Drought-modulated allometric patterns of trees in semi-arid forests

  • 1.

    Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature509, 600–603 (2014).

    CAS  PubMed  Google Scholar 

  • 2.

    Ahlstrom, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science348, 895–899 (2015).

    PubMed  Google Scholar 

  • 3.

    Legates, D. R. & Willmott, C. J. Mean seasonal and spatial variability in gauge-corrected, global precipitation. Int. J. Clim.10, 111–127 (1990).

    Google Scholar 

  • 4.

    Rotenberg, E. & Yakir, D. Contribution of semi-arid forests to the climate system. Science327, 451–454 (2010).

    CAS  PubMed  Google Scholar 

  • 5.

    Huxman, T. E. et al. Convergence across biomes to a common rain-use efficiency. Nature429, 651–654 (2004).

    CAS  PubMed  Google Scholar 

  • 6.

    Lal, R. Carbon sequestration in dryland ecosystems. Environ. Manag.33, 528–544 (2004).

    Google Scholar 

  • 7.

    Arzai, A. H. & Aliyu, B. S. Fruit tree and vine sprayer calibration based on canopy size and length of row: unit canopy row method. Bayero J. Pure Appl. Sci.3, 260–263 (2010).

    Google Scholar 

  • 8.

    Hartmann, H. Will a 385 million year-struggle for light become a struggle for water and for carbon?–How trees may cope with more frequent climate change-type drought events. Glob. Change Biol.17, 642–655 (2011).

    Google Scholar 

  • 9.

    Sitch, S. et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Change Bio.9, 161–185 (2003).

    Google Scholar 

  • 10.

    Zaehle, S., Sitch, S., Smith, B. & Hatterman, F. Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics. Global Biogeochem. Cy. 19, GB3020 (2005).

  • 11.

    Collins, W. D. et al. The Community Climate System Model Version 3 (CCSM3). J. Clim.19, 2122–2143 (2006).

    Google Scholar 

  • 12.

    Levis, S., Bonan, G., Vertenstein, M. & Oleson, K. The Community Land Model’s Dynamic Global Vegetation Model (CLM-DGVM): technical description and user’s guide. NCAR Tech. Note459, 1–50 (2004).

    Google Scholar 

  • 13.

    Zaehle, S. & Friend, A. D. Carbon and nitrogen cycle dynamics in the O-CN land surface model, I: model description, site-scale evaluation and sensitivity to parameter estimates. Global Biogeochem. Cy.24, GB1005 (2010).

  • 14.

    Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob. Biogeochem. Cy.19, GB1015 (2005).

    Google Scholar 

  • 15.

    Haverd, V. et al. A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis. Geosci. Model. Dev.11, 2995–3026 (2018).

    CAS  Google Scholar 

  • 16.

    Friend, A. D., Stevens, A. K., Knox, R. G. & Cannell, M. G. R. A process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid v3.0). Ecol. Model.95, 0–287 (1997).

    CAS  Google Scholar 

  • 17.

    Levy, P. E., Cannell, M. G. R. & Friend, A. D. Modelling the impact of future changes in climate, CO2 concentration and land use on natural ecosystems and the terrestrial carbon sink. Glob. Environ. Change14, 0–30 (2004).

    CAS  Google Scholar 

  • 18.

    Zeng, X., Li, F. & Song, X. Development of the IAP dynamic global vegetation model. Adv. Atmos. Sci.31, 505–514 (2014).

    Google Scholar 

  • 19.

    Smith, B. et al. Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences11, 2027–2054 (2014).

    Google Scholar 

  • 20.

    Sato, H., Itoh, A. & Kohyama, T. SEIB-DGVM: a new dynamic global vegetation model using a spatially explicit individual-based approach. Ecol. Model.200, 279–307 (2007).

    Google Scholar 

  • 21.

    Zhou, R. et al. Estimation of DBH at forest stand level based on multi-parameters and generalized regression neural network. Forests10, 778–796 (2019).

    Google Scholar 

  • 22.

    Franceschini, T. & Schneider, R. Influence of shade tolerance and development stage on the allometry of ten temperate tree species. Oecologia176, 739–749 (2014).

    PubMed  Google Scholar 

  • 23.

    Tao, S., Guo, Q., Li, C., Wang, Z. & Fang, J. Global patterns and determinants of forest canopy height. Ecology12, 3265–3270 (2016).

    Google Scholar 

  • 24.

    Zimmermann, M. H. Hydraulic architecture of some diffuse-porous trees. Can. J. Bot.-Rev. Canadienne De. Botanique56, 2286–2295 (1978).

    Google Scholar 

  • 25.

    Koch, G. W., Sillett, S. C., Jennings, G. M. & Davis, S. D. The limits to tree height. letters to nature. Nature428, 851–854 (2004).

    CAS  PubMed  Google Scholar 

  • 26.

    Sterck, F. J., Bongers, F. & Newbery, D. M. Tree architecture in a Bornean lowland rain forest: intraspecific and interspecific patterns. Plant Ecol.153, 279–292 (2001).

    Google Scholar 

  • 27.

    Stark, S. C. et al. Amazon forest carbon dynamics predicted by profiles of canopy leaf area and light environment. Ecol. Lett.15, 1406–1414 (2012).

    PubMed  Google Scholar 

  • 28.

    McDowell, N. et al. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? N. Phytol.178, 719–739 (2008).

    Google Scholar 

  • 29.

    Risto, S., Christophe, G., Theodore, M. D. & Eero, N. Functional–structural plant models: a growing paradigm for plant studies. Ann. Bot.-Lond.114, 599–603 (2014).

    Google Scholar 

  • 30.

    Kevin, L., Paul, T., Michael, W., Benoît, S. & Martin, F. LiDAR remote sensing of forest structure. Prog. Phys. Geog.27, 88–106 (2003).

    Google Scholar 

  • 31.

    Watt, P. J. & Donoghue, D. N. M. Measuring forest structure with terrestrial laser scanning. Int. J. Remote Sens.26, 1437–1446 (2005).

    Google Scholar 

  • 32.

    Fernández-Sarríaa, A., Velázquez-Martíb, B., Sajdakb, M., Martíneza, L. & Estornella, J. Residual biomass calculation from individual tree architecture using terrestrial laser scanner and ground-level measurements. Comput. Electron. Agr.93, 90–97 (2013).

    Google Scholar 

  • 33.

    Bayer, D., Seifert, S. & Pretzsch, H. Structural crown properties of Norway spruce (Picea abies L. Karst.) and European beech (Fagus sylvatica L.) in mixed versus pure stands revealed by terrestrial laser scanning. Trees27, 1035–1047 (2013).

    Google Scholar 

  • 34.

    Hackenberg, J., Wassenberg, M., Spiecker, H. & Sun, D. Non-destructive method for biomass prediction combining TLS derived tree volume and wood density. Forests6, 1274–1300 (2015).

    Google Scholar 

  • 35.

    Metz, J. et al. Crown modeling by terrestrial laser scanning as an approach to assess the effect of aboveground intra- and interspecific competition on tree growth. For. Ecol. Manag.310, 275–288 (2013).

    Google Scholar 

  • 36.

    Disney, M. Terrestrial LiDAR: a 3D revolution in how we look at trees. N. Phytol.222, 1736–1741 (2018).

    Google Scholar 

  • 37.

    Zeide, B. Primary unit of the tree crown. Ecology74, 1598–1602 (1993).

    Google Scholar 

  • 38.

    Tyree, M. T. & Sperry, J. S. Vulnerability of xylem to cavitation and embolism. Annu. Rev. Plant Physiol. Plant Mol. Biol.40, 19–38 (2003).

    Google Scholar 

  • 39.

    Schepper, V. D., Dusschoten, D., Copini, P., Jahnke, S. & Steppe, K. MRI links stem water content to stem diameter variations in transpiring trees. J. Exp. Bot.63, 2645–2653 (2012).

    PubMed  Google Scholar 

  • 40.

    Pivovaroff, A. L. et al. Multiple strategies for drought survival among woody plant species. Func. Ecol.30, 517–526 (2016).

    Google Scholar 

  • 41.

    Walcroft, A. S. et al. Radiative transfer and carbon assimilation in relation to canopy architecture, foliage area distribution and clumping in a mature temperate rainforest canopy in New Zealand. Agr. For. Meteorol.135, 326–339 (2005).

    Google Scholar 

  • 42.

    Smith, J. M. B. Scrubland. (2009).

  • 43.

    Archibald, S. & Bond, W. J. Growing tall vs growing wide: tree architecture and allometry of Acacia karroo in forest, savanna, and arid environments. Oikos102, 3–14 (2003).

    Google Scholar 

  • 44.

    Erdős, L. et al. The edge of two worlds: a new review and synthesis on Eurasian forest-steppes. Appl. Veg. Sci.21, 345–362 (2018).

    Google Scholar 

  • 45.

    Jackson, T. et al. An architectural understanding of natural sway frequencies in trees. J. R. Soc. Interface16, 20190116 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 46.

    Anav, A. et al. Spatiotemporal patterns of terrestrial gross primary production: a review. Rev. Geophys.53, 785–818 (2015).

    Google Scholar 

  • 47.

    Winkler, A. J., Myneni, R. B., Alexandrov, G. A. & Brovkin, V. Earth system models underestimate carbon fixation by plants in the high latitudes. Nat. Commun.10, 885–893 (2019).

    PubMed  PubMed Central  Google Scholar 

  • 48.

    Parazoo, N. et al. Optimal estimates of global terrestrial gross primary production from satellite fluorescence and DGVMs. 5th Int. Workshop Remote Sens. Vegetation Fluorescence1, 1–12 (2014).

    Google Scholar 

  • 49.

    Xu, X., Wang, Z., Rahbek, C., Sanders, N. J. & Fang, J. Geographical variation in the importance of water and energy for oak diversity. J. Biogeogr.43, 279–288 (2016).

    Google Scholar 

  • 50.

    Fick, S. E. & Hijmans, R. J. (2017) Worldclim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2016).

  • 51.

    Fleck, S. et al. Terrestrial lidar measurements for analysing canopy structure in an old-growth forest. In Proc. ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007 (2007).

  • 52.

    Taubin, G. Estimation of planar curves, surfaces, and nonplanar space-curves defined by implicit equations with applications to edge and range image segmentation. IEEE T. Pattern Anal.13, 1115–1138 (1991).

    Google Scholar 

  • 53.

    Ohashi, Y. Machine vision methods and articles of manufacture for determination of convex hull and convex hull angle. U.S. Patent No. 5,801,966. 1 Sep. (1998).

  • 54.

    Li, Y. et al. Derivation, validation, and sensitivity analysis of terrestrial laser scanning-based leaf area index. Can. J. Remote Sens.42, 719–729 (2016).

    Google Scholar 

  • 55.

    Grossiord, C. et al. Effect of climate change on reference evapotrature, drives functional responses of trees in semi-arid ecosystems. J. Ecol.105, 163–175 (2017).

    Google Scholar 

  • 56.

    Feldpausch, T. R. et al. Height-diameter allometry of tropical forest trees. Biogeosciences8, 1081–1106 (2011).

    Google Scholar 

  • 57.

    Solargis Database. Accessed 21 Nov 2018 (2018).

  • 58.

    Dai, A. & National Center for Atmospheric Research Staff (eds). Last modified 18 Jul 2017. “The Climate Data Guide: Palmer Drought Severity Index (PDSI).” Accessed 5 June 2018 (2017).

  • 59.

    Zomer, R. J. et al. Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agr. Ecosyst. Environ.126, 67–80 (2008).

    Google Scholar 

  • 60.

    Geographic Data Sharing Infrastructure. College of Urban and Environmental Science, Peking University. Accessed 25 Feb 2020 (2020).

  • 61.

    Fang, J., Wang, Z. & Tang, Z. Atlas of Woody Plants in China. (China Higher Education Press, 2009).

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