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 language | English (US) |
|---|---|
| Pages (from-to) | 351-361 |
| Number of pages | 11 |
| Journal | New Phytologist |
| Volume | 226 |
| Issue number | 2 |
| DOIs | |
| State | Published - Apr 1 2020 |
| Externally published | Yes |
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