Coastal Forests of Kenya-Ecology, Biodiversity & Conservation

Aims: the broad objective of this study is to investigate the ecological, biodiversity and conservation status of the coastal forests of Kenya fragments. The specific aims of the study are: (1) to investigate current quantitative trends in plant diversity; (2) develop a spatial and standardised vegetation database for the coastal forests Kenya; (3) investigate forest structure, species diversity and composition across the forests; (4) investigate the effect of forest fragment area on plant species diversity; (5) investigate phylogenetic diversity across these coastal remnants (6) assess vulnerability and provide conservation perspectives to concrete policy issues; (7) investigate plant and butterfly diversity correlation. Methods: I performed various analytical methods including species diversity metrics; multiple regression models for species-area relationship and small island effect; non-metric multidimensional scaling; ANOSIM; PERMANOVA; multiplicative beta diversity partitioning; species accumulation curve and species indicator analysis; statistical tests, rarefaction of species richness; phylogenetic diversity metrics of Phylogenetic diversity index, mean pairwise distance, mean nearest taxon distance, and their null-models: and Co-correspondence analysis. Results: developed the first large standardised, spatial and geo-referenced vegetation database for coastal forests of Kenya consisting of 600 plant species, across 25 forest fragments using 158 plots subdivided into 3160 subplots, 18 sacred forests and seven forest reserves; species diversity, composition and forest structure was significantly different across forest sites and between forest reserves and sacred forests, higher beta diversity, species-area relationship explained significant variability of plant diversity, small Island effect was not evident; sacred forests exhibited higher phylogenetic diversity compared to forest reserves; the threatened Red List species contributed higher evolutionary history; a strong correlation between plants and butterfly diversity. Conclusions: This study provides for the first time a standardized and large vegetation data. Results emphasizes need to improve sacred forests protection status and enhance forest connectivity across forest reserves and sacred forests.

Differential expression analysis for sequence count data via mixtures of negative binomials

The recent advent of Next-generation sequencing technologies has revolutionized the way of analyzing the genome. This innovation allows to get deeper information at a lower cost and in less time, and provides data that are discrete measurements. One of the most important applications with these data is the differential analysis, that is investigating if one gene exhibit a different expression level in correspondence of two (or more) biological conditions (such as disease states, treatments received and so on). As for the statistical analysis, the final aim will be statistical testing and for modeling these data the Negative Binomial distribution is considered the most adequate one especially because it allows for "over dispersion". However, the estimation of the dispersion parameter is a very delicate issue because few information are usually available for estimating it. Many strategies have been proposed, but they often result in procedures based on plug-in estimates, and in this thesis we show that this discrepancy between the estimation and the testing framework can lead to uncontrolled first-type errors. We propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Afterwards, three consistent statistical tests are developed for differential expression analysis. We show that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it is the best one in reaching the nominal value for the first-type error, while keeping elevate power. The method is finally illustrated on prostate cancer RNA-seq data.