James Redcliffe ^1* Scott Creel ^2,3 Ben Goodheart ^2,3 Johnathan Reyes De Merkle ^2,3 Stephi Matsushima ³ Michelo Mungolo ³ Ruth Kabwe ^3,4 Emmanuel Kaseketi ³ Will Donald ⁴ Matthew Becker ⁵ Clive Chifunte ⁶ Matthew Becker ^2,3 Rory Wilson ¹

• ¹ Swansea University, Swansea, United Kingdom
• ² Montana State University, Bozeman, Montana, United States
• ³ Zambian Carnivore Programme, Mfuwe, Zambia
• ⁴ Musekese Conservation, Musekese, Zambia
• ⁵ Zambia Department of National Parks,, Chilanga, Lusaka Province, Zambia
• ⁶ Zambia Department of National Parks and Wildlife, Mumbwa, Zambia

Most large carnivores feed on prey infrequently and may expend large amounts of energy to locate, capture and kill their prey. This makes them probabilistically vulnerable to fluctuating rates of energy acquisition over time, especially within the increasingly human-altered landscapes that dominate their remaining range. Consequently, quantifying their hunting behaviors and success rates is critical, yet direct observation of these events is rarely feasible.This makes them probabilistically susceptible to highly variable rates of energy acquisition over time, particularly in the increasingly human-impacted landscapes characterising their remaining range so it is important to be able to quantify their hunts and successes, but this is rarely accomplished by direct observation. We theorized that we could determine prey pursuit and capture in African wild dogs (Lycaon pictus) using a mechanistic approach by constructing Boolean algorithms applied to accelerometer data derived from collar-mounted tags. Here, we used this method and then iteratively improved algorithms by testing them on observed hunts and kills of collared packs. Using this approach on 47 days of acceleration from three wild dogs in three packs, we identified 29 hunts with 10 kills, all of which were confirmed by direct observation except for a single kill. Our results demonstrate that hunting effort and success can largely be determined from acceleration data using a mechanistic approach. This is particularly valuable when such behaviours are rarely quantified and offers a template for research on foraging in canid species, while also contributing to the expanding body of literature that employs similar methods to quantify hunting in large carnivores.Our results demonstrate that measures of hunting effort and success can be largely resolved from acceleration data using a mechanistic approach, which is particularly valuable if such behaviours are rarely quantified and provides a template for research on foraging in top predators.