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|Title:||Spatial Species Distribution Models To Predict Liver Fluke Disease (Fasciola Gigantica) In Cattle: A Case Study Of Sokoto State, Nigeria.|
|Presented at:||University of Leicester|
|Abstract:||Species distribution models provide an alternative way of observing the distribution of species rather than the conventional methods such as satellite tracking, aerial photography and ground surveys that are both labour and capital intensive. This thesis presents the first application of species distribution models using short-term and long-climate average in predicting the suitability of habitats and transmission pattern of F.gigantica in a semi-arid part of Nigeria in West Africa. The MaxEnt modelling technique was identified in giving better results than BioClim and Domain models in modelling the geographic range of F.gigantica based on six accuracy measures (sensitivity, specificity, Kappa, True Skill Statistics, AUC and Correlation). Also, six scenarios were created with MaxEnt using both Bioclim and non-Bioclim variables, which were validated with independent data obtained during the field work. Finally, Bioclim variables generated from IPCC future climate projections under ‘modest’ RCP 2.6 and ‘aggressive’ RCP 8.5 greenhouse gas emission scenarios were utilised in the construction of the MaxEnt model for two time slices 2041-2060 and 2061-2080. Subsequently, soil moisture was found to be the most significant variable and the distributions of F. gigantica in the study area were significantly associated with it (p<0.05). The predicted area of suitability for the disease prevalence has expanded under both RCP’s for the two future time slices. By combining a species distribution model with satellite based and HadGEM2-es climate projections, risk maps with the aid of GIS were generated indicating which provinces of Sokoto State are predicted to experience an increase in fascioliasis risk in the future. This study validated the short-term model by examining the relationship between the risk indices, and climatic variables with fascioliasis recorded prevalence. This research also used two questionnares through a cross-sectional survey on slaughtered cattle at the abattoirs of the sampled localities in investigating the influence of biological factors on fascioliasis prevalence. Gathering the models developed in this study, coupled with the biological risk factors, can improve our understanding of both the present and future risks. That will no doubt promote the ability to design effective control strategies against this parasite that takes a heavy toll on animals’ health and productivity.|
|Rights:||Copyright © the author. All rights reserved.|
|Appears in Collections:||Leicester Theses|
Theses, Dept. of Geography
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