Managing multiple parks, reserves, and conservation areas collectively as conservation networks is a recent, yet growing trend. But in order for these networks to be ecologically viable, the functional connectivity of the landscape must be ensured. We assessed the connectivity between six African savanna elephant populations in southern Africa to test whether existing conservation networks were functioning and to identify other areas that could benefit from being managed as conservation networks. We used resource selection function models to create an index of habitat selection by males and female elephants. We employed this habitat use index as a resistance surface, and applied circuit theory to assess connectivity between adjacent elephant populations within six clusters of protected areas across southern Africa. Circuit theory current flow maps predicted a high likelihood of connectivity in the central portion of our study area (i.e. between the Chobe, Kafue, Luangwa, and Zambezi cluster). Main factors limiting connectivity across the study area were high human density in the east and a lack of surface water in the west. These factors effectively isolate elephants in the Etosha cluster in Namibia and Niassa clusters in Mozambique from the central region. Our models further identified two clusters where elephants might benefit from being managed as part of a conservation network, 1) northern Zambia and Malawi and 2) northern Mozambique.We conclude that using habitat selection and circuit theory models to identify conservation networks is a data-based method that can be applied to other focal species to identify and conserve functional connectivity.

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Resource selection function (RSF) models are commonly used to quantify species/habitat associations and predict species occurrence on the landscape. However, these models are sensitive to changes in resource availability and can result in a functional response to resource abundance, where preferences change as a function of availability. For generalist species, which utilize a wide range of habitats and resources, quantifying habitat selection is particularly challenging. Spatial and temporal changes in resource abundance can result in changes in selection preference aff ecting the robustness of habitat selection models. We examined selection preference across a wide range of ecological conditions for a generalist megaherbivore, the African savanna elephant Loxodonta africana , to quantify general patterns in selection and to illustrate the importance of functional responses in elephant habitat selection. We found a functional response in habitat selection across both space and time for tree cover, with tree cover being unimportant to habitat selection in the mesic, eastern populations during the wet season. A temporal functional response for water was also evident, with greater variability in selection during the wet season. Selection for low slopes, high tree cover, and far distance from people was consistent across populations; however, variability in selection coeffi cients changed as a function of the abundance of a given resource within the home range. Th is variability of selection coeffi cients could be used to improve confi dence estimations for inferences drawn from habitat selection models. Quantifying functional responses in habitat selection is one way to better predict how wildlife will respond to an ever-changing environment, and they provide promising insights into the habitat selection of generalist species.

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