Quantifying and correcting for systematic population overestimation in camera trapping studies of threatened felids

Reliable assessments of animal abundance are key for successful conservation and management.

With recent technological advancements, camera-traps have been widely adopted to census species that have proven difficult to monitor with traditional survey techniques, such as elusive large carnivores.

Population abundance estimates from camera-trap data are commonly obtained by identifying individuals based on unique natural markings (e.g. spots, stripes). It is assumed that misidentification is negligible in such species and that derived population estimates are accurate.

However, we have robust evidence that 1 in 8 photo captures are misclassified in snow leopard camera trapping, resulting in systematically inflated population abundance estimates by an average 35%.

This suggests an urgent need to quantify these errors in multiple species, why errors occur and to find ways to limit impacts on population estimation.

To directly address this issue we will 1) quantify identification errors in four different felid species with a range of individually-unique markings; tiger, leopard, snow leopard and lynx, 2) examine how experience and training reduces errors, 3) model how survey design minimises error frequency and, 4) assess how errors have influenced population estimates of conservation critical species.

This work is urgent to improve the quality of population estimation in these species, as these estimates inform lethal control measures and global threat classifications.