Semi-arid agroecosystems have a crucial function in supplying food and ecosystem services. However, these ecosystems are under severe threat due to land degradation. To enhance our understanding of environmental trends and their causes and to identify more sustainable land management techniques, it is important to track land degradation in space and time. This study uses land cover, soil organic carbon, and land productivity dynamics maps to evaluate land degradation neutrality (as per Sustainable Development Goal 15, indicator 15.3.1). In this regard, we employed the trends.earth tool in QGIS 3.3, utilising the European Space Agency Climate Change Initiative (ESA CCI) classified LULC maps for 1992, 2000, 2010, and 2020 to assess land degradation. Additionally, we predicted 2050 LULC maps using the MOLUSCE plugin in QGIS, which integrates an artificial neural network (ANN) in cellular automata (CA) modeling (CA-ANN) based on the 2015 LULC map and independent variables such as digital elevation model (DEM) and slope. Our results indicated a significant decrease in bare areas (71%) and an increase in settlements (built-up areas) (163%) between 1992 and 2020. Furthermore, the predicted land cover map shows a significant increase in bare land (238%) and settlements (72%), accompanied by a decrease in water bodies (23%) and forested areas (3.5%). In terms of land degradation, approximately 26.46% of the province exhibited degraded land, accounting for approximately 20,146.35 km 2, while approximately 59.55% (45,337.84 km 2) of the land remained stable. Land cover conversions, particularly from forests to grasslands and settlements, are among the potential drivers of land degradation. Identifying land cover transitions and assessing land degradation is paramount for effective monitoring and planning. By understanding these dynamics, targeted interventions can be developed to mitigate land degradation and promote sustainable land use practices in semi-arid agroecosystems.

Climate change and other global change drivers threaten plant diversity in mountains worldwide. A widely documented response to such environmental modifications is for plant species to change their elevational ranges. Range shifts are often idiosyncratic and difficult to generalize, partly due to variation in sampling methods. There is thus a need for a standardized monitoring strategy that can be applied across mountain regions to assess distribution changes and community turnover of native and non-native plant species over space and time. Here, we present a conceptually intuitive and standardized protocol developed by the Mountain Invasion Research Network (MIREN) to systematically quantify global patterns of native and non-native species distributions along elevation gradients and shifts arising from interactive effects of climate change and human disturbance. Usually repeated every five years, surveys consist of 20 sample sites located at equal elevation increments along three replicate roads per sampling region. At each site, three plots extend from the side of a mountain road into surrounding natural vegetation. The protocol has been successfully used in 18 regions worldwide from 2007 to present. Analyses of one point in time already generated some salient results, and revealed region-specific elevational patterns of native plant species richness, but a globally consistent elevational decline in non-native species richness. Non-native plants were also more abundant directly adjacent to road edges, suggesting that disturbed roadsides serve as a vector for invasions into mountains. From the upcoming analyses of time series even more exciting results especially about range shifts can be expected. Implementing the protocol in more mountain regions globally would help to generate a more complete picture of how global change alters species distributions. This would inform conservation policy in mountain ecosystems, where some conservation policies remain poorly implemented.