Forecasting semi‐arid biome shifts in the Anthropocene. Issue 2 (25th January 2020)

• Record Type: Journal Article
• Authors: Kulmatiski, Andrew; Yu, Kailiang; Mackay, D. Scott; Holdrege, Martin C.; Staver, Ann Carla; Parolari, Anthony J.; Liu, Yanlan; Majumder, Sabiha; Trugman, Anna T.
• Is Part Of: New phytologist. Volume 226:Issue 2(2020)
• Page Start: 351
• Page End: 361
• Publication Date: 2020-01-25
• Subjects: carbon metabolism -- critical threshold -- early‐warning signal -- ecohydrology -- ecophysiology -- lagged mortality -- machine learning -- niche partitioning; Botany -- Periodicals; 580
• DOI: 10.1111/nph.16381
• Languages: English
• ISSNs: 0028-646X

Abstract

Summary: 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.