Good afternoon,

I am currently running single-species, single-season occupancy models in PRESENCE. However, I ran into a problem with some of my models. I ran my models first with a constant occupancy to determine the top detection model. After showing my colleague the models however, he claims that something is wrong with my data as the standard error of my beta models overlaps 0. Here is the null model, where psi overlaps 0.

Untransformed Estimates of coefficients for covariates (Beta's)

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estimate std.error

A1 psi.a1 : 0.133281 0.287953

B1 P[5].b1 : 0.727036 0.195763

However, when I run models with a variable in occupancy, it no longer shows standard error overlapping 0.

Untransformed Estimates of coefficients for covariates (Beta's)

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estimate std.error

A1 psi.a1 : 0.887288 0.428300

A2 psi.Waterbody_Type : -1.643238 0.627444

B1 P[5].b1 : 0.724914 0.196107

I am unsure now whether my models ran properly or not. I was under the assumption that any models that overlap 0 just show that the model is nonsignificant. I thought that whats being shown (in the first model) is that the null for occupancy is nonsignificant but the constant for detection is significant. I am aware that beta values are not usable in their current form and must be calculated to determine the confidence intervals for true significance but I would rather not do all the calculations just to find out I am wasting time.

Does anyone have any thoughts on this or have a resource that may give me more insight? Please let me know if you need more information. Thank you.