fionafoo wrote:Hi everybody,

i am carrying out a survey to estimate the population density of rattus argentiventer by using the software DENSITY 4.4

This live trapping was done for two season.

The 1st season, i did not have any recapture while the 2nd season i do have recapture which is always in the same site (cage) and for both season my data is sparse.

i understand that since my recapture is always in the same cage there is no information about the scale of movement and detection.

So i would like to know if there is any possible way for me to still being able to calculate the population density using this DENSITY with my data.

Fiona

With sparse data like this you can only estimate density by making additional assumptions, but it _is_ possible using ML SECR. If you are willing to assume that movements were similar between seasons then you can fit a pooled, constant model for detection, while allowing density to vary. To do this in DENSITY set up the seasons as different 'sessions' in the one dataset and on the Options | ML SECR page tick 'Use between session model' on the 'Between session model' tab; the default between-session model is exactly what you want (session-specific density, constant g0 and sigma). [You can achieve the same in 'secr' (function secr.fit) with model = list(D~session, g0~1, sigma~1)]. You might also try fitting a model with session-specific g0 and constant sigma.

I guess that you used single-catch traps, and may feel uncomfortable about using ML SECR models for multi-catch traps. This really isn't a worry for density estimation, and from the sparseness of your data I guess the traps weren't saturated (See Efford Borchers & Byrom 2009).

Hope this works

Murray