The ecosystems across the Tibetan Plateau are changing rapidly in response to climate change, which poses unprecedented challenges for the control and mitigation of desertification on the Tibetan Plateau. Sophora moorcroftiana (Benth.) Baker is a drought-resistant plant species that has great potential to be used for desertification and soil degradation control on the Tibetan Plateau. In this study, using a maximum entropy (MaxEnt) niche model, we characterized the habitat distribution of S. moorcroftiana on the Tibetan Plateau under both current and future climate scenarios. To construct a robust model, 242 population occurrence records, gathered from our field surveys, historical data records, and a literature review, were used to calibrate the MaxEnt model. Our results showed that, under current environmental conditions, the habitat of S. moorcroftiana was concentrated in regions along the Yarlung Tsangpo, Lancang, and Jinsha rivers on the Tibetan Plateau. Elevation, isothermality, and minimal air temperature of the coldest month played a dominant role in determining the habitat distribution of S. moorcroftiana. Under future climate scenarios, the increased air temperature was likely to benefit the expansion of S. moorcroftiana over the short term, but, in the long run, continued warming may restrict the growth of S. moorcroftiana and lead to a contraction in its habitat. Importantly, the Yarlung Tsangpo River valley was found to be the core habitat of S. moorcroftiana, and this habitat moved westwards along the Yarlung Tsangpo River under future climate scenarios, but did not detach from it. This finding suggests that, with the current pace of climate change, an increase in efforts to protect and cultivate S. moorcroftiana is necessary and critical to control desertification on the Tibetan Plateau.

Currently, China’s forest ecosystem focus is shifting from a single management objective to multiple management objectives, aiming to improve forest quality and maximize the benefits of ecosystem services. Many difficulties and problems are encountered in the long-term development of most northern state-owned forest farms—for example, the fragmentation and degradation of forest landscapes caused by poor forest management and extensive land use—resulting in an ecosystem that is unable to provide optimal services. This research was conducted on the Fengning Grassland Forest Farm, which is based on the GEF project of state-owned forest farms. We applied lessons from international advanced concepts, such as landscape restoration, and combinecombined all types of existing data and supplementary survey data on forest farms. In addition, we used multivariate statistical analysis and geostatistical analysis methods to optimize spatial layout and forest landscape structure. Strategies of landscape restoration and optimization, forest quality improvement, and grassland ecological restoration were proposed. A forest growth model was established to predict the annual growth of forests, calculate sustainable levels of annual cutting, calculate biomass and carbon sequestration in the management period, and evaluate the value of the ecological service functions of forest ecosystems in forest farms. Finally, a set of forest management methods was developed to effectively improve the sustainable management level of state-owned forest farms and enhance the service function of forest ecosystems.

With the intensification of global climate change, exploring the impact of environmental factors on tree density can provide technical support for sustainable forest management. In this paper, the random forest parameters nTree and mtry were optimized using a particle swarm optimization algorithm. The density, average temperature, soil thickness, forest water consumption, slope, slope direction, slope position, soil type, and diameter at breast height (DBH) of the dominant tree species in Inner Mongolia were fitted using random forest regression with a satisfactory fitting effect ($R^2$ > 0.60). The results show that the average temperature, soil thickness, and forest water consumption were the main factors restricting tree density, and the influence of each factor changed depending on the stage of tree growth. Based on 2018 forest resource data of the Inner Mongolia Autonomous Region, four diameter class models were used to calculate tree density, and Kriging interpolation was used to form a density distribution grid map of the main tree species according to diameter class toward providing a theoretical basis and data support for afforestation and forest management strategies that are justified according to the available environmental resources.