Anticipating global terrestrial ecosystem state change using FLUXNET

Rong Yu, Benjamin L. Ruddell, Minseok Kang, Joon Kim, Daniel Childers

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
JournalGlobal Change Biology
DOIs
StatePublished - Jan 1 2019

Fingerprint

terrestrial ecosystem
Ecosystems
elasticity
Elasticity
ecosystem
air temperature
Climate change
micrometeorology
Carbon
climate change
Air
ecosystem dynamics
Fluxes
eddy covariance
carbon flux
energy flux
global change
savanna
artificial neural network
tropical forest

Keywords

  • eddy covariance
  • FLUXNET
  • functional elasticity
  • information flow
  • phenology
  • precipitation
  • process network
  • radiation
  • structural state
  • temperature

ASJC Scopus subject areas

  • Global and Planetary Change
  • Environmental Chemistry
  • Ecology
  • Environmental Science(all)

Cite this

Anticipating global terrestrial ecosystem state change using FLUXNET. / Yu, Rong; Ruddell, Benjamin L.; Kang, Minseok; Kim, Joon; Childers, Daniel.

In: Global Change Biology, 01.01.2019.

Research output: Contribution to journalArticle

Yu, Rong ; Ruddell, Benjamin L. ; Kang, Minseok ; Kim, Joon ; Childers, Daniel. / Anticipating global terrestrial ecosystem state change using FLUXNET. In: Global Change Biology. 2019.
@article{263f8db32bdd487b8d559d7b80de80b8,
title = "Anticipating global terrestrial ecosystem state change using FLUXNET",
abstract = "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.",
keywords = "eddy covariance, FLUXNET, functional elasticity, information flow, phenology, precipitation, process network, radiation, structural state, temperature",
author = "Rong Yu and Ruddell, {Benjamin L.} and Minseok Kang and Joon Kim and Daniel Childers",
year = "2019",
month = "1",
day = "1",
doi = "10.1111/gcb.14602",
language = "English (US)",
journal = "Global Change Biology",
issn = "1354-1013",
publisher = "Wiley-Blackwell",

}

TY - JOUR

T1 - Anticipating global terrestrial ecosystem state change using FLUXNET

AU - Yu, Rong

AU - Ruddell, Benjamin L.

AU - Kang, Minseok

AU - Kim, Joon

AU - Childers, Daniel

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - eddy covariance

KW - FLUXNET

KW - functional elasticity

KW - information flow

KW - phenology

KW - precipitation

KW - process network

KW - radiation

KW - structural state

KW - temperature

UR - http://www.scopus.com/inward/record.url?scp=85065039004&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85065039004&partnerID=8YFLogxK

U2 - 10.1111/gcb.14602

DO - 10.1111/gcb.14602

M3 - Article

JO - Global Change Biology

JF - Global Change Biology

SN - 1354-1013

ER -