a landscape and climate data logistic model of tsetse distribution in kenya风景和气候数据逻辑模型在肯尼亚采采蝇的分布.pdfVIP

A Landscape and Climate Data Logistic Model of Tsetse Distribution in Kenya Nathan Moore1,2*, Joseph Messina2 1 Department of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China, 2 Department of Geography, Center for Global Change and Earth Observations, Michigan State University, East Lansing, Michigan, United States of America Abstract Background: Trypanosoma spp, biologically transmitted by the tsetse fly in Africa, are a major cause of illness resulting in both high morbidity and mortality among humans, cattle, wild ungulates, and other species. However, tsetse fly distributions change rapidly due to environmental changes, and fine-scale distribution maps are few. Due to data scarcity, most presence/absence estimates in Kenya prior to 2000 are a combination of local reports, entomological knowledge, and topographic information. The availability of tsetse fly abundance data are limited, or at least have not been collected into aggregate, publicly available national datasets. Despite this limitation, other avenues exist for estimating tsetse distributions including remotely sensed data, climate information, and statistical tools. Methodology/Principal Findings: Here we present a logistic regression model of tsetse abundance. The goal of this model is to estimate the distribution of tsetse fly in Kenya in the year 2000, and to provide a method by which to anticipate their future distribution. Multiple predictor variables were tested for significance and for predictive power; ultimately, a parsimonious subset of variables was identified and used to construct the regression model with the 1973 tsetse map. These data were validated against year 2000 Food and Agriculture Organization (FAO) estimates. Mapcurves Goodness-Of- Fit scores were used to