Climate change‐driven conservation strategies commonly project habitat availability but may not account for local adaptation among populations of the same species, which can influence prediction accuracy. Using the giant panda ( Ailuropoda melanoleuca ) as a case study, we developed a regional‐scale species distribution model (SDM) and 33 population‐specific local models to assess niche divergence and climate‐induced habitat s...

Climate change affects biodiversity through multidimensional impacts, influencing not only shifts in habitat range but also changes in habitat quality. In this context, habitat area and bioclimatic velocity have become critical metrics for assessing species‐specific vulnerabilities to climate change. Here, we assessed the extinction risk and exposure risk of giant pandas ( Ailuropoda melanoleuca ) based on habitat area and bio...

With the development of deep learning (DL) techniques, there has been a successful application of this approach to determine biological age from latent information contained in retinal images. Retinal age gap (RAG) defined as the difference between chronological age and predicted retinal age has been established previously to predict the age-related disease. In this study, we performed discovery genome-wide association analysi...