Grazing activities constitute the paramount challenge to grassland conservation over the Qinghai–Tibet Plateau (QTP), underscoring the urgency of obtaining detailed information regarding the extent, patterns, and trends of grazing to enable efficient grassland management and sustainable development. Here, to inform stakeholders about these issues, we provided the first annual Gridded Dataset of Grazing Intensity (GDGI), with a resolution of 100 m, from 1990 to 2020 for the QTP. The five most commonly used machine learning algorithms were leveraged to develop a livestock spatialization model, which spatially disaggregates the livestock census data at the county level into a detailed 100 m × 100 m grid based on seven key predictors from terrain, climate, vegetation, and socio-economic factors. Among these algorithms, the extreme-tree (ET) model performed the best in representing the complex nonlinear relationship between various environmental factors and livestock intensity, with an average absolute error of just 0.081 SU ha⁻² (where SU denotes sheep units), a rate outperforming the other models by 21.58 %–414.60 %. By using the ET model, we further generated the GDGI for the QTP to reveal the spatio-temporal heterogeneity and variations in grazing intensities. The GDGI indicates that grazing intensity remained high and largely stable from 1990 to 1997, followed by a sharp decline from 1997 to 2001 and fluctuations thereafter. Encouragingly, compared to other open-access datasets for grazing distribution on the QTP, the GDGI has the highest accuracy, with the determinant coefficient (R²) exceeding 0.8. Given its high resolution, recentness, and robustness, we believe that the GDGI dataset can significantly enhance our understanding of the substantial threats to grasslands emanating from overgrazing activities. Furthermore, the GDGI product holds considerable potential as a foundational source for other research, facilitating the rational utilization of grasslands, refined environmental impact assessments, and the sustainable development of animal husbandry. The GDGI product developed in this study is available at https://doi.org/10.5281/zenodo.10851119 (Zhou et al., 2024).

National nature reserves (NNRs) are at the forefront of conservation efforts on the Qinghai–Tibetan Plateau (QTP). However, few studies have examined the vegetation dynamics and their driving forces at the whole QTP scale. In this study, we used potential Net Primary Productivity (PNPP), actual NPP (ANPP), and human-activity-induced NPP (HNPP) to analyze the vegetation dynamics of 42 NNRs on the QTP. Further, we determined the driving factors of vegetation dynamics from 2000 to 2020. The results indicate that, during the 21 years studied, ANPP increased at 83.4% of the NNRs area on the QTP. Additionally, the contributions of climate change and anthropogenic factors to ANPP variation were 59.53% and 40.47%, respectively. The contribution of temperature to ANPP variation was considered high and stable, whereas the contribution of precipitation was relatively lower and variable. Residual analysis showed that human activities had both positive (51.30%) and negative effects (48.70%) on ANPP. Using Hurst exponent analysis, we found that 31.60% of the vegetation for the NNRs on the QTP will likely remain a persistent trend, and 65.4% will be stochastic in the future. By contrast, 3.00% of the vegetation mainly located in southern QTP would show a reverse trend, with most of them distributing in southern QTP, which deserves more attention. This study may help policymakers understand the relative impacts of climate change and human activities on vegetation in the different nature reserves on the QTP.

In China, rapid urbanization in recent decades has led to increasingly serious ecological and environmental problems, threatening sustainable development. Thus, a clear understanding of the relationship between urbanization and eco-environment is the basis for achieving regional sustainable development. However, despite the current global explosion of research interests on this topic, few studies have focused on ecologically fragile areas. To fill this gap, taking Aba Autonomous Prefecture in the eastern Qinghai-Tibet Plateau as a case study, we explored the relationship between urbanization and eco-environment from 2001 to 2018 using a coupled coordination degree model. The results show that the urbanization level and eco-environmental quality in Aba Prefecture achieved stable and continuous improvements from 0.202 to 0.428 and 0.372 to 0.422, respectively. Moreover, the coupling degree between them ranged from 0.524 to 0.652, indicating that the study area had transformed from uncoordinated development in the initial stage to transformation development in the final stage. Additionally, over the 18 years, the coordinated state of urbanization and eco-environment improved, with the coordinated level increasing from reluctant to moderate coordination after 2011. Lastly, we confirmed that urbanization in Aba Prefecture had an overall positive effect on the local eco-environment, although it gradually decreased as urbanization progressed. These findings have important implications for political decision-makers to achieve high-quality development in ecologically fragile areas.

Background: Rapid increases in protected wildlife population can lead to increased human–wildlife conflicts, challenging conservation program sustainability. As wildlife continue to thrive and increase on the Tibetan Plateau, human–wildlife conflict is expected to be an ever-present and evolving challenge, but the negative effects of such thriving wildlife are generally overlooked. Result: We found that growing wildlife populations and changes in behavior can have considerable negative effects on Sustainable Development Goals and associated targets. Moreover, these negative effects will likely be intensified as a result of climate change and anthropogenic pressures. Conclusion: If not adequately addressed, then rebounding wildlife populations may cause or reinforce human–wildlife conflict. We urge a reconsideration of traditional approaches to human–wildlife conflict mitigation on the Tibetan Plateau that do not shy away from approaching overabundance-related conflicts directly.