Stand age and species richness dampen interannual variation of ecosystem-level photosynthetic capacity

  • Nature Ecology & Evolution 1, Article number: 0048 (2017)
  • doi:10.1038/s41559-016-0048
  • Download Citation
Published online:


The total uptake of carbon dioxide by ecosystems via photosynthesis (gross primary productivity, GPP) is the largest flux in the global carbon cycle. A key ecosystem functional property determining GPP is the photosynthetic capacity at light saturation (GPPsat), and its interannual variability (IAV) is propagated to the net land–atmosphere exchange of CO2. Given the importance of understanding the IAV in CO2 fluxes for improving the predictability of the global carbon cycle, we have tested a range of alternative hypotheses to identify potential drivers of the magnitude of IAV in GPPsat in forest ecosystems. Our results show that while the IAV in GPPsat within sites is closely related to air temperature and soil water availability fluctuations, the magnitude of IAV in GPPsat is related to stand age and biodiversity (R2 = 0.55, P < 0.0001). We find that the IAV of GPPsat is greatly reduced in older and more diverse forests, and is higher in younger forests with few dominant species. Older and more diverse forests seem to dampen the effect of climate variability on the carbon cycle irrespective of forest type. Preserving old forests and their diversity would therefore be beneficial in reducing the effect of climate variability on Earth's forest ecosystems.

  • Subscribe to Nature Ecology & Evolution for full access:



Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.


  1. 1.

    et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).

  2. 2.

    et al. Trends in the sources and sinks of carbon dioxide. Nat. Geosci. 2, 831–836 (2009).

  3. 3.

    , & Predictability of the terrestrial carbon cycle. Glob. Change Biol. 21, 1737–1751 (2015).

  4. 4.

    , , & Are temporal variations of leaf traits responsible for seasonal and inter-annual variability in ecosystem CO2 exchange? Funct. Ecol. 25, 258–270 (2010).

  5. 5.

    , , , & Linking plant and ecosystem functional biogeography. Proc. Natl Acad. Sci. USA 111, 13697–13702 (2014).

  6. 6.

    , , , & Environmental variation is directly responsible for short- but not long-term variation in forest-atmosphere carbon exchange. Glob. Change Biol. 13, 788–803 (2007).

  7. 7.

    et al. The imprint of plants on ecosystem functioning: A data-driven approach. Int. J. Appl. Earth Obs. Geoinform. 43, 119–131 (2015).

  8. 8.

    et al. Joint control of terrestrial gross primary productivity by plant phenology and physiology. Proc. Natl Acad. Sci. USA 112, 2788–2793 (2015).

  9. 9.

    et al. Potential and limitations of inferring ecosystem photo­synthetic capacity from leaf functional traits. Ecol. Evol. 6, 7352–7366 (2016).

  10. 10.

    Resilience and stability of ecological systems. Annu. Rev. Ecol. System. 4, 1–23 (1973).

  11. 11.

    , , & Stabilizing effects of diversity on aboveground wood production in forest ecosystems: linking patterns and processes. Ecol. Lett. 17, 1560–1569 (2014).

  12. 12.

    , & Soil nutrient heterogeneity modulates ecosystem responses to changes in the identity and richness of plant functional groups. J. Ecol. 99, 551–562 (2011).

  13. 13.

    et al. Does forest continuity enhance the resilience of trees to environmental change? PLoS ONE 9, e113507 (2014).

  14. 14.

    et al. in Old-Growth Forests: Function, Fate and Value (eds Wirth, C., Gleixner, G. & Heimann, M.) 57–79 (2009).

  15. 15.

    , , & Differences in carbon uptake and water use between a managed and an unmanaged beech forest in central Germany. Forest Ecol. Manage. 355, 101–108 (2015).

  16. 16.

    et al. Seasonal and interannual variability of climate and vegetation indices across the Amazon. Proc. Natl Acad. Sci. USA 107, 14685–14690 (2010).

  17. 17.

    et al. Nutrient availability as the key regulator of global forest carbon balance. Nat. Clim. Change 4, 471–476 (2014).

  18. 18.

    Breathing of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Austr. J. Bot. 56, 1–26 (2008).

  19. 19.

    et al. The human footprint in the carbon cycle of temperate and boreal forests. Nature 447, 848–850 (2007).

  20. 20.

    et al. Factors controlling CO2 exchange on timescales from hourly to decadal at Harvard Forest. J. Geophys. Res. Biogeosci. 112, G02020 (2007).

  21. 21.

    , , , & Reconciling leaf physiological traits and canopy flux data: use of the TRY and FLUXNET databases in the Community Land Model version 4. J. Geophys. Res. Biogeosci. 117 , G02026 (2012).

  22. 22.

    , , , & Effects of seasonal variation of photosynthetic capacity on the carbon fluxes of a temperate deciduous forest. J. Geophys. Res. Biogeosci 118, 1703–1714 (2013).

  23. 23.

    in Old-Growth Forests: Function, Fate and Value (eds Wirth, C., Gleixner, G. & Heimann, M.) 465–491 (2009).

  24. 24.

    et al. Nitrogen Processes in Terrestrial Ecosystems (eds Sutton, M. A. et al.) 99–125 (Cambridge Univ. Press, 2011).

  25. 25.

    et al. The European Nitrogen Assessment: Sources, Effects and Policy Perspectives (eds Sutton, M. A. et al.) (Cambridge Univ. Press, 2011).

  26. 26.

    , & Carbon and nitrogen dynamics during forest stand development: a global synthesis. New Phytol. 190, 977–989 (2011).

  27. 27.

    , , , & Does drought influence the relationship between biodiversity and ecosystem functioning in boreal forests? Ecosystems 17, 394–404 (2014).

  28. 28.

    , & Temporal variation of competition and facilitation in mixed species forests in Central Europe. Plant Biol. 16, 166–176 (2014).

  29. 29.

    & Stand age and soils as drivers of plant functional traits and aboveground biomass in secondary tropical dry forest. Canadian J. Forest Res. 44, 604–613 (2014).

  30. 30.

    et al. Influence of stand age on the magnitude and seasonality of carbon fluxes in Canadian forests. Agricult. Forest Meteorol. 165, 136–148 (2012).

  31. 31.

    et al. Old-growth forests as global carbon sinks. Nature 455, 213–215 (2008).

  32. 32.

    , & Measuring and testing dependence by correlation of distances. Ann. Stat. 35, 2769–2794 (2007).

  33. 33.

    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).

  34. 34.

    et al. Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. Biogeosci. Discuss. 2016, 1–33 (2016).

  35. 35.

    et al. Terrestrial Carbon Observations: Protocols for Vegetation Sampling and Data Submission (FAO, 2008).

  36. 36.

    et al. Annual Global Automated MODIS Vegetation Continuous Fields (MOD44B) at 250 m Spatial Resolution for Data Years Beginning Day 65, 2000 - 2010, Collection 5 Percent Tree Cover (Univ. Maryland, 2011);

  37. 37.

    , , & Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosci. 116, G04021 (2011).

  38. 38.

    et al. Retrieving surface parameters for climate models from Moderate Resolution Imaging Spectroradiometer (MODIS)-Multiangle Imaging Spectroradiometer (MISR) albedo products. J. Geophys. Res. Atmos. 112, D10116 (2007).

  39. 39.

    et al. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ. 83, 135–148 (2002).

  40. 40.

    et al. Evaluation of the JRC-TIP 0.01 degrees products over a mid-latitude deciduous forest site. Remote Sens. Environ. 115, 3567–3581 (2011).

  41. 41.

    et al. Gross primary production and light response parameters of four Southern Plains ecosystems estimated using long-term CO2-flux tower measurements. Glob. Biogeochem. Cycles 17, 1071 (2003).

  42. 42.

    & Modern Applied Statistics with S (Springer, 2002).

  43. 43.

    Relative importance for linear regression in R: the package relaimpo. J. Stat. Software 17, 1–27 (2006).

Download references


From Max-Planck Institute for Biogeochemistry we thank U. Weber, M. Jung for providing data, and K. Morris and R. Nair for a language check. We thank P. Jassal from the University of British Columbia and Jens Schumacher from University of Jena for their helpful comments. The authors T.M., M.M., M.D.M., J.K., C.W. and M.R. affiliated with the MPI BGC acknowledge funding by the European Union’s Horizon 2020 project BACI under grant agreement no. 640176. This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia and USCCC. The ERA-Interim reanalysis data are provided by ECMWF and processed by LSCE. The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and Fluxdata project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Center, and the OzFlux, ChinaFlux and AsiaFlux offices. T.M. acknowledges the International Max Planck Research School for global biogeochemical cycles.

Author information


  1. Max Planck Institute for Biogeochemistry, 07745 Jena, Germany

    • Talie Musavi
    • , Mirco Migliavacca
    • , Markus Reichstein
    • , Jens Kattge
    •  & Miguel D. Mahecha
  2. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103 Leipzig, Germany

    • Markus Reichstein
    • , Jens Kattge
    • , Christian Wirth
    •  & Miguel D. Mahecha
  3. Institute of Special Botany and Functional Biodiversity, University of Leipzig, 04103 Leipzig, Germany

    • Christian Wirth
  4. Biometeorology and Soil Physics Group, Faculty of Land and Food Systems, University of British Columbia, 2329 West Mall, Vancouver, British Columbia, Canada

    • T. Andrew Black
  5. University of Antwerpen, Department of Biology, 2610 Wilrijk, Belgium

    • Ivan Janssens
  6. Bioclimatology, Georg-August University of Göttingen, 37077 Göttingen, Germany

    • Alexander Knohl
    •  & Rijan Tamrakar
  7. INRA, ISPA, Centre de Bordeaux Aquitaine, 71 Avenue Edouard Bourlaux, 33140 Villenave-d’Ornon, France.

    • Denis Loustau
  8. UMR Ecologie Fonctionnelle and Biogéochimie des Sols et Agroécosystèmes, SupAgro-CIRAD-INRA-IRD, Montpellier, France

    • Olivier Roupsard
  9. A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow, 119071, Russia

    • Andrej Varlagin
  10. Centre d'Ecologie Fonctionnelle et Evolutive, CEFE, UMR 5175, CNRS, Montpellier, France

    • Serge Rambal
  11. Universidade Federal de Lavras, Lavras, MG, 37200-000, Brazil

    • Serge Rambal
  12. European Commission, Joint Research Centre, Directorate for Sustainable Resources, 21027, Ispra, Italy

    • Alessandro Cescatti
  13. Department of Sustainable Agro-Ecosystems and Bioresources, Research and Innovation Center, Fondazione Edmund Mach, 38010 San Michele all’ Adige Trento, Italy

    • Damiano Gianelle
  14. Foxlab Joint CNR-FEM Initiative, Via E. Mach 1, 38010 San Michele all'Adige, Italy

    • Damiano Gianelle
  15. National Institute of Advanced Industrial Science and Technology (AIST), Onogawa, Tsukuba, Ibaraki, 305-8561, Japan

    • Hiroaki Kondo


  1. Search for Talie Musavi in:

  2. Search for Mirco Migliavacca in:

  3. Search for Markus Reichstein in:

  4. Search for Jens Kattge in:

  5. Search for Christian Wirth in:

  6. Search for T. Andrew Black in:

  7. Search for Ivan Janssens in:

  8. Search for Alexander Knohl in:

  9. Search for Denis Loustau in:

  10. Search for Olivier Roupsard in:

  11. Search for Andrej Varlagin in:

  12. Search for Serge Rambal in:

  13. Search for Alessandro Cescatti in:

  14. Search for Damiano Gianelle in:

  15. Search for Hiroaki Kondo in:

  16. Search for Rijan Tamrakar in:

  17. Search for Miguel D. Mahecha in:


T.M. wrote the manuscript and performed the analysis. T.M., M.M., M.D.M. and M.R. designed the study. M.M., M.D.M., M.R., J.K., I.J., A.K., A.C., C.W., T.A.B. and A.V. discussed the interpretation of the results. M.M., M.D.M., M.R., J.K., C.W., T.A.B., A.C., A.K., D.L. and O.R. contributed significantly to editing the paper. S.R., R.T. and D.G. contributed to editing the paper. T.A.B., I.J., A.K., D.L., O.R., A.V., S.R., A.C., H.K. and T.F. contributed data.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Talie Musavi.

Supplementary information

PDF files

  1. 1.

    Supplementary information

    Supplementary Figures 1–11; Supplementary Tables 1–2

Text files

  1. 1.

    Supplementary R code

    R code used in analyses

CSV files

  1. 1.

    Supplementary Dataset 1

    Information on the sites used in this study including site identifier (site.code), coefficient of variation of GPPsat (cvGPPsat), Number of years with data (No_years), number of abundant species at the sites (Sp.no90), stand age (Age), plant functional type (PFT), Climate Group, canopy height (Height), canopy cover, nutrient availability classes (Nutrient_availability), cv of leaf area index (cvLAI), mean of LAI (mean.LAI), maximum LAI (LAI max), standard deviation of growing season water availability index (sdWAI), sd of growing season air temperature (sdTair), sd of growing season cumulative precipitation (sdPrecip), latitude (LAT), longitude (LONG).

  2. 2.

    Supplementary Dataset 2

    Annual information on the GPPsat, leaf area index (LAI), mean growing season air temperature (avgTair), mean growing season cumulative precipitation (avgPrecip), mean growing season water availability index (avgWAI). site.code is the identifier of the sites.