Enhanced peak growth of global vegetation and its key mechanisms


The annual peak growth of vegetation is critical in characterizing the capacity of terrestrial ecosystem productivity and shaping the seasonality of atmospheric CO2 concentrations. The recent greening of global lands suggests an increasing trend of terrestrial vegetation growth, but whether or not the peak growth has been globally enhanced still remains unclear. Here, we use two global datasets of gross primary productivity (GPP) and a satellite-derived Normalized Difference Vegetation Index (NDVI) to characterize recent changes in annual peak vegetation growth (that is, GPPmax and NDVImax). We demonstrate that the peak in the growth of global vegetation has been linearly increasing during the past three decades. About 65% of the NDVImax variation is evenly explained by expanding croplands (21%), rising CO2 (22%) and intensifying nitrogen deposition (22%). The contribution of expanding croplands to the peak growth trend is substantiated by measurements from eddy-flux towers, sun-induced chlorophyll fluorescence and a global database of plant traits, all of which demonstrate that croplands have a higher photosynthetic capacity than other vegetation types. The large contribution of CO2 is also supported by a meta-analysis of 466 manipulative experiments and 15 terrestrial biosphere models. Furthermore, we show that the contribution of GPPmax to the change in annual GPP is less in the tropics than in other regions. These multiple lines of evidence reveal an increasing trend in the peak growth of global vegetation. The findings highlight the important roles of agricultural intensification and atmospheric changes in reshaping the seasonality of global vegetation growth.

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Fig. 1: Enhanced monthly vegetation growth peak.
Fig. 2: Attribution of peak monthly vegetation growth (NDVImax).
Fig. 3: Higher photosynthetic capacity of croplands.
Fig. 4: Attribution of peak GPP trends (GPPmax) using factorial simulations of the ensemble mean of models with (+nitrogen) and without (−nitrogen) a coupled carbon–nitrogen cycle.
Fig. 5: Correlation of MTE GPPmax with annual GPP.
Fig. 6: Relationship between measured NPP and GPP.

Data availability

The MTE GPP datasets are available at https://www.bgc-jena.mpg.de/geodb/projects/Home.php. The Advanced Very High Resolution Radiometer GIMMS-NDVI3g datasets are available at https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v0. The GOME-2 SIF datasets are available at https://avdc.gsfc.nasa.gov/pub/data/satellite/MetOp/GOME_F. The MODIS EVI data are available from the NASA Land Processes Distributed Active Archive Center at https://lpdaac.usgs.gov. The in situ GPP observations are available from FLUXNET2015 at http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/. The Vcmax data are available from the TRY database15 at http://www.try-db.org. The CRU TS 3.23 climate datasets are available from the CRU (https://crudata.uea.ac.uk/cru/data/hrg/). The shortwave radiation datasets are available from the Terrestrial Hydrology Research Group at http://hydrology.princeton.edu/data/pgf/v2/0.5deg/monthly/. The MsTMIP modelling results are available at https://nacp.ornl.gov/mstmipdata/.


  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).

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Randerson, J. T., Thompson, M. V., Conway, T. J., Fung, I. Y. & Field, C. B. The contribution of terrestrial sources and sinks to trends in the seasonal cycle of atmospheric carbon dioxide. Glob. Biogeochem. Cycles 11, 535–560 (1997).

    Article  CAS  Google Scholar 

  3. 3.

    Keeling, C. D., Chin, J. F. S. & Whorf, T. P. Increased activity of northern vegetation inferred from atmospheric CO2 measurements. Nature 382, 146–149 (1996).

    Article  CAS  Google Scholar 

  4. 4.

    Myneni, R. B., Keeling, C. D., Tucker, C. J., Asrar, G. & Nemani, R. R. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).

    Article  CAS  Google Scholar 

  5. 5.

    Lucht, W. et al. Climatic control of the high-latitude vegetation greening trend and Pinatubo effect. Science 296, 698–702 (1997).

    Google Scholar 

  6. 6.

    Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).

    Article  CAS  PubMed  Google Scholar 

  7. 7.

    Xu, L. et al. Temperature and vegetation seasonality diminishment over northern lands. Nat. Clim. Change 3, 581–586 (2013).

    Article  Google Scholar 

  8. 8.

    Forkel, M. et al. Enhanced seasonal CO2 exchange caused by amplified plant productivity in northern ecosystems. Science 351, 696–699 (2016).

    Article  CAS  PubMed  Google Scholar 

  9. 9.

    Graven, H. D. et al. Enhanced seasonal exchange of CO2 by northern ecosystems since 1960. Science 341, 1085–1089 (2013).

    Article  CAS  PubMed  Google Scholar 

  10. 10.

    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).

    Article  CAS  PubMed  Google Scholar 

  11. 11.

    Zhou, S. et al. Explaining inter-annual variability of gross primary productivity from plant phenology and physiology. Agric. For. Meteorol. 226, 246–256 (2016).

    Article  Google Scholar 

  12. 12.

    Musavi, T. et al. Stand age and species richness dampen interannual variation of ecosystem-level photosynthetic capacity. Nat. Ecol. Evol. 1, 0048 (2017).

    Article  Google Scholar 

  13. 13.

    Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).

    Article  CAS  Google Scholar 

  14. 14.

    Anav, A. et al. Spatiotemporal patterns of terrestrial gross primary production: a review. Rev. Geophys. 53, 2015RG000483 (2015).

    Article  Google Scholar 

  15. 15.

    Kattge, J. et al. TRY—a global database of plant traits. Glob. Change Biol. 17, 2905–2935 (2011).

    Article  Google Scholar 

  16. 16.

    Stein, W. E., Mannolini, F., Hernick, L. V., Landing, E. & Berry, C. M. Giant cladoxylopsid trees resolve the enigma of the Earth's earliest forest stumps at Gilboa. Nature 446, 904–907 (2007).

    Article  CAS  PubMed  Google Scholar 

  17. 17.

    Jung, M. et al. Global patterns of land–atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J. Geophys. Res. Biogeosci. 116, G00J07 (2011).

    Article  Google Scholar 

  18. 18.

    Hickler, T. et al. Precipitation controls Sahel greening trend. Geophys. Res. Lett. 32, L21415 (2005).

    Article  Google Scholar 

  19. 19.

    Guanter, L. et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl Acad. Sci. USA 111, 1327–1333 (2014).

    Article  CAS  Google Scholar 

  20. 20.

    Huntzinger, D. N. et al. The North American Carbon Program Multi-Scale Synthesis and Terrestrial Model Intercomparison Project—Part 1: overview and experimental design. Geosci. Model Dev. 6, 2121–2133 (2013).

    Article  Google Scholar 

  21. 21.

    Medlyn, B. E. et al. Effects of elevated [CO2] on photosynthesis in European forest species: a meta-analysis of model parameters. Plant Cell Environ. 22, 1475–1495 (1999).

    Article  CAS  Google Scholar 

  22. 22.

    Montogomery, R. A. & Givnish, T. J. Adaptive radiation of photosynthetic physiology in the Hawaiian lobeliads: dynamic photosynthetic responses. Oecologia 155, 455–467 (2008).

    Article  Google Scholar 

  23. 23.

    Wilson, K. B., Baldocchi, D. D. & Hanson, P. J. Spatial and seasonal variability of photosynthetic parameters and their relationship to leaf nitrogen in a deciduous forest. Tree Physiol. 20, 565–578 (2000).

    Article  PubMed  Google Scholar 

  24. 24.

    Zeng, N. et al. Agricultural Green Revolution as a driver of increasing atmospheric CO2 seasonal amplitude. Nature 515, 394–397 (2014).

    Article  CAS  PubMed  Google Scholar 

  25. 25.

    Schimel, D., Stephens, B. B. & Fisher, J. B. Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl Acad. Sci. USA 112, 436–441 (2015).

    Article  CAS  PubMed  Google Scholar 

  26. 26.

    Ainsworth, E. A. & Long, S. P. What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol. 165, 351–371 (2005).

    Article  PubMed  Google Scholar 

  27. 27.

    Luo, Y. Q., Hui, D. F. & Zhang, D. Q. Elevated CO2 stimulates net accumulations of carbon and nitrogen in land ecosystems: a meta-analysis. Ecology 87, 53–63 (2006).

    Article  PubMed  Google Scholar 

  28. 28.

    LeBauer, D. S. & Treseder, K. K. Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed. Ecology 89, 371–379 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Xia, J. Y. & Wan, S. Q. Global response patterns of terrestrial plant species to nitrogen addition. New Phytol. 179, 428–439 (2008).

    Article  CAS  PubMed  Google Scholar 

  30. 30.

    Piao, S. et al. Leaf onset in the Northern Hemisphere triggered by daytime temperature. Nat. Commun. 6, 6911 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Niu, S. L. et al. Water-mediated responses of ecosystem carbon fluxes to climatic change in a temperate steppe. New Phytol. 177, 209–219 (2008).

    CAS  PubMed  Google Scholar 

  32. 32.

    Xia, J. Y., Niu, S. L. & Wan, S. Q. Response of ecosystem carbon exchange to warming and nitrogen addition during two hydrologically contrasting growing seasons in a temperate steppe. Glob. Change Biol. 15, 1544–1556 (2009).

    Article  Google Scholar 

  33. 33.

    Rustad, L. et al. A meta-analysis of the response of soil respiration, net nitrogen mineralization and aboveground plant growth to experimental ecosystem warming. Oecologia 126, 543–562 (2001).

    Article  CAS  Google Scholar 

  34. 34.

    Huntzinger, D. N. et al. NACP MsTMIP: Global 0.5-degree Model Outputs in Standard Format, Version 1.0 (ORNL DAAC, 2016); https://doi.org/10.3334/ORNLDAAC/1225

  35. 35.

    Thomas, R. T. et al. Increased light-use efficiency in northern terrestrial ecosystems indicated by CO2 and greening observations. Geophys. Res. Lett. 43, 11339–11349 (2016).

    Article  CAS  Google Scholar 

  36. 36.

    Wei, Y. et al. The North American Carbon Program Multi-scale Synthesis and Terrestrial Model Intercomparison Project—Part 2: environmental driver data. Geosci. Model Dev. 7, 2875–2893 (2014).

    Article  Google Scholar 

  37. 37.

    Gray, J. M. et al. Direct human influence on atmospheric CO2 seasonality from increased cropland productivity. Nature 515, 398–401 (2014).

    Article  CAS  PubMed  Google Scholar 

  38. 38.

    Wenzel, S., Cox, P. M., Eyring, V. & Friedlingstein, P. Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO2. Nature 538, 499–501 (2016).

    Article  CAS  PubMed  Google Scholar 

  39. 39.

    Magnani, F. et al. The human footprint in the carbon cycle of temperate and boreal forests. Nature 447, 849–851 (2007).

    Article  CAS  Google Scholar 

  40. 40.

    Guan, K. et al. Photosynthetic seasonality of global tropical forests constrained by hydroclimate. Nat. Geosci. 8, 284–289 (2015).

    Article  CAS  Google Scholar 

  41. 41.

    Sacks, W. J., Cook, B. I., Buenning, N., Levis, S. & Helkowski, J. H. Effects of global irrigation on the near-surface climate. Clim. Dynam. 33, 159–175 (2009).

    Article  Google Scholar 

  42. 42.

    Zhang, X. et al. Managing nitrogen for sustainable development. Nature 528, 51–59 (2015).

    Article  CAS  PubMed  Google Scholar 

  43. 43.

    Zhao, F. et al. Role of CO2, climate and land use in regulating the seasonal amplitude increase of carbon fluxes in terrestrial ecosystems: a multimodel analysis. Biogeosciences 13, 5121–5137 (2016).

    Article  CAS  Google Scholar 

  44. 44.

    Fernández-Martínez, M. et al. Atmospheric deposition, CO2, and change in the land carbon sink. Sci. Rep. 7, 9632 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Ali, A. A. et al. Global-scale environmental control of plant photosynthetic capacity. Ecol. Appl. 25, 2349–2365 (2015).

    Article  PubMed  Google Scholar 

  46. 46.

    Sitch, S. et al. Evaluation of the terrestrial carbon cycle, future plant geography and climate–carbon cycle feedbacks using five dynamic global vegetation models (DGVMs). Glob. Change Biol. 14, 2015–2039 (2008).

    Article  Google Scholar 

  47. 47.

    Bondeau, A. et al. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Glob. Change Biol. 13, 679–706 (2007).

    Article  Google Scholar 

  48. 48.

    Gervois, S. et al. Including croplands in a global biosphere model: methodology and evaluation at specific sites. Earth Interact. 8, 1–25 (2004).

    Article  Google Scholar 

  49. 49.

    Han, J., Chen, J., Miao, Y. & Wan, S. Multiple resource use efficiency (mRUE): a new concept for ecosystem production. Sci. Rep. 6, 37453 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Lawrence, D. M. et al. The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design. Geosci. Model Dev. 9, 2973–2998 (2016).

    Article  Google Scholar 

  51. 51.

    Chen, M. et al. Regional contribution to variability and trends of global gross primary productivity. Environ. Res. Lett. 12, 105005 (2016).

    Article  CAS  Google Scholar 

  52. 52.

    Running, S. W. A measurable planetary boundary for the biosphere. Science 337, 1458–1459 (2012).

    Article  CAS  PubMed  Google Scholar 

  53. 53.

    Sun, Y. et al. OCO-2 advances photosynthesis observation from space via solar induced chlorophyll fluorescence. Science 358, eaam5747 (2017).

    Article  CAS  PubMed  Google Scholar 

  54. 54.

    Hilton, T. W. et al. Peak growing season gross uptake of carbon in North America is largest in the Midwest USA. Nat. Clim. Change 7, 450–454 (2017).

    Article  CAS  Google Scholar 

  55. 55.

    Yao, Y. et al. Spatiotemporal pattern of gross primary productivity and its covariation with climate in China over the last thirty years. Glob. Change Biol. 24, 184–196 (2018).

    Article  Google Scholar 

  56. 56.

    Carvalhais, N. et al. Global covariation of carbon turnover times with climate in terrestrial ecosystems. Nature 514, 213–217 (2014).

    Article  CAS  PubMed  Google Scholar 

  57. 57.

    Sanderman, J., Hengl, T. & Fiske, G. J. Soil carbon debt of 12,000 years of human land use. Proc. Natl Acad. Sci. USA 114, 9575–9580 (2017).

    Article  CAS  PubMed  Google Scholar 

  58. 58.

    Croft, H. et al. Leaf chlorophyll content as a proxy for leaf photosynthetic capacity. Glob. Change Biol. 23, 3513–3524 (2017).

    Article  Google Scholar 

  59. 59.

    Luo, X. et al. Incorporating leaf chlorophyll content into a two-leaf terrestrial biosphere model for estimating carbon and water fluxes at a forest site. Agric. For. Meteorol. 248, 156–168 (2018).

    Article  Google Scholar 

  60. 60.

    Alton, P. B. Retrieval of seasonal RuBisCO-limited photosynthetic capacity at global FLUXNET sites from hyperspectral satellite remote sensing: impact on carbon modelling. Agric. For. Meteorol. 232, 74–88 (2017).

    Article  Google Scholar 

  61. 61.

    Wieder, W. R., Cleveland, C. C., Smith, W. K. & Todd-Brown, K. Future productivity and carbon storage limited by terrestrial nutrient availability. Nat. Geosci. 8, 441–444 (2015).

    Article  CAS  Google Scholar 

  62. 62.

    Piao, S. et al. Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity. Nat. Commun. 5, 5018 (2014).

    Article  CAS  PubMed  Google Scholar 

  63. 63.

    Tucker, C. J. et al. An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens. 26, 4485–4498 (2005).

    Article  Google Scholar 

  64. 64.

    Zhu, Z. C. et al. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the period 1982 to 2011. Remote Sens. 5, 927–948 (2013).

    Article  Google Scholar 

  65. 65.

    Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002).

    Article  Google Scholar 

  66. 66.

    Ma, X., Huete, A., Moran, S., Ponce-Campos, G. & Eamus, D. Abrupt shifts in phenology and vegetation productivity under climate extremes. J. Geophys. Res. Biogeosci. 120, 2036–2052 (2015).

    Article  Google Scholar 

  67. 67.

    Joiner, J. et al. Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2. Atmos. Meas. Tech. 6, 2803–2823 (2013).

    Article  Google Scholar 

  68. 68.

    Parazoo, N. C. et al. Interpreting seasonal changes in the carbon balance of southern Amazonia using measurements of XCO2 and chlorophyll fluorescence from GOSAT. Geophys. Res. Lett. 40, 2829–2833 (2013).

    Article  CAS  Google Scholar 

  69. 69.

    Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS 3.10 dataset. Int. J. Climatol. 34, 623–642 (2014).

    Article  Google Scholar 

  70. 70.

    Sheffield, J., Goteti, G. & Wood, E. F. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Clim. 19, 3088–3111 (2006).

    Article  Google Scholar 

  71. 71.

    Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change 109, 117–161 (2011).

    Article  Google Scholar 

  72. 72.

    Wei, Y. et al. NACP MsTMIP: Global and North American Driver Data for Multi-Model Intercomparison (ORNL DAAC, 2014); https://doi.org/10.3334/ORNLDAAC/1220

  73. 73.

    Dentener, F. J. Global Maps of Atmospheric Nitrogen Deposition, 1860, 1993, and 2050 (ORNL DAAC, 2006); https://doi.org/10.3334/ORNLDAAC/830

  74. 74.

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

    Article  Google Scholar 

  75. 75.

    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 

  76. 76.

    Papale, D. et al. Towards a standardized processing of net ecosystem exchange measured with eddy covariance technique: algorithms and uncertainty estimation. Biogeosciences 3, 571–583 (2006).

    Article  CAS  Google Scholar 

  77. 77.

    Vetter, D., Rücker, G. & Storch, I. Meta-analysis: a need for well-defined usage in ecology and conservation biology. Ecosphere 4, 1–24 (2013).

    Article  Google Scholar 

  78. 78.

    Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).

    Article  Google Scholar 

  79. 79.

    DeLucia, E. H., Drake, J. E., Thomas, R. B. & Gonzalez-Meler, M. Forest carbon use efficiency: is respiration a constant fraction of gross primary production? Glob. Change Biol. 13, 1157–1167 (2007).

    Article  Google Scholar 

  80. 80.

    Campioli, M. et al. Biomass production efficiency controlled by management in temperate and boreal ecosystems. Nat. Geosci. 8, 843–846 (2015).

    Article  CAS  Google Scholar 

  81. 81.

    Chen, Z., Yu, G. & Wang, Q. Ecosystem carbon use efficiency in China: variation and influence factors. Ecol. Indic. 90, 316–323 (2018).

    Article  Google Scholar 

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This work was financially supported by the National Key R&D Program of China (2017YFA0604603), National Natural Science Foundation (31430015, 41601099 and 41630528) and National 1000 Young Talents Program of China. We thank all the people who worked to provide data for this study, particularly the MsTMIP modelling group. We are grateful for receiving MTE GPP products from MPI-BGC, biweekly NDVI data from the GIMMS team, MODIS EVI products from USGS, climate-forcing data from CRU and Princeton University, CO2 site data from NOAA, and GOME-2 SIF retrievals from Eumetsat. We further thank the TRY initiative for plant traits (http://www.try-db.org). The TRY initiative and database are hosted, developed and maintained by J. Kattge and G. Bönisch (MPI-BGC). The eddy-covariance data of FLUXNET used in this study were mainly acquired by the following networks: AmeriFlux, GHG-Europe, SOERE, FORE-T, the Fluxnet-Canada Research Network (supported by CFCAS, NSERC, BIOCAP, Environment Canada and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia and USCCC. The vector map data were made using Natural Earth. J.B.F. contributed to this paper from the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA, and support was provided by the IDS programme.

Author information




J.X. designed the study. K.H. performed the analysis. J.X. and K.H. wrote the first draft. Y.L., Y.Wang, A.A., J.C., E.C., Z.L., J.W., Y.Q., X.X., L.Y. and C.B. contributed to the idea development. C.S., D.N.H., R.B.C., Y.F., J.B.F., A.M.M., K.S. and Y.Wei provided the modelling results. All authors interpreted the results and revised the manuscript.

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Correspondence to Jianyang Xia.

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Supplementary Information

Supplementary Figures 1–15, Supplementary Tables 1–2 and Supplementary References

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Supplementary Data 1

Eddy covariance flux tower data from 125 flux sites (including forest, grassland and cropland) across the globe

Supplementary Data 2

Database of 466 studies identified in a meta-analysis that investigate the response of plant growth under warming, nitrogen addition and elevated levels of CO2

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Huang, K., Xia, J., Wang, Y. et al. Enhanced peak growth of global vegetation and its key mechanisms. Nat Ecol Evol 2, 1897–1905 (2018). https://doi.org/10.1038/s41559-018-0714-0

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