Landscape drivers of population density of a vulnerable apex predator (Wilkerr/dingo)

Amanda Lo Cascio, Ellisha Martion, Dave Ramsay, Rachel Mason, Ange Pestell, William L Geary, Will Sowersby, Euan G. Ritchie

Apex predators shape ecosystems globally, yet robust monitoring that assesses the effects of management actions and environmental variation on their populations is challenging. The dingo, Australia’s largest terrestrial predator, is ecologically and culturally significant. In many parts of Australia, dingoes now exist in fragmented and isolated populations, and our understanding of how their population abundance and distribution is influenced by environmental and anthropogenic factors is limited. Here, we employed a flexible spatial mark–resight (SMR) modelling framework with strategically placed camera traps to estimate the density of an isolated and genetically distinct dingo population across a fire- and drought-prone, water-limited, semi-arid region of southern Australia. Our approach addressed detection challenges in remote landscapes and enabled the integration of key environmental covariates—including water availability, road proximity, and fire regime—to better understand spatial variation in dingo density. From over two million camera trap images, we identified 20 unique individuals at 38 locations using a combination of machine learning and manual validation. Dingo abundance and density was estimated as 77 (64–94) individuals and 0.014 (0.012–0.017) dingoes/km²—lower than previously reported estimates—and varied significantly in response to aspects of the fire regime and distance from roads. Our findings highlight the value of integrating environmental covariates and resource-focused sampling strategies to improve detection, population estimates and inference of environmental preferences of large carnivores. Our approach can be adopted elsewhere to help inform management of landscapes and apex predator populations through robust population estimation of low-density carnivore populations in remote area contexts.