No abstract

The first-stage of an ecological conservation and restoration project in the Three-River Source Region (TRSR), China, has been in progress for eight years. However, because the ecological effects of this project remain unknown, decision making for future project implementation is hindered. Thus, in this study, we developed an index system to evaluate the effects of the ecological restoration project, by integrating field observations, remote sensing, and process-based models. Effects were assessed using trend analyses of ecosystem structures and services. Results showed positive trends in the TRSR since the beginning of the project, but not yet a return to the optima of the 1970s. Specifically, while continued degradation in grassland has been initially contained, results are still far from the desired objective, 'grassland coverage increasing by an average of 20%-40%'. In contrast, wetlands and water bodies have generally been restored, while the water conservation and water supply capacity of watersheds have increased. Indeed, the volume of water conservation achieved in the project meets the objective of a 1.32 billion m 3 increase. The effects of ecological restoration inside project regions was more significant than outside, and, in addition to climate change projects, we concluded that the implementation of ecological conservation and restoration projects has substantially contributed to vegetation restoration. Nevertheless, the degradation of grasslands has not been fundamentally reversed, and to date the project has not prevented increasing soil erosion. In sum, the effects and challenges of this first-stage project highlight the necessity of continuous and long-term ecosystem conservation efforts in this region.

High-latitude and high-altitude regions contain vast stores of permafrost carbon. Climate warming may result in the release of CO2 from both the thawing of permafrost and accelerated autotrophic respiration, but it may also increase the fixation of CO2 by plants, which could relieve or even offset the CO2 losses. The Tibetan Plateau contains the largest area of alpine permafrost on Earth. However, the current status of the net CO2 balance and feedbacks to warming remain unclear, given that the region has recently experienced an atmospheric warming rate of over 0.3 °C decade−1. We examined 32 eddy covariance sites and found an unexpected net CO2 sink during 2002 to 2020 (26 of the sites yielded a net CO2 sink) that was four times the amount previously estimated. The CO2 sink peaked at an altitude of roughly 4,000 m, with the sink at lower and higher altitudes limited by a low carbon use efficiency and a cold, dry climate, respectively. The fixation of CO2 in summer is more dependent on temperature than the loss of CO2 than it is in the winter months, especially at higher altitudes. Consistently, 16 manipulative experiments and 18 model simulations showed that the fixation of CO2 by plants will outpace the loss of CO2 under a wetting–warming climate until the 2090s (178 to 318 Tg C y−1). We therefore suggest that there is a plant-dominated negative feedback to climate warming on the Tibetan Plateau.

Despite the great progress in human motion prediction, it remains a challenging task due to the complicated structural dynamics of human behaviors. In this paper, we address this problem in three aspects. First, to capture the long-range spatial correlations and temporal dependencies, we apply a transformer-based architecture with the global attention mechanism. Specifically, we feed the network with the sequential joints encoded with the temporal information for spatial and temporal explorations. Second, to further exploit the inherent kinematic chains for better 3D structures, we apply a progressive-decoding strategy, which performs in a central-to-peripheral extension according to the structural connectivity. Last, in order to incorporate a general motion space for high-quality prediction, we build a memory-based dictionary, which aims to preserve the global motion patterns in training data to guide the predictions. We evaluate the proposed method on two challenging benchmark datasets (Human3.6M and CMU-Mocap). Experimental results show our superior performance compared with the state-of-the-art approaches.