Vertical relief facilitates spatial segregation of a high density large carnivore population

Abstract

Quantifying the distribution and size of home ranges is critical for understanding animal spatial dynamics. This is particularly important for large carnivores in fragmented landscapes. Most studies that estimate home range consider only a bivariate frequency distribution represented by a two‐dimensional planimetric surface. The underlying assumption of these approaches is that the animals inhabit landscapes that are completely flat. Of course, this is rarely the case. Here we investigated the influence of vertical relief and three‐dimensional landscape features on the home range patterns of a high density carnivore. Via GPS telemetry‐tracking of a population of Persian leopards Panthera pardus saxicolor (n = 6), and globally‐available digital elevation models (DEMs), we calculated the surface area of home ranges in comparison to traditional planimetric estimates. We also investigated predation patterns of leopards across elevation gradients using GPS location data and kill site analysis. The topographic measurements exceeded planimetric estimates by up to 38% which suggests that planimetric modeling underestimates home range size, particularly when animals inhabit variable terrain. We also observed that resident leopards exhibit significant altitudinal partitioning of predation, suggesting that leopards that have overlapping home ranges may still utilize exclusive hunting territories. We discuss the ways in which planimetric approaches may be underestimating aspects of animal ranging behavior and ecology. We conclude that topography should be considered, not as an ancillary metric, but as an important aspect of home range calculation. Our approach can enhance understanding of spatial requirements, population density, intra‐guild sympatric competition and conflict management of large felids inhabiting rugged landscapes.

Movement behavior of a solitary large carnivore within a hotspot of human-wildlife conflicts in India

“…Movement parameters can provide detailed knowledge of animal behavior with respect to humans, co-predators and livestock 24 . Both intrinsic (age, sex, life stage) and extrinsic (environmental conditions, availability of resources) factors can contribute to variance in movement 23 , 25 . Ecological attributes such as availability of water, human presence, and the proximity to protected areas, forests, roads, and crop fields have been reported to affect hunting success and habitat utilization by large carnivores 26 – 29 .…”

“…2 Savannah Research Ecology Laboratory, University of Georgia, Athens, GA, USA. * email: ssk@wii.gov.in of resources) factors can contribute to variance in movement 23,25 . Ecological attributes such as availability of water, human presence, and the proximity to protected areas, forests, roads, and crop fields have been reported to affect hunting success and habitat utilization by large carnivores [26][27][28][29] .…”

Landscape complexity persists as a critical source of bias in terrestrial animal home range estimation

“…These methods overlook potential vertical stratification of animal movement patterns by ignoring vertical relief structures (Davies andAsner 2014, Farhadinia et al 2019). Areas with broad vertical relief structures could potentially accommodate more individuals by both providing more area for research procurement and by limiting antagonistic inter and intra-specific interaction (Downs et al 2008, Farhadinia et al 2019. Similarly, 2D+ landscape features can act as physical barriers to movement beyond solely increasing area.…”

“…For example, computationally-efficient methods to calculate surface area exist in popular geographical and statistical software like ArcGIS and R (Kamp et al 2003, Rabus et al 2003, Jenness 2004, Pebesma and Bivand 2005. Such methods add functionality to existing estimators and do not require entirely different methods or a great amount of computing power (see Farhadinia et al 2019). Thus, incorporating landscape complexity into home range estimation can be accomplished with relative ease, so long as the scale of available GDEMs is relevant to the study species in question.…”

Understanding decision making in a food-caching predator using hidden Markov models

“…Using the auto-correlated Kernel density estimation method, we estimated a mean home range of 103.4 ± SE 51.8 km 2 for resident male leopards [27]. If threedimensional topographic estimation to account for vertical relief is considered, leopards' home range will increase up to 38%, making them larger than has hitherto been observed in other studies of Asian leopards [28].…”