Hello all and thanks in advance for your time; I run windows 7, R 3.02,
glmmADMB 0.7.7. I have 2 additional questions to the question in the title.
My data set is 2,184 plot records of hare pellet counts - the response
variable, associated with vegetation variables to assess seasonal effects
of  habitat characteristics on hare pellet densities.  Summary is below.
In each of 26 forest stands, 20 summer and 10 winter Plots were sub-sampled
for vegetation metrics Three summers, and Three winters.  Stands are
replicates.  The data are highly skewed, overdispersed (k=0.4, method to
assess overdisp.: *Bolker et. al 2011, revised version, part 1.*)

SEASON and various PARAMETER effects on pellets are of interest, season
being the main fixed effect.

There are 3 repeated measures of summer (2010,2011,2012); 3 of winter
(years 2011,2012,2013) but the time series is NOT of interest, but a
"nuisance" source of random variation.  Data is unbalanced (replication
table below).

Q1:   Have I correctly specified the models to account for the repeated
measures as a "nuisance" random variable?
        Q1a: Does    ...+ (1|plot/stand) adequately address the repeated
measure within seasons?  ,
                  or do I need to specify something like random=year,
though the years don't match seasons?
Q2:   Do I need to scale covariates that are counts, vs. percentages, and
how to do that??
Q3:   Should standardized-area-sampled be added as a covariate or offset?,
because # plots sampled/stand varies?

Two models are shown, sc.all and nb.null2 as a set of many competing models,

##   Relative Effects of species composition (sc) metrics  ##########

##   a priori Hypotheses 1: con.splgs >> all other params, wtr, but not smr
####

##   H 2:  >Decid mid layer > more pellets summer than winter (complement
to H1 ###

##   H 3:  >Conifer trees(>7.6cm dbh, < pellets summer and winter
##########

##   H 4:  MidConifer layer >> everything except con.splgs summer and
winter ##



sc.all <- glmmadmb(pellets ~ season * (dec.splgs + con.splgs +

                                       pctMidCon + pctMidDec.shb +

                                       con.trees+dec.trees) +

                                       offset(ln.days)+(1|plot/stand),

                                       data=hv, family="nbinom")

summary(sc.all)
Call:
glmmadmb(formula = pellets ~ season * (dec.splgs + con.splgs +
    pctMidCon + pctMidDec.shb + con.trees + dec.trees) + offset(ln.days) +
    (1 | plot/stand), data = hv, family = "nbinom")
AIC: 8441.2
Coefficients:
                             Estimate Std. Error z value Pr(>|z|)
(Intercept)                 -4.938011   0.125850  -39.24  < 2e-16 ***
season[T.wtr]                0.234171   0.137270    1.71  0.08802 .
dec.splgs                   -0.001206   0.001913   -0.63  0.52835
con.splgs                    0.001735   0.000758    2.29  0.02205 *
pctMidCon                    0.019846   0.005565    3.57  0.00036 ***
pctMidDec.shb                0.000603   0.004759    0.13  0.89924
con.trees                    0.000848   0.003292    0.26  0.79661
dec.trees                   -0.019391   0.007630   -2.54  0.01105 *
season[T.wtr]:dec.splgs      0.003342   0.002024    1.65  0.09866 .
season[T.wtr]:con.splgs      0.002784   0.000853    3.27  0.00109 **
season[T.wtr]:pctMidCon      0.001967   0.006437    0.31  0.75990
season[T.wtr]:pctMidDec.shb -0.001214   0.005638   -0.22  0.82944
season[T.wtr]:con.trees      0.005451   0.003520    1.55  0.12144
season[T.wtr]:dec.trees     -0.018788   0.010425   -1.80  0.07151 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Number of observations: total=2184, plot=81, plot:stand=508
Random effect variance(s):
Group=plot
             Variance   StdDev
(Intercept) 2.061e-09 4.54e-05
Group=plot:stand
            Variance StdDev
(Intercept)   0.8056 0.8976
Negative binomial dispersion parameter: 1.132 (std. err.: 0.073595)
Log-likelihood: -4203.6

##############  True null without season ################################

nb.null2<- glmmadmb(pellets ~
(1|plot/stand)+offset(ln.days),data=hv,family="nbinom")

summary(nb.null2)

Call:

glmmadmb(formula = pellets ~ (1 | plot/stand) + offset(ln.days),

    data = hv, family = "nbinom")



AIC: 8571.6

Coefficients:

            Estimate Std. Error z value Pr(>|z|)

(Intercept)  -4.6451     0.0579   -80.3   <2e-16 ***

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Number of observations: total=2184, plot=81, plot:stand=508

Random effect variance(s):

Group=plot

            Variance    StdDev

(Intercept) 7.01e-08 0.0002648

Group=plot:stand

            Variance StdDev

(Intercept)   0.9685 0.9841

Negative binomial dispersion parameter: 1.0155 (std. err.: 0.063845)

Log-likelihood: -4281.8

numSummary(hv[,c("pellets","lc","cc","snow","BAsplgs","BAtrees","con.splgs","dec.splgs",

+         "con.trees","dec.trees","totl.splgs","totl.trees","SCU",

+        "pctMidCov","pctMidDec.shb","pctMidCon")],

+         statistics=c("mean", "sd", "IQR", "quantiles", "cv", "skewness"),

+                 quantiles=c(0,.25,.5,.75,1), type="2")



                    mean         sd        IQR        cv   skewness  0%
25%      50%        75%       100%    n

pellets         2.789835   6.011538   3.000000 2.1548003  6.2887020 0.0
0.00000  1.00000   3.000000   90.00000 2184

lc             63.615614  35.443319  59.500000 0.5571481 -0.4526649 0.0
38.00000 85.50000  97.500000  100.00000 2184

cc             82.832234  18.082934  14.575000 0.2183079 -1.9273224 5.5
79.60000 89.70000  94.175000   99.80000 2184

snow           11.140568  17.244647  23.500000 1.5479146  1.3904818 0.0
0.00000  0.00000  23.500000   89.00000 2184

BAsplgs        10.845238   8.811449  12.000000 0.8124717  0.9481797
0.0  4.00000
10.00000  16.000000   48.00000 2184

BAtrees        19.229853  13.086075  16.000000 0.6805083  1.2117637 0.0
10.00000 16.00000  26.000000   70.00000 2184

con.splgs      51.313645  75.954039  70.000000 1.4801919  2.4581968
0.0  0.00000
20.00000  70.000000  500.00000 2184

dec.splgs      24.185440  36.738056  35.000000 1.5190155  2.2157829
0.0  0.00000
10.00000  35.000000  220.00000 2184

con.trees      17.828297  19.442842  30.000000 1.0905608  1.0967674
0.0  0.00000
10.00000  30.000000   90.00000 2184

dec.trees       4.281593   8.176753   5.000000 1.9097453  2.7236491 0.0
0.00000  0.00000   5.000000   60.00000 2184

totl.splgs    100.755037  96.937229 105.000000 0.9621080  1.5514325 0.0
30.00000 75.00000 135.000000  560.00000 2184

totl.trees     23.736722  18.938452  25.000000 0.7978546  0.8897595 0.0
10.00000 20.00000  35.000000   90.00000 2184

SCU           178.128663 224.171445 210.000000 1.2584805  2.4217877 0.0
30.00000 95.00000 240.000000 1500.00000 2184

pctMidCov      40.855210  24.901304  38.888889 0.6095013  0.2805500 0.0
22.22222 38.88889  61.111111  100.00000 2184

pctMidDec.shb   7.186101  14.278952   5.555556 1.9870235  2.6927980 0.0
0.00000  0.00000   5.555556   83.33333 2184

pctMidCon       5.497049  10.078750   5.555556 1.8334837  2.7091625 0.0
0.00000  0.00000   5.555556   66.66667 2184


#### check pellet count Replications of stands w/in periods
###################

reptab<-with(hv,table(stand, period))

print(reptab)

        period

stand    2010smr 2011smr 2011wtr 2012smr 2012wtr 2013wtr

  AF1         20      20      10      20      10      10

  AF2         20      20      10      20      10      10

  AF5         20      20      10      19      10      10

  AF7         20      20      10      20      10      10

  CLREG1      20      19      10      20      10      10

  CLREG2      20      20      10      20      10      10

  CLREG3      19      19      10      19      10      10

  CLREG4      20      20      10      20      10      10

  CLSH4       20      20      10      20      10      10

  CLSH5       20      20      10      20      10      10

  JH01C       20      20      10      20      10      10

  JH02C       20      20      10      20      10      10

  JH03C       19      19      10      19      10      10

  JH04C       20      20      10      20      10      10

  JH05C       20      20       7      20       7       7

  JH54C       20      20      10      20      10      10

  JH56C       20      20      10      20      10      10

  MMX3        10      10      10       9      10      10

  MSW10        0       0       0      20       9      10

  MSW3        20      20      10      20      10      10

  MSW9         0       0       0      20      10      10

  S22         20      20      10      20      10      10

  SM4B        18      18      10      18      10      10

  SM4C        20      20      10      20      10      10

  SM4D        20      20      10      20      10      10

  SM4E        20      19      10      20      10      10

Sorry for the long post; Hopefully I'm avoiding incrementally re-posting
more info!

thanks again
Sheryn
-- 
Sheryn Olson
Graduate Research Assistant
Department of Wildlife Ecology
The University of Maine

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