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


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.


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



Akaike’s Information Criterion


Carbon Land Model Intercomparison Project


Community Land Model


Day of year


Gross ecosystem productivity


Gross ecosystem productivity normalized by photosynthetic photon flux density


Gross primary productivity


(Tukey’s) Honestly Significant Difference test


Rate of photosynthetic electron flow at light saturation


Maximum value of the likelihood function


Day length


Linear model


Number of parameters


Number of instances


Net ecosystem exchange


Photosynthetic photon flux density


Ecosystem respiration


Air temperature


Maximum carboxylation capacity


Initial slope of the light response curve


Net ecosystem exchange at light saturation


Ecosystem respiration calculated as the intercept of the light response curve


Degree of curvature of the non-rectangular hyperbola


Referring to the Mitscherlich model


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


Referring to the non-rectangular hyperbola


Referring to a light response curve parameter or combination of parameters



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.


  1. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723Google Scholar
  2. Amiro BD, Orchansky AL, Barr AG, Black TA, Chambers SD, Chapin FS III, Goulden ML, Litvak M, Liu HP, McCaughey JH, McMillan A, Randerson JT (2006) The effect of post-fire stand age on the boreal forest energy balance. Agric For Meteorol 140(1–4):41–50Google Scholar
  3. Anthoni PM, Knohl A, Rebmann C, Freibauer A, Mund M, Ziegler W, Kolle O, Schulze ED (2004) Forest and agricultural land-use-dependent CO2 exchange in Thuringia, Germany. Glob Change Biol 10(12):2005–2019. doi: 10.1111/j.1365-2486.2004.00863.x Google Scholar
  4. Aradóttir ÁL, Thorgeirsson H, McCaughey JH, Strachan IB, Robertson A (1997) Establishment of a black cottonwood plantation on an exposed site in Iceland: plant growth and site energy balance. Agric For Meteorol 84(1–2):1–9Google Scholar
  5. Asner GP (1998) Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens Environ 64:234–253Google Scholar
  6. Aubinet M, Grelle A, Ibrom A, Rannik Ü, Moncrieff J, Foken T, Kowalski AS, Martin PH, Berbigier P, Bernhofer C, Clement R, Elbers J, Granier A, Grunwald T, Morgenstern K, Pilegaard K, Rebmann C, Snijders W, Valentini R, Vesala T (2000) Estimates of the annual net carbon and water exchange of forests: the EUROFLUX methodology. Adv Ecol Res 30:113–175Google Scholar
  7. Aubinet M, Chermanne B, Vandenhaute M, Longdoz B, Yernaux M, Laitat E (2001) Long term carbon dioxide exchange above a mixed forest in the Belgian Ardennes. Agric For Meteorol 108(4):293–315Google Scholar
  8. Aurela M, Laurila T, Tuovinen JP (2002) Annual CO2 balance of a subarctic fen in northern Europe: importance of the wintertime efflux. J Geophys Res-Atmos 107 (D21):4607. doi: 10.1029/2002JD002055
  9. Baldocchi DD (2008) ‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurements systems. Aust J Bot 56:1–26Google Scholar
  10. Baldocchi D, Falge E, Gu LH, Olson R, Hollinger D, Running S, Anthoni P, Bernhofer C, Davis K, Evans R, Fuentes J, Goldstein A, Katul G, Law B, Lee XH, Malhi Y, Meyers T, Munger W, Oechel W, Paw UKT, Pilegaard K, Schmid HP, Valentini R, Verma S, Vesala T, Wilson K, Wofsy S (2001) FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull Am Meteorol Soc 82(11):2415–2434Google Scholar
  11. Baldocchi DD, Wilson KB, Gu L (2002) How the environment, canopy structure and canopy physiological functioning influence carbon, water and energy fluxes of a temperate broad-leaved deciduous forest - an assessment with the biophysical model CANOAK. Tree Physiol 22:1065–1077PubMedGoogle Scholar
  12. Bauerle WL, Oren R, Way DA, Qian SS, Stoy PC, Thornton PE, Bowden JD, Hoffman FM, Reynolds RF (2012) Photoperiodic regulation of the seasonal pattern of photosynthetic capacity and the implications for carbon cycling. Proc Natl Acad Sci 109(22):8612–8617. doi: 10.1073/pnas.1119131109 PubMedGoogle Scholar
  13. Berbigier P, Bonnefond J-M, Mellmann P (2001) CO2 and water vapour fluxes for 2 years above Euroflux forest site. Agric For Meteorol 108(3):183–197Google Scholar
  14. Berger BW, Davis KJ, Yi C, Bakwin PS, Zhao CL (2001) Long-term carbon dioxide fluxes from a very tall tower in a northern forest: flux measurement methodology. J Atmos Ocean Technol 18:529–542Google Scholar
  15. Bergeron O, Margolis HA, Black TA, Coursolle C, Dunn AL, Barr AG, Wofsy SC (2007) Comparison of carbon dioxide fluxes over three boreal black spruce forests in Canada. Glob Change Biol 13(1):89–107. doi: 10.1111/j.1365-2486.2006.01281.x Google Scholar
  16. Beringer J, Hutley L, Kilinc M, McGuire AD, McHugh I (2006) Water, energy and carbon fluxes from the world’s tallest anigosperm (Eucalyptus regnans) at Wallaby Creek, south-eastern Australia. In: International Geographical Union Conference “Regional Responses to Global Changes: a view from the Antipodes”, Brisbane, 2006Google Scholar
  17. Bernhofer C, Aubinet M, Clement R, Grelle A, Grunwald T, Ibrom A, Jarvis P, Rebmann C, Schulze E-D, Tenhunen J (2003) Spruce forests (Norway and Sitka Spruce, including Douglas Fir): carbon and water fluxes and balances, ecological and ecophysiological determinants. In: Valentini R (ed) Fluxes of carbon, water and energy of european forests. Springer, BerlinGoogle Scholar
  18. Bonan GB, Lawrence PJ, Oleson KW, Levis S, Jung M, Reichstein M, Lawrence DM, Swenson SC (2011) Improving canopy processes in the Community Land Model version 4 (CLM4) using global flux fields empirically inferred from FLUXNET data. J Geophys Res 116 (G2):G02014. doi: 10.1029/2010jg001593
  19. Campbell GS, Norman JM (1998) An introduction to environmental biophysics, 2nd edn. Springer, New YorkGoogle Scholar
  20. Cescatti A, Marcolla B (2004) Drag coefficient and turbulence intensity in conifer canopies. Agric For Meteorol 121(3–4):197–206Google Scholar
  21. Chen J, Falk M, Euskirchen E, Paw UKT, Suchanek TH, Ustin SL, Bond BJ, Brosofske KD, Phillips N, Bi R (2002) Biophysical controls of carbon flows in three successional Douglas-fir stands based on eddy-covariance measurements. Tree Physiol 22(2–3):169–177. doi: 10.1093/treephys/22.2-3.169 PubMedGoogle Scholar
  22. Chesher A (1991) The effect of measurement error. Biometrika 78(3):451–462. doi: 10.1093/biomet/78.3.451 Google Scholar
  23. Chojnicki BH, Urbaniak M, Jozefczyk D, Augustin J, Olejnik J (2007) Measurements of gas and heat fluxes at Rzecin wetland. In: Okruszko T, Maltby E, Szatylowicz J, Miroslow-Swiakek D, Kotowski W (eds) Wetlands: monitoring. modelling and management. Taylor & Francis, London, pp 125–131Google Scholar
  24. Clark KL, Gholz HL, Moncrieff JB, Cropley F, Loescher HW (1999) Environmental controls over net exchanges of carbon dioxide from contrasting Florida ecosystems. Ecol Appl 9(3):936–948Google Scholar
  25. Cook BD, Davis KJ, Wang W, Desai AR, Berger BW, Teclaw RM, Martin JM, Bolstad PV, Bakwin P, Yi C, Heilman W (2004) Carbon exchange and venting anomalies in an upland deciduous forest in northern Wisconsin, USA. Agric For Meteorol 126:271–295Google Scholar
  26. Curtis PS, Hanson PJ, Bolstad P, Barford C, Randolph JC, Schmid HP, Wilson KB (2002) Biometric and eddy-covariance based estimates of annual carbon storage in five eastern North American deciduous forests. Agric For Meteorol 113:3–19Google Scholar
  27. Darwin F (1898) Observations on stomata. Philos Transact R Soc B 190:531–621Google Scholar
  28. de Dios VR, Goulden ML, Ogle K, Richardson AD, Hollinger DY, Davidson EA, Alday JG, Barron-Gafford GA, Carrara A, Kowalski AS, Oechel WC, Reverter BR, Scott RL, Varner RK, Díaz-Sierra R, Moreno JM (2012) Endogenous circadian regulation of carbon dioxide exchange in terrestrial ecosystems. Glob Change Biol 18(6):1956–1970. doi: 10.1111/j.1365-2486.2012.02664.x Google Scholar
  29. de Pury DGG, Ceulemans R (1997) Scaling-up carbon fluxes from leaves to stands in a patchy coniferous/deciduous forest. In: Mohren GMJ (ed) Impacts of global change on tree physiology and forest ecosystems. Kluwer Academic, Dordrecht, pp 263–272Google Scholar
  30. DeForest J, Noormets A, McNulty S, Sun G, Tenney G, Chen J (2006) Phenophases alter the soil respiration–temperature relationship in an oak-dominated forest. Int J Biometeorol 51(2):135–144. doi: 10.1007/s00484-006-0046-7 PubMedGoogle Scholar
  31. Desai AR (2010) Climatic and phenological controls on coherent regional interannual variability of carbon dioxide flux in a heterogeneous landscape. J Geophys Res 115:G00J02. doi: 10.1029/2010jg001423
  32. Desai AR, Bolstad PV, Cook BD, Davis KJ, Carey EV (2005) Comparing net ecosystem exchange of carbon dioxide between an old-growth and mature forest in the upper Midwest, USA. Agric For Meteorol 128:33–55Google Scholar
  33. Desai AR, Noormets A, Bolstad PV, Chen J, Cook BD, Davis KJ, Euskirchen ES, Gough C, Martin JG, Ricciuto DM, Schmid HP, Tang J, Wang W (2008a) Influence of vegetation and seasonal forcing on carbon dioxide fluxes across the Upper Midwest, USA: implications for regional scaling. Agric For Meteorol 148(2):288–308Google Scholar
  34. Desai AR, Richardson AD, Moffat AM, Kattge J, Hollinger DY, Barr AG, Falge E, Noormets A, Papale D, Reichstein M, Stauch VJ (2008b) Cross-site evaluation of eddy covariance GPP and RE decomposition techniques. Agric For Meteorol 148:821–838Google Scholar
  35. Detto M, Molini A, Katul GG, Stoy PC, Palmroth S, Baldocchi DD (2012) Assessing cause and effect in ecological time series: an application of conditional Granger’s spectral causality theory. Am Nat 179:524–535PubMedGoogle Scholar
  36. Dietze M, Vargas R, Richardson AD, Stoy PC, Barr AG, Anderson RS, Arain A, Baker IT, Black TA, Chen JM, Ciais P, Flanagan LB, Gough CM, Grant RF, Hollinger DY, Izaurralde C, Kucharik CJ, Lafleur PM, Liu S, Lokupitiya E, Luo Y, Munger JW, Peng C, Poulter B, Price DT, Ricciuto DM, Riley WJ, Sahoo AK, Schaefer K, Tian H, Verbeeck H, Verma SB (2011) Characterizing the performance of ecosystem models across time scales: a spectral analysis of the North American Carbon Program site-level synthesis. J Geophys Res 116:G04029. doi: 10.1029/2011JG001661 Google Scholar
  37. Dolman AJ, Moors EJ, Elbers JA (2002) The carbon uptake of a mid latitude pine forest growing on sandy soil. Agric For Meteorol 111(3):157–170Google Scholar
  38. Dušek J, Čížková H, Czerný R, Taufarová K, Šmídová M, Janouš D (2009) Influence of summer flood on the net ecosystem exchange of CO2 in a temperate sedge-grass marsh. Agric For Meteorol 149(9):1524–1530Google Scholar
  39. Ehleringer J, Björkman O (1977) Quantum yields for CO2 uptake in C3 and C4 plants. Plant Physiol 59(1):86–90. doi: 10.1104/pp.59.1.86 PubMedPubMedCentralGoogle Scholar
  40. Fang C, Moncrieff JB, Gholz HL, Clark KL (1998) Soil CO2 efflux and its spatial variation in a Florida slash pine plantation. Plant Soil 205(2):135–146. doi: 10.1023/a:1004304309827 Google Scholar
  41. Farquhar GD, von Caemmerer S, Berry JA (1980) A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149:78–90PubMedGoogle Scholar
  42. Finnigan JJ, Leuning R (2000) Long term flux measurements-coordinate systems and averaging. In: Proceedings of the International Workshop for Advanced Flux Network and Flux Evaluation, Center for Global Environmental Research, National Institute for Environmental Studies, Hokkaido, 2000. pp 51–56Google Scholar
  43. Fisher JI, Richardson AD, Mustard JF (2007) Phenology model from surface meteorology does not capture satellite-based greenup estimations. Glob Change Biol 13(3):707–721. doi: 10.1111/j.1365-2486.2006.01311.x Google Scholar
  44. Foken T, Aubinet M, Leuning R (2012) The eddy covariance method. In: Aubinet M, Vesala T, Papale D (eds) Eddy covariance: a practical guide to measurement and data analysis. Springer, Dordrecht, p 460Google Scholar
  45. Foody GM, Atkinson PM (2006) Uncertainty in remote sensing and GIS. Wiley, New York. doi: 10.1002/0470035269.fmatter
  46. Fuller WA (1987) Measurement error models. Wiley, New YorkGoogle Scholar
  47. Giasson M-A, Coursolle C, Margolis HA (2006) Ecosystem-level CO2 fluxes from a boreal cutover in eastern Canada before and after scarification. Agric For Meteorol 140(1–4):23–40Google Scholar
  48. Gilmanov TG, Verma SB, Sims PL, Meyers TP, Bradford JA, Burba GG, Suyker AE (2003) Gross primary production and light response parameters of four Southern Plains ecosystems estimated using long-term CO2-flux tower measurements. Glob Biogeochem Cycle 17:1071Google Scholar
  49. Gioli B, Miglietta F, De Martino B, Hutjes RWA, Dolman HAJ, Lindroth A, Schumacher M, Sanz MJ, Manca G, Peressotti A, Dumas EJ (2004) Comparison between tower and aircraft-based eddy covariance fluxes in five European regions. Agric For Meteorol 127(1–2):1–16Google Scholar
  50. Goulden ML, Daube BC, Fan S-M, Sutton DJ, Bazzaz A, Munger JW, Wofsy SC (1997) Physiological responses of a black spruce forest to weather. J Geophys Res 102 (D24):28987–28996Google Scholar
  51. Goulden ML, Winston GC, McMillan AMS, Litvak ME, Read EL, Rocha AV, Rob Elliot J (2006) An eddy covariance mesonet to measure the effect of forest age on land–atmosphere exchange. Glob Change Biol 12(11):2146–2162. doi: 10.1111/j.1365-2486.2006.01251.x Google Scholar
  52. Grace J, Nichol CJ, Disney MI, Lewis P, Quaife T, Bowyer P (2007) Can we measure photosynthesis from space directly, using spectral reflectance and fluorescence? Glob Change Biol 13:1484–1497. doi: 10.1111/j.1365-2486.2007.01352.x Google Scholar
  53. Granger CWJ (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3):424–438Google Scholar
  54. Granier A, Ceschia E, Damesin C, Dufrêne E, Epron D, Gross P, Lebaube S, Le Dantec V, Le Goff N, Lemoine D, Lucot E, Ottorini JM, Pontailler JY, Saugier B (2000) The carbon balance of a young Beech forest. Funct Ecol 14(3):312–325. doi: 10.1046/j.1365-2435.2000.00434.x Google Scholar
  55. Gray J (2009) Jim Gray on eScience: A transformed scientific method. In: Hey T, Tansley S, Tolle K (eds) The fourth paradigm: data-intensive scientific discovery. Microsoft Research, Redmond, p 284Google Scholar
  56. Groenendijk M, Dolman AJ, Ammann C, Arneth A, Cescatti A, Dragoni D, Gash JHC, Gianelle D, Gioli B, Kiely G, Knohl A, Law BE, Lund M, Marcolla B, van der Molen MK, Montagnani L, Moors E, Richardson AD, Roupsard O, Verbeeck H, Wohlfahrt G (2011) Seasonal variation of photosynthetic model parameters and leaf area index from global Fluxnet eddy covariance data. J Geophys Res 116 (G4):G04027. doi: 10.1029/2011jg001742
  57. Gu L, Meyers T, Pallardy SG, Hanson PJ, Yang B, Heuer M, Hosman KP, Riggs JS, Sluss D, Wullschleger SD (2006) Direct and indirect effects of atmospheric conditions and soil moisture on surface energy partitioning revealed by a prolonged drought at a temperate forest site. J Geophys Res 111 (D16):D16102. doi: 10.1029/2006jd007161
  58. Guerschman JP, Van Dijk AIJM, Mattersdorf G, Beringer J, Hutley LB, Leuning R, Pipunic RC, Sherman BS (2009) Scaling of potential evapotranspiration with MODIS data reproduces flux observations and catchment water balance observations across Australia. J Hydrol 369(1–2):107–119Google Scholar
  59. Hargreaves KJ, Milne R, Cannell MGR (2003) Carbon balance of afforested peatland in Scotland. Forestry 76(3):299–317. doi: 10.1093/forestry/76.3.299 Google Scholar
  60. Harrison SP, Prentice IC, Barboni D, Kohfeld KE, Ni J, Sutra J-P (2010) Ecophysiological and bioclimatic foundations for a global plant functional classification. J Veg Sci 21:300–317Google Scholar
  61. Hastings JW, Astrachan L, Sweeney BM (1961) A persistent daily rhythm in photosynthesis. J Gen Physiol 45:69–76. doi: 10.1085/jgp.45.1.69 PubMedPubMedCentralGoogle Scholar
  62. Havrankova K, Sedlak P (2004) Wind velocity analysis for mountainous site Bily Kriz. Ekologia (Bratislava) 23:46–54Google Scholar
  63. Hays JD, Imbrie J, Shackleton NJ (1976) Variations in the Earth’s orbit: pacemaker of the ice ages. Science 194:1121–1132PubMedGoogle Scholar
  64. Herbst M, Rosier PTW, McNeil DD, Harding RJ, Gowing DJ (2008) Seasonal variability of interception evaporation from the canopy of a mixed deciduous forest. Agric For Meteorol 148(11):1655–1667Google Scholar
  65. Herrick JD, Thomas RB (2003) Leaf senescence and late-season net photosynthesis of sun and shade leaves of overstory sweetgum (Liquidambar styraciflua) grown in elevated and ambient carbon dioxide concentrations. Tree Physiol 23(2):109–118PubMedGoogle Scholar
  66. Hirano T, Segah H, Harada T, Limin S, June T, Hirata R, Osaki M (2007) Carbon dioxide balance of a tropical peat swamp forest in Kalimantan, Indonesia. Glob Change Biol 13(2):412–425. doi: 10.1111/j.1365-2486.2006.01301.x Google Scholar
  67. Hollinger DY, Goltz SM, Davidson EA, Lee JT, Tu K, Valentine HT (1999) Seasonal patterns and environmental control of carbon dioxide and water vapour exchange in an ecotonal boreal forest. Glob Change Biol 5(8):891–902. doi: 10.1046/j.1365-2486.1999.00281.x Google Scholar
  68. Hunt JR, Baldocchi DD, van Ingen C (2009) Redefining ecological science using data. In: Hey T, Tansley S, Tolle K (eds) The fourth paradigm: data-intensive scientific discovery. Microsoft Research, Redmont, p 284Google Scholar
  69. Kattge J, Díaz S, Lavorel S, Prentice IC, Leadley P, Bönisch G, Garnier E, Westoby M, Reich PB, Wright IJ, Cornelissen JHC, Violle C, Harrison SP, Van Bodegom PM, Reichstein M, Enquist BJ, Soudzilovskaia NA, Ackerly DD, Anand M, Atkin O, Bahn M, Baker TR, Baldocchi D, Bekker R, Blanco CC, Blonder B, Bond WJ, Bradstock R, Bunker DE, Casanoves F, Cavender-Bares J, Chambers JQ, Chapin Iii FS, Chave J, Coomes D, Cornwell WK, Craine JM, Dobrin BH, Duarte L, Durka W, Elser J, Esser G, Estiarte M, Fagan WF, Fang J, Fernández-Méndez F, Fidelis A, Finegan B, Flores O, Ford H, Frank D, Freschet GT, Fyllas NM, Gallagher RV, Green WA, Gutierrez AG, Hickler T, Higgins SI, Hodgson JG, Jalili A, Jansen S, Joly CA, Kerkhoff AJ, Kirkup D, Kitajima K, Kleyer M, Klotz S, Knops JMH, Kramer K, Kühn I, Kurokawa H, Laughlin D, Lee TD, Leishman M, Lens F, Lenz T, Lewis SL, Lloyd J, Llusiá J, Louault F, Ma S, Mahecha MD, Manning P, Massad T, Medlyn BE, Messier J, Moles AT, Müller SC, Nadrowski K, Naeem S, Niinemets Ü, Nöllert S, Nüske A, Ogaya R, Oleksyn J, Onipchenko VG, Onoda Y, Ordoñez J, Overbeck G, Ozinga WA, Patiño S, Paula S, Pausas JG, Peñuelas J, Phillips OL, Pillar V, Poorter H, Poorter L, Poschlod P, Prinzing A, Proulx R, Rammig A, Reinsch S, Reu B, Sack L, Salgado-Negret B, Sardans J, Shiodera S, Shipley B, Siefert A, Sosinski E, Soussana JF, Swaine E, Swenson N, Thompson K, Thornton P, Waldram M, Weiher E, White M, White S, Wright SJ, Yguel B, Zaehle S, Zanne AE, Wirth C (2011) TRY – a global database of plant traits. Glob Change Biol 17(9):2905–2935. doi: 10.1111/j.1365-2486.2011.02451.x Google Scholar
  70. Knohl A, Schulze E-D, Kolle O, Buchmann N (2003) Large carbon uptake by an unmanaged 250-year-old deciduous forest in Central Germany. Agric For Meteorol 118:151–167Google Scholar
  71. Krinner G, Viovy N, de Noblet-Ducoudré N, Ogée J, Polcher J, Friedlingstein P, Ciais P, Sitch S, Prentice IC (2005) A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob Biogeochem Cycle 19:GB1015. doi: 10.1029/2003GB002199
  72. Kurbatova J, Arneth A, Vygodskaya NN, Kolle O, Varlargin AV, Milyukova IM, Tchebakova NM, Schulze E-D, Lloyd J (2002) Comparative ecosystem–atmosphere exchange of energy and mass in a European Russian and a central Siberian bog I. Interseasonal and interannual variability of energy and latent heat fluxes during the snowfree period. Tellus B 54 (5):497–513. doi: 10.1034/j.1600-0889.2002.01354.x Google Scholar
  73. Kurbatova J, Li C, Varlagin AB, Xiao X, Vygodskaya NN (2008) Modeling carbon dynamics in two adjacent spruce forests with different soil conditions in Russia. Biogeosciences 5:969–980. doi: 10.5194/bg-5-969-2008 Google Scholar
  74. Lafleur PM, Roulet NT, Bubier JL, Moore TR, Frolking S (2003) Interannual variability in the peatland-atmosphere carbon dioxide exchange at an ombrotrophic bog. Glob Biogeochem Cycle 17:1036. doi: 10.1029/2002GB001983 Google Scholar
  75. Lagergren F, Lindroth A, Dellwik E, Ibrom A, Lankreijer H, Launiainen S, Mölder M, Kolari P, Pilegaard KIM, Vesala T (2008) Biophysical controls on CO2 fluxes of three Northern forests based on long-term eddy covariance data. Tellus B 60(2):143–152. doi: 10.1111/j.1600-0889.2006.00324.x Google Scholar
  76. Lambers H, Chapin FSI, Pons TL (2000) Plant physiological ecology. Springer, New YorkGoogle Scholar
  77. Lasslop G, Reichstein M, Papale D, Richardson AD, Arneth A, Barr AG, Stoy PC, Wohlfahrt G (2010) Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation Glob Change. Biology 16:187–208Google Scholar
  78. Le Maire G, Davi H, Soudani K, Francois C, Le Dantec V, Dufresne E (2005) Modeling annual production and carbon fluxes of a large managed temperate forest using forest inventories, satellite data and field measurements. Tree Physiol 25(7):859–872. doi: 10.1093/treephys/25.7.859 PubMedGoogle Scholar
  79. Lee X, Fuentes JD, Staebler RM, Neumann HH (1999) Long-term observation of the atmospheric exchange of CO2 with a temperate deciduous forest in southern Ontario, Canada. J Geophys Res-Atmos 104 (D13):15975–15984Google Scholar
  80. Lindroth A, Klemedtsson L, Grelle A, Weslien P, Langvall O (2008a) Measurement of net ecosystem exchange, productivity and respiration in three spruce forests in Sweden shows unexpectedly large soil carbon losses. Biogeochemistry 89(1):43–60. doi: 10.1007/s10533-007-9137-8 Google Scholar
  81. Lindroth A, Lagergren F, Aurela M, Bjarnadottir B, Christensen T, Dellwik E, Grelle A, Ibrom A, Johansson T, Lankreijer H, Launiainen S, Laurila T, Molder M, Nikinmaa E, Pilegaard KIM, Sigurdsson BD, Vesala T (2008b) Leaf area index is the principal scaling parameter for both gross photosynthesis and ecosystem respiration of Northern deciduous and coniferous forests. Tellus B 60(2):129–142. doi: 10.1111/j.1600-0889.2007.00330.x Google Scholar
  82. Lund M, Lindroth A, Christensen TR, Strom L (2007) Annual CO2 balance of a temperate bog. Tellus B 59:804–811Google Scholar
  83. Marcolla B, Pitacco A, Cescatti A (2003) Canopy architecture and turbulence structure in a coniferous forest. Bound-Layer Meteorol 108(1):39–59. doi: 10.1023/a:1023027709805 Google Scholar
  84. Matese A, Alberti G, Gioli B, Toscano P, Vaccari FP, Zaldei A (2008) Compact_Eddy: a compact, low consumption remotely controlled eddy covariance logging system. Comput Electron Agric 64(2):343–346Google Scholar
  85. Medlyn BE, Robinson AP, Clement R, McMurtrie R (2005) On the validation of models of forest CO2 exchange using eddy covariance data: some perils and pitfalls. Tree Physiol 25:839–857PubMedGoogle Scholar
  86. Noormets A, Chen J, Crow T (2007) Age-dependent changes in ecosystem carbon fluxes in managed forests in northern Wisconsin, USA. Ecosystems 10(2):187–203. doi: 10.1007/s10021-007-9018-y Google Scholar
  87. Papale D, Reichstein M, Aubinet M, Canfora E, Bernhofer C, Kutsch W, Longdoz B, Rambal S, Valentini R, Vesala T, Yakir D (2006) Towards a standardized processing of net ecosystem exchange measured with eddy covariance technique: algorithms and uncertainty estimation. Biogeosciences 3:571–583Google Scholar
  88. Pavlick R, Drewry DT, Bohn K, Reu B, Kleidon A (2012) The Jena Diversity-Dynamic Global Vegetation Model (JeDi-DGVM): a diverse approach to representing terrestrial biogeography and biogeochemistry based on plant functional tradeoffs. Biogeosci Discuss 9:4627–4726. doi: 10.5194/bgd-9-4627-2012 Google Scholar
  89. Pilegaard K, Hummelshoj P, Jensen NO, Chen Z (2001) Two years of continuous CO2 eddy-flux measurements over a Danish beech forest. Agric For Meteorol 107(1):29–41Google Scholar
  90. R Development Core Team (2011) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  91. Randerson JT, Hoffman FM, Thornton PE, Mahowald NM, Lindsay K, Lee Y-H, Nevison CD, Doney SC, Bonan G, Stöckli R, Covey C, Running SW, Fung IY (2009) Systematic assessment of terrestrial biogeochemistry in coupled climate–carbon models. Glob Change Biol 15(10):2462–2484. doi: 10.1111/j.1365-2486.2009.01912.x Google Scholar
  92. Rayment MB, Jarvis PG (1999) Seasonal gas exchange of black spruce using an automatic branch bag system. Can J For Res 29(10):1528–1538. doi: 10.1139/x99-130 Google Scholar
  93. Reich PB, Walters MB, Ellsworth DS (1991) Leaf age and season influence the relationships between leaf nitrogen, leaf mass per area and photosynthesis in maple and oak trees. Plant Cell Environ 14:251–259Google Scholar
  94. Reichstein M, Falge E, Baldocchi D, Papale D, Aubinet M, Berbigier P, Bernhofer C, Buchmann N, Gilmanov TG, Granier A, Grünwald T, Havránková K, Ilvesniemi H, Janous D, Knohl A, Laurila T, Lohila A, Loustau D, Matteucci G, Meyers T, Miglietta F, Ourcival J-M, Pumpanen J, Rambal S, Rotenberg E, Sanz M, Tenhunen J, Seufert G, Vaccari F, Vesala T, Yakier D, Valentini R (2005) On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob Change Biol 11:1424–1439Google Scholar
  95. Reichstein M, Stoy PC, Desai AR, Lasslop G, Richardson AD (2012) Partitioning of net fluxes. In: Aubinet M, Vesala T, Papale D (eds) Eddy covariance: A practical guide to measurement and data analysis. Springer, Dordrecht, p 460Google Scholar
  96. Ricciuto DM, King AW, Gu L, Post WM (2008) Estimates of terrestrial carbon cycle model parameters by assimilation of FLUXNET data: do parameter variations cause bias in regional flux estimates? Eos Trans AGU 89 (53):Fall Meet. Suppl., Absttact B54–03Google Scholar
  97. Richardson A, Jenkins J, Braswell B, Hollinger D, Ollinger S, Smith M-L (2007a) Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia 152(2):323–334. doi: 10.1007/s00442-006-0657-z PubMedGoogle Scholar
  98. Richardson AD, Hollinger DY, Aber JD, Ollinger SV, Braswell BH (2007b) Environmental variation is directly responsible for short- but not long-term variation in forest-atmosphere carbon exchange. Glob Change Biol 13:788–803Google Scholar
  99. Ryu Y, Baldocchi DD, Ma S, Hehn T (2008) Interannual variability of evapotranspiration and energy exchange over an annual grassland in California. J Geophys Res 113:D09104. doi: 10.1029/2007JD009263 Google Scholar
  100. Ryu Y, Baldocchi DD, Kobayashi H, van Ingen C, Li J, Black TA, Beringer J, van Gorsel E, Knohl A, Law BE, Roupsard O (2011) Integration of MODIS land and atmosphere products with a coupled-process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales. Global Biogeochem Cycles 25 (4):GB4017. doi: 10.1029/2011gb004053
  101. Sagerfors J, Lindroth A, Grelle A, Klemedtsson L, Weslien P, Nilsson M (2008) Annual CO2 exchange between a nutrient-poor, minerotrophic, boreal mire and the atmosphere. J Geophys Res 113 (G1):G01001. doi: 10.1029/2006jg000306
  102. Saleska SR, Miller SD, Matross DM, Goulden M, Wofsy S, da Rocha HR, de Camargo PB, Crill P, Daube BC, de Freitas HC, Hutyra L, Keller M, Kirchhoff V, Menton M, Munger JW, Pyle EH, Rice AH, Silva H (2003) Carbon in amazon forests: unexpected seasonal fluxes and disturbance-induced losses. Science 302:1554–1557PubMedGoogle Scholar
  103. Schaefer K, Schwalm CR, Williams C, Arain MA, Barr A, Chen JM, Davis KJ, Dimitrov D, Hilton TW, Hollinger DY, Humphreys E, Poulter B, Raczka BM, Richardson AD, Sahoo A, Thornton P, Vargas R, Verbeeck H, Anderson R, Baker I, Black TA, Bolstad P, Chen J, Curtis PS, Desai AR, Dietze M, Dragoni D, Gough C, Grant RF, Gu L, Jain A, Kucharik C, Law B, Liu S, Lokipitiya E, Margolis HA, Matamala R, McCaughey JH, Monson R, Munger JW, Oechel W, Peng C, Price DT, Ricciuto D, Riley WJ, Roulet N, Tian H, Tonitto C, Torn M, Weng E, Zhou X (2012) A model-data comparison of gross primary productivity: results from the North American Carbon Program site synthesis. J Geophys Res 117 (G3):G03010. doi: 10.1029/2012jg001960
  104. Schmid HP, Grimmond CSB, Cropley F, Offerle B, Su HB (2000) Measurements of CO2 and energy fluxes over a mixed hardwood forest in the mid-western United States. Agric For Meteorol 103(4):357–374Google Scholar
  105. Schwalm CR, Williams CA, Schaefer K, Anderson R, Arain MA, Baker I, Barr AG, Black TA, Chen G, Chen JM, Ciais P, Davis KJ, Desai AR, Dietze M, Dragoni D, Fischer ML, Flanagan LB, Grant R, Gu L, Hollinger D, Izaurralde RC, Kucharik CJ, Lafleur PM, Law BE, Li L, Li Z, Liu S, Lokupitiya E, Luo Y, Ma S, Margolis H, Matamala R, McCaughey JH, Monson RK, Oechel W, Peng C, Poulter B, Price DT, Riciutto DM, Riley W, Sahoo AK, Sprintsin M, Sun J, Tian H, Tonitto C, Verbeeck H, Verma SB (2010) A model-data intercomparison of CO2 exchange across North America: results from the North American Carbon Program Site Synthesis. J Geophys Res. doi: 10.1029/2009JG001229 Google Scholar
  106. Sellers PJ, Hall F, Margolis H, Kelly B, Baldocchi DD, Den Hartog G, Cihlar J, Ryan MG, Goodison B, Crill P, Ranson KJ, Lettenmaier DP, Wickland DE (1995) The Boreal Ecosystem-Atmosphere Study (BOREAS): an overview and early results from the 1994 field year. 76:1549–1577Google Scholar
  107. Sitch S, Smith B, Prentice IC, Arneth A, Bondeau A, Cramer W, Kaplan JO, Levis S, Lucht W, Sykes M, Thonicke K, Venevsky S (2003) Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob Change Biol 9:161–185Google Scholar
  108. Song C, Katul GG, Oren R, Band LE, Tague CL, Stoy PC, McCarthy HR (2009) Energy, water, and carbon fluxes in a loblolly pine stand: results from uniform and gappy canopy models with comparisons to eddy flux data. J Geophys Res 114:G04021. doi: 10.1029/2009JG000951 Google Scholar
  109. Stoy PC, Katul GG, Siqueira MBS, Juang J-Y, Novick KA, Oren R (2006) An evaluation of methods for partitioning eddy covariance-measured net ecosystem exchange into photosynthesis and respiration. Agric For Meteorol 141:2–18Google Scholar
  110. Stoy PC, Richardson AD, Baldocchi DD, Katul GG, Stanovick J, Mahecha MD, Reichstein M, Detto M, Law BE, Wohlfahrt G, Arriga N, Campos J, McCaughey JH, Montagnani L, Paw UKT, Sevanto S, Williams M (2009) Biosphere-atmosphere exchange of CO2 in relation to climate: a cross-biome analysis across multiple time scales. Biogeosciences 6:2297–2312Google Scholar
  111. Suni T, Rinne J, Reissell A, Altimir N, Keronen P, Rannik U, Dal Maso M, Kulmala M, Vesala T (2003) Long-term measurements of surface fluxes above a Scots pine forest in Hyytiala, southern Finland, 1996–2001. Boreal Environ Res 8:287–301Google Scholar
  112. Thornton PE, Law BE, Gholz HL, Clark KL, Falge E, Ellsworth DS, Goldstein AH, Monson RK, Hollinger D, Falk M, Chen J, Sparks JP (2002) Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agric For Meteorol 113(1–4):185–222Google Scholar
  113. Thum T, Aalto T, Laurila T, Aurela M, Kolari P, Hari P (2007) Parametrization of two photosynthesis models at the canopy scale in a northern boreal Scots pine forest. Tellus B 59(5):874–890. doi: 10.1111/j.1600-0889.2007.00305.x Google Scholar
  114. Tian Y, Woodcock CM, Wang Y, Privette JL, Shabanov NV, Zhou L, Zhang Y, Buermann W, Dong J, Veikkanen B, Hame T, Andersson K, Ozdogan M, Knyazikhin Y, Myneni RB (2002) Multiscale analysis and validation of the MODIS LAI product I. Uncertainty assessment. Remote Sens Environ 83:414–430Google Scholar
  115. Urbanski SP, Barford C, Wofsy S, Kucharik CJ, Pyle EH, Budney J, McKain K, Fitzjarrald D, Czikowsky MJ, Munger JW (2007) Factors controlling CO2 exchange on timescales from hourly to decadal at Harvard Forest. J Geophys Res 112 (G02020). doi: 10.1029/2006JG000293
  116. Valentini R, Matteucci G, Dolman AJ, Schulze ED, Rebmann C, Moors EJ, Granier A, Gross P, Jensen NO, Pilegaard K, Lindroth A, Grelle A, Bernhofer C, Grunwald T, Aubinet M, Ceulemans R, Kowalski AS, Vesala T, Rannik U, Berbigier P, Loustau D, Guomundsson J, Thorgeirsson H, Ibrom A, Morgenstern K, Clement R, Moncrieff J, Montagnani L, Minerbi S, Jarvis PG (2000) Respiration as the main determinant of carbon balance in European forests. Nature 404(6780):861–865PubMedGoogle Scholar
  117. van der Molen MK, van Huissteden J, Parmentier FJW, Petrescu AMR, Dolman AJ, Maximov TC, Kononov AV, Karsanaev SV, Suzdalov DA (2007) The growing season greenhouse gas balance of a continental tundra site in the Indigirka lowlands, NE Siberia. Biogeosciences 4:985–1003. doi: 10.5194/bg-4-985-2007 Google Scholar
  118. van Schaik CP, Terborgh JW, Wright SJ (1993) The phenology of tropical forests: adaptive significance and consequences for primary consumers. Annu Rev Ecol Syst 24:353–377Google Scholar
  119. Verma SB, Baldocchi DD, Anderson DE, Matt DR, Clement RJ (1986) Eddy fluxes of CO2, water vapor, and sensible heat over a deciduous forest. Bound-Layer Meteorol 36(1):71–91. doi: 10.1007/bf00117459 Google Scholar
  120. Webb AAR (2003) The physiology of circadian rhythms in plants. New Phytol 160(2):281–303. doi: 10.1046/j.1469-8137.2003.00895.x Google Scholar
  121. Wilkinson M, Eaton EL, Broadmeadow MSJ, Morison JIL (2012) Inter-annual variation of carbon uptake by a plantation oak woodland in south-eastern England. Biogeosci Discuss 9:9667–9710. doi: 10.5194/bgd-9-9667-2012 Google Scholar
  122. Williams M, Carvalhais N, Hollinger D, Kattge J, Leuning R, Luo Y, Peylin P, Reichstein M, Richardson AD, Santaren D, Stoy PC, Tomelleri I, Trudinger CM, Verbeeck H, Wang YP (2009) Improving land surface models with FLUXNET data. Biogeosciences 6:1341–1359Google Scholar
  123. Wilson KB, Baldocchi DD, Hanson PJ (2000) Spatial and seasonal variability in photosynthetic parameters and their relationship to leaf nitrogen in a deciduous forest. Tree Physiol 20:565–578PubMedGoogle Scholar
  124. Wilson KB, Baldocchi DD, Hanson PJ (2001) Leaf age affects the seasonal pattern of photosynthetic capacity and net ecosystem exchange of carbon in a deciduous forest. Plant Cell Environ 24:571–583Google Scholar
  125. Wofsy SC, Goulden ML, Munger JW, Fan S-M, Bakwin PS, Daube BC, Bassow SL, Bazzaz FA (1993) Net exchange of CO2 in a mid-latitude forest. Science 260(5112):1314–1317. doi: 10.1126/science.260.5112.1314 PubMedGoogle Scholar
  126. Wright SJ, van Schaik CP (1994) Light and the phenology of tropical trees. Am Nat 143:192–199Google Scholar
  127. Yuan W, Liu S, Zhou G, Zhou G, Tieszen LL, Baldocchi D, Bernhofer C, Gholz H, Goldstein AH, Goulden ML, Hollinger DY, Hu Y, Law BE, Stoy PC, Vesala T, Wofsy SC (2007) Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agric For Meteorol 143(3–4):189–207Google Scholar

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

Personalised recommendations