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Camera traps let researchers document rare cats without having to see them in person. Repeated, dated photographs can reveal where and when cats occur; when coat patterns distinguish individuals, researchers can turn those images into capture histories and use statistical models to estimate abundance or density. A photograph is a detection—not, by itself, a population count.
What camera traps can tell researchers
A camera trap is a remotely triggered camera, often using infrared, that records an animal when it passes a sensor. Researchers can use the resulting images to establish a species’ presence, document timing and activity, and—when photographs are adequate—identify individual animals.
Those uses are not interchangeable. A detection confirms that an animal passed a camera. A capture rate summarizes detections relative to survey effort. Abundance and density are model-based estimates that require an appropriate design and assumptions; they cannot be read directly from a pile of photographs.
How photographs become population estimates
- Choose a survey area and camera locations. Researchers place cameras where the target cats are likely to pass, while arranging the sites to sample the area in a way that supports the intended analysis.
- Collect dated detections. Each trigger creates an image and timestamp. Researchers review the images to determine which show the target species and whether the photographs are usable.
- Identify individuals where possible. Coat patterns or other distinctive marks may let researchers recognize the same cat in photographs from different cameras or occasions. Image quality and viewing angle affect whether a match is reliable.
- Build capture histories. For each identified cat, researchers record when and where it was photographed. Capture–recapture models use the pattern of first and repeat detections to estimate animals that were present but not photographed.
- Estimate the quantity the design can support. Spatial capture–recapture (SCR) incorporates the locations of detections and animal space use to estimate density. Other capture–recapture frameworks can estimate abundance and, with suitable repeated sampling, parameters such as survival or recruitment.
In a snow leopard study in Hemis National Park, researchers placed infrared cameras along active travel routes and at scent-marked rocks and scrapes. In a separate nine-year tiger study, researchers used photographic capture–recapture to assess population dynamics. These are examples of the method in specific settings, not a guarantee that the same placement or model will work for every cat or landscape. Jackson et al. (2006), USGS; Karanth et al. (2002), USGS.
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Why individual identification and camera layout matter
Photographs must support dependable matching
Researchers can only build useful individual capture histories if they can distinguish cats reliably. A poor, partial, or one-sided image may not show enough of the coat pattern to identify an animal. In a European wildcat study, 105 photographs were taken in 60 days, and 98 were suitable for individual identification. The researchers used a multiple-mark SCR model that could include left- and right-flank photographs even when the two sides could not be matched with certainty. Maronde et al. (2020), Ecology and Evolution.
Identification mistakes can affect estimates in either direction. Treating one cat as several creates false individuals and may inflate an estimate; treating distinct cats as the same individual can push it downward. In a controlled experiment involving 16 captive snow leopards and eight observers, 12.5% of capture occasions were misclassified, and estimated abundance was inflated by a mean of 35% (standard deviation 21%). Those results describe that experiment; they are not a universal error rate or a correction to apply to other studies. Johansson et al. (2020), Scientific Reports.
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The array has to fit the question and the landscape
For SCR, camera locations are part of the model’s evidence: the array needs to sample space well enough to support estimates of where animals move and how likely they are to encounter cameras. A review of camera-trap SCR studies discusses sampling variables and design choices relevant to precise wild-felid density estimates. A snow leopard study in Pakistan also found density estimates sensitive to camera-array configuration. Difficult terrain and limited access can constrain placement, so a theoretically ideal layout may not be practical in the field. Mammal Review (2023); 2021 study of snow leopard camera-array design.
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There is no universally best placement strategy established by the available comparisons. Trail cameras can intercept animal movement efficiently, while sites away from trails may represent other parts of the landscape. Which approach is more useful depends on the target species, habitat, access, survey goal, and how placement affects the model’s assumptions.
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A 2026 Bhutan study directly compares trail-based and random camera deployment for felid density estimation. Its comparison is a reason to treat placement as a study-design choice rather than assume that the strategy with more detections automatically gives the more representative or precise estimate. Dorji et al. (2026), Biological Conservation.
What study-specific numbers do—and do not—mean
| Study and species | Reported result | How to interpret it |
|---|---|---|
| Snow leopards, Hemis National Park, 2003 and 2004 | 66 captures and 8.91 individuals per 100 trap-nights in 2003; 49 captures and 5.63 individuals per 100 trap-nights in 2004 | These are figures from that study, not expected rates for other sites or surveys. Jackson et al. (2006), USGS. |
| Tigers, nine-year study | 74 individual tigers over 5,725 trap-nights | The study applied robust-design capture–recapture methods to model time-specific abundance, density, survival, recruitment, temporary emigration, and transience. Karanth et al. (2002), USGS. |
| European wildcats, 60-day survey | 105 photographs, of which 98 were suitable for individual identification | The study used a multiple-mark SCR approach to handle unmatched left- and right-sided flank images. Maronde et al. (2020), Ecology and Evolution. |
| Snow leopard identification experiment | 12.5% of capture occasions misclassified; mean abundance overestimate of 35% (SD 21%) | A controlled experiment with 16 captive snow leopards and eight observers; not a general adjustment factor for field estimates. Johansson et al. (2020), Scientific Reports. |
Limits to keep in mind when reading an estimate
- Detection is uneven. Some cats are more likely than others to pass a camera, and individual differences in capture probability can complicate inference.
- More cameras do not guarantee accuracy. Spacing, duration, array configuration, target species, terrain, image quality, and analytical assumptions work together.
- Rare animals can produce small samples. Too few independent detections or reliable individual matches can leave estimates imprecise.
- Not every survey estimates abundance. A study may be designed to document presence, timing, or activity rather than produce a defensible population estimate.
What camera equipment can and cannot do
Infrared wildlife camera traps are used in field studies of elusive cats, including snow leopards. But equipment alone cannot create a reliable population estimate: placement, survey design, image review, individual identification, and suitable statistical analysis determine what the photographs can support. A specialist resource page from the Snow Leopard Network lists camera-trapping and identification resources for researchers. Snow Leopard Network, Research Approaches.
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