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High‐Resolution GPS Tracking Data Reveal Inter‐Individual and Seasonal Variations in Habitat Selection Strategies by the Critically Endangered African White‐Backed Vulture Gyps africanus in the Hwange Ecosystem of Zimbabwe

Mark Zvidzai,M. Masocha,P. Mundy,Fadzai M. Zengeya,Henry Ndaimani,A. Murwira

Published 2025 in African Journal of Ecology

ABSTRACT

Effective conservation of critically endangered species should be guided by empirical evidence on how they interact with the environment at multiple scales. Yet, such information is lacking for many endangered species such as African White‐backed Vultures (AWbV) Gyps africanus. Habitat selection modelling is a promising tool for inferring habitat selection strategies by species to guide conservation planning. This study investigated how habitat selection patterns for AWbV differ in respect of intrinsic individual traits and seasonality. To achieve this goal, six AWbV were captured and attached with solar‐powered Global Positioning Systems (GPS) tracking devices. GPS data were then integrated with biologically relevant environmental predictors. Two modelling frameworks, namely binary logistic regression and Ecological Niche Factor Analysis, were fitted to develop habitat selection models at three scales based on pooled, individual and seasonal data. Results indicate that the six AWbV reflect specialist tendencies, with a narrow ecological niche. Further, results reveal a significant positive relationship between predicted presence of the six AWbV and the human footprint index while a consistent negative relationship with mammalian density index was also uncovered. Complex but significant relationships were also uncovered between AWbV and other variables such as the Normalised Difference Vegetation Index, mean daily temperature and thermal uplift. Results from the study suggest that AWbV response strategies to environmental heterogeneity are individual and season‐specific. This therefore calls for researchers to disaggregate movement ecology data to multiple scales as this may improve the utility of habitat selection modelling to inform biodiversity conservation planning.

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