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 language | English (US) |
|---|---|
| Pages (from-to) | 1-17 |
| Number of pages | 17 |
| Journal | Oecologia |
| DOIs | |
| State | Accepted/In press - Mar 25 2017 |
Fingerprint
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 journal › Article
}
TY - JOUR
T1 - Climate controls over ecosystem metabolism
T2 - insights from a fifteen-year inductive artificial neural network synthesis for a subalpine forest
AU - Albert, Loren P.
AU - Keenan, Trevor F.
AU - Burns, Sean P.
AU - Huxman, Travis E.
AU - Monson, Russell
PY - 2017/3/25
Y1 - 2017/3/25
N2 - 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.
AB - 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.
KW - Coniferous
KW - Eddy covariance
KW - Fluxnet
KW - Model-data assimilation
KW - Photosynthesis
UR - http://www.scopus.com/inward/record.url?scp=85016054995&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85016054995&partnerID=8YFLogxK
U2 - 10.1007/s00442-017-3853-0
DO - 10.1007/s00442-017-3853-0
M3 - Article
C2 - 28343362
AN - SCOPUS:85016054995
SP - 1
EP - 17
JO - Oecologia
JF - Oecologia
SN - 0029-8549
ER -