Spatial patterns of species diversity in Kenya

The most striking feature of Earth is the existence of life and the most striking feature of life is its diversity. Explaining patterns of species diversity is one of the most complex problems in ecology. This is because diversity is usually the outcome of many contributing factors whose relative importance varies with spatial and temporal scales. However, carefully framed questions about species diversity can provide insight into a wide variety of processes on spatial and temporal scales. This doctoral thesis investigates ecological processes responsible for the spatial variability of mainly species richness of birds and large mammals, and to a lesser extent plants in Kenya.

Chapter 1 gives a brief general introduction on biological diversity, the aim of the study, description of the study area and the outline of the thesis. Chapters 2 and 3 introduce two new indices of diversity based on animal body size. In chapter 2, an index of diversity for large herbivores is derived from the inverse relationship between the body size of species and its local abundance. This diversity index has an advantage of incorporating information on species abundances without the need for time-consuming surveys. In addition, by estimating the abundance of every species from its body mass, differences between species are also incorporated in the proposed index. Chapter 3 proposes an index of diversity for large grazing mammals derived from a measure of variation in body size among species. This diversity index also has an advantage of treating species of different sizes as being essentially different. Practically, the two proposed indices (Chapters 2 and 3) may be ideal for rapid appraisal of large herbivore species diversity over large areas because species sampling is on the basis of presence and absence within a sampling unit.

Chapters 4 and 5 focus on the use of Advanced Very High Resolution Radiometer-Normalized Difference Vegetation Index (AVHRR-NDVI) as a means of estimating relevant environmental factors influencing patterns of species richness at landscape scale. In chapter 4, species richness of vascular plants and large mammals were correlated with interannually integrated maximum average NDVI and its variability (standard deviation and coefficient of variation). Species richness correlated negatively with maximum average NDVI and positively with variability of NDVI. Chapter 5 demonstrates the relations between net primary productivity (NPP) derived from maximum average NDVI and biodiversity indicators (species richness and number of individuals). The biodiversity indicators related positively to variability of NDVI (standard deviation) and unimodally to maximum average NDVI, with highest number of species and individuals occurring at intermediate levels of maximum average NDVI (productivity). Given assumptions of the close relationship of NDVI to NPP and variability of NDVI to heterogeneity, this study concludes that both the productivity and spatial heterogeneity hypotheses are important in understanding the potential of remotely sensed data for explaining patterns of species richness.

Chapter 6 explores the relationship between large mammal species richness and indices of habitat diversity derived using spectral reflectance from high resolution satellite (Landsat TM). The influence of spatial scale on the relation between species richness and indices of habitat diversity was also tested. Species richness related positively to habitat diversity and changes of spatial scale in calculations of habitat diversity influence the strength of the correlation with species richness. However, the influence does not change the direction of the relationship between habitat diversity and species richness. This implies that before starting to manage for high levels of species diversity, the spatial scale at which species richness are to be protected must be determined.

Chapter 7 examines environmental correlates of bird species richness at regional (a quarter degree) scale. Mean annual potential evapotranspiration emerged as the overriding environmental factor influencing bird species richness confirming the hypothesis that energy is partitioned among species such that environmentally available energy limits regional species richness. In chapter 8, bird species richness is predicted from multi-temporal data of vegetation reflectance (AVHRR-NDVI) at regional scale. Species richness was correlated with interannually integrated maximum average NDVI and its variability (standard deviation and coefficient of variation). Species richness related positively to maximum average NDVI and negatively to variability of NDVI. Hence, the study establishes that maximum average NDVI relates to environmental factors favoring high bird species richness whereas variability of NDVI represent factors limiting the distribution of bird species. Since high maximum average NDVI may reflect stable vegetation cover, the study concludes that high bird species diversity occurs in stable and predictable environments in Kenya.

Chapter 9 relates regional patterns of large herbivore species richness to remotely sensed data reflecting current ecoclimatic stability. The study reveals that high large herbivore species richness occurs in regions with current ecoclimatic stability. However, persistence of stable regional conditions may also have enhanced development of stable human cultures and agricultural development. Consequently, the pressure on nature is often particularly great in the biologically most unique regions. In view of this, distinctions must be made between stable and unstable regions concerning conservation priorities and management approaches.