Terrestrial ecosystems currently offset one-quarter of anthropogenic carbon dioxide (CO2) emissions because of a slight imbalance between global terrestrial photosynthesis and respiration1. Understanding what controls these two biological fluxes is therefore crucial to predicting climate change2. Yet there is no way of directly measuring the photosynthesis or daytime respiration of a whole ecosystem of interacting organisms; instead, these fluxes are generally inferred from measurements of net ecosystem–atmosphere CO2 exchange (NEE), in a way that is based on assumed ecosystem-scale responses to the environment. The consequent view of temperate deciduous forests (an important CO2 sink) is that, first, ecosystem respiration is greater during the day than at night; and second, ecosystem photosynthetic light-use efficiency peaks after leaf expansion in spring and then declines3, presumably because of leaf ageing or water stress. This view has underlain the development of terrestrial biosphere models used in climate prediction4,5 and of remote sensing indices of global biosphere productivity5,6. Here, we use new isotopic instrumentation7 to determine ecosystem photosynthesis and daytime respiration8 in a temperate deciduous forest over a three-year period. We find that ecosystem respiration is lower during the day than at night—the first robust evidence of the inhibition of leaf respiration by light9,10,11 at the ecosystem scale. Because they do not capture this effect, standard approaches12,13 overestimate ecosystem photosynthesis and daytime respiration in the first half of the growing season at our site, and inaccurately portray ecosystem photosynthetic light-use efficiency. These findings revise our understanding of forest–atmosphere carbon exchange, and provide a basis for investigating how leaf-level physiological dynamics manifest at the canopy scale in other ecosystems.
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Intergovernmental Panel on Climate Change. Climate Change 2013: The Physical Science Basis (Cambridge Univ. Press, 2013)
Schimel, D. et al. Observing terrestrial ecosystems and the carbon cycle from space. Glob. Change Biol. 21, 1762–1776 (2015)
Falge, E. et al. Seasonality of ecosystem respiration and gross primary production as derived from FLUXNET measurements. Agric. For. Meteorol. 113, 53–74 (2002)
Stöckli, R. et al. Use of FLUXNET in the community land model development. J. Geophys. Res. 113, 1–19 (2008)
Parazoo, N. C. et al. Terrestrial gross primary production inferred from satellite fluorescence and vegetation models. Glob. Change Biol. 20, 3103–3121 (2014)
Turner, D. P. et al. Scaling gross primary production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation. Remote Sens. Environ. 88, 256–270 (2003)
Wehr, R. et al. Long-term eddy covariance measurements of the isotopic composition of the ecosystem–atmosphere exchange of CO2 in a temperate forest. Agric. For. Meteorol. 181, 69–84 (2013)
Wehr, R. & Saleska, S. R. An improved isotopic method for partitioning net ecosystem-atmosphere CO2 exchange. Agric. For. Meteorol. 214–215, 515–531 (2015)
Kok, B. On the interrelation of respiration and photosynthesis in green plants. Biochim. Biophys. Acta 3, 625–631 (1949)
Heskel, M. A., Atkin, O. K., Turnbull, M. H. & Griffin, K. L. Bringing the Kok effect to light: a review on the integration of daytime respiration and net ecosystem exchange. Ecosphere 4, 98 (2013)
Sun, J. et al. Estimating daytime ecosystem respiration to improve estimates of gross primary production of a temperate forest. PLoS One 9, http://dx.doi.org/10.1371/journal.pone.0113512 (2014)
Wofsy, S. C. et al. Net exchange of CO2 in a mid-latitude forest. Science 260, 1314–1317 (1993)
Reichstein, M. et al. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob. Change Biol. 11, 1424–1439 (2005)
Lasslop, G. et al. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. Glob. Change Biol. 16, 187–208 (2010)
Dillen, S. Y., Phillips, N., de Beeck, M. O., Hufkens, K. & Buonanduci, M. Agricultural and forest meteorology. Agric. For. Meteorol. 160, 60–68 (2012)
Keenan, T. F. et al. Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment. Ecol. Appl. 24, 1478–1489 (2014)
Tang, X. et al. How is water-use efficiency of terrestrial ecosystems distributed and changing on Earth? Sci. Rep. 4, 7483 (2014)
Yuan, W. et al. Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database. Agric. For. Meteorol. 192–193, 108–120 (2014)
Giasson, M. A. et al. Soil respiration in a northeastern US temperate forest: a 22-year synthesis. Ecosphere 4, 140 (2013)
Savage, K., Davidson, E. A. & Tang, J. Diel patterns of autotrophic and heterotrophic respiration among phenological stages. Glob. Change Biol. 19, 1151–1159 (2013)
De Pury, D. G. G. & Farquhar, G. D. Simple scaling of photosynthesis from leaves to canopies without the errors of big-leaf models. Plant Cell Environ. 20, 537–557 (1997)
Knohl, A. & Baldocchi, D. D. Effects of diffuse radiation on canopy gas exchange processes in a forest ecosystem. J. Geophys. Res. 113, G02023 (2008)
Beer, C. et al. Temporal and among-site variability of inherent water use efficiency at the ecosystem level. Global Biogeochem. Cycles 23, GB2018 (2009)
Grassi, G., Vicinelli, E., Ponti, F., Cantoni, L. & Magnani, F. Seasonal and interannual variability of photosynthetic capacity in relation to leaf nitrogen in a deciduous forest plantation in northern Italy. Tree Physiol. 25, 349–360 (2005)
Constable, G. A. & Rawson, H. M. Effect of leaf position, expansion and age on photosynthesis, transpiration and water use efficiency of cotton. Funct. Plant Biol. 7, 89–100 (1980)
Bassow, S. L. & Bazzaz, F. A. How environmental conditions affect canopy leaf-level photosynthesis in four deciduous tree species. Ecology 79, 2660–2675 (1998)
Restrepo-Coupe, N. et al. What drives the seasonality of photosynthesis across the Amazon basin? A cross-site analysis of eddy flux tower measurements from the Brasil flux network. Agric. For. Meteorol. 182–183, 128–144 (2013)
Wu, J. et al. Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests. Science 351, 972–976 (2016)
Goulden, M. L., Munger, J. W., Fan, S.-M., Daube, B. C. & Wofsy, S. C. Measurements of carbon sequestration by long-term eddy covariance: methods and a critical evaluation of accuracy. Glob. Change Biol. 2, 169–182 (1996)
Yakir, D. & Wang, X. Fluxes of CO2 and water between terrestrial vegetation and the atmosphere estimated from isotope measurements. Nature 380, 515–517 (1996)
Bowling, D. R., Tans, P. P. & Monson, R. K. Partitioning net ecosystem carbon exchange with isotopic fluxes of CO2 . Glob. Change Biol. 7, 127–145 (2001)
Ogée, J. et al. Partitioning net ecosystem carbon exchange into net assimilation and respiration using 13CO2 measurements: a cost-effective sampling strategy. Glob. Biogeochem. Cycles 17, 1070 (2003)
Lai, C.-T., Schauer, A. J., Owensby, C., Ham, J. M. & Ehleringer, J. R. Isotopic air sampling in a tallgrass prairie to partition net ecosystem CO2 exchange. J. Geophys. Res. 108, 4566 (2003)
Knohl, A. & Buchmann, N. Partitioning the net CO2 flux of a deciduous forest into respiration and assimilation using stable carbon isotopes. Global Biogeochem. Cycles 19, GB4008 (2005)
Zhang, J., Griffis, T. J. & Baker, J. M. Using continuous stable isotope measurements to partition net ecosystem CO2 exchange. Plant Cell Environ. 29, 483–496 (2006)
Zobitz, J. M., Burns, S. P., Reichstein, M. & Bowling, D. R. Partitioning net ecosystem carbon exchange and the carbon isotopic disequilibrium in a subalpine forest. Glob. Change Biol. 14, 1785–1800 (2008)
Billmark, K. A. & Griffis, T. J. in Phenology of Ecosystem Processes 143–166 (Springer, 2009)
Fassbinder, J. J., Griffis, T. J. & Baker, J. M. Evaluation of carbon isotope flux partitioning theory under simplified and controlled environmental conditions. Agric. For. Meteorol. 153, 154–164 (2012)
Ogée, J. et al. Partitioning net ecosystem carbon exchange into net assimilation and respiration with canopy-scale isotopic measurements: an error propagation analysis with 13CO2 and CO18O data. Global Biogeochem. Cycles 18, GB2019 (2004)
Commane, R. et al. Seasonal fluxes of carbonyl sulfide in a mid-latitude forest. Proc. Natl. Acad. Sci. USA 112, 14162–14167 (2015)
Yang, X. et al. Solar–induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest. Geophys. Res. Lett. 42, http://dx.doi.org/10.1002/2015GL063201 (2015)
van der Tol, C., Berry, J. A. & Campbell, P. Models of fluorescence and photosynthesis for interpreting measurements of solar–induced chlorophyll fluorescence. J. Geophys. Res. Biogeosci. 119, http://dx.doi.org/10.1002/2014JG002713 (2014)
Gatz, D. F. & Smith, L. The standard error of a weighted mean concentration—I. Bootstrapping vs other methods. Atmos. Environ. 29, 1185–1193 (1995)
This research was supported by the US Department Of Energy (DOE), Office of Science, Terrestrial Ecosystem Science (TES) program (award DE-SC0006741), and the Agnese Nelms Haury Program in Environment and Social Justice at the University of Arizona. Soil chamber data were acquired with the help of K. Savage at the Woods Hole Research Center, under the DOE award. The Harvard Forest Environmental Measurements Site infrastructure is a component of the Harvard Forest Long-Term Ecological Research (LTER) site, supported by the National Science Foundation (NSF), and is additionally supported by the DOE TES program. Below-canopy PAR, soil temperature and soil moisture data were provided by E. Nicoll at the Harvard Forest, supported by the NSF LTER program. OCS data and SIF data were provided by the authors of refs 40 and 41, respectively.
The authors declare no competing financial interests.
Extended data figures and tables
Repeated quantum cascade laser spectrometer measurements (dots, each representing a measurement integrated over 100 s) of the isotopic compositions of δ13C and δ18O in a single known reference cylinder, but measured as if it were an unknown value interspersed among the three years of routine atmospheric measurements. Known reference cylinder values are indicated by the solid grey lines, with 95% confidence intervals indicated by the grey regions. Except for a period in September 2011 (between the vertical orange lines) when an inferior instrument thermal regulation scheme was tested, the precision of the spectrometer’s rapid, in situ isotope measurements is seen to be better than that obtained for the reference cylinder by laboratory-based isotope ratio mass spectrometry7.
Extended Data Figure 2 Composite seasonal cycles of isotopic compositions, isotopic discrimination and isotopic disequilibrium.
Shown are: the isotopic composition of CO2 in the canopy airspace, δn; the apparent fractionation by net photosynthetic assimilation (also called discrimination), εA; the isotopic signatures of net photosynthetic assimilation, δA, and non-foliar respiration, δNR; and the isotopic disequilibrium, D = δNR – δA. Dark lines connect flux-weighted means over all daylight hours for each 12-day bin, except in the case of δNR, where the lines connect simple means over all night-time hours for each bin (because δNR is derived from night-time Keeling plots rather than daytime flux measurements). Light shaded bands show standard errors in the flux-weighted means, calculated according to the ratio variance approximation recommended in ref. 43 (or just standard errors in the means for δNR), and based on variability within each bin (64 ≤ n ≤ 431 for daylight bins, and 16 ≤ n ≤ 33 for δNR). Hatched areas indicate periods of leaf expansion and abscission.
Extended Data Figure 3 Relationships of LUE to APAR, from our isotopic partitioning and from both standard methods.
Scatterplot of the LUE and APAR data from Fig. 2c (solid black circles), along with ordinary least-squares linear fits (black lines), for the period from full leaf expansion to the onset of senescence. These results are from partitioning based on isotopes. Also shown are results from the standard method of ref. 13 (hollow blue triangles), and from the partitioning method of ref. 14 (hollow yellow squares).
Daily LUE is plotted against daily APAR, averaged by day of year across all three years, on the basis of isotopic partitioning (solid circles) and the standard method of ref. 13 (hollow squares), and plotted separately for June, July, August and September. Also shown are linear (ordinary least-squares) fits for the isotopic (solid line) and standard (dotted line) partitioning methods.
Shown are leaf area index (LAI), leaf temperature (Leaf T), APAR, diffuse light fraction, leaf–air water vapour pressure difference (VPDL), canopy stomatal conductance (gs), volumetric soil water content (SWC), and soil temperature at 10 cm depth (Soil T), averaged across the three years, 2011–2013. Lines connect means over all daylight hours within each 12-day bin, and grey bands show standard errors in the means, calculated from variability within each bin (64 ≤ n ≤ 431). Air temperature (Air T), PAR, and the atmospheric water vapour pressure deficit (VPD) are also shown, as dotted lines. Hatched areas indicate leaf expansion and abscission.
Extended Data Figure 6 Effect of varying the prescribed rate of foliar respiration on the seasonal patterns of GEP and DER.
As for Fig. 2, but with the black lines thickened to show the range of GEP and DER values that result from prescribing between 0% and 100% inhibition of leaf respiration by light. The grey standard error bands in Fig. 2 have been removed here for clarity. Hatched areas indicate leaf expansion and abscission. a, GEP and DER. b, Discrepancy between standard and isotopic partitioning (black line), with the gold line showing the 1996–2009 mean seasonal pattern of aboveground respiration (Rabgd) estimated from soil chambers and night-time NEE19. c, Light-use efficiency (LUE; isotopic and standard partitioning), with absorbed photosynthetically active radiation (APAR) inverted in red. d, Intrinsic water-use efficiency (WUEi).
Extended Data Figure 7 Sensitivity of the seasonal cycles of GEP and DER to change in the isotopic fractionation by the photosynthetic enzyme Rubisco, and to restriction to diffuse light conditions.
This figure compares the composite seasonal cycles (across the three years, 2011–2013) of GEP and DER obtained from three variations of the IFP method (restricted to the southwest quadrant to reduce spurious discrepancies caused by differences in the flux tower sampling footprint when subsampling for diffuse light fraction). Top panel: GEP and DER from IFP. Bottom panel: the discrepancy between values of DER obtained from standard partitioning (based on night-time NEE and temperature), and values obtained from isotopic partitioning. The IFP variations shown are: as described in the text (solid black lines); restricted to periods with diffuse light fractions greater than 90% (solid orange lines); and using 27‰ instead of 29‰ for the isotopic fractionation by Rubisco-catalysed fixation of CO2 (dotted purple lines). The lines connect the means (which are from all daylight hours) for each 12-day bin. The light shaded bands around each line in the top panel show the standard error of the mean, calculated from the variability within each bin (25 ≤ n ≤ 130). Hatched areas indicate periods of leaf expansion and abscission.
Extended Data Figure 8 Composite seasonal cycles, from isotopic partitioning and from both standard partitioning methods.
Shown are results from isotopic partitioning (solid black); from standard partitioning based on night-time NEE and temperature (dotted blue); and from standard partitioning incorporating a photosynthetic function of light (dotted yellow). a, GEP and DER. b, LUE, with APAR inverted in red. c, WUEi. Lines connect means over all daylight hours for each 12-day bin; pale bands show standard errors of the means calculated from variability within each bin (64 ≤ n ≤ 431).
Extended Data Figure 9 Seasonal cycles from isotopic partitioning and from both standard partitioning methods, for individual years.
As for Extended Data Fig. 8, but showing the individual years separately (8 ≤ n ≤ 204).
Extended Data Figure 10 Comparison of GEP values obtained from isotopic partitioning with preliminary estimates based on measurements of carbonyl sulfide and solar-induced fluorescence.
Seasonal patterns of GEP from IFP (solid black) and from the standard method of ref. 13 (dotted blue) are compared with those of the OCS flux in 2011 (dashed purple, on an inverted scale) and the SIF signal in 2013 (dashed red). Lines connect means for each 12-day bin, and pale bands show standard errors of the means calculated from variability within each bin (10 ≤ n ≤ 209). The analysis included only data points for which simultaneous GEP and OCS, or GEP and SIF, measurements were available. The OCS data were provided by Commane et al.40, and the SIF data by Yang et al.41.
About this article
Cite this article
Wehr, R., Munger, J., McManus, J. et al. Seasonality of temperate forest photosynthesis and daytime respiration. Nature 534, 680–683 (2016). https://doi.org/10.1038/nature17966
Theoretical Ecology (2021)
Carbon dioxide isotopic compositions during tundra ecosystem respiration and photosynthesis in relation to environmental variables in maritime Antarctica
Polar Biology (2021)
Paddy and Water Environment (2020)
Nature Ecology & Evolution (2019)
Environmental factors associated with seasonal variations of night-time plant canopy and soil respiration fluxes in deciduous conifer forest, Western Himalaya, India