Habitat fragmentation, in which contiguous forests are broken into smaller, isolated patches, threatens biodiversity by disrupting species movement, shrinking populations, and altering ecosystem dynamics. Past assessments suggested declining global fragmentation, but they relied on structure-based metrics that overlook ecological connectivity. We analyzed global forest fragmentation from 2000 to 2020 using complementary metrics that captured patch connectivity, aggregation, and structure. Connectivity-based metrics revealed that 51 to 67% of forests globally—and 58 to 80% of tropical forests—became more fragmented, which is nearly twice the rate suggested by traditional structure-focused methods (30 to 35%). Aggregation-focused metrics confirmed increases in 57 to 83% of forests. Human activities such as agriculture and logging drive this change. Yet protected tropical areas saw up to an 82% reduction in fragmentation, underscoring the potential of targeted conservation.

Urban environments are typically warmer than surrounding rural areas, providing a unique setting for studying phenological responses to climate warming. Phenological differences between urban and rural trees are driven by local climate and species composition. Yet, the extent to which species composition influences phenological responses to urbanization remains poorly understood. To address this, we combine manipulative experiments, satellite-derived phenology data, and georeferenced tree occurrence records. Our findings show that, across Northern Hemisphere cities, differences in the temperature sensitivity of spring phenology between urban and rural areas are largely driven by urban-rural variation in species composition, surpassing the effects of preseason temperature. This pattern is particularly pronounced in Asian cities, where urban areas exhibit 0.74 ± 0.24 days/°C higher temperature sensitivity than rural areas. In-depth analyses using experiments and high-resolution satellite imagery from Beijing further demonstrate species-specific phenological responses to urbanization, with urban-dominant species exhibiting higher temperature sensitivity in urban environments compared to rural ones. These findings show that both interspecific variation in temperature sensitivity and species-specific responses to urbanization contribute to the pronounced impact of species composition on urban-rural phenological patterns. Our study underscores the importance of considering species composition when studying phenological responses to climate warming, especially in urban contexts.

Vegetation phenology and hydrological cycles closely interact from leaf and species levels to watershed and global scales. As one of the most sensitive biological indicators of climate change, vegetation phenology is essential to be simulated accurately in hydrological models. Although the Soil and Water Assessment Tool (SWAT) has been widely used for estimating hydrological cycles, its lack of integration with the phenology module has led to substantial uncertainties. In this study, we developed a process-based vegetation phenology module and coupled it with the SWAT-Carbon model to investigate the effects of vegetation dynamics on runoff in the upper reaches of the Jinsha River watershed in China. The modified SWAT-Carbon model showed reasonable performance in phenology simulation, with root mean square error (RMSE) of 9.89 d for the start of season (SOS) and 7.51 d for the end of season (EOS). Simulations of both vegetation dynamics and runoff were also substantially improved compared to the original model. Specifically, the simulation of leaf area index significantly improved with the coefficient of determination (R²) increasing by 0.62, the Nash–Sutcliffe efficiency (NSE) increasing by 2.45, and the absolute percent bias (PBIAS) decreasing by 69.0 % on average. Additionally, daily runoff simulation also showed notable improvement, particularly in June and October, with R² rising by 0.22 and NSE rising by 0.43 on average. Our findings highlight the importance of integrating vegetation phenology into hydrological models to enhance modeling performance.