Density and Abundance from Distance-Sampling Surveys
Distance Sampling Abundance Estimates
AIC-related fit statistics for detection functions
Automated classical distance analysis
Bias corrected bootstraps
Perform bootstrap iterations
B-spline expansion terms
Check number of numeric integration intervals
Check for the presence of units
Coefficients of an estimated detection function
Add color to result if terminal accepts it
Cosine expansion terms
Estimate multiple-observer line-transect distance functions
Estimate single-observer line-transect distance function
Estimate a distance-based detection function
dfuncEstim error messages
Differentiable likelihoods in Rdistance
Observation distances
Effective Detection Radius (EDR) for point transects
Effective sampling distances
Effort information
Unknown error message
Abundance point estimates
Effective Strip Width (ESW) for line transects
Distance function expansion terms
Gamma distance function
Gamma.start.limits - Start and limit values for Gamma distance functio...
Modes of the Gamma distribution
Reparameterise Gamma parameters for use in dgamma
Set number of cores
Group Sizes
Estimate g(0) or g(x)
Half-normal distance function
Start and limit values for halfnorm distance function
Hazard rate likelihood
Start and limit values for hazrate distance function
Hermite expansion factors
'nlminb' optimizer
Insert oneStep Likelihood breaks
Integration of distance functions
Integrate Gamma line surveys
Integrate Half-normal line surveys
Integrate Half-normal Point transects
Integrate Hazard-rate line survey distance functions
Compute and print distance function integration
Integrate Negative exponential
Integrate Negative exponential point surveys
Numeric Integration
Integrate Line-transect One-step function
Numeric Integration of One-step Function
Integrate Point-survey One-step function
Detect intercept-only distance function
Tests for point surveys
Check RdistDf data frames
Tests for smoothed distance functions
Test whether object is unitless
Likelihood parameter names
lines.dfunc - Line plotting method for distance functions
Find coordinate of function maximum
Distance function maximum likelihood estimates
Rdistance model matrix
Number of covariates
Negative exponential likelihood
Start and limit values for negexp distance function
Negative log likelihood of distances
'nlminb' optimizer
Type of observations
Calculations for one bootstrap iteration
Mixture of two uniforms likelihood
oneStep likelihood start and limit values
'optim' optimizer
Parse Rdistance model
Compute off-transect distances from sighting distances and angles
Plot parametric distance functions
Plot method for distance (detection) functions
Density on transects
Predict distance functions
Predict distance functions
Distance function values at observations
Print abundance estimates
Print method for distance function object
Rdistance - Distance Sampling Analyses for Abundance Estimation
Rdistance optimization control parameters.
Construct Rdistance nested data frames
Numeric second derivatives
Simple polynomial expansion factors
Simpson numerical integration coefficients
Sine expansion terms
Distance function starting values and limits
Summarize abundance estimates
Summarize a distance function object
Summary method for Rdistance data frames
Type of transects
Unit assignment helpers
Unnest an RdistDf data frame
Estimate variance-covariance
Distance-sampling (<doi:10.1007/978-3-319-19219-2>) is a field survey and analytical method that estimates density and abundance of survey targets (e.g., animals) when detection probability declines with observation distance. Distance-sampling is popular in ecology, especially when survey targets are observed from aerial platforms (e.g., airplane or drone), surface vessels (e.g., boat or truck), or along walking transects. Analysis involves fitting smooth (parametric) curves to histograms of observation distances and using those functions to adjust density estimates for missed targets. Routines included here fit curves to observation distance histograms, estimate effective sampling area, density of targets in surveyed areas, and the abundance of targets in a surrounding study area. Confidence interval estimation uses built-in bootstrap resampling. Help files are extensive and have been vetted by multiple authors. Many tutorials are available on the package's website (URL below).
Useful links