I am new to both occupancy modelling, and the unmarked package. I would like to run several questions past those more knowledgeable for your feedback.
I am interested in investigating how the occupancy of a snake species varies in response to burn intensity post bushfires.
I have a detection history across two distinct seasons ( ~ 1 year post fire, and ~2 year post fire) for 70 sites that are high intensity burn, low intensity burn or unburnt. These sites are across four separate national parks. Detection histories were constructed from standardised active searches to locate the snakes. In the first year due to logistical constraints (both site access post-fire and covid), we only had 3-4 four surveys per site, the second season we sampled sites between 6-8 times. For one park (9 sites in total) we only have occupancy for the second season as we were not able to access it the first year.
To account for the different sampling lengths within years and across years I’ve added ‘NA’ values to the detection history so that all sites had equal secondary sampling of 8 (maximum number of visits that occurred at a single site in a season) instances, for both seasons.
Given we sampled across years I ran a dynamic model using the colext() function of unmarked. The data frame was the detection history combined with site covariates of burn intensity (factor) and park/reserve(factor). Year was included as a yearly covariate.
dynamic_occ_mustard <- colext(
psiformula= ~ intensity + park ,
gammaformula = intensity,
epsilonformula = intensity,
pformula = ~ intensity + park + year,
data = mustarddata,
method = "BFGS")
Model selection indicated to drop all factors, expect park and year from the detection formula.
I checked model fit with mb.gof.test and the chi squared statistic was >0.05. Chat was = 0 though.
the inclusion of NAs to account for different secondary sampling lengths
2) Can I run an colext model given for one park (nine sites) there was no sampling in the first year?
3) Why is chat = 0?
3) Are there any general recommendations or further reading you could suggest?
The reason I am seeking feedback is that when I account for different detection across parks and between years, I see a significant drop in the mean among site occupancy between year 1 and 2. From ~0.50 to 0.26. I also see a very high among site extinction probability 0.74. I should note I determined occupancy for each year using the smoothed function, and extracted among site probabilities using predict.
I just want to confirm that I have not made an error in the model as these results, while possible, seem extreme.
Thanks for your time.