PCount - Observation covariate

[Tristan Baird]

Hello,

I am trying to obtain population size estimates for 2 species using the PCount function in unmarked. 

I condensed camera-trap data into daily sampling occasions for each species. Cameras were staggered in time and were left to record constantly when deployed for a period of around 1 month.

The dataset contains 255 daily sampling occasions across 110 sites. 

My site level covariates work just fine. As soon as I incorporate my observation covariate, I get an error. The only observation covariate that I can think of with regards to this camera-trap data is date. However, the dates across many of the sampling occasions will be the same for multiple camera-traps, I am not sure of the effect this haves?

The error I get is 

obsCovData does not have M*ObsNum rows

I am confused and don't know how to resolve this.

Is the Pcount function a viable function for condensed camera-trap data?

I am sorry if this is a simple error on my part, but any help would be greatly appreciated.

Thank you for your time!

Kind regards,

Tristan 

[José Infante]

Hi Tristan,

Pcount is suitable for camera trap data. You'll probably get low detection probability if you use one-day occasions, though (unless your focal species are very common). It is also very appropriate to have several cameras functioning the very same dates if you think about it, because you'll have a better chance to detect patterns of detection probability of the species being influenced by time of year. 

What the output is pointing out is just that you haven't setted your observation level covariate in the correct way. As far as I knew, observation level covariates should have rows = number of sites; and columns = number of occasions (255 in your case). I might be wrong, because the output says rows = number of sites (M) * number of occasions (ObsNum). In general, when I use dates as observation level covariate I prefer julian day, so you have a continuous number that just represents the passage of time. One thing that you must be aware of is that you can also use site level covariates to model detection, if there are well informed reasons to do so. So, you can have site level covariates (one column) and observation level covariates (255 = number of observations (ObsNum)) 

I hope this helps,

José.

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[Tristan Baird]

Hi Jose,

Thank you very much for your feedback!

My obs cov rows and columns matches that of my input dataset, this is why I'm getting confused because they do match.

Otherwise, thank you for your help and input!!

Kind regards,

Tristan

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[ryan.c...@gmail.com]

Hi Tristan,

In your unmarked dataframe  do you have you observational covariates as a list?

example: UMF=unmarkedFramePCount(Data, siteCovs=sitecoves, obsCovs=list(obscovs))

Ryan

[Tristan Baird]

Hi Ryan,

Thank you for your email.

No I don't have it as a list, might that be the issue?

Thanks again.

Kind regards,

Tristan

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[ryan.c...@gmail.com]

Glad to help. Below is an example code from one of my analysis. 

UMF <- unmarkedFramePCount(MCElkArcheryComparisisonDiurnal, obsCovs=list(Date=as.data.frame(MCArcheryComDate),People=as.data.frame(MCArcheryComPeople),

                                                               ClosedRd=as.data.frame(MCArcheryComClosedRd),GameTrail=as.data.frame(MCArcheryComGameTrail),ThreeRd=as.data.frame(MCArcheryCom3digitRd)))

[ryan.c...@gmail.com]

I had the same issue you were having so I assume putting the obscovs in a list will solve the problem.

[Tristan Baird]

Thank you very much!

Kind regards,

Tristan Baird

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