Statistical power to detect a decline in occupancy - number of sites, repeat visits etc

[Storm Borum]

Hello all, 

As part my master's thesis I want to be able to give a recommendation for a monitoring program for my study species. I am therefore trying to figure out how to compare statistical power of different survey efforts to detect declines in occupancy between surveys so I can say "minimum X sites and Y repeat visits are needed to detect a Z% decline in occupancy over 10 years" and weigh effects of doing more sites vs visits. 

Can someone advise me how to do this? So far, I have only been able to find code examples for power to detect a covariate effect. I have detection probability and occupancy estimates from a pilot study (20 sites, 5 repeats) but none of my covariates are significant. 

I am quite new to r so any help is appreciated!

[Jordan Green]

Hi Storm,

I would start by looking at Guillera-Arroita et al 2012 (which I’ve attached a link to). 

The paper focuses on this very question and provides some helpful functions in R to simulate various combinations of sites and visits to assess the power to detect differences in occupancy over a defined time frame.

https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/j.2041-210X.2012.00225.x

Cheers,

Jordan

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[Ken Kellner]

In addition to the Guillera-Arroita paper, there's also an unmarked vignette discussing power analysis in case you haven't seen it

https://cran.r-project.org/web/packages/unmarked/vignettes/powerAnalysis.html

Also in our unmarked demo paper from a couple years ago, we do a power analysis to detect a declining trend over time. However we frame it in terms of power to detect a certain yearly decline rather than a total decline over a multi-year period, which isn't quite the same thing as you are proposing. The code and data we used are linked in the paper.

https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.14123

Ken

[Marc Kery]

Dear Storm,

power analysis in practice always means to repeatedly use simulated data with a known effect present. Then you can tally up the proportion that a significance test "finds" this known effect: this proportion is the power.

Adding to what Jordan and Ken say, attached is a set of slides on data simulation from a course last year, along with the R code to do a power analysis for a trend for the occupancy's "sister model", the Nmix. That may also help you to get started.

Best regards  --- Marc

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