The value of individual identification in studies of free-living hyenas and aardwolves

From population estimates to social evolution, much of our understanding of the family Hyaenidae is drawn from studies of known individuals. The extant species in this family (spotted hyenas, Crocuta crocuta, brown hyenas, Parahyaena brunnea, striped hyenas, Hyaena hyaena, and aardwolves, Proteles cristata) are behaviorally diverse, presenting an equally diverse set of logistical constraints on capturing and marking individuals. All these species are individually identifiable by their coat patterns, providing a useful alternative to man-made markings. Many studies have demonstrated the utility of this method in answering a wide range of research questions across all four species, with some employing a creative fusion of techniques. Despite its pervasiveness in basic research on hyenas and aardwolves, individual identification has rarely been applied to the conservation and management of these species. We argue that individual identification using naturally occurring markings in applied research could prove immensely helpful, as this could further improve accuracy of density estimates, reveal characteristics of suitable habitat, identify threats to population persistence, and help to identify individual problem animals.

Estimates of carnivore densities in a human-dominated agricultural matrix in South Africa

Populations of carnivore species outside protected areas may be of considerable importance for conservation, as many protected areas do not provide sufficient space for viable populations. Data on carnivore population sizes and trends are often biased towards protected areas, and few studies have examined the role of unprotected areas for carnivore conservation. We used camera-trapping data and spatial capture–recapture models to estimate population densities for four sympatric carnivores: the African leopard Panthera pardus, spotted hyaena Crocuta crocuta, brown hyaena Parahyaena brunnea and African civet Civettictis civetta in Platjan, a predominantly agricultural, mixed land-use system, South Africa. Mean densities per 100 km2 for the leopard were 2.20 (95% CI 1.32–3.68) and 2.18 (95% CI 1.32–3.61) for left and right flank data, respectively; spotted hyaena, 0.22 (95% CI 0.06–0.81); brown hyaena, 0.74 (95% CI 0.30–1.88); and African civet 3.60 (95% CI 2.34–5.57; left flanks) and 3.71 (95% CI 2.41–5.72; right flanks). Our results indicate that although densities are lower than those reported for protected areas, humans and predators coexist in this unprotected agricultural matrix. We suggest that increased conservation effort should be focused in such areas, to mitigate human–carnivore conflicts. Our study improves the knowledge available for carnivore populations on privately owned, unprotected land, and may benefit conservation planning.

Assumptions about fence permeability influence density estimates for brown hyaenas across South Africa

Wildlife population density estimates provide information on the number of individuals in an area and influence conservation management decisions. Thus, accuracy is vital. A dominant feature in many landscapes globally is fencing, yet the implications of fence permeability on density estimation using spatial capture-recapture modelling are seldom considered. We used camera trap data from 15 fenced reserves across South Africa to examine the density of brown hyaenas (Parahyaena brunnea). We estimated density and modelled its relationship with a suite of covariates when fenced reserve boundaries were assumed to be permeable or impermeable to hyaena movements. The best performing models were those that included only the influence of study site on both hyaena density and detection probability, regardless of assumptions of fence permeability. When fences were considered impermeable, densities ranged from 2.55 to 15.06 animals per 100 km², but when fences were considered permeable, density estimates were on average 9.52 times lower (from 0.17 to 1.59 animals per 100 km²). Fence permeability should therefore be an essential consideration when estimating density, especially since density results can considerably influence wildlife management decisions. In the absence of strong evidence to the contrary, future studies in fenced areas should assume some degree of permeability in order to avoid overestimating population density.

Impacts of exclusion fencing on target and non-target fauna: a global review

Exclusion fencing is a common tool used to mitigate a variety of unwanted economic losses caused by problematic wildlife. While the potential for agricultural, ecological and economic benefits of pest animal exclusion are often apparent, what is less clear are the costs and benefits to sympatric non-target wildlife. This review examines the use of exclusion fencing in a variety of situations around the world to elucidate the potential outcomes of such fencing for wildlife and apply this knowledge to the recent uptake of exclusion fencing on livestock properties in the Australian rangelands. In Australia, exclusion fences are used to eliminate dingo (Canis familiaris dingo) predation on livestock, prevent crop-raiding by emus (Dromaius novaehollandiae), and enable greater control over total grazing pressure through the reduction of macropods (Macropodidae) and feral goats (Capra hircus). A total of 208 journal articles were examined for location, a broad grouping of fence type, and the reported effects the fence was having on the study species. We found 51% of the literature solely discusses intended fencing effects, 42% discusses unintended effects, and only 7% considers both. Africa has the highest proportion of unintended effects literature (52.0%) and Australia has the largest proportion of literature on intended effects (34.2%). We highlight the potential for exclusion fencing to have positive effects on some species and negative effects on others (such as predator exclusion fencing posing a barrier to migration of other species), which remain largely unaddressed in current exclusion fencing systems. From this review we were able to identify where and how mitigation strategies have been successfully used in the past. Harnessing the potential benefits of exclusion fencing while avoiding the otherwise likely costs to both target and non-target species will require more careful consideration than this issue has previously been afforded.