In Africa we find incredibly diverse ecosystems which are home to a huge diversity of species. Unfortunately, as the latest literature suggests, the population sizes of mammals, birds, fish, amphibians and reptiles have declined by 65% in the last 50 years. The main drivers include deforestation, human population growth, urbanization, collection of natural resources and clearing of land for agriculture. With the African population expected to double in the next 50 years, it is expected an exacerbated effect of the drivers that currently contribute to biodiversity decline. The fact that a third of Africa lives under the poverty line together with high levels of corruption makes it especially important that the sparse existing resources dedicated to biodiversity preservation can be transparently and effectively distributed throughout the most important areas for biodiversity.
Besides the chapters of general introduction and discussion, this thesis contains four chapters representing published or submitted papers. In chapter 1, I propose a new metric that allows the ranking of areas based on biodiversity importance. In chapter 2, I showcase the lack of biodiversity sampling in Africa unveiling an important sampling bias and making predictions on sampling time and effort to sample biodiversity in Africa. In chapter 3, I compile important baseline information on amphibians and reptiles from a poorly known area in terms of biodiversity in northern Mozambique and in chapter 4, we show that endemisms are very dependent on which scale or taxonomic treatment we decide to use in our analysis.
Overall, the work developed in this thesis is a contribution to the transparency and prioritisation procedures at biodiversity management globally and showcases the current situation of the African biodiversity sampling and how it may translate into the future. Furthermore, it provides an example on how biodiversity baseline information can be acquired and made available as well as highlighting the importance of taxonomy and scale when inferring patterns from spatial analysis.
Dr Maria Ana Azeredo de Dornales (Univ. St Andrews)
Dr Luca Santini (Inst. of Research on Terrestrial Ecosystems (IRET) of the Italian National Research Council (CNR)