Multi-season models and temporal structure

[Juliana Vélez]

Hi all,

I’m fitting single species multi-season models to account for seasonal trends in occupancy. Instead of using years as primary time periods, I’m thinking about using season_year (e.g., spring_2024, summer_2024, etc). Is there a way to account for the structure of season nested in year when using spOccupancy?

Thanks in advance for your help!

Juliana

[Jeffrey Doser]

Hi Juliana,

There is not an extremely elegant way for accounting for such nested temporal structure (e.g., within a sort of classic time series framework). Perhaps the best way to try and account for both levels would be to include two unstructured random effects, with one being for "year" and then the other being for "season_year". So, you'll have to have two numeric variables in your occupancy covariates that indicate the specific year and then the specific "season year" in order to fit those both as random effects. The random effects in the model would then be included with something like "occ.formula = ~ (1 | year) + (1 | site_year) + ...", where ... are everything else. This would be equivalent to using the classic lme4 syntax for a nested random effect of season nested in year, but such nested syntax is not supported in spOccupancy (or spAbundance).

Hope all is well!

Jeff

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Jeffrey W. Doser, Ph.D.

Assistant Professor

Department of Forestry and Environmental Resources

North Carolina State University

Statistical Ecology and Forest Science Lab

Pronouns: he/him/his

[Juliana Vélez]

Hi Jeff,

Thanks for your suggestion, and sorry for the delayed reply! That approach makes sense and is probably a better way to handle the temporal autocorrelation structure of this dataset.

Hope all is well with you too!!

Juliana