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2023 | OriginalPaper | Chapter

3. Detectability

Authors : George A. F. Seber, Matthew R. Schofield

Published in: Estimating Presence and Abundance of Closed Populations

Publisher: Springer International Publishing

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Abstract

In much of the early research, little attention was paid to the possibility of incomplete detection, which is now built into most models. Because this involves further unknown parameters, further data is needed, and a further reduction in the number of parameters can be made using regression-type modeling with a link function and covariates. To aid in the theory development, a constant and known probability of detection is assumed to begin with, which then leads to considering a constant unknown probability of detection. Estimates and variance estimates are derived. The theory is extended to the case of constant but unknown detection probability and then to variable unknown detection probabilities. Further data can be obtained using time replicate counts or spatial replication, double sampling, and partial repeat sampling. In some populations, counts out to a certain distance are made from a point, referred to as “point” counts, involving circular plots. Resource selection is considered briefly, and other detection methods arising in later chapters are listed. Finally, some mention is made of the possibilities of using adaptive sampling for sparse and clustered populations.

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Metadata
Title
Detectability
Authors
George A. F. Seber
Matthew R. Schofield
Copyright Year
2023
DOI
https://doi.org/10.1007/978-3-031-39834-6_3

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