Forecasting semi-arid biome shifts in the Anthropocene

  • Andrew Kulmatiski
  • , Kailiang Yu
  • , D. Scott Mackay
  • , Martin C. Holdrege
  • , Ann Carla Staver
  • , Anthony J. Parolari
  • , Yanlan Liu
  • , Sabiha Majumder
  • , Anna T. Trugman

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)351-361
Number of pages11
JournalNew Phytologist
Volume226
Issue number2
DOIs
StatePublished - Apr 1 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Physiology
  • Plant Science

Keywords

  • carbon metabolism
  • critical threshold
  • early-warning signal
  • ecohydrology
  • ecophysiology
  • lagged mortality
  • machine learning
  • niche partitioning

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