Photosynthesis Research

, Volume 119, Issue 1–2, pp 49–64 | Cite as

Controls on seasonal patterns of maximum ecosystem carbon uptake and canopy-scale photosynthetic light response: contributions from both temperature and photoperiod

  • Paul C. Stoy
  • Amy M. Trowbridge
  • William L. Bauerle
Regular Paper

Abstract

Most models of photosynthetic activity assume that temperature is the dominant control over physiological processes. Recent studies have found, however, that photoperiod is a better descriptor than temperature of the seasonal variability of photosynthetic physiology at the leaf scale. Incorporating photoperiodic control into global models consequently improves their representation of the seasonality and magnitude of atmospheric CO2 concentration. The role of photoperiod versus that of temperature in controlling the seasonal variability of photosynthetic function at the canopy scale remains unexplored. We quantified the seasonal variability of ecosystem-level light response curves using nearly 400 site years of eddy covariance data from over eighty Free Fair-Use sites in the FLUXNET database. Model parameters describing maximum canopy CO2 uptake and the initial slope of the light response curve peaked after peak temperature in about 2/3 of site years examined, emphasizing the important role of temperature in controlling seasonal photosynthetic function. Akaike’s Information Criterion analyses indicated that photoperiod should be included in models of seasonal parameter variability in over 90 % of the site years investigated here, demonstrating that photoperiod also plays an important role in controlling seasonal photosynthetic function. We also performed a Granger causality analysis on both gross ecosystem productivity (GEP) and GEP normalized by photosynthetic photon flux density (GEP n ). While photoperiod Granger-caused GEP and GEP n in 99 and 92 % of all site years, respectively, air temperature Granger-caused GEP in a mere 32 % of site years but Granger-caused GEP n in 81 % of all site years. Results demonstrate that incorporating photoperiod may be a logical step toward improving models of ecosystem carbon uptake, but not at the expense of including enzyme kinetic-based temperature constraints on canopy-scale photosynthesis.

Keywords

Eddy covariance Granger causality Gross ecosystem productivity Light response curve Net ecosystem exchange Seasonal variability 

Abbreviations

AIC

Akaike’s Information Criterion

C-LAMP

Carbon Land Model Intercomparison Project

CLM

Community Land Model

DOY

Day of year

GEP

Gross ecosystem productivity

GPPn

Gross ecosystem productivity normalized by photosynthetic photon flux density

GPP

Gross primary productivity

HSD

(Tukey’s) Honestly Significant Difference test

Jmax

Rate of photosynthetic electron flow at light saturation

L

Maximum value of the likelihood function

LDay

Day length

M

Linear model

N

Number of parameters

N

Number of instances

NEE

Net ecosystem exchange

PPFD

Photosynthetic photon flux density

RE

Ecosystem respiration

Ta

Air temperature

Vc,max

Maximum carboxylation capacity

α

Initial slope of the light response curve

β

Net ecosystem exchange at light saturation

γ

Ecosystem respiration calculated as the intercept of the light response curve

θN

Degree of curvature of the non-rectangular hyperbola

M

Referring to the Mitscherlich model

max

Referring to the maximum seasonal value calculated using a second-order polynomial

N

Referring to the non-rectangular hyperbola

p

Referring to a light response curve parameter or combination of parameters

Notes

Acknowledgments

This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE‐FG02‐04ER63917 and DE‐FG02‐04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet‐Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS‐Siberia, USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAOGTOS‐TCO, iLEAPS Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval and Environment Canada and US Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval and Environment Canada and US Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California ‐ Berkeley, University of Virginia. The authors thank Ram Oren of Duke University for the encouragement to pursue the present research and Michael Kleder of Delta Epsilon Technologies, LLC for providing the MATLAB code for the world map with political boundaries. PCS acknowledges funding from the National Science Foundation (‘Scaling ecosystem function: Novel Approaches from MaxEnt and Multiresolution’, Division of Biological Infrastructure #1021095) and the State of Montana. WLB was supported in part by USDA Grant 2009-51181-05768, Cooperative Agreement 58-6618-2-0209 and the State of Colorado.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Paul C. Stoy
    • 1
  • Amy M. Trowbridge
    • 1
  • William L. Bauerle
    • 2
    • 3
  1. 1.Department of Land Resources and Environmental SciencesMontana State UniversityBozemanUSA
  2. 2.Department of Horticulture and Landscape ArchitectureColorado State UniversityFort CollinsUSA
  3. 3.Graduate Degree Program in EcologyColorado State UniversityFort CollinsUSA

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