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4 results for “African elephant (Loxodonta africana)”
Data from: Water for African elephants (Loxodonta Africana): faecal microbial loads affect use of artificial waterholes
In semi-arid protected areas artificial waterholes ensure that water is locally available to animals for extended periods. However, artificial waterholes may limit animal movement, which contributes towards habitat deterioration. Challenges of artificial water provisioning worsen in the presence of ecosystem engineers like African elephants Loxodonta africana, capable of transforming environments. Camera traps were used to monitor elephant visitation at 21 artificial waterholes in the Kruger National Park, South Africa. We also assessed if water quality parameters influenced elephant preference for certain waterholes. There were no significant correlations between elephant abundance and water physicochemical properties. However, there was a strong negative correlation between elephant abundance and levels of Escherichia coli in water. Our findings suggest that elephants avoid drinking water with high levels of faecal microbial loads. Whereas most studies addressing animal management in protected areas consider waterholes as homogeneous units, we posit that water quality could also determine local landscape use and movement patterns of key species like elephants, a finding with relevant implications in reserve management practices.
Data from: Water for African elephants (Loxodonta Africana): faecal microbial loads affect use of artificial waterholes
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Genetic connectivity and population structure of African savanna elephants (Loxodonta africana) in Tanzania
<p>Increasing human population growth, exurban development, and associated habitat fragmentation is accelerating the isolation of many natural areas and wildlife populations across the planet. In Tanzania, rapid and ongoing habitat conversion to agriculture has severed many of the country's former wildlife corridors between protected areas. To identify historically-linked protected areas, we investigated the genetic structure and gene flow of African savanna elephants in Tanzania using microsatellite and mitochondrial DNA markers in 688 individuals. Our results indicate distinct population genetic structure within and between ecosystems across Tanzania, and reveal important priority areas for connectivity conservation. Elephants sampled from the Tarangire-Manyara ecosystem appear marginally, yet significantly isolated from elephants sampled from the greater Serengeti ecosystem (mean FST = 0.03), where two distinct subpopulations were identified.Unexpectedly, elephants in the Lake Manyara region appear to be more closely related to those across the East African Rift wall in the Ngorongoro Conservation Area than they are to the neighboring Tarangire subpopulations. We concluded that the Rift wall has had a negligible influence on genetic differentiation up to this point, but differentiation may accelerate in the future because of ongoing loss of corridors in the area. Interestingly, relatively high genetic similarity was found between elephants in Tarangire and Ruaha although they are separated by >400 km. In southern Tanzania, there was little evidence of female-mediated gene flow between Ruaha and Selous, probably due to the presence of the Udzungwa Mountains between them. Despite observing evidence of significant isolation, the populations of elephants we examined generally exhibited robust levels of allelic richness (mean AR = 9.96), heterozygosity (mean µHE = 0.73), and effective population sizes (mean Ne = 148). Our results may inform efforts to restore wildlife corridors between protected areas in Tanzania in order to facilitate gene flow for long-term survival of elephants and other species.</p>
Genetic connectivity and population structure of African savanna elephants (Loxodonta africana) in Tanzania
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.