I am relatively new to R. I conducted repeated 10 min point counts over one season. I have 50 sites (100 m radius) replicated over 3 surveys, and each individual observation (n = 1108) is recorded at discrete distance intervals (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 covariates were measured for each site on each survey (n = 150).
I wish to test the effect(s) of various habitat metrics on songbird abundance/density, to include detection covariate(s) in my models, and to account for repeated measures in my design. I think that 'distsamp' is most appropriate for this, but am not sure (especially when it comes to how to deal with repeated measures).
I have followed Chandler's 'Distance sampling analysis in unmarked (2011)' and everything seems to work until I add detection covariates (using distsamp; prior to adding abundance/density habitat predictors), when running my models produce the warning: "In lambda * A : longer object length is not a multiple of shorter object length".
(a) Am I using the appropriate fitting function (i.e., distsamp vs. gdistsamp vs. pcount vs. ???) (b) Why am I getting this warning message?
Here is my code:
dists <-read.csv("file/path.csv")
sub-set of variables (to be used as detection covariates)
jdate<-(dists$day.julian)
daytime<-(dists$time.hour.num)
head(dists, 1108)
'point' contains character+numerical site names (e.g., 'sweco03')
levels(dists$point) <- c(levels(dists$point), "sweco03")
levels(dists$point)
individual observations were recorded at 10 m distance intervals 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") `
to test the fit of detection covariates
model1 <-distsamp (~1 ~jdate, umf, keyfun = "hazard")
model2 <-distsamp (~1 ~daytime, umf, keyfun = "hazard")