dsm: Fit a density surface model to segment-specific estimates of abundance or density.

Description

Fits a density surface model (DSM) to detection adjusted counts from a spatially-referenced distance sampling analysis. dsm takes observations of animals, allocates them to segments of line (or strip transects) and optionally adjusts the counts based on detectability using a supplied detection function model. A generalized additive model, generalized mixed model or generalized linear model is then used to model these adjusted counts based on a formula involving environmental covariates.

Usage

dsm(
  formula,
  ddf.obj,
  segment.data,
  observation.data,
  engine = "gam",
  convert.units = 1,
  family = quasipoisson(link = "log"),
  group = FALSE,
  control = list(keepData = TRUE),
  availability = 1,
  segment.area = NULL,
  weights = NULL,
  method = "REML",
  ...
)

Value

a glm, gam, gamm or bam object, with an additional element, $ddf which holds the detection function object.

Arguments

formula: formula for the surface. This should be a valid formula. See "Details", below, for how to define the response.
ddf.obj: result from call to ddf or ds. If multiple detection functions are required a list can be provided. For strip/circle transects where it is assumed all objects are observed, see dummy_ddf. Mark-recapture distance sampling (mrds) models of type io (independent observers) and trial are allowed.
segment.data: segment data, see dsm-data.
observation.data: observation data, see dsm-data.
engine: which fitting engine should be used for the DSM ("glm"/"gam"/"gamm"/"bam").
convert.units: conversion factor to multiply the area of the segments by. See 'Units' below.
family: response distribution (popular choices include quasipoisson, Tweedie/tw and negbin/nb). Defaults quasipoisson.
group: if TRUE the abundance of groups will be calculated rather than the abundance of individuals. Setting this option to TRUE is equivalent to setting the size of each group to be 1.
control: the usual control argument for a gam; keepData must be TRUE for variance estimation to work (though this option cannot be set for GLMs or GAMMs).
availability: an estimate of availability bias. For count models used to multiply the effective strip width (must be a vector of length 1 or length the number of rows in segment.data); for estimated abundance/estimated density models used to scale the response (must be a vector of length 1 or length the number of rows in observation.data). Uncertainty in the availability is not handled at present.
segment.area: if NULL (default) segment areas will be calculated by multiplying the Effort column in segment.data by the (right minus left) truncation distance for the ddf.obj or by strip.width. Alternatively a vector of segment areas can be provided (which must be the same length as the number of rows in segment.data) or a character string giving the name of a column in segment.data which contains the areas. If segment.area is specified it takes precedent.
weights: weights for each observation used in model fitting. The default, weights=NULL, weights each observation by its area (see Details). Setting a scalar value (e.g., weights=1) all observations are equally weighted.
method: The smoothing parameter estimation method. Default is "REML", using Restricted Maximum Likelihood. See gam for other options. Ignored for engine="glm".
...: anything else to be passed straight to glm, gam, gamm or bam.

Units

It is often the case that distances are collected in metres and segment lengths are recorded in kilometres. dsm allows you to provide a conversion factor (convert.units) to multiply the areas by. For example: if distances are in metres and segment lengths are in kilometres setting convert.units=1000 will lead to the analysis being in metres. Setting convert.units=1/1000 will lead to the analysis being in kilometres. The conversion factor will be applied to segment.area if that is specified.

Large models

For large models, engine="bam" with method="fREML" may be useful. Models specified for bam should be as gam. Read bam before using this option; this option is considered EXPERIMENTAL at the moment. In particular note that the default basis choice (thin plate regression splines) will be slow and that in general fitting is less stable than when using gam. For negative binomial response, theta must be specified when using bam.

Author

David L. Miller

Details

The response (LHS of formula) can be one of the following (with restrictions outlined below):

count count in each segment
abundance.est estimated abundance per segment, estimation is via a Horvitz-Thompson estimator
density.est density per segment

The offset used in the model is dependent on the response:

count area of segment multiplied by average probability of detection in the segment
abundance.est area of the segment
density zero

The count response can only be used when detection function covariates only vary between segments/points (not within). For example, weather conditions (like visibility or sea state) or foliage cover are usually acceptable as they do not change within the segment, but animal sex or behaviour will not work. The abundance.est response can be used with any covariates in the detection function.

In the density case, observations can be weighted by segment areas via the weights= argument. By default (weights=NULL), when density is estimated the weights are set to the segment areas (using segment.area or by calculated from detection function object metadata and Effort data). Alternatively weights=1 will set the weights to all be equal. A third alternative is to pass in a vector of length equal to the number of segments, containing appropriate weights.

A example analyses are available at http://examples.distancesampling.org.

References

Hedley, S. and S. T. Buckland. 2004. Spatial models for line transect sampling. JABES 9:181-199.

Miller, D. L., Burt, M. L., Rexstad, E. A., Thomas, L. (2013), Spatial models for distance sampling data: recent developments and future directions. Methods in Ecology and Evolution, 4: 1001-1010. doi: 10.1111/2041-210X.12105 (Open Access)

Wood, S.N. 2006. Generalized Additive Models: An Introduction with R. CRC/Chapman & Hall.

Examples

Run this code

if (FALSE) {
library(Distance)
library(dsm)

# load the Gulf of Mexico dolphin data (see ?mexdolphins)
data(mexdolphins)

# fit a detection function and look at the summary
hr.model <- ds(distdata, truncation=6000,
               key = "hr", adjustment = NULL)
summary(hr.model)

# fit a simple smooth of x and y to counts
mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata)
summary(mod1)

# predict over a grid
mod1.pred <- predict(mod1, preddata, preddata$area)

# calculate the predicted abundance over the grid
sum(mod1.pred)

# plot the smooth
plot(mod1)
}

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