|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM294184333 |
| 003 |
DE-627 |
| 005 |
20231225080933.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231225s2019 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1111/gcb.14602
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n0980.xml
|
| 035 |
|
|
|a (DE-627)NLM294184333
|
| 035 |
|
|
|a (NLM)30793451
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Yu, Rong
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Anticipating global terrestrial ecosystem state change using FLUXNET
|
| 264 |
|
1 |
|c 2019
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 11.10.2019
|
| 500 |
|
|
|a Date Revised 11.10.2019
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status MEDLINE
|
| 520 |
|
|
|a © 2019 John Wiley & Sons Ltd.
|
| 520 |
|
|
|a Ecosystems can be characterized as complex systems that traverse a variety of functional and structural states in response to changing bioclimatic forcings. A central challenge of global change biology is the robust empirical description of these states and state transitions. An ecosystem's functional state can be empirically described using Process Networks (PN) that use timeseries observations to determine the strength of process-level functional couplings between ecosystem components. A globally extensive source of in-situ observations of terrestrial ecosystem dynamics is the FLUXNET eddy-covariance network that provides standardized observations of micrometeorology and carbon, water, and energy flux dynamics. We employ the LaThuile FLUXNET synthesis dataset to delineate each month's functional state for 204 sites, yielding the LaThuile PN version 1.0 database that describes the strength of an ecosystem's functional couplings from air temperature and precipitation to carbon fluxes during each site-month. Then we calculate the elasticity of these couplings to seasonal scale forcings: air temperature, precipitation, solar radiation, and phenophase. Finally, we train artificial neural networks to extrapolate these elasticities from 204 sites to the globe, yielding maps of the estimated functional elasticity of every terrestrial ecosystem's functional states to changing seasonal bioclimatic forcings. These maps provide theoretically novel resource that can be used to anticipate ecological state transitions in response to climate change and to validate process-based models of ecological change. These elasticity maps show that each ecosystem can be expected to respond uniquely to changing forcings. Tropical forests, hot deserts, savannas, and high elevations are most elastic to climate change, and elasticity of ecosystems to seasonal air temperature is on average an order of magnitude higher than elasticity to other bioclimatic forcings. We also observed a reasonable amount of moderate relationships between functional elasticity and structural state change across different ecosystems
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, Non-U.S. Gov't
|
| 650 |
|
4 |
|a Research Support, U.S. Gov't, Non-P.H.S.
|
| 650 |
|
4 |
|a FLUXNET
|
| 650 |
|
4 |
|a eddy covariance
|
| 650 |
|
4 |
|a functional elasticity
|
| 650 |
|
4 |
|a information flow
|
| 650 |
|
4 |
|a phenology
|
| 650 |
|
4 |
|a precipitation
|
| 650 |
|
4 |
|a process network
|
| 650 |
|
4 |
|a radiation
|
| 650 |
|
4 |
|a structural state
|
| 650 |
|
4 |
|a temperature
|
| 700 |
1 |
|
|a Ruddell, Benjamin L
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Kang, Minseok
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Kim, Joon
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Childers, Dan
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t Global change biology
|d 1999
|g 25(2019), 7 vom: 01. Juli, Seite 2352-2367
|w (DE-627)NLM098239996
|x 1365-2486
|7 nnns
|
| 773 |
1 |
8 |
|g volume:25
|g year:2019
|g number:7
|g day:01
|g month:07
|g pages:2352-2367
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1111/gcb.14602
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 25
|j 2019
|e 7
|b 01
|c 07
|h 2352-2367
|