# Mark-recapture distance sampling

Analysis of double observer data to estimate g(0).

M. Louise Burt http://distancesampling.org (CREEM, Univ of St Andrews)https://creem.st-andrews.ac.uk
2020-02-01

This example looks at mark-recapture distance sampling (MRDS) models. The first part of this exercise involves analysis of a survey of a known number of golf tees. This is intended mainly to familiarise you with the double-platform data structure and analysis features in the R function mrds (Laake, Borchers, Thomas, Miller, & Bishop, 2019).

To help understand the terminology using in MRDS and the output produced by mrds, there is a guide available at this link called ‘Interpreting MRDS output: making sense of all the numbers’.

# Aims

The aims of this practical are to learn how to model

• trial and independent-observer configuration
• full and point independence assumptions,
• include covariates in the detection function(s) and
• select between competing models.

## Golf tee data

These data come from a survey of golf tees which conducted by statistics students at the University of St Andrews. The data were collected along transect lines, 210 metres in total. A distance of 4 metres out from the centre line was searched and, for the purposes of this exercise, we assume that this comprised the total study area, which was divided into two strata. There were 250 clusters of tees in total and 760 individual tees in total.

The population was independently surveyed by two observer teams. The following data were recorded for each detected group: perpendicular distance, cluster size, observer (team 1 or 2), ‘sex’ (males are yellow and females are green and golf tees occur in single-sex clusters) and ‘exposure’. Exposure was a subjective judgment of whether the cluster was substantially obscured by grass (exposure=0) or not (exposure=1). The lengths of grass varied along the transect line and the grass was slightly more yellow along one part of the line compared to the rest.

The golf tee dataset is provided as part of the mrds package.

Open R and load the mrds package and golf tee dataset (called book.tee.data). The elements required for an MRDS analysis are contained within the object dataset. These data are in a hierarchical structure (rather than in a ‘flat file’ format) so that there are separate elements for observations, samples and regions. In the code below, each of these tables is extracted to avoid typing long names.


library(knitr)
library(mrds)
# Access the golf tee data
data(book.tee.data)
# Investigate the structure of the dataset
str(book.tee.data)

List of 4
$book.tee.dataframe:'data.frame': 324 obs. of 7 variables: ..$ object  : num [1:324] 1 1 2 2 3 3 4 4 5 5 ...
..$observer: Factor w/ 2 levels "1","2": 1 2 1 2 1 2 1 2 1 2 ... ..$ detected: num [1:324] 1 0 1 0 1 0 1 0 1 0 ...
..$distance: num [1:324] 2.68 2.68 3.33 3.33 0.34 0.34 2.53 2.53 1.46 1.46 ... ..$ size    : num [1:324] 2 2 2 2 1 1 2 2 2 2 ...
..$sex : num [1:324] 1 1 1 1 0 0 1 1 1 1 ... ..$ exposure: num [1:324] 1 1 0 0 0 0 1 1 0 0 ...
$book.tee.region :'data.frame': 2 obs. of 2 variables: ..$ Region.Label: Factor w/ 2 levels "1","2": 1 2
..$Area : num [1:2] 1040 640$ book.tee.samples  :'data.frame': 11 obs. of  3 variables:
..$Sample.Label: num [1:11] 1 2 3 4 5 6 7 8 9 10 ... ..$ Region.Label: Factor w/ 2 levels "1","2": 1 1 1 1 1 1 2 2 2 2 ...
..$Effort : num [1:11] 10 30 30 27 21 12 23 23 15 12 ...$ book.tee.obs      :'data.frame': 162 obs. of  3 variables:
..$object : int [1:162] 1 2 3 21 22 23 24 59 60 61 ... ..$ Region.Label: int [1:162] 1 1 1 1 1 1 1 1 1 1 ...
..$Sample.Label: int [1:162] 1 1 1 1 1 1 1 1 1 1 ...  # Extract the list elements from the dataset into easy-to-access objects detections <- book.tee.data$book.tee.dataframe # detection information
region <- book.tee.data$book.tee.region # region info samples <- book.tee.data$book.tee.samples # transect info
chi.markrecap <- gof.result$chisquare$chi2$chisq chi.total <- gof.result$chisquare$pooled.chi Abbreviated $$\chi^2$$ goodness-of-fit assessment shows the $$\chi^2$$ contribution from the distance sampling model to be 11.5 and the $$\chi^2$$ contribution from the mark-recapture model to be 3.4. The combination of these elements produces a total $$\chi^2$$ of 14.9 with 17 degrees of freedom, resulting in a $$p$$-value of 0.604 The (two) detection functions can be plotted (Figure 3).  # Divide the plot region into 2 columns par(mfrow=c(1,2)) # Plot detection functions plot(fi.mr.dist)  par(mfrow=c(1,1)) The plot labelled • “Observer=1 detections” shows a histogram of Observer 1 detections with the estimated Observer 1 detection function overlaid on it and adjusted for p(0). The dots show the estimated detection probability for all Observer 1 detections. • “Conditional detection probability” shows the proportion of Obs 2’s detections that were detected by Obs 1 (also see the detection tables). The fitted line is the estimated detection probability function for Obs 1 (given detection by Obs 2) - this is the MR model. Dots are estimated detection probabilities for each Obs 1 detection. There is some evidence of unmodelled heterogeneity in that the fitted line in the left-hand plot declines more slowly than the histogram as the distance increases. #### Estimating abundance Abundance is estimated using the dht function. In this function, we need to supply information about the transects and survey regions.  # Calculate density estimates using the dht function tee.abund <- dht(model=fi.mr.dist, region.table=region, sample.table=samples, obs.table=obs) # Print out results in a nice format knitr::kable(tee.abund$individuals$summary, digits=2, caption="Survey summary statistics for golftees") Table 1: Survey summary statistics for golftees Region Area CoveredArea Effort n ER se.ER cv.ER mean.size se.mean 1 1040 1040 130 229 1.76 0.12 0.07 3.18 0.21 2 640 640 80 152 1.90 0.33 0.18 2.92 0.23 Total 1680 1680 210 381 1.81 0.14 0.08 3.07 0.15  knitr::kable(tee.abund$individuals$N, digits=2, caption="Abundance estimates for golftee population with two strata") Table 1: Abundance estimates for golftee population with two strata Label Estimate se cv lcl ucl df 1 356.52 32.35 0.09 294.54 431.53 17.13 2 236.64 44.14 0.19 147.33 380.09 5.06 Total 593.16 60.38 0.10 478.32 735.57 16.06 The estimated abundance is 593 (recall that the true abundance is 760) and so this estimate is negatively biased. The 95% confidence interval does not include the true value. ### Estimation of p(0): distance and other explanatory variables How about including the other covariates, size, sex and exposure, in the MR model? Which MR model would you use? In the command below, distance and sex are included in the detection function - remember sex was defined as a factor earlier on. In the code below, all possible models (excluding interaction terms) are fitted.  # Full independence model # Set up list with possible models mr.formula <- c("~distance","~distance+size","~distance+sex","~distance+exposure", "~distance+size+sex","~distance+size+exposure","~distance+sex+exposure", "~distance+size+sex+exposure") num.mr.models <- length(mr.formula) # Create dataframe to store results fi.results <- data.frame(MRmodel=mr.formula, AIC=rep(NA,num.mr.models)) # Loop through all MR models for (i in 1:num.mr.models) { fi.model <- ddf(method='trial.fi', mrmodel=~glm(link='logit',formula=as.formula(mr.formula[i])), data=detections, meta.data=list(width=4)) fi.results$AIC[i] <- summary(fi.model)$aic } # Calculate delta AIC fi.results$deltaAIC <- fi.results$AIC - min(fi.results$AIC)
# Order by delta AIC
fi.results <- fi.results[order(fi.results$deltaAIC), ] # Print results in pretty way knitr::kable(fi.results, digits=2) MRmodel AIC deltaAIC 7 ~distance+sex+exposure 405.68 0.00 8 ~distance+size+sex+exposure 407.40 1.72 4 ~distance+exposure 433.72 28.04 3 ~distance+sex 434.41 28.74 6 ~distance+size+exposure 435.33 29.65 5 ~distance+size+sex 436.02 30.34 1 ~distance 452.81 47.13 2 ~distance+size 454.58 48.91  # Fit chosen model fi.mr.dist.sex.exp <- ddf(method='trial.fi', mrmodel=~glm(link='logit',formula=~distance+sex+exposure), data=detections, meta.data=list(width=4)) We see that the preferred model contains distance + sex + exposure so check the goodness-of-fit statistics (Figure 4) and detection function plots (Figure 5).  # Check goodness-of-fit ddf.gof(fi.mr.dist.sex.exp, main="FI trial mode\nMR=dist+sex+exp")  Goodness of fit results for ddf object Chi-square tests Distance sampling component: [0,0.4] (0.4,0.8] (0.8,1.2] (1.2,1.6] (1.6,2] Observed 25.000000 16.0000000 16.0000000 22.000000 9.000000 Expected 20.276214 19.3414709 18.0737423 16.344635 14.082691 Chisquare 1.100509 0.5772792 0.2379367 1.956798 1.834432 (2,2.4] (2.4,2.8] (2.8,3.2] (3.2,3.6] (3.6,4] Observed 10.0000000 12.0000000 9.0000000 3.0000000 2.000000 Expected 11.5105010 9.0462873 6.9147393 5.0438158 3.365904 Chisquare 0.1982202 0.9644198 0.6288469 0.8281791 0.554292 Total Observed 124.000000 Expected 124.000000 Chisquare 8.880913 No degrees of freedom for test Mark-recapture component: Capture History 01 [0,0.4] (0.4,0.8] (0.8,1.2] (1.2,1.6] (1.6,2] Observed 1.00000000 2.00000000 2.0000000 6.00000000 5.0000000 Expected 0.85161169 1.61345653 1.5784634 6.25617270 4.2054953 Chisquare 0.02585579 0.09260606 0.1125735 0.01048955 0.1500983 (2,2.4] (2.4,2.8] (2.8,3.2] (3.2,3.6] (3.6,4] Observed 2.000000 6.00000000 6.00000000 2.000000 6.0000000 Expected 4.014580 6.11790214 6.76552359 1.599467 4.9973276 Chisquare 1.010948 0.00227217 0.08661952 0.100300 0.2011779 Total Observed 38.000000 Expected 38.000000 Chisquare 1.792941 Capture History 11 [0,0.4] (0.4,0.8] (0.8,1.2] (1.2,1.6] Observed 21.000000000 15.000000000 16.00000000 20.000000000 Expected 21.148388313 15.386543475 16.42153664 19.743827301 Chisquare 0.001041171 0.009710814 0.01082074 0.003323796 (1.6,2] (2,2.4] (2.4,2.8] (2.8,3.2] (3.2,3.6] Observed 8.00000000 8.0000000 9.000000000 5.0000000 1.0000000 Expected 8.79450467 5.9854201 8.882097863 4.2344764 1.4005328 Chisquare 0.07177638 0.6780697 0.001565048 0.1383941 0.1145468 (3.6,4] Total Observed 1.0000000 104.000000 Expected 2.0026724 104.000000 Chisquare 0.5020052 1.531254 Total chi-square = 12.205 P = 0.66344 with 15 degrees of freedom Distance sampling Cramer-von Mises test (unweighted) Test statistic = 0.0976947 p-value = 0.596294  par(mfrow=c(1,2)) plot(fi.mr.dist.sex.exp) And produce abundance estimates.  # Get abundance estimates tee.abund.fi <- dht(model=fi.mr.dist.sex.exp, region.table=region, sample.table=samples, obs.table=obs) # Print results print(tee.abund.fi)  Summary for clusters Summary statistics: Region Area CoveredArea Effort n k ER se.ER 1 1 1040 1040 130 72 6 0.5538462 0.02926903 2 2 640 640 80 52 5 0.6500000 0.08292740 3 Total 1680 1680 210 124 11 0.5904762 0.03884115 cv.ER 1 0.05284685 2 0.12758061 3 0.06577936 Abundance: Label Estimate se cv lcl ucl df 1 1 119.28976 14.18665 0.1189260 91.64686 155.2704 10.124933 2 2 98.17731 18.59356 0.1893876 63.58200 151.5961 7.838438 3 Total 217.46707 26.05226 0.1197986 169.90392 278.3451 23.213663 Density: Label Estimate se cv lcl ucl df 1 1 0.1147017 0.01364101 0.1189260 0.08812198 0.1492985 10.124933 2 2 0.1534020 0.02905244 0.1893876 0.09934687 0.2368689 7.838438 3 Total 0.1294447 0.01550730 0.1197986 0.10113328 0.1656816 23.213663 Summary for individuals Summary statistics: Region Area CoveredArea Effort n ER se.ER cv.ER 1 1 1040 1040 130 229 1.761538 0.1165805 0.06618107 2 2 640 640 80 152 1.900000 0.3342319 0.17591151 3 Total 1680 1680 210 381 1.814286 0.1391400 0.07669132 mean.size se.mean 1 3.180556 0.2086982 2 2.923077 0.2261991 3 3.072581 0.1537082 Abundance: Label Estimate se cv lcl ucl df 1 1 371.0397 37.86856 0.1020607 297.1733 463.2666 11.904078 2 2 279.7141 67.25221 0.2404320 154.4960 506.4208 5.482653 3 Total 650.7538 82.72648 0.1271241 493.7469 857.6875 11.907386 Density: Label Estimate se cv lcl ucl df 1 1 0.3567690 0.03641207 0.1020607 0.2857436 0.4454487 11.904078 2 2 0.4370533 0.10508158 0.2404320 0.2414000 0.7912825 5.482653 3 Total 0.3873535 0.04924195 0.1271241 0.2938970 0.5105283 11.907386 Expected cluster size Region Expected.S se.Expected.S cv.Expected.S 1 1 3.110407 0.2740170 0.08809682 2 2 2.849071 0.2211204 0.07761141 3 Total 2.992425 0.1758058 0.05875027 This model incorporates the effect of more variables causing the heterogeneity. The estimated abundance is 651 which is less biased than the previous estimate and the 95% confidence interval (494, 858) contains the true value. The model is a reasonable fit to the data (i.e. non-significant $$\chi^2$$ and Cramer von Mises tests). This model has a lower AIC (405.7) than the model with only distance (452.81) and so is to be preferred. ### Point independence A less restrictive assumption than full independence is point independence, which assumes that detections are only independent on the transect centre line i.e. at perpendicular distance zero (Buckland, Laake, & Borchers, 2010). Determine if a simple point independence model is better than a simple full independence one. This requires that a distance sampling (DS) model is specified as well a MR model. Here we try a half-normal key function for the DS model (Figure 6).  # Fit trial configuration with point independence model pi.mr.dist <- ddf(method='trial', mrmodel=~glm(link='logit', formula=~distance), dsmodel=~cds(key='hn'), data=detections, meta.data=list(width=4)) # Summary pf the model summary(pi.mr.dist)  Summary for trial.fi object Number of observations : 162 Number seen by primary : 124 Number seen by secondary (trials) : 142 Number seen by both (detected trials): 104 AIC : 140.8887 Conditional detection function parameters: estimate se (Intercept) 2.900233 0.4876238 distance -1.058677 0.2235722 Estimate SE CV Average primary p(0) 0.9478579 0.02409999 0.02542574 Summary for ds object Number of observations : 124 Distance range : 0 - 4 AIC : 311.1385 Detection function: Half-normal key function Detection function parameters Scale coefficient(s): estimate se (Intercept) 0.6632435 0.09981249 Estimate SE CV Average p 0.5842744 0.04637627 0.07937413 Summary for trial object Total AIC value = 452.0272 Estimate SE CV Average p 0.5538091 0.04615833 0.08334699 N in covered region 223.9038534 22.99246702 0.10268902  # Produce goodness of fit statistics and a qq plot gof.results <- ddf.gof(pi.mr.dist, main="Point independence, trial configuration\n goodness of fit Golftee data") The AIC for this point independence model is 452.03 which is marginally smaller than the first full independence model that was fitted and hence is to be preferred.  # Get abundance estimates tee.abund.pi <- dht(model=pi.mr.dist, region.table=region, sample.table=samples, obs.table=obs) # Print results print(tee.abund.pi)  Summary for clusters Summary statistics: Region Area CoveredArea Effort n k ER se.ER 1 1 1040 1040 130 72 6 0.5538462 0.02926903 2 2 640 640 80 52 5 0.6500000 0.08292740 3 Total 1680 1680 210 124 11 0.5904762 0.03884115 cv.ER 1 0.05284685 2 0.12758061 3 0.06577936 Abundance: Label Estimate se cv lcl ucl df 1 1 130.00869 12.83042 0.09868896 106.66570 158.4601 48.427796 2 2 93.89516 14.30894 0.15239269 66.25307 133.0701 8.094139 3 Total 223.90385 23.21563 0.10368569 181.78332 275.7840 44.038283 Density: Label Estimate se cv lcl ucl df 1 1 0.1250084 0.01233694 0.09868896 0.1025632 0.1523655 48.427796 2 2 0.1467112 0.02235771 0.15239269 0.1035204 0.2079220 8.094139 3 Total 0.1332761 0.01381882 0.10368569 0.1082044 0.1641572 44.038283 Summary for individuals Summary statistics: Region Area CoveredArea Effort n ER se.ER cv.ER 1 1 1040 1040 130 229 1.761538 0.1165805 0.06618107 2 2 640 640 80 152 1.900000 0.3342319 0.17591151 3 Total 1680 1680 210 381 1.814286 0.1391400 0.07669132 mean.size se.mean 1 3.180556 0.2086982 2 2.923077 0.2261991 3 3.072581 0.1537082 Abundance: Label Estimate se cv lcl ucl df 1 1 413.4999 44.00745 0.1064268 332.9536 513.5314 30.28937 2 2 274.4628 53.42627 0.1946576 171.1754 440.0740 5.98750 3 Total 687.9626 79.79845 0.1159924 542.4532 872.5040 25.99319 Density: Label Estimate se cv lcl ucl df 1 1 0.3975960 0.04231485 0.1064268 0.3201477 0.4937801 30.28937 2 2 0.4288481 0.08347854 0.1946576 0.2674615 0.6876156 5.98750 3 Total 0.4095016 0.04749908 0.1159924 0.3228888 0.5193476 25.99319 Expected cluster size Region Expected.S se.Expected.S cv.Expected.S 1 1 3.180556 0.2114629 0.06648615 2 2 2.923077 0.1750319 0.05987935 3 Total 3.072581 0.1391365 0.04528327 This results in an estimated abundance of 688. Can we do better if more covariates are included in the DS model? #### Covariates in the DS model To include covariates in the DS detection function, we need to specify an MCDS model as follows:  # Fit the PI-trial model - DS sex and MR distance pi.mr.dist.ds.sex <- ddf(method='trial', mrmodel=~glm(link='logit',formula=~distance), dsmodel=~mcds(key='hn',formula=~sex), data=detections, meta.data=list(width=4)) Use the summary function to check the AIC and decide if you are going to include any additional covariates in the detection function. Now try a point independence model that has the preferred MR model from your full independence analyses.  # Point independence model, Include covariates in DS model # Use selected MR model, iterate across DS models ds.formula <- c("~size","~sex","~exposure","~size+sex","~size+exposure","~sex+exposure", "~size+sex+exposure") num.ds.models <- length(ds.formula) # Create dataframe to store results pi.results <- data.frame(DSmodel=ds.formula, AIC=rep(NA,num.ds.models)) # Loop through ds models - use selected MR model from earlier for (i in 1:num.ds.models) { pi.model <- ddf(method='trial', mrmodel=~glm(link='logit',formula=~distance+sex+exposure), dsmodel=~mcds(key='hn',formula=as.formula(ds.formula[i])), data=detections, meta.data=list(width=4)) pi.results$AIC[i] <- summary(pi.model)$AIC } # Calculate delta AIC pi.results$deltaAIC <- pi.results$AIC - min(pi.results$AIC)
# Order by delta AIC
pi.results <- pi.results[order(pi.results\$deltaAIC), ]
knitr::kable(pi.results, digits = 2)
DSmodel AIC deltaAIC
2 ~sex 399.26 0.00
6 ~sex+exposure 400.28 1.02
4 ~size+sex 401.06 1.80
7 ~size+sex+exposure 401.94 2.69
1 ~size 407.92 8.66
3 ~exposure 407.97 8.72
5 ~size+exposure 409.89 10.63

This indicates that sex should be included in the DS model. We do this and check the goodness of fit and obtain abundance (Figure 7).


# Fit chosen model
dsmodel=~mcds(key='hn',formula=~sex), data=detections,
meta.data=list(width=4))
summary(pi.ds.sex)

Summary for trial.fi object
Number of observations               :  162
Number seen by primary               :  124
Number seen by secondary (trials)    :  142
Number seen by both (detected trials):  104
AIC                                  :  94.89911

Conditional detection function parameters:
estimate        se
(Intercept)  0.7870962 0.6774633
distance    -1.9435496 0.3706866
sex1         2.8059863 0.6828331
exposure1    3.6094527 0.7332797

Estimate         SE         CV
Average primary p(0) 0.9697357 0.02018876 0.02081883

Summary for ds object
Number of observations :  124
Distance range         :  0  -  4
AIC                    :  304.3594

Detection function:
Half-normal key function

Detection function parameters
Scale coefficient(s):
estimate        se
(Intercept) 0.2525377 0.1327279
sex1        0.5832341 0.2041197

Estimate         SE         CV
Average p 0.5605421 0.04616396 0.08235592

Summary for trial object

Total AIC value =  399.2585
Estimate          SE         CV
Average p             0.5435777  0.04643944 0.08543294
N in covered region 228.1182639 24.21314095 0.10614293

# Check goodness-of-fit
ddf.gof(pi.ds.sex, main="PI trial configutation\nGolfTee DS model sex")

Goodness of fit results for ddf object

Chi-square tests

Distance sampling component:
[0,0.4] (0.4,0.8]  (0.8,1.2] (1.2,1.6]   (1.6,2]
Observed  25.0000000 16.000000 16.0000000 22.000000  9.000000
Expected  21.9165416 20.740242 18.6299046 15.975863 13.181194
Chisquare  0.4338146  1.083396  0.3712525  2.271566  1.326313
(2,2.4] (2.4,2.8] (2.8,3.2] (3.2,3.6]  (3.6,4]
Observed  10.00000000 12.000000  9.000000  3.000000 2.000000
Expected  10.55317365  8.260839  6.353756  4.809648 3.578838
Chisquare  0.02899612  1.692483  1.102121  0.680887 0.696519
Total
Observed  124.000000
Expected  124.000000
Chisquare   9.687346

P = 0.20699 with 7 degrees of freedom

Mark-recapture component:
Capture History 01
[0,0.4]  (0.4,0.8] (0.8,1.2]  (1.2,1.6]   (1.6,2]
Observed  1.00000000 2.00000000 2.0000000 6.00000000 5.0000000
Expected  0.85161169 1.61345653 1.5784634 6.25617270 4.2054953
Chisquare 0.02585579 0.09260606 0.1125735 0.01048955 0.1500983
(2,2.4]  (2.4,2.8]  (2.8,3.2] (3.2,3.6]   (3.6,4]
Observed  2.000000 6.00000000 6.00000000  2.000000 6.0000000
Expected  4.014580 6.11790214 6.76552359  1.599467 4.9973276
Chisquare 1.010948 0.00227217 0.08661952  0.100300 0.2011779
Total
Observed  38.000000
Expected  38.000000
Chisquare  1.792941
Capture History 11
[0,0.4]    (0.4,0.8]   (0.8,1.2]    (1.2,1.6]
Observed  21.000000000 15.000000000 16.00000000 20.000000000
Expected  21.148388313 15.386543475 16.42153664 19.743827301
Chisquare  0.001041171  0.009710814  0.01082074  0.003323796
(1.6,2]   (2,2.4]   (2.4,2.8] (2.8,3.2] (3.2,3.6]
Observed  8.00000000 8.0000000 9.000000000 5.0000000 1.0000000
Expected  8.79450467 5.9854201 8.882097863 4.2344764 1.4005328
Chisquare 0.07177638 0.6780697 0.001565048 0.1383941 0.1145468
(3.6,4]      Total
Observed  1.0000000 104.000000
Expected  2.0026724 104.000000
Chisquare 0.5020052   1.531254

MR total chi-square = 3.3242  P = 0.76719 with 6 degrees of freedom

Total chi-square = 13.012  P = 0.44692 with 13 degrees of freedom

Distance sampling Cramer-von Mises test (unweighted)
Test statistic = 0.081285 p-value = 0.684457

# Get abundance estimates
tee.abund.pi.ds.sex <- dht(model=pi.ds.sex, region.table=region,
sample.table=samples, obs.table=obs)
print(tee.abund.pi.ds.sex)

Summary for clusters

Summary statistics:
Region Area CoveredArea Effort   n  k        ER      se.ER
1      1 1040        1040    130  72  6 0.5538462 0.02926903
2      2  640         640     80  52  5 0.6500000 0.08292740
3  Total 1680        1680    210 124 11 0.5904762 0.03884115
cv.ER
1 0.05284685
2 0.12758061
3 0.06577936

Abundance:
Label Estimate       se         cv       lcl      ucl       df
1     1 125.7678 12.50318 0.09941474 102.97943 153.5991 43.66336
2     2 102.3504 17.53163 0.17129022  68.75817 152.3544  7.39421
3 Total 228.1183 25.15323 0.11026399 182.12573 285.7254 28.04581

Density:
Label  Estimate         se         cv        lcl       ucl       df
1     1 0.1209306 0.01202229 0.09941474 0.09901868 0.1476914 43.66336
2     2 0.1599226 0.02739317 0.17129022 0.10743464 0.2380538  7.39421
3 Total 0.1357847 0.01497216 0.11026399 0.10840817 0.1700746 28.04581

Summary for individuals

Summary statistics:
Region Area CoveredArea Effort   n       ER     se.ER      cv.ER
1      1 1040        1040    130 229 1.761538 0.1165805 0.06618107
2      2  640         640     80 152 1.900000 0.3342319 0.17591151
3  Total 1680        1680    210 381 1.814286 0.1391400 0.07669132
mean.size   se.mean
1  3.180556 0.2086982
2  2.923077 0.2261991
3  3.072581 0.1537082

Abundance:
Label Estimate       se        cv      lcl      ucl        df
1     1 395.0545 36.33952 0.0919861 329.0883 474.2437 79.295718
2     2 299.7763 65.43242 0.2182709 175.5600 511.8809  5.685148
3 Total 694.8307 84.25554 0.1212605 537.2145 898.6908 15.167370

Density:
Label  Estimate         se        cv       lcl       ucl        df
1     1 0.3798601 0.03494185 0.0919861 0.3164310 0.4560035 79.295718
2     2 0.4684004 0.10223816 0.2182709 0.2743125 0.7998140  5.685148
3 Total 0.4135897 0.05015211 0.1212605 0.3197705 0.5349350 15.167370

Expected cluster size
Region Expected.S se.Expected.S cv.Expected.S
1      1   3.141141     0.2081675    0.06627130
2      2   2.928920     0.1866200    0.06371633
3  Total   3.045923     0.1371508    0.04502767

This model estimated an abundance of 695, which is closest to the true value of all the models - it is still less than the true value indicating, perhaps, some unmodelled heterogeneity on the trackline (or perhaps just bad luck - remember this was only one survey).

Was this complex modelling worthwhile? In this case, the estimated $$p(0)$$ for the best model was 0.97 (which is very close to 1). If we ran a conventional distance sampling analysis, pooling the data from the two observers, we should get a very robust estimate of true abundance.

Buckland, S. T., Laake, J. L., & Borchers, D. L. (2010). Double-observer line transect methods: Levels of independence. Biometrics, 66, 169–177. https://doi.org/10.1111/j.1541-0420.2009.01239.x

Laake, J., Borchers, D., Thomas, L., Miller, D., & Bishop, J. (2019). Mrds: Mark-recapture distance sampling.