Hi all,
New to distance sampling at a new job and I'm trying to move into the R framework, having never used Distance software and pretty comfortable in the R environment. I have data that was analyzed through Distance and am attempting to replicate results before moving on to new analyses. Data are bird detections in line transects. For context, it is open grassland surveys (histogram of detection distances attached).
In the original Distance analysis, Uniform key had the lowest AIC, with pretty much identical AIC between hermite poly, simple poly, and cosine adjustments. Half normal was within 2 AIC units.
In R, I was able to get the half normal to work and replicate the Distance software results. But when the key is uniform in R, it gives me a very low detection probability equating to an insanely high density and abundance estimate.
The density under half-normal is 0.1 birds/ha (reasonable, abundance estimate corresponds well to expectations), vs. uniform says 14 birds/ha (far too high).
Where I think the low detection probability comes from 1/w, in this case, with the right 10% truncated, is 149.86.
Therefore, 1/149.86 = 0.00667 = average P for uniform key.
In Distance, the p = 1 for uniform key.
Is there a setting in Distance to indicate this perfect detection because distance doesn't change with detection that has to be differently specified in R? I've been up and down this forum and the Distance R package information without success, and I suspect it's because uniform detection is not usually a good thing and can be a sign of flawed design.
Any assistance with parsing this issue is greatly appreciated!