By the end of this practical you should feel confident doing the following:


First need to load the requisite R libraries

## Loading required package: mrds
## This is mrds 2.2.3
## Built: R 4.0.2; ; 2020-08-01 10:33:56 UTC; unix
## Attaching package: 'Distance'
## The following object is masked from 'package:mrds':
##     create.bins

We can then load the data from the RData file (this needs to be in your R working directory):


Exploring the data

Before setting off fitting detection functions, let’s look at the relationship of various variables in the data.

The most important covariate in a distance sampling analysis is distance itself. We can plot a histogram of the distances to check that (1) we imported the data correctly and (2) it conforms to the usual shape for line transect data.

hist(distdata$distance, xlab="Distance (m)",
     main="Distance to sperm whale observations")
Distribution of observed perpendicular detection distances.

Distribution of observed perpendicular detection distances.

We can also see how many observations were recorded per Beaufort level (some background on Beaufort with photos). We can first use table() to see what values occur and how many there are:

##   0   1 1.8   2 2.4 2.5 2.7 2.8 2.9   3 3.1 3.5 3.6 3.7 3.9   4 4.5   5 
##   5   4   7  31   8  10   1   1   4  41   2   6   1   2   2   8   1   3

The survey protocol on these surveys took an average of several observers’ assessments of the weather, hence we get these decimal values. We can use the breaks= argument to hist() to bin these values to integer values. We’ll plot the values from the segments at the same time, so we can get an idea of what the conditions were like over the whole survey, not just when we saw sperm whales:


hist(distdata$Beaufort, breaks=seq(0, 5, by=1),
     main="Observations per Beaufort level",

hist(segs$Beaufort, breaks=seq(0, 5, by=1),
     main="Segments per Beaufort level",

We can see here that most of the segments had a Beaufort >= 2 and very few had Beaufort of 0-1 (right plot). We can see then that there are few observations at the lowest Beaufort (left plot) because there just wasn’t much time to observe things in those conditions. We see that with increasing Beaufort observations fall off (left plot), even as there were plenty of segments with those higher Beaufort levels (right plot).

We can create a rounded version of the variable using the cut function in R for later:

distdata$BeaufortCut <- cut(distdata$Beaufort, seq(0, 5, by=1),
                            include.lowest=TRUE, ordered.result=TRUE)

Given we are including data from two different surveys we can also check that the surveys have similar histograms of distance (we’ll check with the detection function below whether we need to include a covariate to control for this).


hist(subset(distdata, Survey=="en04395")$distance,
     main="", ylim=c(0, 30),
     xlab="Distance (m)")

hist(subset(distdata, Survey=="GU0403")$distance,
     main="", ylim=c(0, 30),
     xlab="Distance (m)")

Fitting detection functions

It’s now time to fit some detection function models. We’ll use the ds() function from the Distance package to fit the detection function. You can access the help file for the ds() function by typing ?ds – this will give you information about what the different arguments to the function are and what they do.

We can fit a very simple model with the following code:

df_hn <- ds(data=distdata, truncation=6000, key="hn", adjustment=NULL)
## Fitting half-normal key function
## Key only model: not constraining for monotonicity.
## AIC= 2252.06
## No survey area information supplied, only estimating detection function.

Let’s dissect the call and see what each argument means:

Other useful arguments for this practical are:

We set truncation=6000 here, why? The truncation removes observations in the tail of the distribution. We do this because we only care about the detection function near 0, that has the most effect on \(\hat{p}\) (since it’s where the detection function is biggest). We need to trade-off between getting the shape right and not using lots of parameters modelling what’s going on far from the observer and has little effect on the result. Here we use ~96% of the data. Len Thomas suggests \(g(w)\approx 0.15\) (i.e., at the truncation distance the detection function should be about 0.15). You can play around with this if you’re interested here by changing truncation= and seeing what the differences in \(\hat{p}\) are (but push on to the next section if you’re happy!).


We can look at the summary of the fitted detection function using the summary() function:

## Summary for distance analysis 
## Number of observations :  132 
## Distance range         :  0  -  6000 
## Model : Half-normal key function 
## AIC   : 2252.06 
## Detection function parameters
## Scale coefficient(s):  
##             estimate         se
## (Intercept) 7.900732 0.07884776
##                        Estimate          SE         CV
## Average p             0.5490484  0.03662569 0.06670757
## N in covered region 240.4159539 21.32287580 0.08869160

Goodness of fit

Goodness of fit quantile-quantile plot and test results can be accessed using the gof_ds() function:

Goodness of fit QQ plot of half-normal detection function.

Goodness of fit QQ plot of half-normal detection function.

## Goodness of fit results for ddf object
## Distance sampling Cramer-von Mises test (unweighted)
## Test statistic = 0.396179 p-value = 0.0739475


We can plot the models simply using the plot() function: