Estimating the Occupancy of Spotted Owl Habitat Areas by Sampling and Adjusting for Bias


Book Description

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.




Estimating the Occupancy of Spotted Owl Habitat Areas by Sampling and Adjusting for Bias


Book Description

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.







Occupancy Estimation and Modeling


Book Description

Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition, provides a synthesis of model-based approaches for analyzing presence-absence data, allowing for imperfect detection. Beginning from the relatively simple case of estimating the proportion of area or sampling units occupied at the time of surveying, the authors describe a wide variety of extensions that have been developed since the early 2000s. This provides an improved insight about species and community ecology, including, detection heterogeneity; correlated detections; spatial autocorrelation; multiple states or classes of occupancy; changes in occupancy over time; species co-occurrence; community-level modeling, and more. Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition has been greatly expanded and detail is provided regarding the estimation methods and examples of their application are given. Important study design recommendations are also covered to give a well rounded view of modeling. - Provides authoritative insights into the latest in occupancy modeling - Examines the latest methods in analyzing detection/no detection data surveys - Addresses critical issues of imperfect detectability and its effects on species occurrence estimation - Discusses important study design considerations such as defining sample units, sample size determination and optimal effort allocation
















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