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

Nature Ecology & Evolution volume 1, Article number: 0048 (2017) Cite this article

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

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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.

Interannual variability (IAV) of the net carbon dioxide exchange over land is globally the main determinant of the variability of atmospheric CO2 growth rate1,2. So understanding the factors controlling the IAV in CO2 fluxes is essential to improve the predictability of the global carbon cycle3. Ecosystem biotic properties — such as soil and canopy nutrient status, rates of change in physiological properties of the vegetation, or the sensitivity of these properties to environmental factors — influence ecosystem CO2 exchange. Recent studies have shown that the IAV of the carbon budget can be better explained by variation in biotic properties of ecosystems such as photosynthetic capacity (GPPsat) than directly by environmental and climatic drivers4–6. GPPsat is defined as the value of gross primary productivity (GPP) at saturating light under non-stressed conditions, minimizing the influence of anomalous hydrometeorological conditions (for example, droughts and heatwaves), which potentially affect photosynthesis. A robustly retrieved characterization of GPPsat can be regarded as an ecosystem functional property reflecting the physiological response of the ecosystem to the environment. Given that IAV of GPPsat must propagate to observed GPP, this quantity is thought to be a key variable in understanding IAV of carbon fluxes7. In fact, recent studies demonstrated that GPPsat correlates more strongly than any climatic variable with annual GPP8, but also correlates with net ecosystem CO2 exchange5. The magnitude of IAV in GPPsat has been shown to exhibit considerable variation across ecosystems9, yet no obvious explanation for this pattern has been reported in the literature. However, the consequences are important: a low IAV in GPPsat would suggest that ecosystem functioning is not very sensitive to climatic variability, and that it preserves its functionality under the influence of that variability — and, likewise, high IAV is a consequence of high sensitivity. The capability of an ecosystem to preserve its functioning and structure over time (after external disturbances or climate extremes), is often defined as ecosystem stability and is linked to ecosystem resilience10. Using this terminology, low values of IAV in GPPsat can be understood as a characterization of high ecosystem functional stability.

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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.

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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.

Author information

Authors and Affiliations

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

    Talie Musavi, Mirco Migliavacca, Markus Reichstein, Jens Kattge & Miguel D. Mahecha

  2. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, 04103, Germany

    Markus Reichstein, Jens Kattge, Christian Wirth & Miguel D. Mahecha

  3. Institute of Special Botany and Functional Biodiversity, University of Leipzig, Leipzig, 04103, Germany

    Christian Wirth

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

    T. Andrew Black

  5. Department of Biology, University of Antwerpen, Wilrijk, 2610, Belgium

    Ivan Janssens

  6. Bioclimatology, Georg-August University of Göttingen, Göttingen, 37077, 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, 37200-000,, MG, Brazil

    Serge Rambal

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

    Alessandro Cescatti

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

    Damiano Gianelle

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

    Damiano Gianelle

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

    Hiroaki Kondo

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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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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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