Using landscape and bioclimatic features to predict the distribution of lions, leopards and spotted hyaenas in Tanzania's Ruaha landscape

Abstract

Tanzania's Ruaha landscape is an international priority area for large carnivores, supporting over 10% of the world's lions and important populations of leopards and spotted hyaenas. However, lack of ecological data on large carnivore distribution and habitat use hinders the development of effective carnivore conservation strategies in this critical landscape. Therefore, the study aimed to (i) identify the most significant ecogeographical variables influencing the potential distribution of lions, leopards and spotted hyaenas across the Ruaha landscape; (ii) identify zones with highest suitability for harbouring those species; and (iii) use species distribution modelling algorithms (SDMs) to define important areas for conservation of large carnivores. Habitat suitability was calculated based on environmental features from georeferenced presence-only carnivore location data. Potential distribution of large carnivores appeared to be strongly influenced by water availability; highly suitable areas were situated close to rivers and experienced above average annual precipitation. Net primary productivity and tree cover also exerted some influence on habitat suitability. All three species showed relatively narrow niche breadth and low tolerance to changes in habitat characteristics. From 21,050 km2 assessed, 8.1% (1,702 km2) emerged as highly suitable for all three large carnivores collectively. Of that area, 95.4% (1,624 km2) was located within 30 km of the Park-village border, raising concerns about human-carnivore conflict. This was of particular concern for spotted hyaenas, as they were located significantly closer to the Park boundary than lions and leopards. This study provides the first map of potential carnivore distribution across the globally important Ruaha landscape, and demonstrates that SDMs can be effective for understanding large carnivore habitat requirements in poorly sampled areas. This approach could have relevance for many other important wildlife areas that only have limited, haphazard presence-only data, but which urgently require strategic conservation planning.

Figure 1. Map of the Rungwa-Ruaha region, highlighting the location of Tanzania's Ruaha landscape.

Location of the Rungwa-Ruaha region, southern Tanzania, East Africa, composed of the Ruaha National Park, Pawaga-Idodi Wildlife Management Area, adjacent game reserves and village land. The dashed black line highlights the study area, the Ruaha landscape, comprised by the Ruaha National Park, Pawaga-Idodi Wildlife Management Area and village land.

Figure 2. Representation of variables used for predicting the distribution of large carnivores in the Ruaha landscape.

Representation of raster files used for the predictive modelling of the distribution of lions, leopards and spotted hyaenas in the Ruaha landscape. A. Elevation (m), B. Rainfall (mm), C. Slope (degrees), D. Geology – cation exchange capacity, E. Distance to rivers (km), F. NDVI, G. Distance to households (km) and H. VCF (% tree cover).

Figure 3. Predictive map of the potential distribution of large carnivores in Tanzania's Ruaha Landscape.

Map of potential distribution of large carnivores across Tanzania's Ruaha landscape. The maps were generated using ensemble modelling approach based on the outputs of Maxent, ENFA and SVMs. The colour gradient indicates probability of species occurrence, with darker areas representing the highly suitable areas (h.s.> species median suitability) for species occurrence.

Figure 4. Distribution of highly-suitable areas for large carnivores in relation to proximity to village lands.

Distribution of grid cells deemed highly suitable for large carnivores in relation to proximity to village land. The grey areas represent the probability density of the data. The horizontal black bar represents the first-to-third interquartile range, and the horizontal black line represents the 1.5 times the interquartile range. The median is represented by the white dot.