Estimating mesocarnivore abundance on commercial farmland using distance sampling with camera traps

Abstract

Mesocarnivores are of particular interest in wildlife management. Their adaptability makes them a focus of public attention globally, as negative interactions with people occur regularly, but their importance to wider ecosystem function is increasingly apparent. Robust methods for estimating mesocarnivore densities are essential for long-term management strategies. Estimating densities of unmarked populations remains challenging, but new methods, based on camera trapping, have recently become available and require field testing. We conducted two camera trap surveys over two 200 km2 areas of commercial farmland in South Africa. One survey sampled 25 locations, while the second used a migrating grid to sample 59 locations; total sampling effort was similar across the two surveys. We applied distance sampling with camera traps (CTDS), developing a method to estimate animal distances by applying a distance measurement overlay grid to camera trap images. We aimed to establish meaningful density estimates of the mesocarnivore guild and evaluate CTDS's suitability for broader use with these types of species. We obtained density estimates for four carnivores, African civet Civettictis civetta, black-backed jackal Canis mesomelas, brown hyena Hyaena brunnea and caracal Caracal caracal, providing valuable insight into their status in commercial farmland. Imprecision in the estimates was almost exclusively due to encounter rate variance, which was not reduced with the migrating camera grid. We explored the sensitivity of our results to assumptions determining the value of the ‘snapshot interval’, demonstrating that careful selection of this parameter is vital to ensuring reliable estimates when using rapid-fire photo burst modes. CTDS can provide useful density estimates for mesocarnivores, but future studies should aim to maximize precision and reliability by increasing sampling locations. More studies are required in areas with known densities to promote confidence in accuracy.