Hello,

I'm relatively new to PRESENCE and occupancy modeling in general, but I have scoured manuals, forums, and the internet in general and have not yet been able to find a solution for my current problem.

I am trying to assess the impacts of variables on detection. Occupancy at my sites for my species does not change, so I've set psi to 1 for a single-season, single-species model. All of my covariates are sampling-specific, and I do not have any site-specific covariates. I have the continuous covariates of water temperature and trap hours (Z-transformed), and the categorical covariates of recent rain (binary), weather, and meteorological season (converted to indicators). I have 5 sites and 72 sampling occasions for each site.

My issue lies in the beta outputs for my categorical models. The standard error for betas is coming back as -1.#IND00. When I do not include all the covariates (for example, if I run a model including spring, summer, and fall, excluding winter), I do not have the issue with the SE. The model is reaching convergence with 7.8 significant digits, but the output does read ***** neg. std.err(s) in VC matrix.

Untransformed Estimates of coefficients for covariates (Beta's)

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estimate std.error

A1 psi.a1 : 0.000000 118632.832030

B1 P[72].b1 : -1.756447 -1.#IND00

B2 P[1].Spring : 0.370153 -1.#IND00

B3 P[1].Summer : -2.463060 -1.#IND00

B4 P[1].Fall : 0.542003 -1.#IND00

B5 P[1].Winter : -0.203647 -1.#IND00

Is there a problem with the way I am inputting/running things, or is my data set unable to work with this? Is there anything I can do to fix it? Season is coming up as my top model and makes biological sense, but I obviously don't want to include something that is incorrect. I would really appreciate any guidance that anyone is able to give me, and I'm happy to supply any further information requested.

Thanks,

Krista