unmarked (version 0.9-1)

unmarked-package: Models for Data from Unmarked Animals

Description

unmarked fits hierarchical models of animal occurrence and abundance to data collected on species subject to imperfect detection. Examples include single- and multi-season site occupancy models, binomial mixture models, and multinomial mixture models. The data can arise from survey methods such as occurrence sampling, temporally replicated counts, removal sampling, double observer sampling, and distance sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. General treatment of these models can be found in MacKenzie et al. (2006) and Royle and Dorazio (2008).

Details

ll{ Package: unmarked Type: Package Version: 0.9-1 License: GPL (>= 3) } Overview of Model-fitting Functions: occu fits occurrence models with no linkage between abundance and detection (MacKenzie et al. 2002). occuRN fits abundance models to presence/absence data by exploiting the link between detection probability and abundance (Royle and Nichols 2003). colext fits the mutli-season occupancy model of MacKenzie et al. (2003). pcount fits N-mixture models (aka binomial mixture models) to repeated count data (Royle 2004a, Kéry et al 2005). distsamp fits the distance sampling model of Royle et al. (2004) to distance data recorded in discrete intervals. gdistsamp fits the generalized distance sampling model described by Chandler et al. (2011) to distance data recorded in discrete intervals. multinomPois fits the multinomial-Poisson model of Royle (2004b) to data collected using methods such as removal sampling or double observer sampling. gmultmix fits a generalized form of the multinomial-mixture model of Royle (2004b) that allows for estimating availability and detection probability. pcountOpen fits the open population model of Dail and Madsen (in press) to repeated count data. This is a genearlized form of the Royle (2004a) N-mixture model that includes parameters for recruitment and apparent survival. Data: All data are passed to unmarked's estimation functions as a formal S4 class called an unmarkedFrame, which has child classes for each model type. This allows metadata (eg as distance interval cut points, measurement units, etc...) to be stored with the response and covariate data. See unmarkedFrame for a detailed description of unmarkedFrames and how to create them. Model Specification: unmarked's model-fitting functions allow specification of covariates for both the state process and the detection process. For two-level hierarchical models, (eg occu, occuRN, pcount, multinomPois, distsamp) covariates for the detection process (at the site or observation level) and the state process (at the site level) are specified with a double right-hand sided formula, in that order. Such a formula looks like $~ x1 + x2 + \ldots + x_n ~ x_1 + x_2 + \ldots + x_n$ where $x_1$ through $x_n$ are additive covariates of the process of interest. Using two tildes in a single formula differs from standard R convention, but it is informative about the model being fit. The meaning of these covariates, or what they model, is full described in the help files for the individual functions and is not the same for all functions. For models with more than two processes (eg colext, gmultmix, pcountOpen), single right-hand sided formulas (only one tilde) are used to model each parameter. Utility Functions: unmarked contains several utility functions for organizing data into the form required by its model-fitting functions. csvToUMF converts an appropriately formated comma-separated values (.csv) file to a list containing the components required by model-fitting functions.

References

Dail, D. and L. Madsen. In press. Models for estimating abundance from repeated counts of an open metapopulation. Biometrics Kéry, M., Royle, J. A., and Schmid, H. 2005 Modeling avian abundance from replicated counts using binomial mixture models. Ecological Applications 15:1450--1461. MacKenzie, D. I., J. D. Nichols, G. B. Lachman, S. Droege, J. A. Royle, and C. A. Langtimm. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83: 2248--2255. MacKenzie, D. I., J. D. Nichols, J. E. Hines, M. G. Knutson, and A. B. Franklin. 2003. Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology 84:2200--2207. MacKenzie, D. I., J. D. Nichols, J. A. Royle, K. H. Pollock, L. L. Bailey, and J. E. Hines. 2006. Occupancy Estimation and Modeling. Amsterdam: Academic Press. Royle, J. A. 2004a. N-Mixture models for estimating population size from spatially replicated counts. Biometrics 60:108--105. Royle, J. A. 2004b. Generalized estimators of avian abundance from count survey data. Animal Biodiversity and Conservation 27:375--386. Royle, J. A., D. K. Dawson, and S. Bates. 2004. Modeling abundance effects in distance sampling. Ecology 85:1591--1597. Royle, J. A., and R. M. Dorazio. 2006. Hierarchical models of animal abundance and occurrence. Journal Of Agricultural Biological And Environmental Statistics 11:249--263. Royle, J. A. and R. M. Dorazio. 2008. Hierarchical Modeling and Inference in Ecology. Academic Press. Royle, J. A. and J. D. Nichols. 2003. Estimating Abundance from Repeated Presence-Absence Data or Point Counts. Ecology, 84:777--790.

Examples

## An example of site-occupancy analysis

# Simulate occupancy data
nSites <- 100
nReps <- 5
covariates <- data.frame(veght=rnorm(nSites),
    habitat=factor(c(rep('A', 50), rep('B', 50))))

psipars <- c(-1, 1, -1)
ppars <- c(1, -1, 0)
X <- model.matrix(~veght+habitat, covariates) # design matrix
psi <- plogis(X %*% psipars)
p <- plogis(X %*% ppars)

y <- matrix(NA, nSites, nReps)
Z <- rbinom(nSites, 1, psi)       # true occupancy state
for(i in 1:nSites) {
    y[i,] <- rbinom(nReps, 1, Z[i]*p[i])
    }

# Organize data and look at it
umf <- unmarkedFrameOccu(y = y, siteCovs = covariates)
head(umf)
summary(umf)


# Fit some models
fm1 <- occu(~1 ~1, umf)
fm2 <- occu(~veght+habitat ~veght+habitat, umf)
fm3 <- occu(~veght ~veght+habitat, umf)


# Model selection
fms <- fitList(m1=fm1, m2=fm2, m3=fm3)
modSel(fms)

# Model-averaged prediction and plots

# psi in each habitat type
newdata1 <- data.frame(habitat=c('A', 'B'), veght=0)
Epsi1 <- predict(fms, type="state", newdata=newdata1)
with(Epsi1, {
    plot(1:2, Predicted, xaxt="n", xlim=c(0.5, 2.5), ylim=c(0, 0.5),
        xlab="Habitat",
        ylab=expression(paste("Probability of occurrence (", psi, ")")),
        cex.lab=1.2,
        pch=16, cex=1.5)
    axis(1, 1:2, c('A', 'B'))
    arrows(1:2, Predicted-SE, 1:2, Predicted+SE, angle=90, code=3, length=0.05)
    })


# psi and p as functions of vegetation height
newdata2 <- data.frame(habitat=factor('A', levels=c('A','B')),
    veght=seq(-2, 2, length=50))
Epsi2 <- predict(fms, type="state", newdata=newdata2, appendData=TRUE)
Ep <- predict(fms, type="det", newdata=newdata2, appendData=TRUE)

par(mfrow=c(2, 1), mai=c(0.9, 0.8, 0.2, 0.2))
with(Epsi2, {
    plot(veght, Predicted, type="l", lwd=2, col="blue", ylim=c(0,1),
        xlab="Vegetation height (standardized)",
        ylab=expression(paste("Probability of occurrence (", psi, ")")))
    lines(veght, Predicted-SE, col="blue")
    lines(veght, Predicted+SE, col="blue")
    })
with(Ep, {
    plot(veght, Predicted, type="l", lwd=2, col="blue", ylim=c(0,1),
        xlab="Vegetation height (standardized)",
        ylab=expression(paste("Detection probability (", italic(p), ")")))
    lines(veght, Predicted-SE, col="blue")
    lines(veght, Predicted+SE, col="blue")
    })