Hi everyone, I am currently working on a project focused on occupancy patterns of a carnivore species.
Our data comes from camera-trap surveys conducted originally for other studies, with different objectives.
In total we have detection histories for 169 sites, distributed in 11 study areas. While distance between different areas can be more than 100km, sites within each area are spatially clustered and are not independent as distance between camera-trap stations is around 1km, a short distance considering the vital area of our target species.
Originally we used single season occupancy models to identify main factors (environmental covariates) influencing occupancy probabilities considering all the stations in the analysis. However, we are unsure on the validity of our approach since we do not include any explicit spatial processes on the modelling procedure.
We are aware that due to the lack of independence among stations within the same study area we are actually looking at the probability of use rather than probability of true occupancy. Nonetheless, we do not fully understand the consequences of clumping together two very different sampling scales.
Therefore we would really appreciate any opinion on the best way to proceed with this kind of data. Are single season occupancy models a valid approach or is there any better method that accommodates spatial relations between trapping stations? I was suggested to include an additional factor with 11 levels (one per area), however I don't think my data can support that many parameters. Another option might be to try to include a random effect of study area in WinBUGS, but I fear I might have the same problem.
Thank you in advance,
Gonçalo Curveira Santos