Volume 6, Issue 6, November 2018, Page: 226-236
Geostatistical Analysis of Spatial Distribution of Endoclita signifer Larvae on Eucalyptus
Xiuhao Yang, Department of Guangxi Forestry Pest Management, Bureau of Guangxi Forestry, Nanning, China
Jianglin Qin, Guangxi Institute of Meteorological Disaster-Reducing Research, Nanning, China
Youqing Luo, Forestry College, Beijing Forestry University, Beijing, China
Zhongwu Yang, Forestry Pest Management Station of Guiling Districts, Guiling, China
Jiguang Wei, Agriculture College, Guangxi University, Nanning, China
Received: Oct. 14, 2018;       Accepted: Nov. 10, 2018;       Published: Dec. 24, 2018
DOI: 10.11648/j.ajaf.20180606.20      View  39      Downloads  13
Abstract
Endoclita signifer is a native wood borer species adapted to infest Eucalyptus in Guangxi of China. To better understand its spatial distribution in Eucalyptus forests, geostatistical approach was employed to analyze survey data of small scale (plantation level) and large scale (provincial level). The small scale results showed that the spatial distribution pattern was clumped regardless of infestation level. But the distance of spatial dependence was different with different infestation levels. These distances were 20.0 m, 40.3 m and 69.4 m for light, moderate and severe infestation levels, respectively. This indicated that as infestation level increased, the spatial distribution was less clumped. At the large scale level, the spatial distribution pattern was random when infestation was light. At the moderate and severe infestation level, the distribution pattern was clumped. The distance of spatial dependence was 43.6 km, 15.5 km and 12.47 km for light, moderate and severe infestation, respectively. This trend was opposite to that of the small scale. The large scale survey results reflected the occurrence of E. signifer in Guangxi province. Eucalyptus trees are cultivated in every county of Guangxi. Plantations with light infestation were scattered in a random pattern across the province. Moderate and severe infested plantations were mainly distributed in the central and southern areas of the province where Eucalyptus cultivation has longer history compared to other areas. Furthermore, the distance of spatial dependence was 17.4 km and 21.3 km for the 2nd generation forests (ratoon) and the 1st generation forests (established via seedlings), respectively. The observed spatial distribution patterns of E. signifer larvae seemed closely related to its biology and its successful adaptation to attack exotic Eucalyptus. These results provide fundamental knowledge for forecasting and evaluating E. signifer infestation and damage of Eucalyptus forests.
Keywords
Endoclita signifer, Spatial Distribution Pattern, Geostatistics, Eucalyptus
To cite this article
Xiuhao Yang, Jianglin Qin, Youqing Luo, Zhongwu Yang, Jiguang Wei, Geostatistical Analysis of Spatial Distribution of Endoclita signifer Larvae on Eucalyptus, American Journal of Agriculture and Forestry. Vol. 6, No. 6, 2018, pp. 226-236. doi: 10.11648/j.ajaf.20180606.20
Copyright
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Yang XH, Yu YH, Wu YJ, et al., First report of Endoclita signifer (Lepidoptera: Hepialidae) as a new pest on Eucalyptus. Journal of Economic Entomology, 106:866-873 (2013).
[2]
Yang XH, Yu YH, Cao SG, et al., Morphology and biology of Endoclita signifer Walker, a new borer damage on Eucalyptus. Forest research, 26: 34-40 (2013).
[3]
Qi SX, Eucalyptus in China. Beijing, Chinese Forest Press (2002).
[4]
Yang XH, Cheng SW, Yu YH, et al., Damage occurred monitoring and risk analysis of Endoclita signifer Walker in Guangxi. Anhui Agricultural Science Bulletin., 18:84-86 (2012).
[5]
Qin JL, Yang XH, Fu H, and Lei XF, A novel nonlinear algorithm for area-wide near surface air temperature retrieval. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 9:3283-3296 (2016).
[6]
Xu RM, Population ecology of insects. Beijing: Beijing Normal University Press (1987).
[7]
Ding YQ, Mathematical ecology of insects. Beijing, Science Press (994).
[8]
Wang ZQ, Application of geostatistics in ecology. Beijing, Science Press (1999).
[9]
Wang ZJ, Li DM, Theories and methods of geostatistics and their application in insect ecology. Entomological Knowledge, 39:405-502 (2002).
[10]
Zong SX, Jia FY, Xu ZC, et al. Spatial distribution pattern and sampling method of Holcocerus hippophaecolus larvae. Entomological Knowledge, 41:552-555 (2004).
[11]
Zong SX, Luo YQ, Xu ZC, et al. Geostatistical analysis on spatial distribution of Holcocerus hippophaecolus eggs and larvae. Acta Ecologica Sinica, 25:831-836 (2005).
[12]
Zong SX. Studies on the bio-ecological characteristics of seabuckthorn carpenter moth: Holcocerus hippophaecolus (Lepidoptera:Cossidae). Doctoral dissertation, Beijing, Beijing Forestry University (2006).
[13]
Yuan ZM, Fu W, Li FY, Spatial distribution pattern of Chilo suppressalis analyzed by classical method and geostatistics. Chinese Journal of Applied Ecology, 15:610- 614 (2004).
[14]
Williams L, Schotzko DJ, McCaffrey JP, Geostatistical description of the spatial distribution of Limonius californicus (Coleoptera: Elateridae) wire-worms in the northwestern United States, with comments on sampling. Environ. Entomol., 21: 983–995 (1992).
[15]
Qin JL, Yang XH, Luo JT, et al. An improved novel nonlinear algorithm of area-wide near surface air temperature retrieval, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens, 11: 830-844 (2018).
[16]
Robertson GP, GS+ Geostatistics for the Environmental Sciences. Gamma Design Software, Plainwell, Michigan USA (2008).
[17]
Duarte F, Calvo MV, Borges A., and Scatoni IB, Geostatistics applied to the study of the spatial distribution of insects and its use in integrated pest management. Rev. Agron. Noroeste Argent, 35:9-20 (2015).
[18]
Southwood TRE, Henderson PA, Ecological Methods. 3rd ed. Blackwell Sciences, Oxford, 592 pp (2000).
[19]
Shi GS, Li DM, Geostatistic analysis of spatial pattern of Dendrolimus punctatus. Chinese Journal of Applied Ecology, 8:612-616 (1997).
[20]
Li HX, Guo SP, ZHou ZJ, et al. Spatial distribution pattern and sampling method of the larvae of Phassus excrescens. Journal of Northwest Forestry University, 24:140-142 (2009).
[21]
Schotzko DJ and O’Keeffe LE, Geostatistical description of the spatial distribution of Lygus hesperus (Heteroptera: Miridae) in lentils. J. Econ. Entomol., 82:1277-1288 (1989).
[22]
Cao SG, Pang ZH, Yang XH, et al. Preliminary study on spatial distribution pattern of Endoclita signifer Walker Larva. Journal of Anhui Agri. Sci., 39:11492-11495 (2011).
[23]
Midgarden DG, Youngman RR, Fleischer SJ, Spatial analysis of counts of western corn root worm (Coleoptera: Chrysomelidae) adults on yellow sticky traps in corn: geostatistics and dispersion indices. Environ. Entomol., 22: 1124–1133 (1993).
[24]
Li YC, Xia NB, Tu UQ, et al. A geostatistical analysis on spatial patterns of Anoplophora glabripennis in poplars. Acta Ecologica Sinica, 17:301-309 (1997).
[25]
Liebhold AM, Rossi RE, Kemp WP, Geostatistics and geographic information systems in applied insect ecology. Annu. Rev. Entomol., 38: 303–327 (1993).
Browse journals by subject