Although the spotted hyaena (Crocuta crocuta) has been widely considered to be resilient to human disturbance, the species is now thought to be undergoing widespread population declines. Nevertheless, only a handful of population density estimates are available for the species, despite the importance of this information for informing conservation management. This is a consequence of both a lack of surveys and logistical challenges associated with processing spotted hyaena data. In this study, we collaborated with a cohort of students to process camera trap data from the Ruaha-Rungwa landscape initially collected to estimate lion (Panthera leo) and leopard (Panthera pardus) population density. By doing so, we provide the first spatially explicit population density estimates for spotted hyaena in Tanzania, via spatially explicit capture-recapture (SECR) modelling. We also examine the relationship between population densities of spotted hyaena, lion and leopard at each site. Spotted hyaena densities varied from 3.55 ± 0.72 adults and sub-adults per 100 km2 in a miombo (Brachystegia-Julbernardia) woodland area of Ruaha National Park, to 10.80 ± 1.08 per 100 km2 in a prey-rich open woodland savannah habitat in Ruaha National Park, with intermediate densities recorded in Rungwa Game Reserve and MBOMIPA Wildlife Management Area. Our results suggest that spotted hyaena density is influenced by prey availability and protection, and the species may be less resilient to human pressures than widely thought. Spotted hyaena densities were generally positively correlated with densities of lion and leopard, suggesting that prey availability and anthropogenic disturbance had a greater impact than interspecific effects in shaping large carnivore densities in this system. Overall, our study provides some of the first insights into an under-studied species in an under-researched part of its range, while shedding light into the impact of anthropogenic versus interspecific effects in shaping population status of spotted hyaena in human-impacted African systems.

As landscape-scale conservation models grow in prominence, assessments of how wildlife utilise multiple-use landscapes are required to inform effective conservation and management planning. Such efforts should incorporate multi-species perspectives to maximise value for conservation, and should account for scale to accurately capture species-environment relationships.
We show that the random forest machine learning algorithm can be used to model large-scale sign-based data in a multi-scale framework. We used this method to investigate scale-dependent habitat associations for 16 mammal species of high conservation importance across the southern Kavango Zambezi (KAZA) Transfrontier Conservation Area in Botswana and Zimbabwe.
Our findings revealed substantial variation in factors shaping habitat use across species, and illustrate that different species often have divergent responses to the same environmental and anthropogenic factors, and differ in the scales at which they respond to them. For all variables across all species, scale optimisation most often selected our largest scale.
Precipitation, soil nutrients, and vegetation appeared to be the most important factors determining mammal distributions, likely through their associations with food resources for herbivores and, in turn, prey availability for carnivores.
Anthropogenic pressures also had an important influence, with many species selecting against areas with high cattle density. The variety of relationships with human density indicated that species vary in their tolerance of humans. We found a consistent positive relationship with areas under high protection, and negative relationship with unprotected and less-strictly protected areas.
Policy implications. Through a novel application of random forest modelling to spoor data from 16 mammal species, this study highlights the importance of adopting a multi-scale, multi-species approach for decision-making processes that depend on understanding wildlife distributions and habitat associations, such as protected area and corridor prioritisation. The findings identify changing rainfall patterns and increasing livestock numbers as emerging trends that may impact wildlife distributions, both within sub-Saharan Africa and on a global scale. Wildlife management authorities should use modelling exercises and adaptive management to ensure that protected area networks remain fit for purpose under anticipated changes in rainfall under climate change, and explore initiatives that promote coexistence of wildlife and livestock.