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Relative importance of climate changes at different time scales on net primary productivity—a case study of the Karst area of northwest Guangxi, China

  • Huiyu Liu
  • Mingyang Zhang
  • Zhenshan Lin
Article
  • 200 Downloads

Abstract

Climate changes are considered to significantly impact net primary productivity (NPP). However, there are few studies on how climate changes at multiple time scales impact NPP. With MODIS NPP product and station-based observations of sunshine duration, annual average temperature and annual precipitation, impacts of climate changes at different time scales on annual NPP, have been studied with EEMD (ensemble empirical mode decomposition) method in the Karst area of northwest Guangxi, China, during 2000–2013. Moreover, with partial least squares regression (PLSR) model, the relative importance of climatic variables for annual NPP has been explored. The results show that (1) only at quasi 3-year time scale do sunshine duration and temperature have significantly positive relations with NPP. (2) Annual precipitation has no significant relation to NPP by direct comparison, but significantly positive relation at 5-year time scale, which is because 5-year time scale is not the dominant scale of precipitation; (3) the changes of NPP may be dominated by inter-annual variabilities. (4) Multiple time scales analysis will greatly improve the performance of PLSR model for estimating NPP. The variable importance in projection (VIP) scores of sunshine duration and temperature at quasi 3-year time scale, and precipitation at quasi 5-year time scale are greater than 0.8, indicating important for NPP during 2000–2013. However, sunshine duration and temperature at quasi 3-year time scale are much more important. Our results underscore the importance of multiple time scales analysis for revealing the relations of NPP to changing climate.

Keywords

Multiple time scales Climate changes Net primary productivity Ensemble empirical mode decomposition (EEMD) model Partial least squares regression (PLSR) model 

Notes

Funding

This research has been supported by National Natural Science Foundation of China (No. 31470519, 31370484), Natural Science Foundation of Jiangsu Province (BK20131399) and funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

References

  1. Aghakouchak, A., Cheng, L., Mazdiyasni, O., & Farahmand, A. (2014). Global warming and changes in risk of concurrent climate extremes: insights from the 2014 California drought. Geophysical Research Letters, 41, 8847–8852.CrossRefGoogle Scholar
  2. Bloor, J. M. G., & Bardgett, R. D. (2012). Stability of above-ground and below-ground processes to extreme drought in model grassland ecosystems, interactions with plant species diversity and soil nitrogen availability. Perspectives in Plant Ecology, Evolution & Systematics, 14, 193–204.CrossRefGoogle Scholar
  3. Chapin, F. S., Matson, P. A., Vitousek, P., & Chapin, M. C. (2012). Principles of terrestrial ecosystem ecology (2nd ed.). New York: Springer.Google Scholar
  4. Chen, Z., Chen, Y., Bai, L., & Xu, J. (2016). Multiscale evolution of surface air temperature in the arid region of Northwest China and its linkages to ocean oscillations. Theoretical and Applied Climatology.  https://doi.org/10.1007/s00704.
  5. Cherwin, K., & Knapp, A. (2012). Unexpected patterns of sensitivity to drought in three semi-arid grasslands. Oecologia, 169, 845–852.CrossRefGoogle Scholar
  6. Chong, I. G., & Jun, C. H. (2005). Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems, 78, 103–112.CrossRefGoogle Scholar
  7. Craine, J. M., Nippert, J. B., Elmore, A. J., Adam, M., Skibbe, A. M., Hutchinson, S. L., & Brunsell, N. A. (2012). Timing of climate variability and grassland productivity. PNAS, 109, 3401–3405.CrossRefGoogle Scholar
  8. Eisfelder, C., Klein, I., Niklaus, M., & Kuenzer, C. (2014). Net primary productivity in Kazakhstan, its spatio-temporal patterns and relation to meteorological variables. Journal of Arid Environments, 103, 17e30.CrossRefGoogle Scholar
  9. Fay, P. A., Blair, J. M., Smith, M. D., Nippert, J. B., Carlisle, J. D., & Knapp, A. K. (2011). Relative effects of precipitation variability and warming on tallgrass prairie ecosystem function. Biogeosciences, 8, 3053–3068.CrossRefGoogle Scholar
  10. Field, C. B., Behrenfeld, M. J., Randerson, J. T., & Falkowski, P. (1998). Primary production of the biosphere: integrating terrestrial and oceanic components. Science, 281, 237–240.CrossRefGoogle Scholar
  11. Gherardi, L. A., Osvaldo, E., & Sala, O. E. (2015). Enhanced precipitation variability decreases grass- and increases shrub-productivity. PNAS, 112, 12735–12740.CrossRefGoogle Scholar
  12. Guo, Q., Hu, Z., Li, S., Li, X., & Sun, X. (2012). Spatial variations in aboveground net primary productivity along a climate gradient in Eurasian temperate grassland: effects of mean annual precipitation and its seasonal distribution. Global Change Biology, 18, 3624–3631.CrossRefGoogle Scholar
  13. Heim, R. H., Jürgens, N., Große-Stoltenberg, A., & Oldeland, J. (2015). The effect of epidermal structures on leaf spectral signatures of ice plants (Aizoaceae). Remote Sensing, 7, 16901–16914.CrossRefGoogle Scholar
  14. Hoeppner, S. S., & Dukes, J. S. (2012). Interactive responses of old-field plant growth and composition to warming and precipitation. Global Change Biology, 18, 1754–1768.CrossRefGoogle Scholar
  15. Hoerling, M., Hurrell, J., Kumar, A., Terray, L., Eischeid, J., Pegion, P., Zhang, T., Quan, X. W., & Xu, T. Y. (2011). On North American decadal climate for 2011–2020. Journal of Climate, 24, 4519–4528.CrossRefGoogle Scholar
  16. Hsu, J. S., Powell, J., & Adler, P. B. (2012). Sensitivity of mean annual primary production to precipitation. Global Change Biology, 18, 2246–2255.CrossRefGoogle Scholar
  17. Huang, N. E., Shen, Z., & Long, S. R. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proceedings of the Royal Society of London A, 454, 903–995.CrossRefGoogle Scholar
  18. Jia, X., Xie, B., Shao, M., & Zhao, C. (2015). Primary productivity and precipitation-use efficiency in temperate grassland in the Loess Plateau of China. PLoS One, 10, e0135490.CrossRefGoogle Scholar
  19. Johnson, R. A., & Wichern, D. W. (2002). Applied multivariate statistical analysis. New Jersey: Prentice Hall.Google Scholar
  20. Liu, C., Dong, X., & Liu, Y. (2015). Changes of NPP and their relationship to climate factors based on the transformation of different scales in Gansu, China. Catena, 125, 190–199.CrossRefGoogle Scholar
  21. Liu, H. Y., Lin, Z. S., Qi, X. Z., Li, Y. X., Yu, M. T., Yang, H., & Shen, J. (2012). Possible link between Holocene East Asian monsoon and solar activity obtained from the EMD method. Nonlinear Processes in Geophysics, 19, 421–430.CrossRefGoogle Scholar
  22. Liu, Y. G., Liu, C. C., Wang, S. J., Guo, K., Yang, J., Zhang, X. S., & Li, G. Q. (2013). Organic carbon storage in four ecosystem types in the karst region of southwestern China. PLoS One, 8, e56443.CrossRefGoogle Scholar
  23. Monteith, J. L. (1972). Solar radiation and productivity in tropical ecosystem. Journal of Applied Ecology, 9, 747–766.CrossRefGoogle Scholar
  24. Ni, J. (2011). Impacts of climate change on Chinese ecosystems: key vulnerable regions and potential thresholds. Regional Environmental Change, 11, S49–S64.CrossRefGoogle Scholar
  25. Ouyang, S., Wang, X., Wu, Y., & Sun, O. J. (2014). Contrasting responses of net primary productivity to inter-annual variability and changes of climate among three forest types in northern China. Journal of Plant Ecology, 7, 309–320.CrossRefGoogle Scholar
  26. Parise, M., Dewaele, J., & Gutierrez, F. (2008). Engineering and environmental problems in Karst—an introduction. Engineering Geology, 99, 91–94.CrossRefGoogle Scholar
  27. Pérez-Enciso, M., & Tenenhaus, M. (2003). Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach. Human Genetics, 112, 581–592.Google Scholar
  28. Potter, C., Klooster, S., & Genovese, V. (2012). Net primary production of terrestrial ecosystems from 2000 to 2009. Climatic Change, 115, 365–378.CrossRefGoogle Scholar
  29. Robinson, T. M. P., Pierre, K. J. L., & Vadeboncoeur, M. A. (2013). Seasonal, not annual precipitation drives community productivity across ecosystems. Oikos, 122, 727–738.CrossRefGoogle Scholar
  30. Seddon, A. W. R., Macias-Fauria, M., Long, P. R., Benz, D., & Willis, K. J. (2016). Sensitivity of global terrestrial ecosystems to climate variability. Nature, 531, 229–232.CrossRefGoogle Scholar
  31. Sullivan, M. J. P., Thomsen, M. A., & Suttle, K. B. (2016). Grassland responses to increased rainfall depend on the timescale of forcing. Global Change Biology, 22, 1655–1665.CrossRefGoogle Scholar
  32. Thevs, N., Wuchererb, W., & Buras, A. (2013). Spatial distribution and carbon stock of the Saxaul vegetation of the winter-cold deserts of Middle Asia. Journal of Arid Environments, 90, 29–35.CrossRefGoogle Scholar
  33. Thomey, M. L., Collins, S. L., Vargas, R., Johnson, J. E., Brown, R. F., Natvig, D. O., & Friggens, M. T. (2011). Effect of precipitation variability on net primary production and soil respiration in a Chihuahuan Desert grassland. Global Change Biology, 17, 1505–1515.CrossRefGoogle Scholar
  34. Vicente-Serranoa, S. M., Gouveia, C., Camarerod, J. J., Begueria, S., Trigo, R., Lopez-Moreno, J. I., Azorin-Molina, C., Pasho, E., Lorenzo-Lacruz, J., Revuelto, J., Morán-Tejeda, E., & Sanchez-Lorenzo, A. (2013). Response of vegetation to drought time-scales across global land biomes. PNAS, 110, 52–57.CrossRefGoogle Scholar
  35. Wang, H., Liu, G., Li, Z., Ye, X., Wang, M., & Gong, L. (2016). Impacts of climate change on net primary productivity in arid and semiarid regions of China. Chinese Geographical Science, 26, 35–47.CrossRefGoogle Scholar
  36. Wang, J., Wang, K., Zhang, M., & Zhang, C. (2015). Impacts of climate change and human activities on vegetation cover in hilly southern China. Ecological Engineering, 81, 451–461.CrossRefGoogle Scholar
  37. Wold, S. (1995). PLS for multivariate linear modeling. In H. van der Waterbeemd (Ed.), Chemometric methods in molecular design: methods and principles in medicinal chemistry (pp. 195–218). Weinheim: Verlag-Chemie.Google Scholar
  38. Wold, S., Algano, C., & Dunn, M. (1983). Pattern regression finding and using regularities in multivariate data. London: Analysis Applied Science Publication.Google Scholar
  39. Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58, 109–130.CrossRefGoogle Scholar
  40. Wu, Z. H., & Huang, N. E. (2004). A study of the characteristics of white noise using the empirical mode decomposition method. Proceedings of the Royal Society of London A, 460, 1597–1611.CrossRefGoogle Scholar
  41. Wu, Z. H., & Huang, N. E. (2009). Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1, 1–41.CrossRefGoogle Scholar
  42. Xu, H., & Wang, X. (2016). Effects of altered precipitation regimes on plant productivity in the arid region of northern China. Ecological Informatics, 31, 137–146.CrossRefGoogle Scholar
  43. Xu, X., Sherry, R., Niu, S., Li, D., & Luo, Y. (2013). Net primary productivity and rain-use efficiency as affected by warming, altered precipitation, and clipping in a mixed-grass prairie. Global Change Biology, 19, 2753–2764.CrossRefGoogle Scholar
  44. Yin, Y., Tang, Q., Wang, L., & Liu, X. (2016). Risk and contributing factors of ecosystem shifts over naturally vegetated land under climate change in China. Scientific Reports.  https://doi.org/10.1038/srep20905.
  45. Yuan, D. X., & Cai, G. H. (1988). The science of karst environment (in Chinese). Chongqing: Chongqing Science and Technology Publishing House.Google Scholar
  46. Zhang, M., Wang, K., Liu, H., Zhang, C., Wang, J., Yue, Y., & Qi, X. (2015). How ecological restoration alters ecosystem services: an analysis of vegetation carbon sequestration in the karst area of northwest Guangxi, China. Environmental Earth Sciences, 74, 5307–5317.CrossRefGoogle Scholar
  47. Zhang, M. Y., Zhang, C. H., Wang, K. L., Yue, Y. M., Qi, X. K., & Fan, F. D. (2011). Spatiotemporal variation of karst ecosystem service values and its correlation with environmental factors in northwest Guangxi, China. Environmental Management, 48, 933–944.CrossRefGoogle Scholar
  48. Zhao, M. S., Heinsch, F. A., Nemani, R. R., & Running, S. W. (2005). Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment, 95(2), 164–176.CrossRefGoogle Scholar
  49. Zhu, L., & Southworth, J. (2013). Disentangling the relationships between net primary production and precipitation in southern Africa savannas using satellite observations from 1982 to 2010. Remote Sensing, 5, 3803–3825.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.College of Geography ScienceNanjing Normal UniversityNanjingChina
  2. 2.State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province)NanjingChina
  3. 3.Key Laboratory of Virtual Geographic EnvironmentNanjing Normal University, Ministry of EducationNanjingChina
  4. 4.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and ApplicationNanjingChina
  5. 5.Key Laboratory of Agro-ecological Processes in Subtropical RegionInstitute of Subtropical Agriculture, Chinese Academy of SciencesChangshaChina

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