# Occupancy Covaraite Predictions

Hey y'all,

I am working on multi-season occupancy data for semi-aquatic mammals, determining what covariates are affecting the occupancy of sites by my species. I am currently trying to run code to make predictions of my top models, to determine at what point the covariate is affecting occupancy (i.e., stream density is positively affecting American beaver, I want to know at what point (2 streams per km?) does the stream density begin affecting them).

I have tried using some different tutorials I found online but nothing is working out for me. Is any familiar with what I am attempting to do and have some insight for me?

Every task new to the user can be approached advantageously with f(x) = y.

In R terms f, x \& y are objects.

x is the object at hand, y is the object desired and f is the object, acting as a function, that transforms x to y.

I'm going to speculate about x, and adjustments to the approach outlined will be required based on the actual data.

Let x be organized as a tidy data frame keyed to the date and time of observation as a column (Time is needed only for the purpose of ensuring unique rows).

The form of a predictive model is Y ~ X_i...X_n + \epsilon

Y is the response variable, which will have a great influence on the choice of f. It may be continuous, categorical or binary, depending on how the data was collected and the characteristic of interest.

A Y about population characteristics might be a census count, a binning into categories (low, medium, high) or presence/absence. For some species, such as ants, the continuous count might be meaningful, for others that are encountered in smaller quantity, it may be the second and for family unit species, binary.

The X covariates are categorical, which may be similarly classified. Categories of stream density is one example.

P(Y|X_i) can be tested with many possible f. The first step, however, is properly to understand the nature of x.

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