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

A Publisher Correction to this article was published on 11 February 2019

This article has been updated

Abstract

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.

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Figure 1: Relationship of cvGPPsat with stand age and species richness.
Figure 2: Relationship of species richness with stand age and cvGPPsat.
Figure 3: Frequency distribution of the distance correlation coefficients computed between the annual ecosystem photosynthetic capacity (GPPsat) and the environmental variables WAI and Tair (n = 50).

Change history

  • 11 February 2019

    In the version of this Article originally published, the wrong Supplementary Information pdf was uploaded, in which the figures did not correspond with those mentioned in the main text and the R code was not presented properly. This has now been replaced.

References

  1. 1

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

    Article  Google Scholar 

  2. 2

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

    Article  Google Scholar 

  3. 3

    Luo, Y., Keenan, T. F. & Smith, M. Predictability of the terrestrial carbon cycle. Glob. Change Biol. 21, 1737–1751 (2015).

    Article  Google Scholar 

  4. 4

    Ma, S. Y., Baldocchi, D. D., Mambelli, S. & Dawson, T. E. Are temporal variations of leaf traits responsible for seasonal and inter-annual variability in ecosystem CO2 exchange? Funct. Ecol. 25, 258–270 (2010).

    Article  Google Scholar 

  5. 5

    Reichstein, M., Bahn, M., Mahecha, M. D., Kattge, J. & Baldocchi, D. D. Linking plant and ecosystem functional biogeography. Proc. Natl Acad. Sci. USA 111, 13697–13702 (2014).

    CAS  Article  Google Scholar 

  6. 6

    Richardson, A. D., Hollinger, D. Y., Aber, J. D., Ollinger, S. V. & Braswell, B. H. Environmental variation is directly responsible for short- but not long-term variation in forest-atmosphere carbon exchange. Glob. Change Biol. 13, 788–803 (2007).

    Article  Google Scholar 

  7. 7

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

    Article  Google Scholar 

  8. 8

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

    CAS  Article  Google Scholar 

  9. 9

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

    Article  Google Scholar 

  10. 10

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

    Article  Google Scholar 

  11. 11

    Jucker, T., Bouriaud, O., Avacaritei, D. & Coomes, D. A. Stabilizing effects of diversity on aboveground wood production in forest ecosystems: linking patterns and processes. Ecol. Lett. 17, 1560–1569 (2014).

    Article  Google Scholar 

  12. 12

    Garcia-Palacios, P., Maestre, F. T. & Gallardo, A. Soil nutrient heterogeneity modulates ecosystem responses to changes in the identity and richness of plant functional groups. J. Ecol. 99, 551–562 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

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

    Article  Google Scholar 

  14. 14

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

  15. 15

    Herbst, M., Mund, M., Tamrakar, R. & Knohl, A. 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).

    Article  Google Scholar 

  16. 16

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

    CAS  Article  Google Scholar 

  17. 17

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

    CAS  Article  Google Scholar 

  18. 18

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

    CAS  Article  Google Scholar 

  19. 43

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

    Article  Google Scholar 

  20. 19

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

    Article  Google Scholar 

  21. 20

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

  22. 21

    Bonan, G. B., Oleson, K. W., Fisher, R. A., Lasslop, G. & Reichstein, M. 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).

  23. 22

    Medvigy, D., Jeong, S. J., Clark, K. L., Skowronski, N. S. & Schafer, K. V. R. Effects of seasonal variation of photosynthetic capacity on the carbon fluxes of a temperate deciduous forest. J. Geophys. Res. Biogeosci 118, 1703–1714 (2013).

    Article  Google Scholar 

  24. 23

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

  25. 24

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

    Google Scholar 

  26. 25

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

    Book  Google Scholar 

  27. 26

    Yang, Y. H., Luo, Y. Q. & Finzi, A. C. Carbon and nitrogen dynamics during forest stand development: a global synthesis. New Phytol. 190, 977–989 (2011).

    CAS  Article  Google Scholar 

  28. 27

    Grossiord, C., Granier, A., Gessler, A., Jucker, T. & Bonal, D. Does drought influence the relationship between biodiversity and ecosystem functioning in boreal forests? Ecosystems 17, 394–404 (2014).

    CAS  Article  Google Scholar 

  29. 28

    Del Rio, M., Schutze, G. & Pretzsch, H. Temporal variation of competition and facilitation in mixed species forests in Central Europe. Plant Biol. 16, 166–176 (2014).

    CAS  Article  Google Scholar 

  30. 29

    Becknell, J. M. & Powers, J. S. 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).

    CAS  Article  Google Scholar 

  31. 30

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

    Article  Google Scholar 

  32. 31

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

    CAS  Article  Google Scholar 

  33. 32

    Szekely, G. J., Rizzo, M. L. & Bakirov, N. K. Measuring and testing dependence by correlation of distances. Ann. Stat. 35, 2769–2794 (2007).

    Article  Google Scholar 

  34. 33

    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).

    Article  Google Scholar 

  35. 34

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

    Google Scholar 

  36. 35

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

    Google Scholar 

  37. 36

    DiMiceli, C. M. 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); http://glcf.umd.edu/data/vcf/

    Google Scholar 

  38. 37

    Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosci. 116, G04021 (2011).

  39. 38

    Pinty, B. 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).

    Article  Google Scholar 

  40. 39

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

    Article  Google Scholar 

  41. 40

    Pinty, B. 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).

    Article  Google Scholar 

  42. 41

    Gilmanov, T. G. 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).

    Article  Google Scholar 

  43. 42

    Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).

    Book  Google Scholar 

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Acknowledgements

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.

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Authors

Contributions

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.

Corresponding author

Correspondence to Talie Musavi.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary information

Supplementary Figures 1–11; Supplementary Tables 1–2 (PDF 1425 kb)

Supplementary R code

R code used in analyses (PDF 1040 kb)

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). (CSV 10 kb)

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. (CSV 31 kb)

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Musavi, T., Migliavacca, M., Reichstein, M. et al. Stand age and species richness dampen interannual variation of ecosystem-level photosynthetic capacity. Nat Ecol Evol 1, 0048 (2017). https://doi.org/10.1038/s41559-016-0048

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