Research Article: Royle, J. A., Karanth, K. U., Gopalaswamy, A. M., & Kumar, N. S. (2009). Bayesian inference in camera trapping studies for a class of spatial capture–recapture models. Ecology, 90(11), 3233-3244.
Blog Author: Shashank Dalvi
Key highlights:
- Tiger detections on camera traps are not purely random.
- Detection probability depends strongly on the distance between a tiger’s home range center and a camera trap.
- Spatial Capture-Recapture models use these detection patterns to estimate tiger density more accurately.
- The study provided a robust framework that has become the foundation of modern camera-trap based wildlife monitoring.
Every wildlifer who visits the tiger reserve has noticed a small camera strapped on a tree or post next to a road or small trail and often wondered what the chances are that a tiger will actually trigger this camera? At first, it may seem like a matter of luck. After all, tigers often roam vast landscapes, and a camera covers a tiny point in their territory. However, the science behind camera trapping tells a different story. In this landmark study, Royle et al (2009) demonstrated that detecting tigers is not a random chance but the detection is governed by tigers’ movement and the location of camera traps. Sounds simple enough right?
However, the core concept behind this study is to identify the tiger’s activity center, a core area of its territory where it spends most of its time. While tigers may roam across large territories, they tend to move more frequently near these centers. As a result, camera traps located closer to a tiger’s activity center have a much higher probability of photographing that individual than traps located farther away. In simple words, there is a better chance of encountering a person right outside their apartment gate, rather than some 10 kilometres away. Tigers behave similarly. Therefore, the probability of a tiger triggering a camera trap depends largely on the distance between the camera and the tiger’s activity center.
To test this idea, the researchers analyzed camera-trap data collected in Nagarahole Tiger Reserve (NTR), Karnataka. Camera trapping across NTR, with the help of 120 camera trap locations over 38 nights, the study identified 44 different individuals, thanks to their unique stripe patterns. However, with this dataset and data of where and how often the individual tigers were photographed, biologists developed a statistical analytical framework known as Special Capture-Recapture (SCR). This framework explicitly takes into consideration species movement and spatial information and helps estimate how many tigers are present, along with how the species is distributed across the landscape.
Using this framework, the study estimated a tiger density of approximately 13.4 tigers per 100 km² in Nagarahole. More importantly, the method produced more reliable and precise estimates than previous approaches, which overestimated tiger densities of approximately 14.3 tigers per 100 km².
Why does this matter?
Conservation decisions should depend on accurate population estimation of the species. By knowing if the species numbers are increasing or decreasing or which habitat supports the best possible densities, authorities can know if the conservation actions are working for the species or not. Today, this approach is the backbone of modern camera-trap studies, which helps estimate the species population around the world.
To access the original article, click here.
Keywords: Tiger Conservation, Camera Traps, Spatial Capture-Recapture, Tiger Density, Wildlife Monitoring, Nagarahole Tiger Reserve, Conservation Science
