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Understanding Long-Term Trends in Annual Net Ecosystem Exchange of CO2
Published: November 15, 2016
Posted: August 03, 2016

Slow ecosystem responses conditionally regulate annual carbon balance over 15 years in a Californian oak-grass savanna.

The Science
Long-term carbon flux measurements over Mediterranean-type ecosystems enabled observations of ecosystem metabolism responses to a wide range of physical, biological, and ecological conditions.

The Impact
The study’s findings showed that biotic and abiotic extremes and legacies can introduce variations to annual ecosystem carbon balance. These variations are different from those that might be explained by the fast responses to factors like light and temperature.

Summary
Many ecophysiological and biogeochemical processes respond rapidly to changes in biotic and abiotic conditions, while ecosystem-level responses develop much more slowly (e.g., over months, seasons, years, or decades). To better understand the role of the slow responses in regulating interannual variability in net ecosystem exchange (NEE), the study partitioned NEE into two major ecological terms: gross primary productivity (GPP) and ecosystem respiration (Reco). The researchers tested a set of hypotheses on seasonal scales using flux and environment data collected from 2000 to 2015 in an oak-grass savanna area in California, where ecosystems annually experience a wet winter and spring and five-month-long summer drought. Results showed that the spring season (April through June) contributed more than 50% of annual GPP and Reco. An analysis of outliers found that each season could introduce significant anomalies in annual carbon budgets. The magnitude of the contribution depends on biotic and abiotic seasonal circumstances across the year and the particular sequences. The study found that (1) extremely wet springs reduced GPP in the years of 2006, 2011, and 2012; (2) soil moisture left from those extremely wet springs enhanced summer GPP; (3) groundwater recharged during the spring of 2011 was associated with the snowpack depth accumulated during the winter between 2010 and 2011; (4) dry autumns (October–December) and winters (January–March) decreased Reco significantly; and (5) grass litter produced in previous seasons might increase Reco, and the effect of litter legacy on Reco was more observable in the second year of two consecutive wet springs. These findings confirm that biotic and abiotic extremes and legacies can introduce variations to annual ecosystem carbon balance, other than those that might be explained by the fast responses.

Contacts
BER Program Managers
Daniel Stover and Jared DeForest
SC-23.1
Daniel.Stover@science.doe.gov (301-903-0289)
Jared.DeForest@science.doe.gov (301-903-1678)

Principal Investigator
Dennis Baldocchi
University of California, Berkeley
Berkeley, CA 94720
Baldocchi@berkeley.edu

Funding
This research was conducted at the sites that are members of the AmeriFlux and Fluxnet networks. The research was supported in part by the Terrestrial Carbon project of the Office of Biological and Environmental Research, within the U.S. Department of Energy (DOE) Office of Science, Grant No. DE-FG02-03Reco63638, and through the Western Regional Center of the National Institute for Global Environmental Change under Cooperative Agreement No. DE-FC02-03Reco63613. Other sources of support included the Kearney Soil Science Foundation, the National Science Foundation, and the Californian Agricultural Experiment Station. S. Wolf acknowledges support from a Marie Curie International Outgoing Fellowship (European Commission, grant 300083).

Publications
Ma, S., D. Baldocchi, S. Wolf, and J. Verfaillie. "Slow ecosystem responses conditionally regulate annual carbon balance over 15 years in Californian oak-grass savanna." Agricultural and Forest Meteorology 228229, 252–264 (2016). [DOI:10.1016/j.agrformet.2016.07.016]

Topic Areas:

  • Research Area: Terrestrial Ecosystem Science
  • Research Area: Carbon Cycle, Nutrient Cycling

Division: SC-33.1 Earth and Environmental Sciences Division, BER

 

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