Ranavirus infections in amphibians, especially anurans, have been increasingly documented, raising concerns over their role in population declines. This integrative review examines the prevalence, epidemiology, and global distribution of ranavirus in anurans over the past decade. The study concludes with recommendations for enhancing global surveillance and risk assessment strategies, particularly in biodiversity hotspots, where vulnerable species face a high risk of extinction.

Plant-feeding insects can have large impacts on the photosynthetic rate of their host plants. Through reducing photosynthesis, and thus carbon assimilation by the plant, these impacts might have large-scale influences on ecosystem carbon cycling. Nevertheless, these effects are rarely considered in ecosystem-level studies. Here we propose an approach to incorporating these changes in plant physiology into estimates of ecosystem productivity.

Machine learning models outperform simple mechanistic models in predicting pollinator visitation rates. We use deep learning to infer rules from land cover maps to estimate pollination services globally. Results suggest deep learning can improve predictions by identifying complex patterns in landscape composition, especially in data-rich but knowledge-poor areas. The challenge is to make deep learning algorithms more interpretable so that experts can validate prediction rules for pollination.