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Alex Nankivell

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Jun 6, 2021, 6:30:05 PMJun 6
to distance...@googlegroups.com

Hi Eric,

 

I am doing a multi species analysis using the following formula and I can’t for life of me figure out what might be causing the warning message below?

 

head(mult.spec_2020)

 

Region.Label Area       date  transects Sample.Label repeats Effort species size distance max.temp min.temp month

1   Witchelina 4219 13/07/2020 TS-W-T1-P1            1       1      1     roo    1      129     18.1      2.5  July

2   Witchelina 4219 13/07/2020 TS-W-T1-P1            2       1      1  rabbit    1       54     18.1      2.5  July

3   Witchelina 4219 13/07/2020 TS-W-T1-P3            3       1      1     roo    1      228     18.1      2.5  July

4   Witchelina 4219 13/07/2020 TS-W-T1-P4            4       1      1     roo    1      196     18.1      2.5  July

5   Witchelina 4219 13/07/2020 TS-W-T1-P4            5       1      1     roo    1      203     18.1      2.5  July

6   Witchelina 4219 13/07/2020 TS-W-T1-P5            6       1      1     roo    1      380     18.1      2.5  July

 

dht.spec.ests_2020 <- dht2(ddf = mult.spec.est_2020,

                           flatfile = mult.spec_2020,

                           strat_formula = ~ species,

                           convert_units = 0.001,

                           stratification = "object")

 

Warning messages:

1: In `[<-.data.frame`(`*tmp*`, flatfile$Sample.Label %in% sl_diff,  :

  provided 14 variables to replace 13 variables

2: In `[<-.data.frame`(`*tmp*`, flatfile$Sample.Label %in% sl_diff,  :

  provided 14 variables to replace 13 variables

 

Help greatly appreciated.

 

Cheers

 

Alex

 

Sent from Mail for Windows 10

 

Eric Rexstad

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Jun 8, 2021, 4:14:26 AMJun 8
to Alex Nankivell, distance...@googlegroups.com

Alex

Thanks for sending along your code and data.  From your survey you are trying to produce annual estimates of kangaroo abundance from surveys stretching over three years.

In this instance, you need not adopt the approach that uses `dht2` as described in the example at https://examples.distancesampling.org, designed for producing species-specific estimates.  If you wish to have annual estimates, treat year as strata (Region.Label) and use Region.Label as a covariate.  This will provide year-specific detection functions (from the covariate) and will report year-specific estimates, without the need for `dht2`.

Here's a snippet of the code:

#====================
roo_all$Region.Label <- as.factor(roo_all$year)
#====================

mult.spec.est_year <-ds(roo_all,
                        key = "hn",
                        adjustment = NULL,
                        transect = "point",
                        convert.units = 0.001,
                        formula = ~Region.Label,
                        truncation = "5%")

summary(mult.spec.est_year)
plot(mult.spec.est_year, pdf= TRUE)
gof_ds(mult.spec.est_year)

and a snippet of the output from `ds`

Summary for individuals

Summary statistics:
  Region  Area CoveredArea Effort   n        ER      se.ER      cv.ER mean.size    se.mean
1   2019  4219   44.849710    104 127 1.2211538 0.07094362 0.05809556  1.270000 0.06942040
2   2020  4219    4.743719     11   9 0.8181818 0.12196734 0.14907120  1.000000 0.00000000
3   2021  4219   10.781180     25  48 1.9200000 0.25768197 0.13420936  2.000000 0.25537696
4  Total 12657   60.374609    140 184 1.3142857 0.07455639 0.05672769  1.383459 0.07373279

Abundance:
  Label  Estimate       se        cv      lcl       ucl       df
1  2019  53290.37  6791.47 0.1274427 41487.33  68451.34 190.5947
2  2020  44820.02 22380.80 0.4993482 17633.24 113923.14 139.3424
3  2021  32670.74 12502.74 0.3826893 15738.55  67819.31 152.7468
4 Total 130781.13 26519.06 0.2027743 87963.44 194441.06 151.3944

Density:
  Label  Estimate       se        cv      lcl      ucl       df
1  2019 12.631043 1.609735 0.1274427 9.833451 16.22454 190.5947
2  2020 10.623375 5.304763 0.4993482 4.179484 27.00240 139.3424
3  2021  7.743717 2.963438 0.3826893 3.730398 16.07474 152.7468
4 Total 10.332712 2.095209 0.2027743 6.949786 15.36233 151.3944

You will note the quite large CVs on the 2020 and 2021 estimates, to be expected from the small number of detections in those years.

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Eric Rexstad
Centre for Ecological and Environmental Modelling
University of St Andrews
St Andrews is a charity registered in Scotland SC013532
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