How Do You Count a Ghost Cat?

A single male can range over more than a thousand square kilometers in the course of his life. An animal like this is rarely seen and is difficult to count. For most of the history of trying to count this species, the honest answer has been that nobody knew. That matters more than it might seem.

Knowing roughly how many snow leopards there are and where they live helps identify populations in trouble, track whether they are recovering or declining, and determine which landscapes need protection. As climate change alters the mountain landscapes snow leopards depend on, getting a clearer picture of their populations is increasingly urgent. Reliable population data also helps determine where research, funding, and conservation support are needed most to protect this keystone species.

Why Snow Leopard Counts Have Been Unreliable

Good estimates have been so scarce because of two persistent problems. Study areas were often small, leading to an overestimation of site density. Researchers also tended to survey the best habitats, the well-protected valleys with the most prey and cover, and those numbers were then applied across the whole landscape. That kind of gap can mean the difference between a population that looks secure on paper and one that’s quietly disappearing.

Pakistan is a great example. Early assessments, drawn from interviews and sign surveys, placed the national population between 300 and 420 animals, and the higher figure was widely repeated. Later camera-trap and genetic work in the core habitats suggested the actual number was closer to 80-120. The earlier estimates had extrapolated limited data across large areas, counted the same cats more than once as they moved between sites, and assumed the animals were present in places they were not.

Reducing Identification Errors

Even the method now considered the gold standard carries a hidden version of the same problem. Camera-trap surveys identify individual snow leopards by the spot patterns on their coats, then use the frequency with which each is photographed to estimate the broader population. The whole approach depends on the assumption that individuals can be positively identified. For a long time, that assumption went untested.

Researchers call this a “ghost”: when one cat’s photos or scat samples get mistakenly split into two, the survey counts an animal that does not exist. This problem is the focus of the 2024 study Ghostbusting: Reducing bias due to identification errors in spatial capture-recapture histories. In one Nepal study, this kind of error turned 34 individuals identified from DNA into an extrapolated estimate of 144. Small identity errors, multiplied across a landscape, can disproportionately inflate the headline number.

When SLT scientists tested individual identification accuracy, they found that observers misclassified one in eight capture events. While modern Spatial Capture-Recapture (SCR) models handle data better than older methods, misidentification remains a challenge. To address this, a recent collaborative study co-led by SLT researchers offers guidance on reducing errors caused by misidentifying snow leopards in photographs. The method improves population estimates by focusing on individuals photographed at least twice and excluding cats recorded only once, as single sightings are more likely to reflect identification errors.

Building a Common Standard with PAWS

The counting has to be done carefully and consistently everywhere, so that a figure from Pakistan can be compared with one from Mongolia. That is the problem the range countries set out to solve after 2017. Through the Global Snow Leopard and Ecosystem Protection Program (GSLEP), the intergovernmental alliance of all twelve snow leopard range states, Snow Leopard Trust helped launch a shared approach to population assessment known as PAWS (Population Assessment of the World’s Snow Leopards).

PAWS works in two stages. First, interviews, sign surveys, and camera traps are used to map snow leopard presence across large areas. Then, intensive camera-trap or genetic sampling estimates abundance, with sites chosen to cover the full range of habitat quality rather than only the best. The design is overseen by a panel of statisticians and field scientists from across the range and beyond, and tested in the field. In one of the large-scale efforts across the entire snow leopard habitat of Himachal Pradesh in India, an area larger than many previous snow leopard population studies, confirmed that the earlier opinion-based figure for the state had been substantially overstated.

Counting accurately is slow and expensive. A single assessment can take the better part of a year, and several rounds of training. The statistical tools are still being refined. One recent method for identifying snow leopards before they enter the count was developed by a group of scientists, including one from the Snow Leopard Trust.

What the New Assessments Are Showing

Earlier this year, the Snow Leopard Network, a community of more than 800 researchers and practitioners, published status assessments for all twelve range countries that underpin the ongoing IUCN Red List review. Six of those countries have now completed PAWS-standardized assessments, and China, which holds around 60% of the world’s snow leopard habitat, produced its first standardized national estimate.

The picture these assessments describe is uneven. While there are more snow leopards in some parts than we previously thought, many other places show far fewer cats than previously believed. What the assessments offer is a picture built on consistent, cross-border measurement, one that lets range countries speak about the state of the species with a confidence they didn’t have before.

From PAWS to Long-Term Monitoring

While PAWS provides a baseline of snow leopard locations and numbers, the next step is to monitor selected representative populations over time. Through Continuous Long-term Assessment of the World’s Snow Leopards, or CLAWS, range countries can track whether populations are stable, growing, or declining, and whether protections already in place are working. Repeated monitoring can also flag emerging threats and identify critical habitats and movement corridors that need to remain connected.

PAWS provides the shared methods that make those country-level assessments comparable across borders. PAWS provides the shared methods that enable those comparisons. This year, the Snow Leopard Network published status assessments for all twelve range countries in Snow Leopard Reports, bringing those country-level findings together in one place.

Snow leopards earned the name “ghost of the mountains” because they are so difficult to see. Even people who share their landscapes may live alongside them without ever seeing one. But being elusive does not mean being beyond human reach. A 2016 TRAFFIC analysis estimated that 55% of illegal snow leopard killings were in retaliation for livestock attacks. The species is also threatened by extinction, giving the ghost metaphor a deeper meaning. A snow leopard may disappear because it is hidden somewhere in the mountains, or because it is no longer there. That is part of why accurate counting matters. We need to know where snow leopards still live, where their populations are declining, and where conservation efforts are most needed.

Koustubh Sharma, Snow Leopard Trust’s Science and Conservation Director, explains why the shift in how snow leopards are sampled matters:

“PAWS has brought range-country governments, scientists, institutions, and local communities together, following a shared, rigorous standard for assessing snow leopard distribution and abundance. While expanding the survey coverage across High Asia from just 2% to 8% is a major milestone, what truly matters is where and how the species is sampled. By moving away from sampling only prime, high-density sites and embracing a balanced sampling approach across high-, medium-, and low-quality habitats, we are finally replacing guesswork with reliable, replicable science.”

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Photo/video credits: SLCF Mongolia

Acknowledgements:

Ale, S. B. & Mishra, C. (2018). The snow leopard’s questionable comeback. Science, 359(6380), 1110.

Suryawanshi, K. R., Khanyari, M., Sharma, K., Lkhagvajav, P. & Mishra, C. (2019). Sampling bias in snow leopard population estimation studies. Population Ecology, 61(3), 268–276.

Johansson, Ö., Samelius, G., Wikberg, E., Chapron, G., Mishra, C. & Low, M. (2020). Identification errors in camera-trap studies result in systematic population overestimation. Scientific Reports, 10, 6393.

Chetri, M., Odden, M., Sharma, K., Flagstad, Ø. & Wegge, P. (2019). Estimating snow leopard density using fecal DNA in a large landscape in north-central Nepal. Global Ecology and Conservation, 17, e00548.

Suryawanshi, K. R. et al. (2021). Estimating snow leopard and prey populations at large spatial scales. Ecological Solutions and Evidence, 2, e12115.

Kodi, A. R., Howard, J., Borchers, D. L., Worthington, H., Johansson, Ö., Samelius, G., Low, M. & Sharma, K. (2024). Ghostbusting: Reducing bias due to identification errors in spatial capture-recapture histories. Methods in Ecology and Evolution, 15(6).

PAWS (Population Assessment of the World’s Snow Leopards) guidelines and process outline. Global Snow Leopard and Ecosystem Protection Program (GSLEP) Secretariat.

Snow Leopard Reports, Vol. 5 (2026). The Status of Snow Leopards Across High Asia. Snow Leopard Network and Swedish University of Agricultural Sciences.

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