Shrubland biomass and root-shoot allocation along a climate gradient in China
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Supplementary Files

Supplementary file 1
Supplementary file 2

Keywords

allometric relationship
biomass allocation
China
climate
shrublands

How to Cite

Guo, J., Guo, Y., Chai, Y., Liu, X. and Yue, M. (2021) “Shrubland biomass and root-shoot allocation along a climate gradient in China”, Plant Ecology and Evolution, 154(1), pp. 5-14. doi: 10.5091/plecevo.2021.1570.

Abstract

Background – Shrublands are receiving increasing attention because of climate change. However, knowledge about biomass allocation of shrublands at the community level and how this is regulated by climate is of limited availability but critical for accurately estimating carbon stocks and predicting global carbon cycles.
Methods – We sampled 50 typical shrublands along a climate gradient in China and investigated the biomass allocation of shrubland at the community level and the effect of climate on biomass allocation. Shrub biomass was estimated using species-specific allometric relationships and the biomass of understory herbs was collected by excavating the whole plant. Regression analysis was used to examine the relationships between the biomass and the climate factors. RMA were conducted to establish the allometric relationships between the root and the shoot biomass at the community level.
Key results – Shoot, root, and total biomass of shrub communities across different sites were estimated with median values of 206.5, 145.8, and 344.5 g/m2, respectively. Shoot, root, and total biomass of herb communities were estimated at 68.2, 58.9, and 117.2 g/m2, respectively. The median value of the R/S ratio of shrub communities was 0.58 and that of herb communities was 0.84. The R/S ratio of the shrub community showed a negative relationship with mean annual temperature and mean annual precipitation and a positive relationship with total annual sunshine and the aridity index. The R/S ratio of the herb community however showed a weak relationship with climate factors. Shoot biomass of the shrub community was nearly proportional to root biomass with a scaling exponent of 1.17, whereas shoot biomass of the herb community was disproportional to root biomass with a scaling exponent of 2.1.
Conclusions – In shrublands, root biomass was more affected than shoot biomass by climate factors and this is related to water availability as a result of biomass allocation change of the shrub community. The understory herb community was less affected by climate due to the modification of the overstory–understory interaction to the climate-induced biomass allocation pattern. Shoot biomass of shrubs scales isometrically with root biomass at the community level, which supports the isometric theory of above-ground and below-ground biomass partitioning.

https://doi.org/10.5091/plecevo.2021.1570
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References

Brown S. 2002. Measuring carbon in forests: current status and future challenges. Environmental Pollution 116(3): 363–372. https://doi.org/10.1016/S0269-7491(01)00212-3

Cahoon S.M., Sullivan P.F., Shaver G.R., Welker J.M. & Post E. 2012. Interactions among shrub cover and the soil microclimate may determine future Arctic carbon budgets. Ecology Letters 15(12): 1415–1422. https://doi.org/10.1111/j.1461-0248.2012.01865.x

Cairns M.A., Brown S., Helmer E.H., Baumgardner G.A. & Helmer E.H. 1997. Root biomass allocation in the world’s upland forests. Oecologia 111: 1–11. https://doi.org/10.1007/s004420050201

Castro H. & Freitas H. 2009. Above-ground biomass and productivity in the Montado: from herbaceous to shrub dominated communities. Journal of Arid Environments 73(4–5): 506–511. https://doi.org/10.1016/j.jaridenv.2008.12.009

Chen M.R. 1983. Climate and agriculture in the Qinling Mountains. Shaanxi People’s Press, Xi’an.

Cheng D.L. & Niklas K.J. 2006. Above- and below-ground biomass relationships across 1534 forested communities. Annals of Botany 99(1): 95–102. https://doi.org/10.1093/aob/mcl206

Chew R.M. & Chew A.E. 1965. The primary productivity of a desert-shrub (Larrea tridentata) community. Ecological Monographs 35(4): 355–375. https://doi.org/10.2307/1942146

Corona P., Pasta S., Giardina G., La Mantia T. & Giardina G. 2012. Assessing the biomass of shrubs typical of Mediterranean pre-forest communities. Plant Biosystems 146(2): 252–257. https://doi.org/10.1080/11263504.2011.593200

de Martonne E. 1926. L’indice d’aridité. Bulletin de l’Association de Géographes français 3: 3–5.

Ding Y. & Dai X. 1994. Temperature variation in China during the last 100 years. Meteorological Monthly 20(12): 19–26. [In Chinese].

Enquist B.J. & Niklas K.J. 2002. Global allocation rules for patterns of biomass partitioning in seed plants. Science 295(5559): 1517–1520. https://doi.org/10.1126/science.1066360

Fang J.Y., Oikawa T., Kato T., Mo W.H. & Wang Z.H. 2005. Biomass carbon accumulation by Japan’s forests from 1947 to 1995. Global Biogeochemical Cycles 19(2): GB2004. https://doi.org/10.1029/2004GB002253

Goodale C.L., Apps M.J., Birdsey R.A., et al. 2002. Forest carbon sinks in the Northern Hemisphere. Ecological applications 12(3): 891–899. https://doi.org/10.1890/1051-0761(2002)012%5B0891:FCSITN%5D2.0.CO;2

Goodale C.L. & Davidson E.A. 2002. Carbon cycle: uncertain sinks in the shrubs. Nature 418: 593–594. https://doi.org/10.1038/418593a

He J., Wang Q. & Hu D. 1997. Studies on the biomass of typical shrubland and their regeneration capacity after cutting. Acta Phytoecologica Sinica 21(6): 512–520. [In Chinese].

Houghton R.A. 2005. Aboveground forest biomass and the global carbon balance. Global Change Biology 11(6): 945–958. https://doi.org/10.1111/j.1365-2486.2005.00955.x

Hu H.F., Wang Z.H., Liu G.H. & Fu B.J. 2006. Vegetation carbon storage of major shrublands in China. Journal of Plant Ecology 30(4): 539–544. [In Chinese].

Hui D. & Jackson R.B. 2006. Geographical and interannual variability in biomass partitioning in grassland ecosystems: a synthesis of field data. New Phytologist 169(1): 85–93. https://doi.org/10.1111/j.1469-8137.2005.01569.x

IPCC 2007. Climate change 2007: impacts, adaptation, and vulnerability. Cambridge University Press, New York.

Jackson R.B., Canadell J., Ehleringer J.R., Mooney H.A., Sala O.E. & Schulze E.D. 1996. A global analysis of root distributions for terrestrial biomes. Oecologia 108: 389–411. https://doi.org/10.1007/BF00333714

Johnson L.C. & Matchett J.R. 2001. Fire and grazing regulate belowground processes in tallgrass prairie. Ecology 82(12): 3377–3389. https://doi.org/10.1890/0012-9658(2001)082%5B3377:FAGRBP%5D2.0.CO;2

Lambers H., Shane M.W., Cramer M.D., Cramer M.D., Pearse S.J. & Veneklaas E.J. 2006. Root structure and functioning for efficient acquisition of phosphorus: matching morphological and physiological traits. Annals of Botany 98(4): 693–713. https://doi.org/10.1093/aob/mcl114

Lecerf A., Evangelista C., Cucherousset J. & Boiché A. 2016. Riparian overstory–understory interactions and their potential implications for forest-stream linkages. Forest Ecology and Management 367: 112–119. https://doi.org/10.1016/j.foreco.2016.02.031

Li C.P. & Xiao C.W. 2007. Above- and belowground biomass of Artemisia ordosica communities in three contrasting habitats of the Mu Us desert, northern China. Journal of Arid Environment 70(2): 195–207. https://doi.org/10.1016/j.jaridenv.2006.12.017

Li Y., Li K., Tao B. & Xu M. 2010. Simulating and assessing the adaptability of geographic distribution of vegetation to climate change in China. Progress in Geography 29(11): 1326–1332.

Liu C., Zhang X. & Zhang Y. 2002. Determination of daily evaporation and evapotranspiration of winter wheat and maize by large-scale weighing lysimeter and micro-lysimeter. Agricultural and Forest Meteorology 111(2): 109–120. https://doi.org/10.1016/S0168-1923(02)00015-1

McConnaughay K. & Coleman J. 1999. Biomass allocation in plants: ontogeny or optimality? A test along three resource gradients. Ecology 80(8): 2581–2593. https://doi.org/10.1890/0012-9658(1999)080%5B2581:BAIPOO%5D2.0.CO;2

Mokany K., Raison R.J. & Prokushkin A.S. 2006. Critical analysis of root: shoot ratios in terrestrial biomes. Global Change Biology 12(1): 84–96. https://doi.org/10.1111/j.1365-2486.2005.001043.x

Moreno G., Bartolome J.W., Gea-Izquierdo G. & Cañellas I. 2013. Overstory–understory relationships. In: Campos P. et al. (eds) Mediterranean oak woodland working landscapes. Landscape Series, vol. 16: 145–179. Dordrecht, Springer. https://doi.org/10.1007/978-94-007-6707-2_6

Návar J., Méndez E., Nájera A., Graciano J., Dale V. & Parresol B. 2004. Biomass equations for shrub species of Tamaulipan thornscrub of North-eastern Mexico. Journal of Arid Environments 59(4): 657–674. https://doi.org/10.1016/j.jaridenv.2004.02.010

Nemani R.R., Keeling C.D., Hashimoto H., et al. 2003. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300(5625): 1560–1563. https://doi.org/10.1126/science.1082750

Niklas K.J. 2005. Modelling below- and above-ground biomass for non-woody and woody plants. Annals of Botany 95(2): 315–321. https://doi.org/10.1093/aob/mci028

Piao S., Fang J., Zhou L., Ciais P. & Zhu B. 2006. Variations in satellite-derived phenology in China’s temperate vegetation. Global Change Biology 12(4): 672–685. https://doi.org/10.1111/j.1365-2486.2006.01123.x

Piñeiro G., Paruelo J.M., Jobbágy E.G., Jackson R.B. & Oesterheld M. 2009. Grazing effects on belowground C and N stocks along a network of cattle enclosures in temperate and subtropical grasslands of South America. Global Biogeochemical Cycles 23(2): GB2003. https://doi.org/10.1029/2007GB003168

Poorter H., Jagodzinski A.M., Ruiz‐Peinado R., et al. 2015. How does biomass distribution change with size and differ among species? An analysis for 1200 plant species from five continents. New Phytologist 208(3): 736–749. https://doi.org/10.1111/nph.13571

Poorter H., Niklas K.J., Reich P.B., Oleksyn J., Poot P. & Mommer L. 2012. Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. New Phytologist 193(1): 30–50. https://doi.org/10.1111/j.1469-8137.2011.03952.x

QGIS Development Team 2012. QGIS Geographic Information System. Version 2.14. Open Source Geospatial Foundation Project. Available from https://qgis.org [accessed 3 Aug. 2020].

Ren G., Guo J., Xu M., et al. 2005. Climate changes of China’s mainland over the past half century. Acta Meteorologica Sinica 63: 942–956. [In Chinese].

Sah J.P., Ross M.S., Koptur S. & Snyder J.R. 2004. Estimating aboveground biomass of broadleaved woody plants in the understory of Florida Keys pine forests. Forest Ecology and Management 203(1–3): 319–329. https://doi.org/10.1016/j.foreco.2004.07.059

Schimel D.S., House J.I., Hibbard K.A., et al. 2001. Recent patterns and mechanisms of carbon exchange by terrestrial ecosystems. Nature 414: 169–172. https://doi.org/10.1038/35102500

Scurlock J., Johnson K. & Olson R. 2002. Estimating net primary productivity from grassland biomass dynamics measurements. Global Change Biology 8(8): 736–753. https://doi.org/10.1046/j.1365-2486.2002.00512.x

Serreze M.C., Walsh J.E., Chapin F.S., et al. 2000. Observational evidence of recent change in the northern high-latitude environment. Climatic Change 46: 159–207. https://doi.org/10.1023/A:1005504031923

Shoshany M. 2012. The rational model of shrubland biomass, pattern and precipitation relationships along semi-arid climatic gradients. Journal of Arid Environments 78: 179–182. https://doi.org/10.1016/j.jaridenv.2011.10.013

Sturm M., Racine C. & Tape K. 2001. Increasing shrub abundance in the Arctic. Nature 411: 546–547. https://doi.org/10.1038/35079180

Throop H.L., Reichmann L.G, Sala O.E. & Archer S.R. 2012. Response of dominant grass and shrub species to water manipulation: an ecophysiological basis for shrub invasion in a Chihuahuan Desert Grassland. Oecologia 169: 373–383. https://doi.org/10.1007/s00442-011-2217-4

Wang X.P., Fang J.Y. & Zhu B. 2008. Forest biomass and root–shoot allocation in northeast China. Forest Ecology and Management 255(12): 4007–4020. https://doi.org/10.1016/j.foreco.2008.03.055

Wang L., Li L., Chen X., Tian X., Wang X. & Luo G. 2014. Biomass allocation patterns across China’s terrestrial biomes. PLOS ONE 9(4): e93566. https://doi.org/10.1371/journal.pone.0093566

Warton D.I., Duursma R.A., Falster D.S. & Taskinen S. 2012. Smatr 3- an R package for estimation and inference about allometric lines. Methods in Ecology and Evolution 3(2):257–259. https://doi.org/10.1111/j.2041-210X.2011.00153.x

Yang Y.H., Fang J.Y., Ji C.J. & Han W.X. 2009. Above- and belowground biomass allocation in Tibetan grassland. Journal of Vegetation Science 20(1): 177–184. https://doi.org/10.1111/j.1654-1103.2009.05566.x

Yang Y.H., Fang J.Y., Ma W.H., Guo D.L. & Mohammat A. 2010. Large-scale pattern of biomass partitioning across China’s grassland. Global Ecology and Biogeography 19(12): 268–277. https://doi.org/10.1111/j.1466-8238.2009.00502.x

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