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Niche Distribution Pattern of Rüppell's Vulture () and Conservation Implication in Kenya

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Journal Ecol Evol
Date 2024 Dec 9
PMID 39650540
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Abstract

Rüppell's vultures are critically endangered, primarily due to anthropogenic activities such as habitat degradation, climate change, and intentional and unintentional poisoning, which have led to the loss of nesting and breeding sites. To aid in the conservation and protection of these species, habitat evaluation and niche mapping are crucial. Species distribution modeling (SDM) is a valuable tool in conservation planning, providing insights into the ecological requirements of species under conservation concerns. This study employed an ensembling modeling approach to assess the habitat suitability and distribution of Rüppell's vultures across Kenya. We utilized four algorithms; Gradient Boosting Machine, Generalized Linear Model, Generalized Additive Model, and Random Forest. Data on Rüppell's vultures were sourced from the Global Biodiversity Information Facility, while key environmental variables influencing the species' distribution were obtained from WorldClim. The resultant species distribution map was overlaid with a conservation area map to evaluate the overlap between suitable habitats and existing protected areas. Our analysis identified suitable habitats in regions such as the Masai Mara Game Reserve, Mount Kenya National Park, Nairobi National Park, Tsavo East National Park, and Hell's Gate National Park, with the majority of these habitats located outside protected areas, except those within Hell's Gate National Park. Precipitation and elevation emerged as the primary environmental predictors of the distribution of Rüppell's vultures. Based on these findings, we recommend establishing vulture sanctuaries in suitable habitats and hotspots to enhance the conservation of Rüppell's vultures outside the protected areas.

References
1.
Milanesi P, Mori E, Menchetti M . Observer-oriented approach improves species distribution models from citizen science data. Ecol Evol. 2020; 10(21):12104-12114. PMC: 7663073. DOI: 10.1002/ece3.6832. View

2.
Mateo-Tomas P, Olea P . Anticipating knowledge to inform species management: predicting spatially explicit habitat suitability of a colonial vulture spreading its range. PLoS One. 2010; 5(8):e12374. PMC: 2928263. DOI: 10.1371/journal.pone.0012374. View

3.
Ngila P, Chiawo D, Owuor M, Wasonga V, Mugo J . Mapping suitable habitats for globally endangered raptors in Kenya: Integrating climate factors and conservation planning. Ecol Evol. 2023; 13(9):e10443. PMC: 10468911. DOI: 10.1002/ece3.10443. View

4.
Ogada D . The power of poison: pesticide poisoning of Africa's wildlife. Ann N Y Acad Sci. 2014; 1322:1-20. DOI: 10.1111/nyas.12405. View

5.
Kelling S, Fink D, La Sorte F, Johnston A, Bruns N, Hochachka W . Taking a 'Big Data' approach to data quality in a citizen science project. Ambio. 2015; 44 Suppl 4:601-11. PMC: 4623867. DOI: 10.1007/s13280-015-0710-4. View