Literature DB >> 7981397

Detectability in conventional and adaptive sampling.

S K Thompson1, G A Seber.   

Abstract

In this paper a simple but very general method is given for estimating a population total with any sampling design when objects in sampled units are observed with imperfect detectability--a problem characteristic of many surveys of natural and human populations. In the most general case, the method consists of dividing the value of the variable of interest associated with each detected object by the detection probability for that object and then proceeding to use the estimation method that would ordinarily be used under the design if there were no detectability problems. Examples illustrating the method include simple random sampling, conventional unequal probability sampling, and adaptive cluster sampling.

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Year:  1994        PMID: 7981397

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  5 in total

1.  Modeling the relations between flow regime components, species traits, and spawning success of fishes in warmwater streams.

Authors:  Scott W Craven; James T Peterson; Mary C Freeman; Thomas J Kwak; Elise Irwin
Journal:  Environ Manage       Date:  2010-06-18       Impact factor: 3.266

2.  Effect of imperfect detectability on adaptive and conventional sampling: simulated sampling of freshwater mussels in the upper Mississippi River.

Authors:  David R Smith; Brian R Gray; Teresa J Newton; Doug Nichols
Journal:  Environ Monit Assess       Date:  2009-11-28       Impact factor: 2.513

3.  Modeling the effects of potential salinity shifts on the recovery of striped bass in the Savannah River estuary, Georgia-South Carolina, United States.

Authors:  Thomas R Reinert; James T Peterson
Journal:  Environ Manage       Date:  2008-05       Impact factor: 3.266

4.  Temporally adaptive acoustic sampling to maximize detection across a suite of focal wildlife species.

Authors:  Cathleen Balantic; Therese Donovan
Journal:  Ecol Evol       Date:  2019-08-22       Impact factor: 2.912

5.  A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis.

Authors:  Stephanie M Bilodeau; Austin W H Schwartz; Binfeng Xu; V Paúl Pauca; Miles R Silman
Journal:  PLoS One       Date:  2022-02-02       Impact factor: 3.240

  5 in total

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