unmarked-package: Models for Data from Unmarked Animals

Description

Estimate wildlife abundance or occurrence.

Arguments

Details

ll{ Package: unmarked Type: Package Version: 0.8-5 License: GPL (>= 2) }

Unmarked estimates wildlife parameters for many popular sampling methods including occurrence and point count data.

Overview of Model-fitting Functions: Unmarked provides several functions for fitting integrated likelihood models for wildlife abundance and occurrence to replicated survey data. occu fits occurrence models with no linkage between abundance and detection (MacKenzie et al. 2006). occuRN fits abundance models to presence/absence data by exploiting the link between detection probability and abundance (Royle and Nichols 2003). pcount fits N-mixture models for repeated count data (Royle 2004, Kéry et al 2005). distsamp fits the distsance sampling model of Royle et al. (2004) to distance data recorded in discrete intervals. All of these functions allow the user to specify covariates that affect the detection process and several also allow covariates for the state process.

Data: All data is 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: Most of unmarked's model-fitting functions allow specification of covariates for both the state process and the detection process. 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 $~ x1 + x2 + ... + x3 ~ x_1 + x_2 + \ldots + x_n$ where $x_1$ through $x_n$ are additive covariates of the process of interest. 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.

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

MacKenzie, D. I. et al. (2006) Occupancy Estimation and Modeling. Amsterdam: Academic Press.

Royle, J. A. and Nichols, J. D. (2003) Estimating Abundance from Repeated Presence-Absence Data or Point Counts. Ecology, 84(3) pp. 777--790.

Royle, J. A. (2004) N-Mixture Models for Estimating Population Size from Spatially Replicated Counts. Biometrics 60, pp. 108--105.

Royle, J. A., D. K. Dawson, and S. Bates (2004) Modeling abundance effects in distance sampling. Ecology 85, pp. 1591-1597.

Kéry, M., Royle, J. A., and Schmid, H. (2005) Modeling Avaian Abundance from Replicated Counts Using Binomial Mixture Models. Ecological Applications 15(4), pp. 1450--1461.

Royle, J. A. and Link W. A. (2005) A general class of multinomial mixture models for anuran calling survey data. Ecology, 86(9), pp. 2505--2512.