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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1111/nph.16381
|2 doi
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|a pubmed24n1015.xml
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|e rakwb
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|a eng
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| 100 |
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|a Kulmatiski, Andrew
|e verfasserin
|4 aut
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| 245 |
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|a Forecasting semi-arid biome shifts in the Anthropocene
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|c 2020
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 14.05.2021
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|a Date Revised 14.05.2021
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2019 The Authors. New Phytologist © 2019 New Phytologist Trust.
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|a 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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|a Journal Article
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|a carbon metabolism
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|a critical threshold
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|a early-warning signal
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|a ecohydrology
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|a ecophysiology
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|a lagged mortality
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|a machine learning
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| 650 |
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|a niche partitioning
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| 700 |
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|a Yu, Kailiang
|e verfasserin
|4 aut
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| 700 |
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|a Mackay, D Scott
|e verfasserin
|4 aut
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| 700 |
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|a Holdrege, Martin C
|e verfasserin
|4 aut
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| 700 |
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|a Staver, Ann Carla
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Parolari, Anthony J
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Yanlan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Majumder, Sabiha
|e verfasserin
|4 aut
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| 700 |
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|a Trugman, Anna T
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t The New phytologist
|d 1979
|g 226(2020), 2 vom: 15. Apr., Seite 351-361
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|x 1469-8137
|7 nnns
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| 773 |
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|g volume:226
|g year:2020
|g number:2
|g day:15
|g month:04
|g pages:351-361
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|u http://dx.doi.org/10.1111/nph.16381
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