Climate controls over ecosystem metabolism: insights from a fifteen-year inductive artificial neural network synthesis for a subalpine forest

Loren P. Albert, Trevor F. Keenan, Sean P. Burns, Travis E. Huxman, Russell Monson

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Eddy covariance (EC) datasets have provided insight into climate determinants of net ecosystem productivity (NEP) and evapotranspiration (ET) in natural ecosystems for decades, but most EC studies were published in serial fashion such that one study’s result became the following study’s hypothesis. This approach reflects the hypothetico-deductive process by focusing on previously derived hypotheses. A synthesis of this type of sequential inference reiterates subjective biases and may amplify past assumptions about the role, and relative importance, of controls over ecosystem metabolism. Long-term EC datasets facilitate an alternative approach to synthesis: the use of inductive data-based analyses to re-examine past deductive studies of the same ecosystem. Here we examined the seasonal climate determinants of NEP and ET by analyzing a 15-year EC time-series from a subalpine forest using an ensemble of Artificial Neural Networks (ANNs) at the half-day (daytime/nighttime) time-step. We extracted relative rankings of climate drivers and driver–response relationships directly from the dataset with minimal a priori assumptions. The ANN analysis revealed temperature variables as primary climate drivers of NEP and daytime ET, when all seasons are considered, consistent with the assembly of past studies. New relations uncovered by the ANN approach include the role of soil moisture in driving daytime NEP during the snowmelt period, the nonlinear response of NEP to temperature across seasons, and the low relevance of summer rainfall for NEP or ET at the same daytime/nighttime time step. These new results offer a more complete perspective of climate–ecosystem interactions at this site than traditional deductive analyses alone.

Original languageEnglish (US)
Pages (from-to)1-17
Number of pages17
JournalOecologia
DOIs
StateAccepted/In press - Mar 25 2017

Fingerprint

net ecosystem production
subalpine forests
artificial neural network
neural networks
metabolism
eddy covariance
climate
synthesis
evapotranspiration
ecosystems
ecosystem
productivity
snowmelt
time series analysis
temperature
network analysis
soil water
rain
ranking
summer

Keywords

  • Coniferous
  • Eddy covariance
  • Fluxnet
  • Model-data assimilation
  • Photosynthesis

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics

Cite this

Climate controls over ecosystem metabolism : insights from a fifteen-year inductive artificial neural network synthesis for a subalpine forest. / Albert, Loren P.; Keenan, Trevor F.; Burns, Sean P.; Huxman, Travis E.; Monson, Russell.

In: Oecologia, 25.03.2017, p. 1-17.

Research output: Contribution to journalArticle

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