I am relatively new to R. I have repeated 10 minute counts in one season. I have 50 sites (100m radius) replicated over 3 surveys, and each individual observation (n = 1108) is recorded at discrete distances (i.e. the number of observations for each site during each survey is not always equal). Habitat variables were measured once for each site (n = 50), and detection covariances were measured for each site in each study (n = 150).

I want to test the effect of different habitat metrics on songbird abundance / density, incorporate detection covariance (s) in my models, and account for repetitive measures in my design. I think "distsamp" is the most suitable for this, but I'm not sure (especially when it comes to how to deal with repetitive measures).

I followed Chandler's sample analysis "Measuring Distance Sampling in Unlabeled (2011)" and everything seems to work until I add detection covariance (using distsamp, prior to adding habitat / habitat density predictors) when mine models issue warning: In lambda * A: length of long object is not a multiple of short length of object ".

(a) Am I using an appropriate fitting function (i.e. distsamp vs. gdistsamp vs. pcount vs.) (b) Why am I getting this warning?

Here is my code:

dists <-read.csv("file/path.csv")

subset of variables (for use as detection covariances)

jdate<-(dists$day.julian)


daytime<-(dists$time.hour.num)

head(dists, 1108) 

      

        
        
        
      

    

'point' contains characters + numeric site names (e.g. 'sweco03')

levels(dists$point) <- c(levels(dists$point), "sweco03") 
levels(dists$point) 

      

        
        
        
      

    

individual observations were recorded at a distance of 10 m from a distance of up to 100 m

umf <-unmarkedFrameDS(y = as.matrix(yDat), survey = "point", dist.breaks =   c(0,10,20,30,40,50,60,70,80,90,100), unitsIn = "m")

      

        
        
        
      

    

summary(umf)

to determine the best detection function

`hn_Null <- distsamp (~1 ~1, umf, keyfun = "halfnorm", output = "density", unitsOut = "ha") 
haz_Null <-distsamp (~1 ~1, umf, keyfun = "hazard")                #lowest AIC 
uni_Null <- distsamp (~1 ~1, umf, keyfun = "uniform") 
exp_Null <- distsamp (~1 ~1, umf, keyfun = "exp") `

      

        
        
        
      

    

for matching detection covariance

model1 <-distsamp (~1 ~jdate, umf, keyfun = "hazard")
model2 <-distsamp (~1 ~daytime, umf, keyfun = "hazard")

      

        
        
        
      

    

etc .. When I try to run these models, I get a warning message.