The Cape Griffon Gyps coprotheres is listed as a globally threatened species, because of threats posed by, inter alia, poisoning, loss of habitat, food shortages, electrocutions and drowning in high-walled farm reservoirs. The Cape Griffon has undergone major decline in South Africa, including the Eastern Cape. A detailed investigation into the causes of this decline is vital to ensure the survival of the species. Since it is known that electrocution on powerlines is a major cause of mortality of the griffons in the Eastern Cape, the study therefore focuses on this factor in this region. More specifically, the overall aim or objective of the study is to investigate, through the application of appropriate GIS techniques, the use of descriptive, empirical, habitat and foraging models to prioritise powerline networks, in the Eastern Cape, for mitigation to prevent electrocution of Cape Griffons Gyps coprotheres. The specific activities associated with the aim of the study are: (1) to identify the broad habitat types used by the griffons, (2) to determine the mean daily foraging range size of the griffons, (3) to map currently active foraging ranges and to categorise them according to their role, and the level of importance, in the foraging and breeding behaviour of the species, (4) to map the locations of networks of griffon-unfriendly powerline networks, and (5) to integrate the outcomes of (1) to (4) above, to produce a spatially explicit product that ranks griffon-unfriendly powerline networks according to their priority level for mitigation. The study uses simple descriptive models to investigate, spatially, the threat posed by griffon-unfriendly powerlines to the Cape Griffon in the Eastern Cape. The modelling approach includes a broad level simple habitat model (Categorical model), a Maxent model, a spatial foraging model and an electrocution model. Several map outputs were produced from the analyses. Cape Griffon habitat was successfully modelled using ecological input variables: biome, vegetation xvii type (suitable/unsuitable for griffon foraging), stock (cattle, sheep and goats), and stock farming types (commercial & communal). All outputs were modelled through the use of a Geographical Information System (GIS). Opportunistic sightings data were used for the study, which means that the data were collected on a non-systematic basis and are therefore considered incomplete. In ecology and zoogeography, incomplete datasets are common. To address this issue, a maximum entropy (Maxent) model was applied to the available dataset. Maxent produces predictions or conclusions from incomplete information, and was therefore useful for this study. The mean daily foraging area (coverage) of the Cape Griffon in the study area was estimated from published and unpublished marked bird studies conducted elsewhere, but in generally similar habitats. Based on all the data from these studies, it was considered appropriate to use a circular area, with a radius of 40 km, to represent the mean daily foraging range of the Cape Griffon in the Eastern Cape. It was considered appropriate to use a roughly circular area to depict the daily foraging area, given that the Cape Griffon qualifies as a central place forager. This means that the griffons are tied to a permanent site (roosting site or breeding site) and forage within a certain area around that central site. The mean daily foraging area (40 km) was applied to the currently active griffon sites: breeding sites, regular roosting sites, seasonal/occasional sites, and roosting sites (uncertain status). This produced four foraging area types, which formed the basis for the creation of two spatial foraging area models: Hierarchical Foraging Area Model (HFAM) and the Intersecting Foraging Area Model (IFAM). The HFAM produced three outputs: Primary, Secondary and Tertiary Foraging Areas. These three areas represent the hierarchical presentation of the relative importance of the four foraging area types. The outcome was areas of different predicted griffon occurrence: ‘high’, ‘medium’, ‘low’. The IFAM is essentially an extension of the HFAM, which represents a single area where foraging ranges based on all four types are intersected. The outcome represents the area of predicted highest griffon occurrence. The identified areas of different predicted griffon occurrence, or density (outputs of the HFAM and IFAM), were individually intersected with the layer that indicates the locations of the ‘unsafe’ powerlines. These intersects are depicted in hierarchical mode and expressed according to a number of four risk categories (very high, high, medium and low). The final spatial output of the study is a map that identifies priority powerlines for mitigation against griffon mortality caused by electrocutions and collisions with powerline infrastructure.

The Cape Vulture (Gyps coprotheres), a southern African endemic, is a species currently listed on the IUCN Red List as “Threatened” due to its 66-81% decline over the past 50 years. South Africa and Lesotho hold 90% of the global population, which are focused at two core areas, namely northern central South Africa and eastern South Africa. This species provides important ecological services yet faces numerous anthropogenic threats. An emerging threat in the south eastern part of South Africa is that of wind energy development. Understanding the movement and roosting behaviour of the Cape Vulture may mitigate potential collisions if areas of high use are avoided. Juvenile Cape Vultures, who naturally suffer high mortality rates, are known to forage extensively over a wider landscape and as a consequence may face a greater assortment of threats. The overall aim of this study therefore was to determine the ranging and roosting behaviour of juvenile Cape Vultures in the Eastern Cape Province, South Africa. In order to achieve this aim, i) the ranging behaviour and habitat use were determined and ii) roost sites and factors that influence roost site selection were determined. Using five juvenile Cape Vultures tagged with Global Positioning System (GPS/GSM) transmitters, home range sizes, distance travelled from the nest and habitat use were determined. Home range was determined through kernel density estimates and distance travelled from the nest was determined through the Euclidean distance. Habitat use was determined by overlaying the home range size onto a merged layer of all protected areas and the National Land Cover Database of South Africa. To determine roost sites, data from the tagged vultures were split into encamped and exploratory movements using a mixture model in a cluster analysis setting. Encamped movements were associated with roost sites. Roost density was determined around the natal colony using predetermined buffer sizes and a generalised linear mixed model (GLMM) fitted to the data. Conditions considered favourable for roost sites were based on previous research conducted on cliff-nesting species and a GLMM conducted. Juveniles increased their home range progressively for the first two months, then exhibited a rapid increase in size associated with dispersal from the colony. Distance from the nest increased rapidly following the dispersal period. Protected areas and woody vegetation were areas of preferred habitat. The highest density of roosts for juveniles was located within 20 km’s from the breeding colony and decreased further away. Roost sites that were favoured for juveniles and adult birds were those that were located close to colonies, had low accessibility to terrestrial predators and were in areas of high wind speed. The orientation of the cliff into the prevailing wind direction was also a determining factor. Roosting sites and foraging areas are important spatial determinants of Cape Vulture behaviour and the identification of such areas can help with conservation management. With the additional threat of wind development in areas highly utilised, wind farms located too close to colonies could have a devastating impact on the Cape Vulture population. Therefore, this study highlights the importance of conservation buffer zones as no-go areas for wind energy development around vulture colonies.