Browsing by Author "Dalu, Tatenda"

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    Deeper knowledge of shallow waters: reviewing the invertebrate fauna of southern African temporary wetlands
    (Spring Nature Limited, 2018) Bird, Mathew, S.; Day, Jenny, A.; Mlambo, Musa, C.; Wasserman, Ryan, J.; Dalu, Tatenda; Holland, Alexandra, J.; Villet, Martin, H.; Bilton, David, T.; Barber-James, Helen, M.; Brendonck, Luc
    Temporary lentic wetlands are becoming increasingly recognised for their collective role in contributing to biodiversity at the landscape scale. In southern Africa, a region with a high density of such wetlands, information characterising the fauna of these systems is disparate and often obscurely published. Here we provide a collation and synthesis of published research on the aquatic invertebrate fauna inhabiting temporary lentic wetlands of the region. We expose the poor taxonomic knowledge of most groups, which makes it difficult to comment on patterns of richness and endemism. Only a few groups (e.g. large branchiopods, ostracods, copepods and cladocerans) appear to reach higher richness and/or endemicity in temporary wetlands compared to their permanent wetland counterparts. IUCN Red List information is lacking for most taxa, thus making it difficult to comment on the conservation status of much of the invertebrate fauna. However, except for a few specialist groups, many of the taxa inhabiting these environments appear to be habitat generalists that opportunistically exploit these waterbodies and this is hypothesised as one of the reasons why endemism appears to be low for most taxa. Given that taxonomy underpins ecology, the urgent need for more foundational taxonomic work on these systems becomes glaringly apparent.
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    From benthic to floating: phytoplankton dynamics in African freshwater lakes and reservoirs
    (Elsevier, 2023) Dube, Timothy; Madzivanzira, Takudzwa C; Mungenge, Chipo P; Dalu, Tatenda
    This chapter reviewed phytoplankton communities in African freshwater lakes and reservoirs and further assessed the latitudinal diversity gradient (LDG) which has been used to explain species variations in other taxonomic groups. The chapter also identified freshwater reservoirs on the continent, heavily impacted by anthropogenic impacts, and assessed how these have led to changes in phytoplankton communities. From the systematic review, phytoplankton information was available for 51 reservoirs in Africa with 1633 freshwater phytoplankton species belonging to nine taxa recorded from the 51 reservoirs. There was strong evidence that supports LDG for phytoplankton species richness increased from the poles toward the Equator. Species that highly occurred in all reservoirs included Microcystis aeruginosa among other species. Despite the basal trophic importance of phytoplankton, undesirable phytoplankton blooms have been reported from several reservoirs on the continent.
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    Spatio–temporal variation of vegetation heterogeneity in groundwater dependent ecosystems within arid environments
    (Ecological Informatics, 2022) Mpakairi, Kudzai S; Dube, Timothy; Dondofema, Farai; Dalu, Tatenda
    Climate change, land cover change and the over–abstraction of groundwater threaten the existence of Groundwater-Dependent Ecosystems (GDE), despite these environments being regarded as biodiversity hotspots. The vegetation heterogeneity in GDEs requires routine monitoring in order to conserve and preserve the ecosystem services in these environments. However, in–situ monitoring of vegetation heterogeneity in extensive, or transboundary, groundwater resources remain a challenge. Inherently, the Spectral Variation Hypothesis (SVH) and remotely-sensed data provide a unique way to monitor the response of GDEs to seasonal or intra–annual environmental stressors, which is the key for achieving the national and regional biodiversity targets. This study presents the first attempt at monitoring the intra–annual, spatio–temporal variations in vegetation heterogeneity in the Khakea–Bray Transboundary Aquifer, which is located between Botswana and South Africa, by using the coefficient of variation derived from the Landsat 8 OLI Operational Land Imager (OLI).