Hostname: page-component-76fb5796d-vfjqv Total loading time: 0 Render date: 2024-04-25T07:03:08.527Z Has data issue: false hasContentIssue false
  • English
  • Français

Occupancy-abundance models for predicting densities of three leaf beetles damaging the multipurpose tree Sesbania sesban in eastern and southern Africa

Published online by Cambridge University Press:  09 March 2007

G. Sileshi ,
G. Hailu  and
P.L. Mafongoya
G. Sileshi*
Affiliation:
World Agroforestry Centre (ICRAF), Zambia-ICRAF Agroforestry Project, PO Box 510089, Chipata, Zambia
G. Hailu
Affiliation:
World Agroforestry Centre (ICRAF), United Nations Avenue, Gigiri, PO Box 30677, Nairobi, 00100 GPO, Kenya
P.L. Mafongoya
Affiliation:
World Agroforestry Centre (ICRAF), Zambia-ICRAF Agroforestry Project, PO Box 510089, Chipata, Zambia
*
*PO Box 511118, Chipata, Zambia Fax: 260 6 221404 E-mail: sgwelde@yahoo.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Mesoplatys ochroptera Stål, Exosoma and Ootheca spp. seriously damage sesbania, Sesbania sesban (L.) Merril, a multipurpose leguminous tree widely used in tropical agroforestry. This is discouraging farmers from expanding the planting of sesbania in various agroforestry systems in eastern and south-central Africa. Rapid methods are needed for estimation of population densities of these beetles for decisionmaking in pest management. A study was conducted with the objectives of determining the existence of any positive relationship between the occupancy and abundance of Mesoplatys, Exosoma and Ootheca and determining the model that best predicts abundance from occupancy for rapid estimation of population densities. The Poisson model assuming spatial randomness, the negative binomial distribution (NBD) model assuming spatial aggregation, the Nachman model without any distribution assumption, and a General model incorporating spatial variance-abundance and occupancy-abundance relationships were fitted to data on adult M. ochroptera, Exosoma and Ootheca from western Kenya, southern Malawi and eastern Zambia. Very strong variance to abundance relationships were observed in the spatial pattern of all three beetles. The occupancy-abundance relationships were also positive and strong in all beetles. The occupancy and abundance predicted by the four models were closest to the observed at lower densities compared with higher beetle densities. At higher population densities, the NBD and the General model gave better fit for M. ochroptera and Exosoma. For Ootheca populations, the Poisson and NBD models gave better fit at higher population densities. The relationships established here can be used as guide to estimate beetle densities for decision-making in pest management.

Keywords

binomial samplingExosomaMesoplatysOothecapower law
Type
Review Article
Information
Bulletin of Entomological Research , Volume 96 , Issue 1 , February 2006 , pp. 61 - 69
Copyright
Copyright © Cambridge University Press 2006

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

Allen, D.J., Ampofo, J.K.O. & Wortman, C.S. (1996) Pests, diseases, and nutritional disorders of the common bean in Africa: A field guide. CIAT Publication No. 260. Cali, Colombia.Google Scholar
Anscombe, F.J. (1949) The analysis of insect counts based on the negative binomial distribution. Biometrics 5, 165–173.Google Scholar
Bliss, C.I. & Fisher, R.A. (1953) Fitting the negative binomial distribution to biological data and note on efficient fitting of the negative binomial. Biometrics 9, 176–200.CrossRefGoogle Scholar
Brown, J.H., Gupta, V.K., Li, B., Milne, B.T., Restrepo, C. & West, G.B. (2002) The fractal nature of nature: power laws, ecological complexity and biodiversity. Philosophical Transactions of the Royal Society of London 357, 619–626.CrossRefGoogle ScholarPubMed
Conrad, K.F., Perry, J.N. & Woiwood, I.P. (2001) An abundance-occupancy time-lag during the decline of an arctid tiger moth. Ecology Letters 4, 300–303.CrossRefGoogle Scholar
Evans, D.O. & Rotar, P.P. (1987) Sesbania in agriculture. Colorado, Westview, Press Boulder.Google Scholar
Gaston, K.J. & Curnutt, J.L. (1998) The dynamics of abundance-range size relationships. Oikos 81, 38–44.Google Scholar
Gaston, K., Blackburn, T.M., Greenwood, J.J.D., Gregory, R., Quinn, R.M. & Lawton, J.H. (2000) Abundance-occupancy relationships. Journal of Applied Ecology 37, 39–59.CrossRefGoogle Scholar
Hartley, S. (1998) A positive relationship between local abundance and regional occupancy is almost inevitable (but not all positive relationships are the same). Journal of Animal Ecology 67, 992–994.CrossRefGoogle ScholarPubMed
He, F. & Gaston, K.J. (2002) Occupancy-abundance relationships and sampling scales. Ecography 23, 503–511.CrossRefGoogle Scholar
He, F. & Gaston, K.J. (2003) Occupancy, spatial variance, and the abundance of species. American Naturalist 162, 366–375.Google Scholar
He, F., Gaston, K.J. & Wu, J. (2002) On species occupancy-abundance models. Ecoscience 9, 119–126.CrossRefGoogle Scholar
Karel, A.K. (1989) Response of Ootheca bennigseni (Coleoptera: Chrysomelidae) to extracts from neem. Journal of Economic Entomology 82, 1799–1803.CrossRefGoogle Scholar
Kilpatrick, A.M. & Ives, A.R. (2003) Species interactions can explain Taylor's power law for ecological time series. Nature 422, 65–68.CrossRefGoogle ScholarPubMed
Kuno, E. (1986) Evaluation of statistical precision and design of efficient sampling for the population estimation based on frequency of occurrence. Research in Population Ecology 28, 305–319.Google Scholar
Kwesiga, F.R., Franzel, S., Place, F., Phiri, D. & Simwanza, C.P. (1999) Sesbania sesban improved fallows in eastern Zambia: their inception, development and farmer enthusiasm. Agroforestry Systems 47, 49–66.CrossRefGoogle Scholar
Mchowa, J.W. & Ngugi, D.N. (1994) Pest complex in agroforestry systems: the Malawi experience. Forest Ecology and Management 64, 277–284.CrossRefGoogle Scholar
MacKenzie, D.T., Nichols, J.D., Lachman, G.B., Droege, S., Royle, J.A. & Langtimm, C.A. (2002) Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248–2255.Google Scholar
Marquet, P.A., Quiñones, R.A., Abades, S., Labra, F., Tognelli, M., Arim, M. & Rivadeneira, M. (2005) Scaling and power-laws in ecological systems. Journal of Experimental Biology 208, 1749–1769.CrossRefGoogle ScholarPubMed
Nachman, G. (1981) A mathematical model of the functional relationship between density and spatial distribution of a population. Journal of Animal Ecology 50, 453–460.CrossRefGoogle Scholar
Ndungu, J.N. & Boland, D.J. (1994) Sesbania seed collection in southern Africa: developing a model for collaboration between a CGIAR Centre and NARS. Agroforestry Systems 27, 129–143.CrossRefGoogle Scholar
Onsager, J.A. (1981) A note on the Poisson distribution in integrated pest management. Bulletin of the Entomological Society of America 27, 119–120.CrossRefGoogle Scholar
Perry, J.N. & Taylor, L.R. (1986) Stability of real interacting populations in space and time: implications, alternatives and the negative binomial k c. Journal of Animal Ecology 55, 1053–1068.CrossRefGoogle Scholar
Ross, S. (1998) Farmers' perception of bean pest problems in Malawi. Network on Bean Research in Africa. Working Occasional Publication Series, No. 25. CIAT, Kampala, Uganda CIAT.Google Scholar
Royle, J.A. & Nichols, J.D. (2003) Estimating abundance from repeated presence absence data or point counts. Ecology 84, 777–790.CrossRefGoogle Scholar
Royle, J.A., Nichols, J.D. & Kery, M. (2005) Modelling occurrence and abundance of species when detection is imperfect. Oikos 110, 353–359.CrossRefGoogle Scholar
SAS Institute Inc (2003) SAS/STAT, Release 9.1 Cary, North Carolina, SAS Institute Inc.Google Scholar
Sileshi, G., Maghembe, J.A., Rao, M.R., Ogol, C.K.P.O. & Sithanantham, S. (2000) Insects feeding on sesbania in natural stands and agroforestry systems in southern Malawi. Agroforestry Systems 49, 41–52.CrossRefGoogle Scholar
Sileshi, G., Baumgaertner, J., Sithanantham, S. & Ogol, C.K.P.O. (2002) Spatial distribution and sampling plans for Mesoplatys ochroptera Stål (Coleoptera: Chrysomelidae) on sesbania. Journal of Economic Entomology 95, 499–506.Google Scholar
Sileshi, G., Sithanantham, S., Mafongoya, P.L., Ogol, C.K.P.O. & Rao, M.R. (2003) Biology of Mesoplatys ochroptera Stål (Chrysomelidae: Coleoptera), a pest of Sebania species in southern central Africa. African Entomology 11, 49–58.Google Scholar
Sithanantham, S., Irving, N.S. & Sohati, P.H. (1989) Recent entomological research on bean and cowpea in Zambia. pp. 135–156 in Mulila Mitti, J.M., Kannaiyan, J. & Sithanantham, S. (eds). Recent progress in food legume research and improvement in Zambia. Proceedings of the national workshop on food legume research and improvement in Zambia9–11 March, 1988Mfuwe, Zambia.Google Scholar
Strayer, D. (1999) Statistical power of presence-absence data to detect population declines. Conservation Biology 13, 1034–1038.Google Scholar
Taylor, L.R. (1961) Aggregation, variance and the mean. Nature 189, 732–735.Google Scholar
Taylor, L.R., Woiwod, I.P. & Perry, J.N. (1979) The negative binomial as a dynamic ecological model and the density-dependence of k. Journal of Animal Ecology 48, 289–304.Google Scholar
Warren, M., McGeoch, M.A. & Chown, S.L. (2003) Predicting abundance from occupancy: a test for an aggregated insect assemblage. Journal of Animal Ecology 72, 468–477.Google Scholar
Wilson, L.T. & Room, P.M. (1983) Clumping patterns of fruit and arthropods in cotton, with implications for binomial sampling. Environmental Entomology 12, 50–54.Google Scholar
Wilson, L.T., Pickel, C., Mount, R.C. & Zalom, F.G. (1983) Presence-absence sequential sampling for cabbage aphid and green peach aphid (Homoptera: Aphididae) on Brussels sprout. Journal of Economic Entomology 76, 476–479.Google Scholar
Wright, D.H. (1991) Correlation between incidence and abundance area expected by chance. Journal of Biogeography 18, 463–466.CrossRefGoogle Scholar
Yaninek, J.S., Baumgaertner, J. & Guitierrez, A.P. (1991) Sampling Mononychellus tanajoa (Acari: Tetranychidae) on cassava in Africa. Bulletin of Entomological Research 81, 201–208.Google Scholar