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Hello,

I'm using DENSITY 4.4.5.2 to estimate density of a cat species in Southeast Asia. I have an array of 26 camera trap stations that were active for 4 months. I got 59 "captures" from only 4 individuals, so the data are somewhat limited. (The capture heterogeneity is also huge: 48, 6, 4, and 1 captures per individual.) The density models run fine -unsurprisingly Mh2 is the best-fit model -and give realistic, if fairly imprecise, density estimates. I'm using ML-SERC with a habitat mask and otherwise default settings.

However, I'm now trying to test the Goodness-of-fit of the best-fit model. In the "Options>ML SERC>Advanced" tab I checked the boxes for "Monte Carlo test" and "Report each replicate". My understanding is that I compare the observed deviance/df with the distribution of resampled deviance/df estimates. Upon clicking "Go", the model runs fine at first but then fails with a "Floating point division by zero" error message after usually ~2 replicate runs. A google search suggested that that error could have something to do with the processor being too fast for the software (seems crazy to me), so I tried re-running the model on an older machine. Then it got up to about 6 replicates before failing with the same message.

Any ideas? Thanks so much!!!!!

Sunda

Sorry for the delay. Here are some comments, but maybe not what you wanted to hear!

Widely different numbers of captures do not necessarily imply unmodelled heterogeneity in a spatially explicit capture-recapture model (some animals may just have lived closer to the cameras). How did you decide Mh2 was the best model? Are we talking spatial or nonspatial models here? I advise againt fitting SECR mixture models to sparse datasets, and I would be very surprised if they are supported by AIC in your case.

Four individuals is really too few for a formal capture-recapture population analysis. You are free to fit a simple model, and the estimates may be better than nothing, but please don't expect much from model selection etc.

The error you report must be a bug in the program, probably exposed by small sample sizes. The message tells you essentially nothing except that there's a bug. I'm will track it down if you send an example dataset (email traps and captures to address in Help | About Density). If you really want to pursue a Monte Carlo goodness of fit test then it is better to use the R package 'secr' (see e.g. help for function sim.secr). This should be more robust than the equivalent in Density (and 'secr' is being actively maintained).

Murray