title: A Bayesian geostatistical Moran Curve model for estimating net changes of tsetse populations in Zambia.
abstract: For the first time a Bayesian geostatistical version of the Moran Curve, a logarithmic form of the Ricker stock recruitment curve, is proposed that is able to give an estimate of net change in population demographic rates considering components such as fertility and density dependent and density independent mortalities. The method is applied to spatio-temporally referenced count data of tsetse flies obtained from fly-rounds. The model is a linear regression with three components: population rate of change estimated from the Moran curve, an explicit spatio-temporal covariance, and the observation error optimised within a Bayesian framework. The model was applied to the three main climate seasons of Zambia (rainy--January to April, cold-dry--May to August, and hot-dry--September to December) taking into account land surface temperature and (seasonally changing) cattle distribution. The model shows a maximum positive net change during the hot-dry season and a minimum between the rainy and cold-dry seasons. Density independent losses are correlated positively with day-time land surface temperature and negatively with night-time land surface temperature and cattle distribution. The inclusion of density dependent mortality increases considerably the goodness of fit of the model. Cross validation with an independent dataset taken from the same area resulted in a very accurate estimate of tsetse catches. In general, the overall framework provides an important tool for vector control and eradication by identifying vector population concentrations and local vector demographic rates. It can also be applied to the case of sustainable harvesting of natural populations.
authors.affiliation: Department of Zoology, University of Oxford, Oxford, United Kingdom.
authors.forename: Luigi
authors.initials: L
authors.lastname: Sedda
authors.affiliation: Department of Veterinary and Livestock Development, Ministry of Agriculture and Co-operatives, Lusaka, Zambia.
authors.forename: Cornelius
authors.initials: C
authors.lastname: Mweempwa
authors.affiliation: Avia-GIS, Zoersel, Belgium.
authors.forename: Els
authors.initials: E
authors.lastname: Ducheyne
authors.affiliation: Animal Health Department, Institute of Tropical Medicine, Antwerp, Belgium.
authors.forename: Claudia
authors.initials: C
authors.lastname: De Pus
authors.affiliation: Avia-GIS, Zoersel, Belgium.
authors.forename: Guy
authors.initials: G
authors.lastname: Hendrickx
authors.affiliation: Department of Zoology, University of Oxford, Oxford, United Kingdom.
authors.forename: David J
authors.initials: DJ
authors.lastname: Rogers
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D006801:Humans; D007300:Insect Control; D007303:Insect Vectors; D016013:Likelihood Functions; D016014:Linear Models; D011157:Population Dynamics; D014370:Tsetse Flies; D015024:Zambia
references.citation: Int J Technol Assess Health Care. 2012 Jul;28(3):294-300
references.pmid: 22980707
references.citation: Med Vet Entomol. 2002 Jun;16(2):170-6
references.pmid: 12109711
references.citation: Bull Entomol Res. 2001 Apr;91(2):79-86
references.pmid: 11260721
references.citation: Biostatistics. 2007 Apr;8(2):158-83
references.pmid: 16809429
references.citation: Ecol Appl. 2009 Apr;19(3):553-70
references.pmid: 19425416
references.citation: Bull Entomol Res. 2001 Feb;91(1):25-35
references.pmid: 11228585