I am working with camera trap distance sampling, and I want to use those distance data from the cameras to make a DSM. As I have not seen yet any studies using CTDS data to do some DSM, my first question is actually
1) Is it ok to use the data from CT, which are highly non independent in a GAM and in the function dsm that is built under R in the package dsm right now?
I still dived into DSM with my CT data without being sure I was not outrageously violating some modelling requirements, I manage I think to control well for the effort in it, and I obtained sensible results. I use as predictors a couple of spatial predictors, like distance to the river, a human population density that is weighted with the distance cost from the village to my points and a couple of other. I have strong concurvity between some spatial predictors and my latitude and longitude that are in my model. I first removed all the predictors that were having the “worst” concurvity check above 0.8 with my latitude and longitude and kept my s(X, Y) term. But then I do not explain why I have those spatial variations in abundance biologically… So my second question would be
2) Can I instead, remove s(X, Y) from my model ? In all the examples I read and worked through, the lat and long are always in the model. If I remove s(X, Y) in the model, do I have to add a specific term to take into account what I think s(X,Y) does which is if I have a lot of individuals at my specific point, I will more likely have more individuals close to this points ?
Finally, I am struggling with something a bit more specific, linked to the area we are sampling. I am working in a highly mosaic habitat region, mostly bushy and woody savanna, with a bit of gallery forest. We are studying chimpanzees, and they mostly use the gallery forest. In the design, we stratified by habitat, placing more cameras in gallery forest, a bit less in woody savanna, and less in bushy savanna. For the DSM I combined all my distance data together to have this one detection function. The issue I am encountering is that my number of capture or distances I have of chimpanzees locally in each camera is mostly driven by which habitat the camera was placed in. Yet, I did not integrate in my model this local habitat where my camera is placed. Because all of my predictors are on larger scale, like percentage of gallery forest in a 2000m buffer around my point (roughly small chimp territory size). I am not capturing this very local variation between my cameras. But if my camera is set locally in a savanna habitat, but has high forest gallery around it, I am going to have points with zero capture with high value of forest gallery and vice versa. I ended up with the forest having no effect on my count of chimp which I know is not true. My predictors as they are now do not explain why I have more capture at one point and less at the other point. So here is my third question:
3) How could I consider the habitat my cameras are set in in the model formulation for dsm? My problem being that I do not see how I could possibly integrate this very specific local habitat into a predictor value for my predictor cells (the habitat map I have is 10m x10 m resolution VS predictor cell I am using so far 1km² (I could make it finer, but I do not think it makes sense to do a prediction on 10x10m grid cell?)
I hope my questions are clear enough, and I would greatly appreciate any input on any of my three questions!