New method may boost tiger monitoring in large landscapes

A new methodology developed by the Indian Statistical Institute and the Wildlife Conservation Society (WCS) may revolutionise monitoring of tigers and other big cats over large landscapes as well as help in their conservation efforts.

The new method -- called 'Bayesian Smoothing Model' (BSM) -- may better help in extrapolating the exact population counts at large geographical scales -- critical information for scientists working to conserve these wildlife icons.

Currently, for smaller areas, such as protected reserves, the scientists rely on information collected using rigorous but resource-intensive survey methods such as camera trapping to provide reliable results.

However, they are compelled to use weak surrogate indices, such as track counts, while surveying large landscapes of 10,000 sq km or more.

The current statistical method of integrating these two types of data, known as 'Index-Calibration' was developed decades ago, and is known to generate misleading population estimates.

Conversely, the new BSM method develops a far more complex but realistic model for combining information obtained at different geographical scales, the researchers said.

"BSM offered a superior, more rigorous methodology to combine these two types of data to yield more transparent, reliable estimates," said Mohan Delampady, Professor at the Indian Statistical Institute, Bengaluru.

The findings are detailed in the Journal of Agricultural, Biological and Environmental Statistics.

For the study, the team applied the BSM technique to the information from actual data sets which included tiger abundance derived from camera surveys and habitat occupancy estimated from counts of tiger signs such as tracks.

"The progress on scientific techniques we describe can significantly impact and greatly inform how we direct our efforts in saving these iconic species into the future," added Ullas Karanth, Director at WCS - Asia.