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Re: [unmarked] ranef() warning message: In dbinom(ya[i, j, t], N, pa[i, j, t]) : non-integer x

kazld...@gmail.com (Kaz) — Thu, 16 May 2013 02:28:37 UT

Cool, thanks for the suggestions Andy.

Re: [unmarked] mapping spatial predictions using pcount

hoke...@wcs.org (Hannah OKelly) — Wed, 15 May 2013 14:08:43 UT

Thanks Richard, I see the distinction you point out here, and it is
important, so my approach is not entirely satisfactory, but maybe still
useful in relative terms. Especially as I’ll be comparing these results
with patrol data consisting of a similar measure – encounters per unit

Re: [unmarked] ranef() warning message: In dbinom(ya[i, j, t], N, pa[i, j, t]) : non-integer x

jaro...@gmail.com (Jeffrey Royle) — Wed, 15 May 2013 11:38:01 UT

hi Kaz,
I don't think this class of models is really very appropriate for the data
you have, so my recommendation is to not use them. Alternatively, I suggest
a standard type of GLMM which somehow you can fit in R using existing
packages for doing those things.
regards,
andy

Re: [unmarked] ranef() warning message: In dbinom(ya[i, j, t], N, pa[i, j, t]) : non-integer x

kazld...@gmail.com (Kaz) — Wed, 15 May 2013 04:32:03 UT

Thanks Andy,
To be more precise the data I have is for shorebirds during the non
breeding season, with counts taken while the birds are roosting. There are
therefore either 0 or hundreds of birds visible during a count. The total
population across the sites does not vary much, though it does appear to be

Re: [unmarked] ranef() warning message: In dbinom(ya[i, j, t], N, pa[i, j, t]) : non-integer x

jaro...@gmail.com (Jeffrey Royle) — Wed, 15 May 2013 01:46:47 UT

hi Kaz,
I recommend against using the N-mixture models for your situation. I'm
guessing there is so much variation in N at that level of abundance that
the model can't possibly produce meaningful predictions (not to mention the
computational problem you encountered).
The warning message seems to be due to the scaling. dbinom() has to take

ranef() warning message: In dbinom(ya[i, j, t], N, pa[i, j, t]) : non-integer x

kazld...@gmail.com (Kaz) — Wed, 15 May 2013 00:41:41 UT

Hi,
I'm trying to estimate yearly abundance of birds at 33 sites over 20 years,
with 4 counts per year. Counts range from 0 to 6600 individuals, with lots
of NAs. I've therefore had to use scale() to prevent pcountOpen() from
crashing R with such a large K.
However, when I then try to use ranef(), I therefore get the following

Re: [unmarked] mapping spatial predictions using pcount

richard.chandl...@gmail.com (Richard Chandler) — Mon, 13 May 2013 14:49:10 UT

This is why it is better, in my opinion, to model density rather
abundance in some unknown area. Since you don't have either fixed area
plots or distance data, you can't really get at density, but you
should be able to map the expected number of snares exposed to
sampling, given some transect length and the pixel-level covariates.

Re: [unmarked] mapping spatial predictions using pcount

hoke...@wcs.org (Hannah OKelly) — Mon, 13 May 2013 14:33:51 UT

Hi Marc,

Yes, a measure of abundance per km walked was what I thought I was getting,
possibly more appropriate as we just did one pass through each site and so
effectively sampled only a minimal proportion of it. My difficulty comes
when trying to translate this into a map, because I am not sure how to

RE: [unmarked] mapping spatial predictions using pcount

marc.k...@vogelwarte.ch (Kery Marc) — Fri, 10 May 2013 20:53:22 UT

Hi Hannah,

btw, I find this a very neat application of an Nmix model ! And WinBUGS (and be sure to try JAGS as well) is not a bullet to bite, but rather a sweet cherry to enjoy ;)

Regards --- Marc

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RE: [unmarked] mapping spatial predictions using pcount

marc.k...@vogelwarte.ch (Kery Marc) — Fri, 10 May 2013 20:48:43 UT

hi Richard and Hannah,
I'm not sure why simply using log(length) as an offset on abundance should not be sensible ? This should give you an estimate of snare abundance per unit-length transect. But yes, we have used length in a slightly more complicated way as a proxy for the proportion of the nominal sample area covered (in Royle et al, Ecol. Mono., 2007), because our transects sampled a 1km2 quadrat in a highly sinuous way with frequent loops.

Re: [unmarked] mapping spatial predictions using pcount

hoke...@wcs.org (Hannah OKelly) — Fri, 10 May 2013 14:09:05 UT

Yes, you’re right, it was actually the inverse of transect length that Marc
and Andy used to deal with the “exposed population” issue, but I didn’t
think this was appropriate in my context because we never came anywhere
close to saturation in terms of sampling each site. In any case, now I have

Re: [unmarked] mapping spatial predictions using pcount

richard.chandl...@gmail.com (Richard Chandler) — Fri, 10 May 2013 13:03:58 UT

... and yes, this code is correct:

lam5 <- betafm5[1] + betafm5[2]*distvil2 + betafm5[3]*distvil2^2 +
betafm5[4]*distbd2 + betafm5[5]*distst2
pred <- exp(lam5)

except that you need to add in the offset.

Richard

On Fri, May 10, 2013 at 8:25 AM, Richard Chandler

Re: [unmarked] mapping spatial predictions using pcount

richard.chandl...@gmail.com (Richard Chandler) — Fri, 10 May 2013 12:25:41 UT

Hi Hannah,

Maybe Marc and Andy can weigh in on what they think is the best
option. I know that in the past they have used transect length in some
way as an offset of the number of individuals exposed to sampling.
That might be a good option, but I don't think they simply used
offset(log(length)). One paper that proposes a nice modeling framework

Re: [unmarked] mapping spatial predictions using pcount

hoke...@wcs.org (Hannah OKelly) — Fri, 10 May 2013 10:22:40 UT

Many Thanks Richard,

I was, in part, following Kéry et al. 2005, who also had transects within
quadrants and used route length as an abundance covariate to account for
similar variation within effective sampled area. My transects were not
fixed width and there is considerable variation in terms of visibility

Re: [unmarked] mapping spatial predictions using pcount

richard.chandl...@gmail.com (Richard Chandler) — Thu, 09 May 2013 15:26:52 UT

Hi Hannah,

As you suspected, you need to include the offset in the linear model.
However, I don't think it is ideal to use transect length as an offset
of abundance. Can you use the area sampled instead? If the transects
had fixed widths, and were narrow enough to make distance-related
heterogeneity in detection negligible, then you could use (log)