Climate controls over ecosystem metabolism: insights from a fifteen-year inductive artificial neural network synthesis for a subalpine forest
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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.
KeywordsConiferous Model-data assimilation Photosynthesis Fluxnet Eddy covariance
We are grateful for support from the US National Science Foundation (DEB Awards 1256526 and 0918565) and the US Department of Energy (NIGEC; Cooperative Agreement DE-FC03-90ER61010, BER, Grant No. DE-FG02-03ER63637, and funds from the AmeriFlux Management Project administered through DOE Lawrence-Berkeley Laboratory). We wish to thank Peter Blanken and his lab for continuing support of the US-NR1 tower. We are also grateful to Drs. David Moore, Laura Scott-Denton and Pascal Mickelson for sharing Matlab scripts to assist in analyses, and to Drs Greg Barron-Gafford, Dave Breshears, and Scott Saleska for comments that improved the manuscript. We thank two anonymous reviewers for their feedback that greatly improved the manuscript. All procedures in this research were conducted in accordance with the legal and ethical standards of the US National Science Foundation and US Department of Energy. No human subjects or animals were studied in this research.
Author contribution statement
RKM conceived the study and obtained financial support for the work. LPA performed the artificial neural network analysis and wrote the initial manuscript draft. LPA, TFK, RKM and TEH collaborated to conduct and interpret the synthesis, as well as develop the text of the manuscript. SPB collected and synthesized the Niwot Ridge AmeriFlux data and participated in writing the manuscript.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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