Description Usage Arguments Details Value Author(s) References See Also Examples
Estimate abundance (or density) given an estimated detection function and supplemental information on observed group sizes, transect lengths, area surveyed, etc. Also computes confidence intervals of abundance (or density) using the bias corrected bootstrap method.
1 2  abundEstim(dfunc, detectionData, siteData, area = 1, ci = 0.95,
R = 500, plot.bs = FALSE, bySite = FALSE, showProgress = TRUE)

dfunc 
An estimated 'dfunc' object produced by 
detectionData 
A data.frame with each row representing one detection
(see example dataset,

siteData 
A data.frame with each row representing one site
(transect or point) (see example dataset,
If the data in 
area 
Total study area size. If 
ci 
A scalar indicating the confidence level of confidence intervals.
Confidence intervals are computed using the bias corrected bootstrap
method. If 
R 
The number of bootstrap iterations to conduct when 
plot.bs 
A logical scalar indicating whether to plot individual bootstrap iterations. 
bySite 
A logical scalar indicating whether to compute sitelevel
estimates of abundance. The default ( 
showProgress 
A logical indicating whether to show a textbased
progress bar during bootstrapping. Default is 
The abundance estimate for line transect surveys (if no covariates are included in the detection function) is
N = n.indiv*area / (2*ESW*tot.trans.len)
where n.indiv
is either avg.group.size * n
or
sum(group.sizes)
, and ESW
is the effective strip width
computed from the estimated distance function (i.e., ESW(dfunc)
).
The confidence interval for abundance assumes that the fundamental units of
replication (lines or points, hereafter "sites") are independent.
The bias corrected bootstrap
method used here resamples the units of replication (sites) and
recalculates the model's parameter estimates. If a doubleobserver data
frame is included in dfunc
, rows of the doubleobserver data frame
are resampled each bootstrap iteration. No model selection is performed.
By default, R
= 500 iterations are performed, after which the bias
corrected confidence intervals are computed using the method given in Manly
(1997, section 3.4).
Setting plot.bs=FALSE
and showProgress=FALSE
suppresses all intermediate output. This is good when calling
abundEstim
from within other functions or during simulations.
If bySite
is FALSE, an 'abundance estimate' object, a list of
class c("abund", "dfunc")
, containing all the components of a "dfunc"
object (see dfuncEstim
), plus the following:
abundance 
Estimated abundance in the study area (if 
n 
The number of detections (not individuals, unless all group sizes = 1) used in the estimate of abundance. 
area 
Total area of inference. Study area size 
esw 
Effective strip width for linetransects, effective
radius for pointtransects. Both derived from 
or EDR
for formulas.
n.sites 
Total number of transects for linetransects, total number of points for pointtransects. 
tran.len 
Total transect length. NULL for pointtransects. 
avg.group.size 
Average group size 
ci 
The bias corrected bootstrap confidence interval for

B 
A vector or length 
alpha 
The (scalar) confidence level of the
confidence interval for 
If bySite
is TRUE, a data frame containing sitelevel
estimated abundance. The data frame is an exact copy of siteData
with the following columns tacked onto the end:
effDist 
The effective sampling distance at the site. For line transects, this is ESW at the site. For points, this is EDR. 
pDetection 
Average probability of detection at the site. If only sitelevel covariates appear in the distance function, pDetection is constant within a site. When detectionlevel covariates are present, pDetection is the average at the site. 
observedCount 
The total number of individuals detected at a site. 
abundance 
Estimated abundance at the site. This is the sum of inflated group sizes at the site. i.e., each group size at the site is divided by its pDetection, and then summed. 
density 
Estimated density at the site. This is abundance at the site divided by the sampled area at the site. E.g., for line transects, this is abundance divided by 2*w*length. For points, this is abundance divided by pi*w^2. 
effArea 
The effective area sampled at the site. This could be used as an offset in a subsequent linear model. For line transects, this is 2*ESW*length. For points, this is pi*EDR^2. 
Trent McDonald, WEST Inc., tmcdonald@westinc.com
Aidan McDonald, WEST Inc., aidan@mcdcentral.org
Jason Carlisle, University of Wyoming and WEST Inc.,
jcarlisle@westinc.com
Manly, B.F.J. (1997) Randomization, bootstrap, and montecarlo methods in biology, London: Chapman and Hall.
Buckland, S.T., D.R. Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers, and L. Thomas. (2001) Introduction to distance sampling: estimating abundance of biological populations. Oxford University Press, Oxford, UK.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  # Load example sparrow data (line transect survey type)
data(sparrowDetectionData)
data(sparrowSiteData)
# Fit halfnormal detection function
dfunc < dfuncEstim(formula=dist~1,
detectionData=sparrowDetectionData,
likelihood="halfnorm", w.hi=100, pointSurvey=FALSE)
# Estimate abundance given a detection function
# Note, area=10000 converts to density per hectare (for distances measured in meters)
# Note, a person should do more than R=20 iterations
fit < abundEstim(dfunc, detectionData=sparrowDetectionData,
siteData=sparrowSiteData, area=10000, R=20, ci=0.95,
plot.bs=TRUE, bySite=FALSE)
# Print results
fit

Rdistance (version 2.1.3)
Computing bootstrap confidence interval on N...

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Call: dfuncEstim(formula = dist ~ 1, detectionData =
sparrowDetectionData, likelihood = "halfnorm", pointSurvey = FALSE,
w.hi = 100)
Coefficients:
Estimate SE z p(>z)
Sigma 46.3587 2.549913 18.1805 7.365789e74
Convergence: Success
Function: HALFNORM
Strip: 0 to 100
Effective strip width (ESW): 56.30084
Probability of detection: 0.5630084
Scaling: g(0) = 1
Log likelihood: 1484.297
AICc: 2970.605
Abundance estimate: 0.8634171 ; 95% CI=( 0.6679225 to 0.9652753 )
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