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Forecasting semi-arid biome shifts in the Anthropocene
by
Yu, Kailiang
, Trugman, Anna T.
, Parolari, Anthony J.
, Holdrege, Martin C.
, Staver, Ann Carla
, Kulmatiski, Andrew
, Mackay, D. Scott
, Majumder, Sabiha
, Liu, Yanlan
in
Anthropocene
/ Anthropocene epoch
/ Aridity
/ artificial intelligence
/ Balances (scales)
/ canopy
/ carbon
/ carbon metabolism
/ climate
/ Climate Change
/ Climate models
/ Coexistence
/ Computer simulation
/ Continental interfaces, environment
/ critical threshold
/ Drought
/ Droughts
/ early‐warning signal
/ Ecohydrology
/ ecophysiology
/ Ecosystem
/ Ecosystems
/ Encroachment
/ forest decline
/ Forests
/ hydrologic cycle
/ lagged mortality
/ Learning algorithms
/ Machine learning
/ monitoring
/ Mortality
/ niche partitioning
/ Ocean, Atmosphere
/ Plant cover
/ Plant growth
/ prediction
/ Remote sensing
/ Research review
/ Sciences of the Universe
/ shrubs
/ Tracers
/ Trees
/ Vegetation
/ Water flow
/ Wildfires
2020
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Forecasting semi-arid biome shifts in the Anthropocene
by
Yu, Kailiang
, Trugman, Anna T.
, Parolari, Anthony J.
, Holdrege, Martin C.
, Staver, Ann Carla
, Kulmatiski, Andrew
, Mackay, D. Scott
, Majumder, Sabiha
, Liu, Yanlan
in
Anthropocene
/ Anthropocene epoch
/ Aridity
/ artificial intelligence
/ Balances (scales)
/ canopy
/ carbon
/ carbon metabolism
/ climate
/ Climate Change
/ Climate models
/ Coexistence
/ Computer simulation
/ Continental interfaces, environment
/ critical threshold
/ Drought
/ Droughts
/ early‐warning signal
/ Ecohydrology
/ ecophysiology
/ Ecosystem
/ Ecosystems
/ Encroachment
/ forest decline
/ Forests
/ hydrologic cycle
/ lagged mortality
/ Learning algorithms
/ Machine learning
/ monitoring
/ Mortality
/ niche partitioning
/ Ocean, Atmosphere
/ Plant cover
/ Plant growth
/ prediction
/ Remote sensing
/ Research review
/ Sciences of the Universe
/ shrubs
/ Tracers
/ Trees
/ Vegetation
/ Water flow
/ Wildfires
2020
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Do you wish to request the book?
Forecasting semi-arid biome shifts in the Anthropocene
by
Yu, Kailiang
, Trugman, Anna T.
, Parolari, Anthony J.
, Holdrege, Martin C.
, Staver, Ann Carla
, Kulmatiski, Andrew
, Mackay, D. Scott
, Majumder, Sabiha
, Liu, Yanlan
in
Anthropocene
/ Anthropocene epoch
/ Aridity
/ artificial intelligence
/ Balances (scales)
/ canopy
/ carbon
/ carbon metabolism
/ climate
/ Climate Change
/ Climate models
/ Coexistence
/ Computer simulation
/ Continental interfaces, environment
/ critical threshold
/ Drought
/ Droughts
/ early‐warning signal
/ Ecohydrology
/ ecophysiology
/ Ecosystem
/ Ecosystems
/ Encroachment
/ forest decline
/ Forests
/ hydrologic cycle
/ lagged mortality
/ Learning algorithms
/ Machine learning
/ monitoring
/ Mortality
/ niche partitioning
/ Ocean, Atmosphere
/ Plant cover
/ Plant growth
/ prediction
/ Remote sensing
/ Research review
/ Sciences of the Universe
/ shrubs
/ Tracers
/ Trees
/ Vegetation
/ Water flow
/ Wildfires
2020
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Journal Article
Forecasting semi-arid biome shifts in the Anthropocene
2020
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Overview
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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