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Isabirye B (2015)
Modeling the Potential Geographical Distribution and Ecological Niche of Selected Fruit Fly (Diptera: Tephritidae) Species in Uganda
Journal of Plant and Pest Science 2(1) 18-32.
Despite their overwhelming economic importance, efforts to assess the distribution of fruit flies (Diptera: Tephritidae) in Uganda have been minimal. Consequently, in this study, potential geographical distributions and climatic envelopes of 10 selected fruit fly species were modeled. Two presence-only predictive models namely, Maxent and Bioclim, were run using 19 bioclimatic parameters at a resolution of 30 arc seconds. New detections and existing records of fruit flies were used in the model. The climatic profiles of the selected fruit flies were described and the relative importance of the bioclimatic variables was explored. There was a close agreement between the two models about the distribution and suitability patterns matching the main fruit agro ecological zones. Precipitation (PC-1 = 61.4190%) and temperature (PC-2 = 29.214%) significantly shaped fruit fly niches across the country. Central and mid north zones provided the most suitable niches, while the western, northeastern and areas around Albert Nile were characterized as marginally suitable. The models were mostly robust in performance (AUC: 0.815 – 0.974), with model test performance ranging from random (C . capitata : 0.486) to excellent ( C. cosyra: 0.965). Predicted marginal sites, such as higher altitude zones matched negative areas of the models, which reflected higher model prediction abilities. These results provide an initial insight into the bioclimatic tolerance ranges of fruit flies in Uganda and should assist in identification of sites for future sampling efforts and fruit fly management planning.
Keywords: Bioclim, Ecological Niche, Fruit Flies, Maxent, Uganda
Kindt R, Lillesø J, van Breugel P, Bingham M, Demissew S, Dudley C et al. (2014)
Correspondence in forest species composition between the Vegetation Map of Africa and higher resolution maps for seven African countries
Applied Vegetation Science 17(1) 162-171.
Abstract Question How well does the forest classification system of the 1:5,000,000 vegetation map of Africa developed by Frank White correspond with classification systems and more extensive information on species assemblages of higher resolution maps developed for Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda and Zambia? Methods We reviewed various national and sub-national vegetation maps for their potential in increasing the resolution of the African map. Associated documentation was consulted to compile species assemblages, and to identify indicator species, for national forest vegetation types. Indicator species were identified for each regional forest type by selecting those species that, among all the species listed for the same phytochorion (regional centre of endemism), were listed only for that forest type. For each of the national forest types, we counted the number of indicator species of the anticipated regional type. Floristic relationships (expressed by four different ecological distance measures) among national forest types were investigated based on distance-based redundancy analysis, permutational multivariate analysis of variance (PERMANOVA) using distance matrices and hierarchical clustering. Results For most of the national forests, the analysis of indicator species and floristic relationships confirmed the regional classification system for the majority of national forest types, including the allocation to different phytochoria. Permutation tests confirmed allocation of national forest types to regional typologies, although the number of possible permutations limited inferences for the Zambezian and Lake Victoria phytochoria. Two forest types from Ethiopia and Kenya did not correspond to regional forest types. Conclusions Our analysis provides support that as the classification systems are compatible, the resolution and information content of the vegetation map of Africa can be directly improved by adding information from national maps, probably leading to improved liability of its application domains. We found statistical evidence for a distinct Afromontane phytochorion. We suggest expanding the regional forest classification system with ‘Afromontane moist transitional forest’. Among the various application domains of the higher resolution maps, these maps allow for an enhanced phytochoristic analysis of eastern Africa.
Keywords: Ethiopia, Frank White, Kenya, Kulczynski distance, Malawi, Rwanda, Tanzania, Uganda, Zambia, beta-sim distance, indicator species, phytochorion