Forecasting semi-arid biome shifts in the Anthropocene
© 2019 The Authors. New Phytologist © 2019 New Phytologist Trust.
| Veröffentlicht in: | The New phytologist. - 1979. - 226(2020), 2 vom: 15. Apr., Seite 351-361 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2020
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| Zugriff auf das übergeordnete Werk: | The New phytologist |
| Schlagworte: | Journal Article carbon metabolism critical threshold early-warning signal ecohydrology ecophysiology lagged mortality machine learning niche partitioning |
| Zusammenfassung: | © 2019 The Authors. New Phytologist © 2019 New Phytologist Trust. 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 |
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| Beschreibung: | Date Completed 14.05.2021 Date Revised 14.05.2021 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1469-8137 |
| DOI: | 10.1111/nph.16381 |