SECR with unpaired cameras
When animals can be individually identified by their natural
markings, as is the case with most cats, camera traps can be
used to gather data for density estimation with
SECR. Because markings differ on
each side of the animal, cameras are usually set in pairs to
simultaneously record both sides. I have just been looking at
ways to analyse data from unpaired cameras, and was surprised to
find that unpaired cameras can be preferable to paired cameras
when the number of cameras is limited.|
This was based on simulations with just one set of parameter values, but it does suggest that projects with limited resources should consider using unpaired cameras, as this allows more locations to be sampled.
Update 5 Nov 2018: See this new paper by Ben Augustine et al (2018) Spatial capture–recapture with partial identity: An application to camera traps, Annals of Applied Statistics 12 (1) 67-95
Analysing unpaired data
Let's look at a practical example. Wang and Macdonald (2009) used unpaired cameras to collect data on leopards and tigers in Bhutan. For leopards, they identified 25 left flanks and 23 right flanks; separate analyses with program capture gave very similar estimates of the population. For tigers, they got 14 left and only 3 right flanks; they analyzed only the left flank data, discarding the right flanks.
A more immediate example is an ongoing study of clouded leopards in Nepal. The objective is to estimate occupancy, so unpaired cameras are deployed. But it may be possible to use SECR to estimate density too if enough recaptures are obtained.
We should be able to combine the data for the 2 flanks into a single analysis as we know that the density of left and right flanks must be equal, and can assume that the capture probabilities are also equal. But we can't just lump all the data into a single capture history, as that would involve an unknown degree of double-counting.
A straightforward solution with the
I think I know how to enforce the equal-density constraint in a JAGS model using data augmentation, but want to run simulations to check.
I used the same general layout as in a
previous post, where we looked at different designs for
estimating small populations of tigers. That used an irregular
habitat patch of 2000 km2
with approx 80 camera traps in systematic-randomly distributed
clusters. This time I used a larger population of 20 animals, 1
animal per 100 km2. I
used the same values for the capture parameters
I considered 4 designs. The first 3 used clusters of up to 8 traps with a total of around 85 (numbers differed among simulations as clusters near the edge had fewer traps).
I did 300 iterations of each (300 gives a nice beeswarm plot) which took just over 1 hr using 7 cores in parallel on my desktop. The resulting beeswarm plots are below:
Each dot in the plot represents the result of one simulation. The red dots are "dead bees", simulations where the data could not be analysed as there were no recaptures or all recaptures were at the same location. The true density is indicated by the dotted red line.
The key points from these results:
That suggests that if you have only a small number of cameras available, an unpaired design may be better.
A lot of work was done on the analysis of this kind of data. See
| Updated 5 Nov
2018 by Mike Meredith|