Identification of individual Amur leopard cats possible using face and ventral patterns

The Amur leopard cat (Prionailurus bengalensis euptilurus) is the largest leopard cat. Unlike most leopard cats with bold black spots on a tawny background, their spot pattern is rather dim. Some researchers have said is it difficult or impossible to discern the patterns and individual from camera trap images. Until now.

A new research paper from South Korea has demonstrated that it is possible to identify individual Amur leopard cats using their face and ventral patterns.

Comparison of markings from Amur leopard cat A) face from high-resolution images and B) vent from low-resolution camera trap images from Park et al. (2019).

The researchers used high and low resolution camera trap images and the image comparison software HotSpotter to reliably identify leopard cats baited using valerian scent lures.

I too found that face and ventral markings are practical for individual leopard cat identification, and they can be a lot less confusing than the sea of spots on the flanks. Of course, sometimes more data and angles is best. This seems promising for the Amur leopard cat, and I am looking forward to seeing population density estimates using the application of this method.


Park H., Lim A., Choi T-Y., Baek S-Y., Song E-G., Park Y.C. 2019. Where to spot: individual identification of leopard cats (Prionailurus bengalensis euptiluris) in South Korea. Journal of Ecology and Environment 43: 39. doi:10.1186/s41610-019-0138-z

Leopard cat activity is higher with increased understory vegetation in oil palm plantations, which seems to result in reduced rat numbers

Leopard cats are known to use oil palm plantations as a hunting ground for rats, and have been proposed as a possible biological control for rodent pests in plantations. Related to this, there have been questions raised regarding how understory vegetation management in oil palm plantations [many places consider these weeds and clear them] may influence leopard cat use of these sites. Finally, a study examines these unknowns.

A recently published study by Hood et al. (2019) showed that leopard cat habitat use is higher with increased understory vegetation. They also demonstrate reduced rat numbers with higher leopard cat activity. The authors suggest that the results indicate that how oil palm plantations are managed can affect leopard cat use, and possible rodent control possibilities. All these only possible with their manipulative experiments on understory vegetation management.

Experimental vegetation treatments in oil palm plantation by Hood et al. (2019) showing reduced, normal, and enhanced understory vegetation.

Curiously, there was no effect of understorey vegetation on rat numbers or rat damage on oil palm in this study. It could be that the rats benefit from more vegetation, but the higher activity of leopard cats in those environment leads to higher rat predation level, which nullified the benefits to rats. Neat if that could somehow be studied in future.

Hood et al. (2019) Understory vegetation in oil palm plantations promotes leopard cat activity, but does not affect rats or rat damageFrontiers in Forest and Global Change 2: 51. doi: 10.3389/ffgc.2019.00051

Leopard cat publication update 2015 to 2017

Some leopard cat publications were out in the last two years, and I try to keep track of as many as possible. Some quick thoughts and summary:

Nakanishi & Izawa took a look at the importance of frogs in the diet of the leopard cats from Iriomote Island, Japan. I must say that the Japanese are the gold standard in leopard cat species biology work with the population on Iriomote Island. They examined the stomach contents and compared the results to scat analysis, which is traditionally more frequently used as it is less invasive. Frogs appeared to be important, but under represented compared to scat studies.

Meanwhile. Srivathsa et al. were one of the first to use camera traps to estimate leopard cat population density in India. The density in in forests there appear to be similar to Sabah, but below what we have on Pulau Tekong, Singapore.

Going back about 5,000 years ago, it seems that leopard cats had some close interaction or relationship with Neolithic people in China. The authors (Vigne et al.) use the term “domestic”, but I’ll hesitate to do so in the strict sense of the word.

And finally, a molecular phylogeography of the leopard cat sampled across its global distribution. Rather important that this is done, and the coverage is quite admirable. I cannot say the results are unexpected though (see image below).

Distribution of leopard cat subspecies suggested by Patel et al. (2017). Image from paper.

Arjun Srivathsa, Ravishankar Parameshwaran, Sushma Sharma, K. Ullas Karanth. (2015) Estimating population sizes of leopard cats in the Western Ghats using camera surveys. Journal of Mammalogy 96(4): 742-750. doi: 10.1093/jmammal/gyv079

Nakanishi, N. & Izawa, M. (2016) Importance of frogs in the diet of the Iriomote cat based on stomach content analysis. Mammal Research 61: 35. doi:10.1007/s13364-015-0246-9

Riddhi P. Patel, Saskia Wutke, Dorina Lenz, Shomita Mukherjee, Uma Ramakrishnan, Géraldine Veron, Jörns Fickel, Andreas Wilting, Daniel W. Förster. (2017) Genetic Structure and Phylogeography of the Leopard Cat (Prionailurus bengalensis) Inferred from Mitochondrial Genomes. J Hered 2017 esx017. doi: 10.1093/jhered/esx017

Vigne J-D, Evin A, Cucchi T, Dai L, Yu C, Hu S, et al. (2016) Earliest “Domestic” Cats in China Identified as Leopard Cat (Prionailurus bengalensis). PLoS ONE 11(1): e0147295.

Camera Trap Tool Tip: Trap Duration Calculator

An important measure when analysing camera trap data is the trapping effort of each camera trap and the total for a study. This is typically measured in number of trap nights, usually defined as a continuous 24-hour period when a camera trap is set to be active. The information can then be used for other analysis, such as mark-recapture to estimate population size.

As my camera traps are in the field for long periods of time, I like to be able to visualise the camera trap data graphically to track patterns more easily. To do this, I create a master map to visualise how long each trap is active and on which days leopard cats were recorded at each site. This is done with Microsoft Excel to plot dates and shade cells with colour codes when traps were active and when leopard cats were recorded. This also allows me to summarise camera trap effort.

Visual representation of camera trap data

However, being poor in math, calculation of camera trap effort is a real challenge. Imagine the pain of calculating camera trap effort for a time period between 5 Oct 2012 4:42pm to 2 Dec 2012 1:10am! For this, I am glad there are tools which other camera trap practitioners may also find useful.

Web calculator has a web-based time duration calculator that allows the user to enter start and end date and time. Saves time.

Date and time duration calculator from

Excel alternative

A non-web based alternative is to use this Excel formula: =INT(B2-A2)&””
Where depending on format, A2 is the start date and time [e.g., dd/mm/yyyy hh:mm] and B2 is the end date and time.

Good ole Excel

My poor brain is thankful for all these things.