is needed.

Spatial heterogeneity in vegetation greenness (VG) can influence earth surface process and resource ecology. However, its long-term change and driving forces remain poorly understood. In this study, MODIS enhanced vegetation index (EVI) data was used to examine the change in spatial heterogeneity in VG and its relationships with vegetation greening in mainland China during 2000–2017. Moving window standard deviation and range of growing season mean EVI (GSEVI) were used as proxies for spatial heterogeneity in VG. It was found that moving window standard deviation of GSEVI increased significantly over 33.8% to 53.7% of mainland China during 2000–2017, while it decreased significantly over less than 5% of mainland China. The results of moving window range of GSEVI were similar to moving window standard deviation of GSEVI. These may be explained by: (1) increased standard deviation and range of GSEVI accompanied by increased GSEVI value; and (2) faster greening speed in dense than in sparse vegetated areas. Additionally, the increased spatial heterogeneity in VG means the increased difference in VG between arid and humid regions, and between urban cores (UCs) and rural areas. These may primarily be attributed to slower greening speed in arid than in humid regions, and in UCs than in rural areas. Thus some benefits from vegetation greening may be much less in arid than in humid regions, and in UCs than in rural areas. Overall, this study analyzed an interesting phenomenon that vegetation greening may increase spatial heterogeneity in VG, which has a series of implications for environment and human activities.

Evaluating the uncertainties in regional/global carbon flux estimates is essential for better understanding of terrestrial carbon dynamics. At the regional scale, climate input data is an important source of model simulation uncertainty. In this study, a process-based ecosystem model, CEVSA, was run driven by four climate input datasets during 1980–2004, i.e., climate input datasets interpolated from 756 (756s) and 2400 weather stations (2400s), the NCEP/NCAR and Princeton reanalysis datasets. We used the 2400s dataset as the reference because it was derived from high density weather station interpolation. The simulated Net Primary Productivity (NPP) based on interpolated climate data from the 756s and the two reanalysis datasets were compared with that from the 2400s dataset. Then, we quantified the uncertainty of model simulations at regional-scale caused by climate input data, and evaluated the performance of different climate datasets across different eco-regions. Our results suggest that the 756s, Princeton and NCEP/NCAR reanalysis datasets overestimated the 25-year mean annual temperature by 7.66%–12.25% and the precipitation by 2.83%–8.43%, respectively; accordingly, the simulated NPP ranged from 3.53 to 3.96 Pg C, 6% to 12% higher than the reference over the entire China. The 756s and the two reanalysis datasets captured well the trend and interannual variations of annual NPP during the study period, but showed systematic errors in the total amount of NPP compared with the 2400s dataset. To increase the station density in the eco-regions with a station density greater than 1.0 station per 10 4 km 2 (1.0 s/10 4 km 2 ) would not decrease the uncertainty for model simulation at a 0.1° spatial resolution. The NCEP/NCAR and Princeton reanalysis datasets showed larger uncertainties in most eco-regions compared with the interpolated datasets. Our results also suggest that the accuracy of the NCEP/NCAR reanalysis data should be further improved in most eco-regions. On Qinghai-Tibet Plateau and in northwestern China, all four climate input datasets had relatively lower accuracy due to the limited observation data available. Future work should further evaluate the simulated NPP against observations and quantify the accuracy of driving climate data to decrease the uncertainty of model simulations at the regional scale.

Clarifying the spatial and temporal variations in precipitation-use efficiency (PUE) is helpful for advancing our knowledge of carbon and water cycles in Tibetan grassland ecosystems. Here we use an integrated remote sensing normalized difference vegetation index (NDVI) and in-situ above-ground net primary production (ANPP) measurements to establish an empirical exponential model to estimate spatial ANPP across the entire Tibetan Plateau. The spatial and temporal variations in PUE (the ratio of ANPP to mean annual precipitation (MAP)), as well as the relationships between PUE and other controls, were then investigated during the 2001–2012 study period. At a regional scale, PUE increased from west to east. PUE anomalies increased significantly (>0.1 g·m–2·mm–1/10 yr) in the southern areas of the Tibetan Plateau yet decreased (>0.02 g·m–2·mm–1/10 yr) in the northeastern areas. For alpine meadow, we obtained an obvious breaking point in trend of PUE against elevation gradients at 3600 m above the sea level, which showed a contrasting relationship. At the inter-annual scale, PUE anomalies were smaller in alpine steppe than in alpine meadow. The results show that PUE of Tibetan grasslands is generally high in dry years and low in wet years.

An ecosystem model serves as an important tool to understand the carbon cycle in the forest ecosystem. However, the sensitivities of parameters and uncertainties of the model outputs are not clearly understood. Parameter sensitivity analysis (SA) and uncertainty analysis (UA) play a crucial role in the improvement of forest gross primary productivity GPP simulation. This study presents a global SA based on an extended Fourier amplitude sensitivity test (EFAST) method to quantify the sensitivities of 16 parameters in the Flux-based ecosystem model (FBEM). To systematically evaluate the parameters&rsquo, sensitivities, various parameter ranges, different model outputs, temporal variations of parameters sensitivity index (SI) were comprehensively explored via three experiments. Based on the numerical experiments of SA, the UA experiments were designed and performed for parameter estimation based on a Markov chain Monte Carlo (MCMC) method. The ratio of internal CO2 to air CO2 ( , canopy quantum efficiency of photon conversion , maximum carboxylation rate at 25 ( were the most sensitive parameters for the GPP. It was also indicated that were influenced by temperature throughout the entire growth stage. The result of parameter estimation of only using four sensitive parameters (RMSE =1.657) is very close to that using all the parameters (RMSE = 1.496). The results of SA suggest that sensitive parameters, such as strongly influence on the forest GPP simulation, and the temporal characteristics of the parameters` SI on GPP and NEE were changed in different growth. The sensitive parameters were a major source of uncertainty and parameter estimation based on the parameter SA could lead to desirable results without introducing too great uncertainties.

Climate conditions are the major driving factors of vegetation phenology. However, there is limited effort to monitor dynamics of vegetation phenology and its responses to climate change and urbanization. Using NDVI data from 1982 to 2015, this study investigated the spatiotemporal change of spring green-up date (GUD) across the Yellow River Basin (YRB) and estimated the possible effects of different climatic factors on it. Additionally, the urban-rural differences in GUD and its linkage to spring land surface temperature (LST) for YRB’s major cities over 2001–2015 were investigated. The results showed that the GUD significantly advanced at a rate of 0.42 days yr−1 and delayed spatially from southeast to northwest. The interannual variations in regionally averaged GUD were driven mainly by preseason min-temperature (Tmin). Spatially, the effect of preseason Tmin was strongest in the central and western region. The confounding effects of preseason maximum temperature (Tmax) and precipitation jointly affected the GUD, while insolation had a weak impact on GUD. Moreover, the sensitivity of GUD to preseason Tmin and precipitation weakened with an increasing mean annual preseason precipitation gradient, but the sensitivity to preseason Tmax was gradually enhanced. Furthermore, the difference in the GUD between urban and rural areas presented a significant logarithmic relationship with the distance away from the urban center, and it was strongly related to the regional LST. Our findings confirmed the importance of Tmin and urbanization in regulating changes in GUD and further suggested that LST should be considered to develop an improved model of GUD under future climate change and urban development.