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Ecological niche and potential geographic distribution of the invasive fruit fly Bactrocera invadens (Diptera, Tephritidae)

Published online by Cambridge University Press:  27 March 2009

M. De Meyer*
Affiliation:
Royal Museum for Central Africa, Entomology Section, Tervuren, B-3080Tervuren, Belgium
M.P. Robertson
Affiliation:
Department of Zoology and Entomology, University of Pretoria, Pretoria, 0001, South Africa
M.W. Mansell
Affiliation:
Department of Zoology and Entomology, University of Pretoria, Pretoria, 0001, South Africa United States Department of Agriculture, APHIS, Pretoria, 0001, South Africa
S. Ekesi
Affiliation:
International Centre of Insect Physiology and Ecology, PO Box 30772-00100, GPO, Nairobi, Kenya
K. Tsuruta
Affiliation:
Moji Plant Protection Station, MAFF, Nishikaigan, Moji-ku, Kitakyushu, 801-0841, Japan
W. Mwaiko
Affiliation:
Ministry of Agriculture and Food Security, Plant Health Services, P.O.Box 9071, Dar es Salaam, Tanzania
J-F Vayssières
Affiliation:
CIRAD, UPR Production Fruitière, Montpellier, F-34398France; IITA, Cotonou, Bénin
A.T. Peterson
Affiliation:
Natural History Museum and Biodiversity Research Center, University of Kansas, Lawrence, Kansas66045USA
*
*Author for correspondence Fax: +32 (0)2 7695695 E-mail: demeyer@africamuseum.be

Abstract

Two correlative approaches to the challenge of ecological niche modeling (genetic algorithm, maximum entropy) were used to estimate the potential global distribution of the invasive fruit fly, Bactrocera invadens, based on associations between known occurrence records and a set of environmental predictor variables. The two models yielded similar estimates, largely corresponding to Equatorial climate classes with high levels of precipitation. The maximum entropy approach was somewhat more conservative in its evaluation of suitability, depending on thresholds for presence/absence that are selected, largely excluding areas with distinct dry seasons; the genetic algorithm models, in contrast, indicate that climate class as partly suitable. Predictive tests based on independent distributional data indicate that model predictions are quite robust. Field observations in Benin and Tanzania confirm relationships between seasonal occurrences of this species and humidity and temperature.

Type
Research Paper
Copyright
Copyright © 2009 Cambridge University Press

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