& Jacobson, 1979; Nichols et al., 2000) are viewed by some as highly time and effort

consuming.

 In many situations, presence-absence (more properly, detection-nondetection) data on

sampling units may more easily be obtained. Methods using such data have been developed

independently several times (Azuma, Baldwin & Noon, 1990; Bailey, 1952; Bayley & Peterson,

2001; Geissler & Fuller, 1987; MacKenzie, Nichols, Lachman, Droege, Royle & Langtimm,

2002; Nichols & Karanth, 2002) and appear to be useful for a variety of monitoring programs

(e.g. patch occupancy by spotted owls in western North America, area occupancy of tigers in

India, wetland occupancy by anurans throughout North America).

 Royle and Nichols (2003) have constructed a model by linking the probability of detecting

presence and the abundance at a sampling unit. By using repeated detection-nondetection data

gathered from occupancy surveys, they suggest a maximum likelihood approach at estimating

the parameters (that includes abundance). They also emphasize that likelihood-based inference is

not a small-sample procedure, and this should be considered in any study. In spite of the relative

ease with which presence-absence data may be gathered, achieving large samples for analysis as

suggested by Royle and Nichols (2003) for even practical estimates of the parameters might be

difficult.

 Bayesian approaches at parameter estimation have found themselves to be useful in a

variety of ecological applications (Dennis, 1996; Dixon & Ellison, 1996; Ellison, 1996; Hilborn

& Mangel, 1997) and have many strengths and limitations (Dennis, 1996; Ellison, 1996). Field

biologists often encounter logistic difficulties that curtail them to work with very low sample

sizes and yet have the need to use such information. Bayesian inferential procedures under

certain circumstances better makes use of such prior beliefs in parameter estimation.