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African elephants (Loxodonta africana) utilise corridors to access limited resources, that is forage and water scattered across heterogeneous habitats they roam. The existence of small elephant metapopulations depend on the intactness of these corridors to access the scarce resources. Due to the sedentarisation of the previously nomadic Maasai people, elephant corridors have been exposed to increased fragmentation from human‐induced activities across the Amboseli ecosystem in Kenya. In this study, we sought to compare the scale of fragmentation between corridors and their immediate landscapes (noncorridors) in the Amboseli ecosystem, Kenya. We used a Brownian Bridge Movement Model (BBMM) to identify corridors used by elephants from global positioning system (GPS) collar data. The scale of fragmentation between corridors and noncorridors was determined using the effective mesh size fragmentation metric (meff). Our results showed that elephant corridors were significantly less fragmented (Wilcoxon sum rank test: W = 6,121.5, p < 0.05) when compared to the noncorridors. The presence of fragmentation geometries in the corridors remains a major cause of concern for wildlife managers as they have the potential to invade and constrict the existing corridors. Our results underscore the need to extend management of elephant habitats to migration corridors outside protected areas. [ABSTRACT FROM AUTHOR]

Understanding factors affecting the behaviour and movement patterns of the African elephant is important for wildlife conservation, especially in increasingly human-dominated savanna landscapes. Currently, knowledge on how landscape fragmentation and vegetation productivity affect elephant speed of movement remains poorly understood. In this study, we tested whether landscape fragmentation and vegetation productivity explains elephant speed of movement in the Amboseli ecosystem in Kenya. We used GPS collar data from five elephants to quantify elephant speed of movement for three seasons (wet, dry and transitional). We then used multiple regression to model the relationship between speed of movement and landscape fragmentation, as well as vegetation productivity for each season. Results of this study demonstrate that landscape fragmentation and vegetation productivity predicted elephant speed of movement poorly (R² < 0.4) when used as solitary covariates. However, a combination of the covariates significantly (p < 0.05) explained variance in elephant speed of movement with improved R² values of 0.69, 0.45, 0.47 for wet, transition and dry seasons, respectively. [ABSTRACT FROM AUTHOR]

Understanding factors affecting the distribution of the African elephant is important for its conservation in increasingly human-dominated savannah landscapes. However, understanding how landscape fragmentation and vegetation productivity affect elephant habitat utilization remains poorly understood. In this study, we tested whether landscape fragmentation and vegetation productivity explain elephant habitat utilization in the Amboseli ecosystem in Kenya. We used GPS (Global Positioning System) telemetry data from five elephants to quantify elephant habitat utilization. Habitat utilization was determined by calculating the time elephants spent within a unit area. We then used generalized additive models (GAMs) to model the relationship between time density and landscape fragmentation, as well as vegetation productivity. Results show that landscape fragmentation and vegetation productivity significantly ( P < 0.05) explain elephant habitat utilization. A significant ( P < 0.05) unimodal relationship between vegetation productivity and habitat utilization was observed. Results suggest that elephants spend much of their time in less fragmented landscapes of intermediate productivity. [ABSTRACT FROM AUTHOR]

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