Forecasting semi-arid biome shifts in the Anthropocene

Kulmatiski, A and Yu, K and Mackay, DS and Holdrege, MC and Staver, AC and Parolari, AJ and Liu, Y and Majumder, S and Trugman, AT (2020) Forecasting semi-arid biome shifts in the Anthropocene. In: New Phytologist, 226 (2). pp. 351-361.

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Abstract

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. © 2019 The Authors. New Phytologist © 2019 New Phytologist Trust

Item Type:	Journal Article
Publication:	New Phytologist
Publisher:	Blackwell Publishing Ltd
Additional Information:	The copyright for this article belongs to the Authors.
Keywords:	Anthropocene; biome; climate change; drought; early warning system; ecohydrology; ecophysiology; flow modeling; forecasting method; machine learning; metabolism; mortality; niche partitioning; paleoclimate; semiarid region; threshold; vegetation history, climate change; drought; ecosystem; forest; tree, Climate Change; Droughts; Ecosystem; Forests; Trees
Department/Centre:	Division of Physical & Mathematical Sciences > Physics
Date Deposited:	24 Jan 2023 06:08
Last Modified:	24 Jan 2023 06:08
URI:	https://eprints.iisc.ac.in/id/eprint/79385

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