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Spatial and Temporal Dynamics of the Weed Community in a Seashore Paspalum Turf

Published online by Cambridge University Press:  20 January 2017

Xin-Ming Xie ,
You-Zhi Jian  and
Xiao-Na Wen
Xin-Ming Xie*
Affiliation:
Department of Grassland, College of Agriculture, South China Agricultural University, Guangzhou 510642, China
You-Zhi Jian
Affiliation:
Department of Grassland, College of Agriculture, South China Agricultural University, Guangzhou 510642, China
Xiao-Na Wen
Affiliation:
Department of Grassland, College of Agriculture, South China Agricultural University, Guangzhou 510642, China
*
Corresponding author's E-mail: [email protected]
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Abstract

The temporal dynamics of spatial heterogeneity was studied for the weed communities in a seashore paspalum turf with the use of a power-law model. Surveys were conducted in January, March, May, July, September, and November in 2007. In every survey, we set 100 quadrats (50 by 50 cm) referred to as L quadrats on a 50-m line transect at the same position in the turf. Each L quadrat was then divided into four S quadrats (25 by 25 cm) and all plant species occurring in each of these S quadrats were identified and recorded. These data were summarized into frequency distributions and the percentage of S quadrats containing a given species, and the variance of each species was estimated. The power law was used to evaluate the spatial heterogeneity (δ) and frequency of occurrence (p) for each species in the weed communities in six survey months. The results showed that weeds emerged more frequently in the summer–spring season than in winter–autumn, and the spatial heterogeneity was much higher in summer–spring than winter–autumn, especially in summer. The Shannon–Wiener diversity indexes (H') from large to small were July (5.9202) > May (5.6775) > September (5.6631) > March (5.5727) > January (5.1742) > November (4.9668). Likewise, the spatial heterogeneity index (δc ) of the whole community was also different in different months. The biggest δc (0.2790) was in July, and the smallest (0.1811) in November. Meanwhile, manilagrass had a high p (= 1.0), indicating that it occurred in all S quadrats in every weed community of every month. However, the turfgrass, seashore paspalum, only emerged in March, May, July, and November, and possessed a low p, indicating the seashore paspalum turf has been naturally replaced by manilagrass.

Keywords

Manilagrass, Zoysia matrella (L.) Merr.seashore paspalum, Paspalum vaginatum Sw. PASVA.Power lawspatial heterogeneityspecies diversitylawn weedregression analysis
Type
Weed Biology and Ecology
Information
Weed Science , Volume 57 , Issue 3 , June 2009 , pp. 248 - 255
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Chen, J., Huang, D., Shiyomi, M., Hori, Y., Yamamura, Y., and Yiruhan, . 2007. Special heterogeneity and diversity of vegetation at the landscape level in Inner Mongolia, China, with special reference to water resources. Landsc. Urban Plann. 82:222–232.Google Scholar
Colbach, N., Forcella, F., and Johnson, G. A. 2000. Spatial and temporal variability of weed populations over five years. Weed Sci. 48:366–377.Google Scholar
Cyril, J., Powell, G. L., Duncan, R. R., and Baird, W. V. 2002. Changes in membrane polar lipid fatty acids of seashore paspalum in response to low temperature exposure. Crop Sci. 42:2031–2037.Google Scholar
Dieleman, J. A. and Mortensen, D. A. 1999. Characterizing the spatial pattern of Abutilon theophrasti seedling patches. Weed Res. 39:455–467.Google Scholar
Gou, W. L., Zhang, X. Q., Bai, S. Q., and Shi, Z. T. 2002. Research advances in manilagrass. Pratacultural Sci. 19 (3):62–65. [In Chinese].Google Scholar
Guan, D. S., He, K. Z., and Chen, Y. J. 1998. The soil characteristic of Guangzhou urban vegetation and its effects on tree growth. Res. Environ. Sci. 11 (4):51–54. [In Chinese].Google Scholar
Heijting, S., Van Der Werf, W., Kruijer, W., and Stein, A. 2007. Testing the spatial significance of weed patterns in arable land using Mead's test. Weed Res. 47:396–405.Google Scholar
Jurado-Expósito, M., López-Granados, F., González-Andújar, J. L., and García-Torres, L. 2004. Spatial and temporal analysis of Convolvulus arvensis L. populations over four growing seasons. Eur. J. Agron. 21:287–296.Google Scholar
Madden, L. P. and Hughes, G. 1995. Plant disease incidence: distribution, heterogeneity, and temporal analysis. Annu. Rev. Phytopathol. 33:529–564.Google Scholar
Perry, J. N., Winder, L., Holland, J. M., and Alston, R. D. 1999. Red–blue plots for detecting clusters in count data. Ecol. Lett. 2:106–113.Google Scholar
Shiyomi, M., Egawa, T., Sei, K., Tstsumi, M., Yamamoto, Y., and Kitahara, N. 1997. Beta-binomial series and power law in occurrence of plant population in semi-natural grassland. Pages 35–43. in. B. Li, ed. International Symposium on Grassland Management in Mongolian Plateau. Hohhot The Inner Mongolia University Press.Google Scholar
Shiyomi, M., Takahashi, S., Yoshimura, J., Yasuda, T., Tsutsumi, M., Tsuiki, M., and Yoshimichi, H. 2001. Spatial heterogeneity in grassland community: use of power law. Ecol. Res. 16:487–495.Google Scholar
Song, Z., Huang, D., Shiyomi, M., Wang, Y., Takahashi, S., Yoshimichi, H., Yamamuru, Y., and Chen, J. 2005. Spatial heterogeneity and variability of a large-scale vegetation community using a power-law model. Tsinghua Sci. Technol. 10:469–477.Google Scholar
Sutherland, W. J. 1990. The response of plants to patchy environments. Pages 45–56. in. B. Shorrochs and L. R. Swingland, eds. Living in a Patchy Environment. London Oxford Scientific.Google Scholar
Taylor, L. R. 1961. Aggregation, variance and the mean. Nature. 189:732–735.Google Scholar
Tsuiki, M., Wang, Y. S., Yiruhan, , Tsutsumi, M., and Shiyomi, M. 2005. Analysis of grassland vegetation of the Southwest Heilongjiang steppe (China) using the power law. J. Integr. Plant Biol. 47:917–926.Google Scholar
Wang, Y., Shiyomi, M., Tsuiki, M., Tsutsumi, M., Yu, X., and Yi, R. 2002. Spatial heterogeneity of vegetation under different grazing intensities in the northwest Heilongjiang steppe of China. Agric. Ecosyst. Environ. 90:217–229.Google Scholar
Whittaker, R. H., Gilbert, L. E., and Connell, J. H. 1979. Analysis of two-phase pattern in a mesquite grassland. Texas. J. Ecol. 67:935–952.Google Scholar
Whittaker, R. H. and Navesh, Z. 1979. Analysis of two phase patterns. Pages 157–165. in. G. P. Patil and M. L. Rosenzweig, eds. Contemporary Quantitative Ecology and Related Econometrics, Statistical Ecology. Series 12. Burtonsville, MD International Cooperative.Google Scholar
Xie, X. M., Tang, W., Zhong, P. T., and Shiyomi, M. 2008. Analysis of spatial heterogeneity of the weed community in a manilagrass lawn using power-law. Acta Hortic. 783:529–535.Google Scholar
Zhang, N. and Wang, M. 2002. Precision agriculture—a world overview. Comput. Electron. Agric. 36:113–132.Google Scholar