Hostname: page-component-76fb5796d-dfsvx Total loading time: 0 Render date: 2024-04-25T14:30:46.100Z Has data issue: false hasContentIssue false
  • English
  • Français

Grass habitat analysis and phytolith-based quantitative reconstruction of Asian monsoon climate change in the sand-loess transitional zone, northern China

Published online by Cambridge University Press:  24 June 2019

Hanlin Wang Open the ORCID record for Hanlin Wang [Opens in a new window] ,
Huayu Lu ,
Hongyan Zhang ,
Shuangwen Yi ,
Yao Gu  and
Chenghong Liang
Hanlin Wang
Affiliation:
School of Geography and Ocean Science, Jiangsu Collaborative Innovation Centre for Climate Change, Nanjing University, Nanjing 210023, China
Huayu Lu*
Affiliation:
School of Geography and Ocean Science, Jiangsu Collaborative Innovation Centre for Climate Change, Nanjing University, Nanjing 210023, China
Hongyan Zhang
Affiliation:
School of Geography and Ocean Science, Jiangsu Collaborative Innovation Centre for Climate Change, Nanjing University, Nanjing 210023, China
Shuangwen Yi
Affiliation:
School of Geography and Ocean Science, Jiangsu Collaborative Innovation Centre for Climate Change, Nanjing University, Nanjing 210023, China
Yao Gu
Affiliation:
School of Geography and Ocean Science, Jiangsu Collaborative Innovation Centre for Climate Change, Nanjing University, Nanjing 210023, China
Chenghong Liang
Affiliation:
School of Geography and Ocean Science, Jiangsu Collaborative Innovation Centre for Climate Change, Nanjing University, Nanjing 210023, China
*
*Corresponding author at e-mail address: huayulu@nju.edu.cn (H. Lu).
Get access Rights & Permissions [Opens in a new window]

Abstract

We investigated climate niches of grasses at regional scales and quantitatively reconstruct Asian monsoon precipitation at the sand-loess transitional zone in northern China. Our results provide direct evidence that certain grass lineages have been specialized in specific habitats: Pooideae grasses stand out and occupy a much cooler environment than all other subfamilies; Pooideae, Aristidoideae, and Chloridoieae occupy dry environments. Pooideae grasses occupy the coldest and driest environments compared to all other subfamilies, with a mean annual temperature (MAT) and precipitation (MAP) of ~13.6 to ~15.3°C and 224 to ~1674 mm, respectively, at a regional scale. We built a database for grasses and their corresponding climate parameters. Based on this database, past climate parameters at the margin of the Asian summer monsoon since ~70 ka were quantitatively reconstructed by phytolith assemblages. They show that this area was dominated by cold- and dry-adapted grasses since ~70 ka with a MAT and MAP of ~3.3 to ~11.0 °C and ~442 to ~900 mm, respectively, generally consistent with the results of phytolith-based transfer function reconstructions and with the results of previous nearby pollen-based quantitative reconstructions. With the improvement of the species-climate and ecosystem dataset, our database-based method is a promising quantitative reconstruction approach to past climatic change in the monsoon region.

Keywords

PhytolithSand-loess depositAsian monsoonQuantitative reconstructionNorthern China
Type
Research Article
Information
Quaternary Research , Volume 92 , Issue 2 , September 2019 , pp. 519 - 529
Copyright
Copyright © University of Washington. Published by Cambridge University Press, 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Berger, A., Loutre, M.F., 1991. Insolation values for the climate of the last 10 million years. Quaternary Science Reviews 10, 297–317.Google Scholar
Birks, H.J.B., 1998. Numerical tools in palaeolimnology—progress, potentialities, and problems. Journal of Paleolimnology 20, 307–332.Google Scholar
Birks, H.J.B., Heiri, O., Seppä, H, Bjune, A.E., 2011. Strengths and weaknesses of quantitative climate reconstructions based on late-Quaternary biological proxies. The Open Ecology Journal 3, 68–110.Google Scholar
Blaauw, M., Christen, J.A., 2011. Flexible paleoclimate age-depth models using an autoregressive gamma process. Bayesian Analysis 6, 457–474.Google Scholar
Cai, Z., Ge, S., 2017. Machine learning algorithms improve the power of phytolith analysis: a case study of the tribe Oryzeae (Poaceae). Journal of Systematics and Evolution 55, 377–384.Google Scholar
Dunn, R.E., Stromberg, C.A.E., Madden, R.H., Kohn, M.J., Carlini, A.A., 2015. Linked canopy, climate, and faunal change in the Cenozoic of Patagonia. Science 347, 258–261.Google Scholar
Edwards, E.J., Smith, S.A., 2010. Phylogenetic analyses reveal the shady history of C4 grasses. Proceedings of the National Academy of Sciences of the United States of America 107, 2532–2537.Google Scholar
Gu, Y., Liu, H., Wang, H., Li, R., Yu, J., 2016. Phytoliths as a method of identification for three genera of woody bamboos (Bambusoideae) in tropical southwest China. Journal of Archaeological Science 68, 46-53.Google Scholar
Intergovernmental Panel on Climate Change, 2013. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Climate Change 2013: The Physical Science Basis. Cambridge University Press, New York.Google Scholar
Lisiecki, L.E., Raymo, M.E., 2005. A Pliocene-Pleistocene stack of 57 globally distributed benthic δ18O records. Paleoceanography 20, PA1003. http://dx.doi.org/10.1029/2004PA001071.Google Scholar
Liu, T. 1985. Loess and environment. China Ocean Press, Beijing.Google Scholar
Lu, H., Miao, X., Zhou, Y., Mason, J., Swinehart, J., Zhang, J., Zhou, L., Yi, S., 2005. Late Quaternary aeolian activity in the Mu Us and Otindag dune fields (north China) and lagged response to insolation forcing. Geophysical Research Letters 32, L21716. http://dx.doi.org/10.1029/2005GL024560.Google Scholar
Lu, H., Stevens, T., Yi, S., Sun, X., 2006. An erosional hiatus in Chinese loess sequences revealed by closely spaced optical dating. Chinese Science Bulletin 51, 2253–2259.Google Scholar
Lu, H., Yi, S., Liu, Z., Mason, J.A., Jiang, D., Cheng, J., Stevens, T., Xu, Z., et al. , 2013a. Variation of East Asian monsoon precipitation during the past 21 k.y. and potential CO2 forcing. Geology 41, 1023–1026.Google Scholar
Lu, H., Yi, S., Xu, Z., Zhou, Y., Zeng, L., Zhu, F., Feng, H., Dong, L., et al. , 2013b. Chinese deserts and sand fields in Last Glacial Maximum and Holocene Optimum. Chinese Science Bulletin 58, 2775–2783.Google Scholar
Lü, H., Wu, N., Yang, X., Jiang, H., Liu, K., Liu, T., 2006. Phytoliths as quantitative indicators for the reconstruction of past environmental conditions in China I: phytolith-based transfer functions. Quaternary Science Reviews 25, 945–959.Google Scholar
Lü, H., Wu, N., Yang, X., Jiang, H., Liu, K., Liu, T., 2007. Phytoliths as quantitative indicators for the reconstruction of past environmental conditions in China II: palaeoenvironmental reconstruction in the Loess Plateau. Quaternary Science Reviews 26, 759–772.Google Scholar
McInerney, F.A., Stroemberg, C.A.E., White, J.W.C., 2011. The Neogene transition from C3 to C4 grasslands in North America: stable carbon isotope ratios of fossil phytoliths. Paleobiology 37, 23–49.Google Scholar
Mohtadi, M., Prange, M., Steinke, S., 2016. Palaeoclimatic insights into forcing and response of monsoon rainfall. Nature 533, 191–199.Google Scholar
Mosbrugger, V., Utescher, T., 1997. The coexistence approach—a method for quantitative reconstructions of Tertiary terrestrial palaeoclimate data using plant fossils. Palaeogeography, Palaeoclimatology, Palaeoecology 134, 61–86.Google Scholar
Piperno, D.R., 2006. Phytoliths: A Comprehensive Guide for Archaeologists and Paleoecologists. AltaMira Press, New York.Google Scholar
Prebble, M., Schallenberg, M., Carter, J., Shulmeister, J., 2002. An analysis of phytolith assemblages for the quantitative reconstruction of late Quaternary environments of the Lower Taieri Plain, Otago, South Island, New Zealand I. Modern assemblages and transfer functions. Journal of Paleolimnology 27, 393–413.Google Scholar
Prebble, M., Shulmeister, J., 2002. An analysis of phytolith assemblages for the quantitative reconstruction of late Quaternary environments of the Lower Taieri Plain, Otago, South Island, Zealand II. Paleoenvironmental reconstruction. Journal of Paleolimnology 27, 415–427.Google Scholar
Soreng, R.J., Peterson, P.M., Romaschenko, K., Davidse, G., Teisher, J.K., Clark, L.G., Barberá, P., Gillespie, L.J., Zuloaga, F.O., 2017. A worldwide phylogenetic classification of the Poaceae (Gramineae) II: an update and a comparison of two 2015 classifications. Journal of Systematics and Evolution 55, 259–290.Google Scholar
Soreng, R.J., Peterson, P.M., Romaschenko, K., Davidse, G., Zuloaga, F.O., Judziewicz, E.J., Filgueiras, T.S., Davis, J.I., Morrone, O., 2015. A worldwide phylogenetic classification of the Poaceae (Gramineae). Journal of Systematics and Evolution 53, 117–137.Google Scholar
Stevens, T., Buylaert, J., Thiel, C., Újvári, G., Yi, S., Murray, A.S., Frechen, M., Lu, H., 2018. Ice-volume-forced erosion of the Chinese Loess Plateau global Quaternary stratotype site. Nature communications 9, 983.Google Scholar
Strömberg, C.A.E., 2004. Using phytolith assemblages to reconstruct the origin and spread of grass-dominated habitats in the great plains of North America during the late Eocene to early Miocene. Palaeogeography, Palaeoclimatology, Palaeoecology 207, 239–275.Google Scholar
Strömberg, C.A.E., Dunn, R.E., Madden, R.H., Kohn, M.J., Carlini, A.A., 2013. Decoupling the spread of grasslands from the evolution of grazer-type herbivores in South America. Nature Communications 4, 1478.Google Scholar
Sun, A., Feng, Z., 2013. Holocene climatic reconstructions from the fossil pollen record at Qigai Nuur in the southern Mongolian Plateau. Holocene 23, 1391–1402.Google Scholar
Sun, J., 2000. Origin of eolian sand mobilization during the past 2300 years in the Mu Us Desert, China. Quaternary Research 53, 78–88.Google Scholar
Telford, R.J., Birks, H.J.B., 2005. The secret assumption of transfer functions: problems with spatial autocorrelation in evaluating model performance. Quaternary Science Reviews 24, 2173–2179.Google Scholar
Wu, J., Lu, H., Yi, S., Xu, Z., Gu, Y., Liang, C., Cui, M., Sun, X., 2018. Establishing a high-resolution luminescence chronology for the Zhenbeitai sand-loess section at Yulin, North-Central China. Quaternary Geochronology 49, 78–84.Google Scholar
Wu, N., Lü, H., Sun, X., Guo, Z., Liu, J., 1994. Climatic transfer function from Opal Phytolith and its application in paleoclimate reconstruction of China loess-paleosol sequence. Quaternary Sciences 4, 270–279.Google Scholar
Xu, Q., Xiao, J., Li, Y., Tian, F., Nakagawa, T., 2010. Pollen-based quantitative reconstruction of Holocene climate changes in the Daihai Lake area, Inner Mongolia, China. Journal of Climate 23, 2856–2868.Google Scholar
Supplementary material: File

Wang et al. supplementary material

Wang et al. supplementary material 1

Download Wang et al. supplementary material(File)
File 5 MB