Kulmatiski A Yu K Mackay DS Holdrege MC Staver AC Parolari AJ Liu Y Majumder S Trugman AT 2020 Shrub encroachment, forest decline and wildfires have caused large-scale changes in semi-arid vegetation over the past 50 years. Climate is a primary determinant of plant growth in semi-arid ecosystems, yet it remains difficult to forecast large-scale vegetation shifts (i.e. biome shifts) in response to climate change. We highlight recent advances from four conceptual perspectives that are improving forecasts of semi-arid biome shifts. Moving from small to large scales, first, tree-level models that simulate the carbon costs of drought-induced plant hydraulic failure are improving predictions of delayed-mortality responses to drought. Second, tracer-informed water flow models are improving predictions of species coexistence as a function of climate. Third, new applications of ecohydrological models are beginning to simulate small-scale water movement processes at large scales. Fourth, remotely-sensed measurements of plant traits such as relative canopy moisture are providing early-warning signals that predict forest mortality more than a year in advance. We suggest that a community of researchers using modeling approaches (e.g. machine learning) that can integrate these perspectives will rapidly improve forecasts of semi-arid biome shifts. Better forecasts can be expected to help prevent catastrophic changes in vegetation states by identifying improved monitoring approaches and by prioritizing high-risk areas for management. https://sonar.ch/global/documents/213546 eng info:eu-repo/semantics/altIdentifier/doi/10.1111/nph.16381 info:eu-repo/semantics/altIdentifier/pmid/31853979 The New phytologist. - 2020 carbon metabolism critical threshold early-warning signal ecohydrology ecophysiology lagged mortality machine learning niche partitioning Forecasting semi-arid biome shifts in the Anthropocene. http://purl.org/coar/resource_type/c_6501