This tutorial is a beginner's guide to doing point transect distance-sampling analysis using
Rdistance. Topics covered include input data requirements, fitting a detection function, estimating abundance (or density), and selecting the best fit detection function using AICc. We use the internal datasets
thrasherSiteData (point transect surveys of brown thrashers). This tutorial is current as of version
r packageVersion("Rdistance") of
If you haven't already done so, install the latest version of
Rdistance. In the R console, issue
install.packages("Rdistance"). After the package is installed, it can be loaded into the current session as follows:
For this tutorial, we use two datasets collected by
J. Carlisle on brown thrashers in central Wyoming that are
The first dataset,
thrasherDetectionData, is a detection data.frame
with one row for each detected object. Columns in the data frame are:
siteID= Factor, the site (point) and transect surveyed. Levels are five character codes like 'TTXPP' where TT is transect number and PP is the point within the transect.
groupsize= Numeric, the number of individuals within the detected group.
dist= Numeric, the radial distance from the point to the detected group. Obtain access to the example dataset of thrasher detections and observed distances (
thrasherDetectionData) using the following commands:
The second required dataset,
thrasherSiteData, is a transect data.frame, with one row for each transect surveyed, and the following required columns:
siteID= Factor, the site (point) and transect surveyed.
...= Any additional transect-level covariate columns (these will not be used in this tutorial).
Load the example dataset of thrasher transects (
thrasherSiteData) using the following commands:
Once the data are imported, the first step is to fit a detection function. Before we do so, explore the distribution of the distances:
hist(thrasherDetectionData$dist, n=40, col="grey", main="", xlab="distance (m)") summary(thrasherDetectionData$dist)
Next, we fit a detection function using
to the radial distances collected from the point transects and
plot it. We specify point transects using option
in the call to
dfuncEstim and specify the the half-normal
distance function using option
In section 5, we demonstrate an automated process to fit
multiple detection functions and compare them using AICc.
dfunc <- dfuncEstim(formula = dist ~ 1, detectionData = thrasherDetectionData, pointSurvey = TRUE, likelihood = "halfnorm") plot(dfunc) dfunc
The effective detection radius (EDR) is the essential information from the detection function that will be used to estimate abundance in section 4. The EDR is calculated by integrating the detection function to compute area under the detection function. See the help documentation for
EDR for details.
Estimating abundance requires the additional information contained in the the thrasher site dataset, described in section 2, where each row represents one transect. Load the example dataset of surveyed thrasher transects from the package.
We estimate abundance (or density in this case) using
area = 1, density is given in the squared units of the distance measurements --- in this case, thrashers per square meter. If we set
area = 10000, we convert to thrashers per hectare (1 ha == 10,000 m^2^). The equation used to calculate the abundance estimate is detailed in the help documentation for
Confidence intervals for abundance are calculated using a bias-corrected bootstrapping method (see
abundEstim). Note that, as with all bootstrapping procedures, there may be slight differences in the confidence intervals between runs. Increasing the number of bootstrap iterations (
R = 100 used here for brevity) may be necessary to stabilize CI estimates.
# Estimate Abundance - Density; fatalities per m2 fit <- abundEstim(dfunc = dfunc, detectionData = thrasherDetectionData, siteData = thrasherSiteData, area = 10000, # density per hectare R = 100, ci = 0.95)
The abundance estimate can be extracted from the
The confidence interval (in this case 95%) can be extracted from the
Fitting several detection functions, choosing the best fitting, and
estimating abundance (sections 3 and 4) can be automated using the
autoDistSamp. The function attempts to fit multiple
detection functions, uses AICc (by default, but see help
criterion for other options)
to find the 'best' detection function, then proceeds to estimate
abundance using the best fit detection function (the distance
function with lowest AICc). By default,
autoDistSamp tries a
large subset of
Rdistance's built-in detection functions, but
you can control exactly which detection functions are attempted
(see help documentation for
produces a plot of each detection function as it is estimated.
plot.bs=TRUE plots the selected distance function
each iteration of the bootstrap procedure. In this example, we
fit the half-normal, hazard rate, exponential, and uniform likelihoods
with no expansion terms, we do not plot all fitted functions
plot=FALSE), but we plot the best distance function
fitted during each bootstrap iteration.
# Automated Fit - fit several models, choose the best model based on AIC autoDS <- autoDistSamp(formula = thrasherDetectionData$dist ~ 1, detectionData = thrasherDetectionData, siteData = thrasherSiteData, pointSurvey = TRUE, expansions = c(0), likelihoods = c("halfnorm", "hazrate", "negexp", "uniform"), plot = FALSE, area = 10000, R = 100, ci = 0.95, plot.bs = FALSE)
The detection function with the lowest AICc value (and thus selected as the 'best') is the hazard rate likelihood with 0 cosine expansion terms.
In sections 3 and 4, we fitted a half-normal detection function and used that function to estimate thrasher density. Our estimate was
r round(fit$n.hat, 2) thrashers per ha (95% CI =
r round(fit$ci[], 2) to
r round(fit$ci[], 2)). In section 5, we used AICc to estimate a
better fitting detection function and used it to estimate thrasher density. The thrasher density estimated by the better-fitting model
r round(autoDS$n.hat, 2) thrashers per ha (95% CI =
r round(autoDS$ci[], 2) to
r round(autoDS$ci[], 2)). (Note, CI estimates may vary slightly from these due to minor 'simulation slop' inherent in bootstrapping methods).
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