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Integrating multiple plant functional traits to predict ecosystem productivity

  • Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838 (2010).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Badgley, G., Field, C. B. & Berry, J. A. Canopy near-infrared reflectance and terrestrial photosynthesis. Sci. Adv.3, e1602244 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chapin, F. S. III Effects of plant traits on ecosystem and regional processes: a conceptual framework for predicting the consequences of global change. Ann. Bot. 91, 455–463 (2003).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chu, C. et al. Does climate directly influence NPP globally? Global Chan. Biol. 22, 12–24 (2016).

    Article 

    Google Scholar 

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

    Article 

    Google Scholar 

  • Fang, J., Lutz, J. A., Wang, L., Shugart, H. H. & Yan, X. Using climate-driven leaf phenology and growth to improve predictions of gross primary productivity in North American forests. Global Chan. Biol. 26, 6974–6988 (2020).

    Article 

    Google Scholar 

  • Fernández-Martínez, M. et al. The role of climate, foliar stoichiometry and plant diversity on ecosystem carbon balance. Global Chan. Biol. 26, 7067–7078 (2020).

    Article 

    Google Scholar 

  • Migliavacca, M. et al. The three major axes of terrestrial ecosystem function. Nature 598, 468–472 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Funk, J. L. et al. Revisiting the Holy Grail: using plant functional traits to understand ecological processes. Biol. Rev. 92, 1156–1173 (2017).

    Article 
    PubMed 

    Google Scholar 

  • Lavorel, S. & Garnier, É. Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail. Fun. Ecol. 16, 545–556 (2002).

    Article 

    Google Scholar 

  • Enquist, B. J. et al. in Advances in Ecological Research 52 (eds Samraat P, Guy W, & Anthony I. D) 249–318 (Academic Press, 2015).

  • Garnier, E. et al. Plant functional markers capture ecosystem properties during secondary succession. Ecology 85, 2630–2637 (2004).

    Article 

    Google Scholar 

  • Enquist, B. J. et al. Assessing trait-based scaling theory in tropical forests spanning a broad temperature gradient. Global Ecol. Biogeogr. 26, 1357–1373 (2017).

    Article 

    Google Scholar 

  • Fyllas, N. M. et al. Solar radiation and functional traits explain the decline of forest primary productivity along a tropical elevation gradient. Ecol. Lett. 20, 730–740 (2017).

    Article 
    PubMed 

    Google Scholar 

  • Van der Plas, F. et al. Plant traits alone are poor predictors of ecosystem properties and long-term ecosystem functioning. Nat. Ecol. Evol. 4, 1602–1611 (2020).

    Article 
    PubMed 

    Google Scholar 

  • Ali, A., Yan, E.-R., Chang, S. X., Cheng, J.-Y. & Liu, X.-Y. Community-weighted mean of leaf traits and divergence of wood traits predict aboveground biomass in secondary subtropical forests. Sci. Total Environ. 574, 654–662 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Yang, J., Cao, M. & Swenson, N. G. Why Functional Traits Do Not Predict Tree Demographic Rates. Trend Ecol. Evol. 33, 326–336 (2018).

    Article 

    Google Scholar 

  • Šímová, I. et al. The relationship of woody plant size and leaf nutrient content to large-scale productivity for forests across the Americas. J. Ecol. 107, 2278–2290 (2019).

    Article 

    Google Scholar 

  • Li, Y. et al. Leaf size of woody dicots predicts ecosystem primary productivity. Ecol. Lett. 23, 1003–1013 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • He, N. et al. Ecosystem Traits Linking Functional Traits to Macroecology. Trend. Ecol. Evol. 34, 200–210 (2019).

    Article 

    Google Scholar 

  • Rubio, V. E., Zambrano, J., Iida, Y., Umaña, M. N. & Swenson, N. G. Improving predictions of tropical tree survival and growth by incorporating measurements of whole leaf allocation. J. Ecol. 109, 1331–1343 (2021).

    Article 

    Google Scholar 

  • Drake, J. E. et al. Increases in the flux of carbon belowground stimulate nitrogen uptake and sustain the long-term enhancement of forest productivity under elevated CO2. Ecol. Lett. 14, 349–357 (2011).

    Article 
    PubMed 

    Google Scholar 

  • Hilty, J., Muller, B., Pantin, F. & Leuzinger, S. Plant growth: the What, the How, and the Why. New Phytol. 232, 25–41 (2021).

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Suding, K. N. et al. Scaling environmental change through the community-level: A trait-based response-and-effect framework for plants. Global Chan. Biol. 14, 1125–1140 (2008).

    Article 

    Google Scholar 

  • Liu, C., Li, Y., Yan, P. & He, N. How to Improve the Predictions of Plant Functional Traits on Ecosystem Functioning? Front. Plant Sci. 12, 622260 (2021).

  • Reich, P. B., Walters, M. B. & Ellsworth, D. S. From tropics to tundra: global convergence in plant functioning. Proc. Natl. Acad. of Sci. 94, 13730–13734 (1997).

    Article 
    CAS 

    Google Scholar 

  • Reich, P. B. The world-wide ‘fast–slow’ plant economics spectrum: a traits manifesto. J. Ecol. 102, 275–301 (2014).

    Article 

    Google Scholar 

  • Monteith, J. L. Climate and the efficiency of crop production in Britain. Philosophical Transactions of the Royal Society of London. B. Biol. Sci. 281, 277–294 (1977).

    Google Scholar 

  • Garnier, E. Resource capture, biomass allocation and growth in herbaceous plants. Trend. Ecol. Evol. 6, 126–131 (1991).

    Article 
    CAS 

    Google Scholar 

  • Farnsworth, K. D., Albantakis, L. & Caruso, T. Unifying concepts of biological function from molecules to ecosystems. Oikos 126, 1367–1376 (2017).

    Article 

    Google Scholar 

  • Zhang, R. et al. Biodiversity alleviates the decrease of grassland multifunctionality under grazing disturbance: A global meta-analysis. Global Ecol. Biogeogr. 31, 155–167 (2022).

    Article 

    Google Scholar 

  • Jing, X. et al. The links between ecosystem multifunctionality and above-and belowground biodiversity are mediated by climate. Nat. Commun. 6, 1–8 (2015).

    Article 

    Google Scholar 

  • Peters, M. K. et al. Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88–92 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Hu, W. et al. Aridity-driven shift in biodiversity–soil multifunctionality relationships. Nat. Commun. 12, 1–15 (2021).

    Article 

    Google Scholar 

  • Jing, X. et al. The influence of aboveground and belowground species composition on spatial turnover in nutrient pools in alpine grasslands. Global Ecol. Biogeogr. 31, 486–500 (2022).

    Article 

    Google Scholar 

  • Jing, X. et al. Above-and belowground complementarity rather than selection drives tree diversity-productivity relationships in European forests. Funct Ecol. 35, 1756–1767 (2021).

  • He, N. et al. Predicting ecosystem productivity based on plant community traits. Trend. Plant Sci. 28, 43–53 (2023).

  • Maynard, D. S. et al. Global relationships in tree functional traits. Nat. Commun. 13, 1–12 (2022).

    Article 

    Google Scholar 

  • Michaletz, S. T., Kerkhoff, A. J. & Enquist, B. J. Drivers of terrestrial plant production across broad geographical gradients. Global Ecol. Biogeogr. 27, 166–174 (2018).

    Article 

    Google Scholar 

  • Shipley, B. Net assimilation rate, specific leaf area and leaf mass ratio: which is most closely correlated with relative growth rate? A meta-analysis. Funct. Ecol. 20, 565–574 (2006).

    Article 

    Google Scholar 

  • Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).

    Article 

    Google Scholar 

  • Jucker, T., Bouriaud, O. & Coomes, D. A. Crown plasticity enables trees to optimize canopy packing in mixed-species forests. Funct. Ecol. 29, 1078–1086 (2015).

    Article 

    Google Scholar 

  • McGill, B. J. Matters of Scale. Science 328, 575 (2010).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Penuelas, J. et al. Increasing atmospheric CO2 concentrations correlate with declining nutritional status of European forests. Communi. Biol. 3, 1–11 (2020).

    Google Scholar 

  • Weemstra, M. et al. Towards a multidimensional root trait framework: a tree root review. New Phytol. 211, 1159–1169 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Oehri, J., Schmid, B., Schaepman-Strub, G. & Niklaus, P. A. Biodiversity promotes primary productivity and growing season lengthening at the landscape scale. Proc. Natl. Acad. Sci. 114, 10160–10165 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Diaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2015).

    Article 
    PubMed 

    Google Scholar 

  • Liu, Y. et al. The optimum temperature of soil microbial respiration: Patterns and controls. Soil Biol. Biochem. 121, 35–42 (2018).

    Article 
    CAS 

    Google Scholar 

  • Zhao, N. et al. Coordinated pattern of multi-element variability in leaves and roots across Chinese forest biomes. Global Ecol. Biogeogr. 25, 359–367 (2016).

    Article 

    Google Scholar 

  • Zhang, J. et al. C: N: P stoichiometry in China’s forests: From organs to ecosystems. Funct. Ecol. 32, 50–60 (2018).

    Article 

    Google Scholar 

  • Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 1–20 (2017).

    Article 

    Google Scholar 

  • Dirk Nikolaus, K. et al. Climatologies at high resolution for the earth’s land surface areas. EnviDat. https://doi.org/10.16904/envidat.332 (2021).

  • Kerkhoff, A. J., Enquist, B. J., Elser, J. J. & Fagan, W. F. Plant allometry, stoichiometry and the temperature-dependence of primary productivity. Global Ecol. Biogeogr. 14, 585–598 (2005).

    Article 

    Google Scholar 

  • Wright, I. J. et al. Global climatic drivers of leaf size. Science 357, 917–921 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Zhang, Y. et al. A global moderate resolution dataset of gross primary production of vegetation for 2000-2016. Sci. Data 4, 170165 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Jolliffe, I. T. & Cadima, J. Principal component analysis: a review and recent developments. Philos. Trans. Soc. A Math. Phys. Eng. Sci. 374, 20150202 (2016).

    Article 

    Google Scholar 

  • Wieczynski, D. J. et al. Climate shapes and shifts functional biodiversity in forests worldwide. Proc. Natl. Acad. Sci. 116, 587–592 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Method Ecol. Evol. 7, 573–579 (2016).

    Article 

    Google Scholar 

  • Bürkner, P.-C. Advanced bayesian multilevel modeling with the R Package brms. R J. 10, 395–411 (2018).

  • Bürkner, P.-C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Software 80, 1–28 (2017).

    Article 

    Google Scholar 

  • Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).

    Article 

    Google Scholar 

  • Vehtari, A. et al. loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models. R package version 2, 1003 (2019).

    Google Scholar 

  • Gabry, J. & Mahr, T. bayesplot: Plotting for Bayesian models. R package version 1 (2017).

  • Mac Nally, R. & Walsh, C. J. Hierarchical partitioning public-domain software. Biodivers. Conserv. 13, 659 (2004).

    Article 

    Google Scholar 

  • Murray, K. & Conner, M. M. Methods to quantify variable importance: implications for the analysis of noisy ecological data. Ecology 90, 348–355 (2009).

    Article 
    PubMed 

    Google Scholar 

  • Yan, P., He, N., Yu, K., Xu, L. & Van Meerbeek, K. Integrating multiple functional traits to predict ecosystem productivity. figshare (2023). Dataset. https://doi.org/10.6084/m9.figshare.22081634.v1.


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