Data from: Counting cats: spatially explicit population estimates of cheetah (Acinonyx jubatus) using unstructured sampling data

Many ecological theories and species conservation programmes rely on
accurate estimates of population density. Accurate density estimation,
especially for species facing rapid declines, requires the application of
rigorous field and analytical methods. However, obtaining accurate density
estimates of carnivores can be challenging as carnivores naturally exist
at relatively low densities and are often elusive and wide-ranging. In
this study, we employ an unstructured spatial sampling field design along
with a Bayesian sex-specific spatially explicit capture-recapture (SECR)
analysis, to provide the first rigorous population density estimates of
cheetahs (Acinonyx jubatus) in the Maasai Mara, Kenya. We estimate adult
cheetah density to be between 1.22 ± 0.301 and 1.28 ± 0.322
individuals/100km2 across four candidate models specified in our analysis.
Our spatially explicit approach revealed 'hotspots' of cheetah
density, highlighting that cheetah are distributed heterogeneously across
the landscape. The SECR models incorporated a movement range parameter
which indicated that male cheetah moved four times as much as females,
possibly because female movement was restricted by their reproductive
status and/or the spatial distribution of prey. We show that SECR can be
used for spatially unstructured data to successfully characterise the
spatial distribution of a low density species and also estimate population
density when sample size is small. Our sampling and modelling framework
will help determine spatial and temporal variation in cheetah densities,
providing a foundation for their conservation and management. Based on our
results we encourage other researchers to adopt a similar approach in
estimating densities of individually recognisable species.