@phdthesis{Soultan2018Bioge-42257,
  year={2018},
  title={Biogeography and conservation of the neglected biodiversity},
  author={Soultan, Alaaeldin},
  address={Konstanz},
  school={Universität Konstanz}
}

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    <dcterms:title>Biogeography and conservation of the neglected biodiversity</dcterms:title>
    <dcterms:abstract xml:lang="eng">Unprecedented change in climate conditions has been documented during the last decades; this change is expected, with high confidence, to continue in the next decades. Several species have adjusted their distribution ranges to cope with this change. However, species ability to adjust its range could be possible for some widespread species, whereas, narrow-ranged species could not be able to adjust its ranges and, as a consequence, they could go extinct. The Desert biome, particularly the Sahara and the Arabian Desert, is amongst the most threatened regions by climate change and its biodiversity would plummet along with the associated unique ecological functions. Given the limited capacity and financial support for conservation, the main focus for conservationists and policymakers is to prioritise the conservation efforts in order to maximise the conservation benefit and minimising the costs. Traditionally, conservation actions prioritise richness/hotspot areas or threatened species, however, in these approaches, endemic species are likely to be overlooked and thus under-represented or even neglected because of their limited geographic ranges. Hence, conservation actions should prioritise regions characterised by high diversity and endemism, and also have the capability to maintain both ecological and evolutionary processes. The rapid development of ecological modelling and multivariate statistical approaches, coupled with increasing the availability of species spatial data in freely-accessible portals, provide ecologists with unprecedented opportunities. I took advantage of these opportunities in my dissertation where I study the diversity patterns and biogeographical distribution of some endemic tetrapod species in the Afro-Arabian region. The motivation behind this dissertation is to provide conservationists and biodiversity-related stakeholders with the required knowledge for halting species extinction in the Afro-Arabian region. I used spatial species range polygons of endemic tetrapod species including amphibians, reptiles, birds, and mammals, provided by the International Union for Conservation of Nature and Natural Resources (IUCN) and BirdLife, to delineate biogeographical affiliations and biodiversity hotspots in the Afro-Arabian region. I show that these four tetrapod classes share two main biodiversity hotspots in the north-west of North Africa along the Mediterranean Coast and around the East African Rift (chapter 1). This indicates that prioritising hotspots for one well-documented tetrapod class could represent hotspots for other less-documented classes. The distinct biogeographical regions for each tetrapod class—four discrete regions for each of amphibians and reptiles, and five discrete regions for each of mammals and birds—reflects the influence of environmental conditions and historical geographical processes in shaping these biogeographical regions. These identified biogeographical regions represent a fundamental step in conservation prioritisation, as they ensure that both ecological and evolutionary processes are considered. The next important step is the identification of the threatened species within each biogeographical region. This step is indispensable for conservation actions as it allows for identifying the priority species for conservation in the Afro-Arabian region. Species Distribution Models (SDMs) have been widely used to quantify species-environment relationship, and assessing species vulnerability to climate changes. However, because of the heterogeneity in the quality of the species data available, particularly for endemic species, it was important to assess the sensitivity of SDMs to noisy species data (e.g. small sample size and low positional accuracy) prior to identifying the threatened species. A wealth of studies has assessed the impact of noise in species occurrence data on the performance of SDMs, but almost no attempt has been made to assess the impact of such data on the reliability of SDMs. Therefore, I studied the impact of noisy species data on the reliability of SDMs considering the possible interacting roles of SDM algorithms, species specialisation, and grid resolution (chapter 2). I show that species specialisation is the main linchpin of the factors affecting the reliability of SDMs. My results indicate that, in general, SDMs are robust to the noise in species occurrence data in case of widespread species, whereas for narrow-ranged species, SDMs are highly sensitive to noisy data. This implies that in order to achieve a reliable SDM for endemic species, the noise in species occurrence data should be as small as possible. I also show that there is not a single optimal SDM algorithm, and the choice of these algorithms is governed by species specialisation and number of species occurrences. I provide through this analysis a work-flow that could help practitioners and modellers to adopt the appropriate SDM algorithm according to the characteristics of the species of interest and its available data. Following this work-flow, I assessed the impact of climate change on the distribution of the endemic mammal species in the Afro-Arabian region (chapter 3). My analyses show that about 20% of the endemic mammals in the Afro-Arabian region could go extinct before 2050. This finding supports previous studies that have predicted that about 60% of the vertebrate species could go extinct soon. I re-assessed the conservation status of the endemic mammal species following IUCN Red List criterion A3(c). I found that a substantial number of mammal species that are currently under IUCN Least Concern category would become Extinct or Critically Endangered in the near future. These results call for urgent conservation intervention in Afro-Arabian region. Throughout my dissertation work, altogether I combined multivariate and spatial analyses to improve the knowledge on biodiversity patterns of tetrapod classes and extinction risk of endemic mammal species to the projected climate change in the Afro-Arabian region, one of the most neglected biomes. This work provides conservationists and biodiversity-related stakeholders with the necessary information for implementing effective conservation intervention programs and highlights the urgency in doing so soon.</dcterms:abstract>
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