Hello all, I am carrying out a survey to understand human-wolf interactions in a human dominated landscape in the state of West Bengal, India. The project has two stages- first being the estimation of habitat use of the wolves in the study area (with which I am facing the problem) and second are interviews.

The work:

The first stage of the project is to understand the wolf habitat-use in the area. The study area is relatively small ~200 sq kms. For this, the grid size thus was selected to be of 4 sq kms (2km by 2km). A total of 44 grids were surveyed, with survey tracks ranging from 1 to 6 kms. Now, owing to logistic, time and other fieldwork related constraints I have used spatially replicated sign surveys. I collected the sign data for every 250m segments and later aggregated it to 1km segments. I am also using 7 covariates for psi according to the ecology of wolves and 2 covariates for detection probability. Out of the total 44 surveyed grids 15 sites were occupied (grids with at least one sign of wolves). I am very new to occupancy analysis and having difficulty going through the process.

The issue:

While analyzing the data on PRESENCE using correlated detections model, whenever I am running models, I get warnings of 'numerical convergence not reached'. Now, for most models the significant digits are more than 3 or 4 and sometimes more than 5. However, whenever I put more than two covariates to run a model for psi, the significant digits drop down to 1 and the standard errors increase by manifolds or go into negative SE! The habitat use probability of individual sites also show just 1 and 0s. Additionally, I am very confused with the th0pi() parameter.

Could my issue be because of less detection data and thus not much variation in the data? Could the issue be related to the design matrix? Can I use single covariates to model habitat use? What would be the proper way ahead?

I can provide additional information if needed for clarification.

Thanks,

Aakash