Aims

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

Preamble

First need to load the requisite R libraries

library(Distance)
## 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
library(ggplot2)
library(knitr)

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

load("spermwhale.RData")

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:

table(distdata$Beaufort)
## 
##   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:

par(mfrow=c(1,2))

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

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

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).

par(mfrow=c(1,2))

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!).

Summaries

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

summary(df_hn)
## 
## 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:

gof_ds(df_hn)
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

Plotting

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

plot(df_hn)
Half-normal detection function.

Half-normal detection function.

The dots on the plot indicate the distances where observations are. We can remove them (particularly useful for a model without covariates) using the additional argument showpoints=FALSE (try this out!).

Now you try…

Now try fitting a few models and comparing them using AIC. Don’t try to fit all possible models, just try a selection (say, a hazard-rate, a model with adjustments and a couple with different covariates). You can also try out changing the truncation distance.

Here’s an example to work from. Some tips before you start:

Here’s an example of adding covariates into the model using the formula= argument:

df_hr_ss_size <- ds(distdata, truncation=6000, adjustment=NULL,
                    key="hr", formula=~Beaufort+size)
## Fitting hazard-rate key function
## AIC= 2249.327
## No survey area information supplied, only estimating detection function.

Once you have the hang of writing models and looking at the differences between them, you should move onto the next section.

Model selection

Looking at the models individually can be a bit unwieldy – it’s nicer to put that data into a table and sort the table by the relevant statistic. The function summarize_ds_models() in the Distance package performs this task for us.

The code below will make a results table with relevant statistics for model selection in it. The summarize_ds_models() function takes a series of object names as its first argument. We can do that with the two models that I fitted like so:

model_table <- summarize_ds_models(df_hn, df_hr_ss_size, output="plain")
kable(model_table, digits=3, row.names = FALSE, escape=FALSE,
      caption = "Comparison of half normal and hazard rate with sea state and group size.")
Comparison of half normal and hazard rate with sea state and group size.
Model Key function Formula C-vM \(p\)-value Average detectability se(Average detectability) Delta AIC
df_hr_ss_size Hazard-rate ~Beaufort + size 0.880 0.355 0.074 0.000
df_hn Half-normal ~1 0.074 0.549 0.037 2.733

(You can add the models you fitted above into this list.)

A further note about model selection for the sperm whale data

Note that there is a considerable spike in our distance data. This may be down to observers guarding the trackline (spending too much time searching near zero distance). It’s therefore possible that the hazard-rate model is overfitting to this peak. So we’d like to investigate results from the half-normal model too and see what the effects are on the final spatial models.

Estimating abundance

Just for fun, let’s estimate abundance from these models using a Horvtiz-Thompson-type estimator.

We know the Horvitz-Thompson estimator has the following form: \[ \hat{N} = \frac{A}{a} \sum_{i=1}^n \frac{s_i}{p_i} \] we can calculate each part of this equation in R:

  • A is the total area of the region we want to estimate abundance for. This was \(A=5.285e+11 m^2\).
  • a is the total area we surveyed. We know that the total transect length was 9,498,474m and the truncation distance. Knowing that \(a=2wL\) we can calculate \(a\).
  • \(s_i\) are the group sizes, they are stored in df_hn$ddf$data$size.
  • \(p_i\) are the probabilities of detection, we can obtain them using predict(df_hn$ddf)$fitted.

We know that in general operations are vectorised in R, so calculating c(1, 2, 3)/c(4, 5, 6) will give c(1/4, 2/5, 3/6) so we can just divide the results of getting the \(s_i\) and \(p_i\) values and then use the sum() function to sum them up.

Try out estimating abundance using the formula below using both df_hn and your favourite model from above:

Note that in the solutions to this exercise (posted on the course website) I show how to use the function dht() to estimate abundance (and uncertainty) for a detection function analysis. This involves some more complex data manipulation steps, so we’ve left it out here in favour of getting to grips with the mathematics.

Accounting for perception bias

It’s common, especially in marine surveys, for animals at zero distance to be missed by observers. There are several ways to deal with this issue. For now, we are just going to use a very simply constant correction factor to inflate the abundance.

From Palka (2006), we think that observations on the track line were such that \(g(0)=0.46\), we can apply that correction to our abundance estimate (in a very primitive way):

This kind of correction works fine when it’s appropriate to use a single number to adjust by, in general we’d like to model the perception bias, for example using “mark-recapture distance sampling” techniques.

Save model objects

Save your top few models in an RData file, so we can load them up later on. We’ll also save the distance data we used to fit out models.

save(df_hn, df_hr_ss_size, # add you models here, followed by commas!
     distdata,
     file="df-models.RData")

You can check it worked by using the load() function to recover the models.

References

Palka, D. L. (2006) Summer Abundance Estimates of Cetaceans in US North Atlantic Navy Operating Areas. Northeast Fisheries Science Center Reference Document 06-03