Estimating the occupancy of spotted owl habitat areas by sampling and adjusting for bias

Abstract

A basic sampling scheme is proposed to estimate the proportion of sampled units (Spotted Owl Habitat Areas (SOHAs) or randomly sampled 1000-acre polygon areas (RSAs)) occupied by spotted owl pairs. A bias adjustment for the possibility of missing a pair given its presence on a SOHA or RSA is suggested. The sampling scheme is based on a fixed number of visits to a sample unit (a SOHA or RSA) in which the occupancy is to be determined. Once occupancy is determined, or the maximum number of visits is reached, the sampling is completed for that unit. The resulting data are summarized as a set of independent Bernoulli trials; a zero (no occupancy) or one (occupancy) is recorded for each unit. The occupancy proportion is the sum of these Bernoulli trials divided by the sample size. The bias adjustment estimates this occupancy proportion for the estimated number of units on which a pair of owls was present but not detected. The bias adjustment requires the recording of the number of the visit during which occupancy was first detected. The distributional assumptions are checked with five different sets of data.

Sustainability Assessment for Fishing Effects (SAFE): A new quantitative ecological risk assessment method and its application to elasmobranch bycatch in an Australian trawl fishery

“…Estimating the proportion of a geographical area occupied by a particular species from such data has been considered useful in long-term monitoring programs and metapopulation studies (Azuma et al, 1990;MacKenzie et al, 2004). A particular concern of using detection-nondetection data is the presence of false-negative (or false-absence) errors.…”

WAS IT THERE? DEALING WITH IMPERFECT DETECTION FOR SPECIES PRESENCE/ABSENCE DATA†

“…There have been a number of different approaches to the problem of estimating the fraction of sites occupied by a species that is imperfectly detected (Giessler & Fuller, 1987;Azuma, Baldwin & Noon, 1990;MacKenzie et al, 2002;Tyre et al, 2003), although here the method of MacKenzie et al (2002) is reviewed as it allows for the simultaneous estimation of occupancy and detectability, and associated variances and covariances. The independently developed methods of Tyre et al (2003) are closely related, but not as flexible.…”

Precision and Bias of Methods for Estimating Point Survey Detection Probabilities

“…Methods for estimating detection rates for abundance counts provide a means for adjusting counts to estimate population density. The use of presence/absence data in monitoring and habitat studies has increased rapidly in the past 10 years (e.g., Azuma et al 1990, Pereira and Itami 1991, Buckland and Elston 1993, Wenjun Li et al 1997, NSW NPWS 2000, Fleishman et al 2001, MacKenzie et al 2002. However, less attention has been given to the estimation of detection rates (or false negative observation rates) in presence/ absence point surveys goal of the survey is to ascertain whether survey locations are occupied by a given species and to estimate the overall proportion of sites that are occupied across a large region.…”

“…A. Wintle, M. A. Burgman, and R. P. Kavanagh, unpublished manuscript), their effect on survey design and data analysis (Stauffer et al 2002), habitat model performance (Dettmers et al 1999, Tyre et al 2003, population monitoring (Azuma et al 1990, Kery 2002, MacKenzie et al 2002, species richness estimates (Boulinier et al 1998), metapopulation modeling (Moilanen 2002), and population viability analysis (Goldwasser et al 2000). A. Wintle, M. A. Burgman, and R. P. Kavanagh, unpublished manuscript), their effect on survey design and data analysis (Stauffer et al 2002), habitat model performance (Dettmers et al 1999, Tyre et al 2003, population monitoring (Azuma et al 1990, Kery 2002, MacKenzie et al 2002, species richness estimates (Boulinier et al 1998), metapopulation modeling (Moilanen 2002), and population viability analysis (Goldwasser et al 2000).…”