India’s upcoming All India Tiger Estimation 2018 is all set to go hi-tech. The plan is to digitise data records with the help of an Android mobile application and eliminate the manual process of recording, which is slow and prone to errors.

The Wildlife Institute of India developed the app, called the Monitoring System for Tiger-Intensive Protection and Ecological Status or M-STrIPES).

“M-STrIPES is designed to address many of the issues difficult to handle in patrolling surveys,” said PS Somasekhar, inspector general of India’s National Tiger Conservation Authority’s Southern Region. “Data related to the carnivore’s signs, the relative abundance of prey, habitat and human impact in the area would be digitally recorded through the app. It is user friendly and easy for information management.”

Bengal tiger eyes the photographer in Ranthambhore National Park, Rajasthan, India. Photo credit: Bjørn Christian Tørrissen, CC BY-SA 3.0.
Bengal tiger eyes the photographer in Ranthambhore National Park, Rajasthan, India. Photo credit: Bjørn Christian Tørrissen, CC BY-SA 3.0.

If procurement of the app goes according to plan, it will be used for the 2018 all-India tiger estimation, added Somasekhar. The National Tiger Conservation Authority plans to provide one app-loaded smartphone for data logging in each of the beats (area covered by a forest guard) in the country’ tiger reserves.

The M-STrIPES app, when loaded onto an Android-based smartphone, can help forest guards collect data along with pictures and GPS coordinates. When a guard is at a location where there is Internet connectivity, the data are transferred automatically to a central server. However, since most parts of the tiger terrain are outside the reach of such connectivity, the data are stored on the mobile phone and can be uploaded at the forest manager’s office.

Training of trainers

“The Wildlife Institute of India is conducting a country-wide training of trainers,” said K Sivakumar, scientist at Wildlife Institute of India, Dehradun. “It is a mammoth task, as the estimation is set to begin in January.”

The trainers will go on to train forest officials, field staff, and technical operators to use the app, according to Ambadi Madhav, director of Bandipur Tiger Reserve in Karnataka.

Sivakumar stated that the use of the app would help to make the tiger survey process quicker and easier. “We used to have a formulated data sheet in which patrolling guards recorded data manually,” he said. “This would then be compiled and sent to the higher authorities. This is a slow and complicated process. But, with the M-STrIPES app, the data logged on the spot will be automatically transmitted to the main server. Those managing the estimation can immediately access the data.”

Besides recording information related to tigers, the app also has features designed to record prey populations, patrol effort, and unusual activities, such as poaching and human-wildlife conflict. “If there are any issues, the software can quickly analyse it, and the local managers can take relevant action,” Sivakumar explained. “This will also help assess if current conservation measures are working.”

He added that tiger reserves in the states of Maharashtra and Madhya Pradesh have been successfully using the M-STRiPES app to assist monitoring and reserve management for nearly five years. There have been improvements in the app since managers first started using it. Now it is more user friendly, and it provides managers with instant analysis capability to make decisions related to surveillance, monitoring, and patrolling.

Improves accuracy and reliability of data

“M-STrIPES will be used in the first phase of the tiger estimation in all forested areas,” said YV Jhala, senior scientist at Wildlife Institute of India and nodal officer of the Wildlife Institute of India-National Tiger Conservation Authority Tiger Cell. In this phase, a beat forest guard covers 10 to 15 kilometers in each forest range, looking for signs [e.g. tracks, scat, or scrapes on trees] related to the presence of tigers. By walking transects and recording pictures, the guard can also quantify the animal’s prey base.

“As the information of both tiger signs and prey is logged, the app automatically records the geographical coordinates. This improves the accuracy and reliability of the data,” said Jhala. “Earlier, the data could be faked or there would be errors in manual recording of coordinates. This would make the data worthless or require a re-visit to the area. With the M-STrIPES app, such errors are minimized and effort is saved.”

The entire national estimation of the tiger population is expected to take a year to cover about 400,000 sq km of forested area in 18 states.

To inform tiger conservation measures, the Government of India started the national tiger estimation in 2006. The NTCA conducts the estimation once every four years and has established a protocol that is implemented in three phases as part of the procedure.

Phase I includes ground survey data collected by trained field personnel. Phase II involves habitat characterization using satellite data. Phase III includes computation of tiger density using camera traps. In areas where it is difficult to conduct ground surveys or establish camera taps, genetic testing of tiger scat is used to obtain tiger density. Data from all three phases are then analysed together using statistical models to estimate tiger populations.

The problem is with the sampling

The last all-India tiger estimation, conducted in 2014, noted a 30% increase in tiger numbers, from 1,411 to 2,226. However, experts have contested these numbers, citing discrepancies in the methodology.

Can M-STrIPES help to eliminate these discrepancies? “Shifting to app-based data collection has certainly helped us to minimise some of the human errors,” said Sanjay Gubbi, scientist with the Nature Conservation Foundation and a member of the Karnataka State Wildlife Board. “Using a smartphone app is only a change in tool for data collection and is not a change in sampling methodology.”

Screenshot of the M-STrIPES app. The ecological module is on the left, and the patrol module on the right. Photo credit: Ashok Kumar
Screenshot of the M-STrIPES app. The ecological module is on the left, and the patrol module on the right. Photo credit: Ashok Kumar

Arjun Gopalaswamy, a scientist associated with the Wildlife Conservation Society (India), agrees with Gubbi. “The M-STrIPES app has not been designed to estimate wildlife numbers. It is essentially a data-recording device for patrolling staff. But the errors in India’s tiger estimation procedure are coming from more foundational problems which are not resolved by an app such as M-STrIPES.”

Gopalaswamy and his associates published a study stating that estimates of tiger abundance reported in India’s tiger estimation reports are more unreliable than previously thought, since these estimates are coming from unpredictable models.

The study states that the index-calibration method used by the Indian government to estimate tiger population numbers suffers from “overdispersion”. What this means is that there is greater variability in the data than would be expected.

Index calibration involves calibrating animal numbers obtained by accurate measures, such as camera trapping and DNA sampling in a small region, with animal densities obtained by approximate measures, such as surveys of tiger signs. The calibrated index is then used to extrapolate actual animal numbers over larger regions.

Using a mathematical model, Gopalaswamy’s study showed that the index calibration model had a high extent of noise and yielded irreproducible results. As a consequence, both the mean estimates of tiger abundance as well as the associated statistical upper and lower limits reported in India’s tiger estimation reports are unreliable.

“A re-analysis of all these tiger data will help in assessing the number of tigers in India better,” Gopalaswamy concluded.

A sleepy-looking male tiger in India’s Ranthambhore National Park. Photo credit: Koshy Koshy CC 2.0
A sleepy-looking male tiger in India’s Ranthambhore National Park. Photo credit: Koshy Koshy CC 2.0

This article first appeared on Mongabay.