The Estimation of Biological Populations

Chapman, Douglas G.

doi:10.1214/aoms/1177728844

Cited by 114 publications

(58 citation statements)

References 17 publications

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“…In isotope dilution, known amount of labeled trout would be added to the pond and then the ratio of labeled-to-unlabeled measured from a representative sample as illustrated in Scheme 1. While this paradigm is not entirely accurate when coupled with probabilistic sampling theory (it gives slightly biased results for small populations) [2], nevertheless it demonstrates the exact working principle of isotope dilution.…”

Section: History Of Isotope Dilutionmentioning

confidence: 99%

Paradigms in isotope dilution mass spectrometry for elemental speciation analysis

Meija

Mester

2008

Analytica Chimica Acta

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Section: History Of Isotope Dilutionmentioning

confidence: 99%

Paradigms in isotope dilution mass spectrometry for elemental speciation analysis

Meija

Mester

2008

Analytica Chimica Acta

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“…The frrst CIR estimators were given by Kelker (1940), who proposed the method for estimating the size of deer, elk, and pheasant populations based on changes in sex ratios due to a single harvest Chapman (1954) developed the frrst statistical model for Kelker's method. This model was then extended to allow multiple sampling periods with multiple intervening removal periods (Chapman 1955).…”

Section: Historical Developmentmentioning

confidence: 99%

“…Under certain assumptions about the encounter probabilities, no information is lost by conditioning on the sample size. This gives the product multinomial likelihoods of Chapman (1954Chapman ( , 1955 and Otis (1980). H sample sizes are ftxed so that sampling efforts are random, then the likelihood is a constant multiple of that for the ftxed effort case (Udevitz 1989).…”

Section: Sampling With Replacement Modelsmentioning

confidence: 99%

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Change-In-Ratio Methods for Estimating Population Size

Udevitz

Pollock

1992

Wildlife 2001: Populations

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Abstract. Change-in-ratio (CIR) methods can provide an effective, low cost approach for estimating the size of wildlife populations. They rely on being able to observe changes in proportions of population subclasses that result from the removal of a known number of individuals from the population. These methods were fIrst introduced in the 1940's to estimate the size of populations with 2 subclasses under the assumption of equal subclass encounter probabilities. Over the next 40 years, closed population CIR models were developed to consider additional subclasses and use additional sampling periods. Models with assumptions about how encounter probabilities vary over time, rather than between subclasses, also received some attention. Recently, all of these eIR models have been shown to be special cases of a more general model. Under the general model, information from additional samples can be used to test assumptions about the encounter probabilities and to provide estimates of subclass sizes under relaxations of these assumptions. These developments have greatly extended the applicability of the methods. CIR methods are attractive because they do not require the marking of individuals, and subclass proportions often can be estimated with relatively simple sampling procedures. However, CIR methods require a carefully monitored removal of individuals from the population, and the estimates will be of poor quality unless the removals induce substantial changes in subclass proportions. In this paper, we review the state of the art for closed population estimation with CIR methods. Our emphasis is on the assumptions of CIR methods and on identifying situations where these methods are likely to be effective. We also identify some important areas for future CIR research.

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“…Alguns pesquisadores tais como Chapman (1954), Darroch (1958Darroch ( , 1959, Seber (1965Seber ( , 1982Seber ( , 1986), Leite et. al.…”

Section: Introductionunclassified

O Uso de Prioris Não Informativas para Estimação do Tamanho Populacional

Zacharias¹,

Leite²,

Diniz³

2004

TEMA - Tendências Em Matemática Aplicada E Computacional

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Resumo. Neste trabalho, apresentamos um modelo Bayesiano de captura-recaptura onde consideramos prioris não informativas para os parâmetros populacionais. Determinamos condições para que as respectivas distribuições a posteriori dos parâmetros existam e apresentamos os resultados originais referentes a existência dessas posterioris. Obtivemos, através das expressões exatas das estimativas, as características a posteriori do tamanho populacional, N . IntroduçãoO método de captura-recaptura consiste, inicialmente, da seleção aleatória (ou não) de uma amostra de uma população, marcação de todos os seus indivíduos e sua devolução (ou uma parte deles)à população. Em seguida, são selecionados, em cada uma das k amostras(k ≥ 2), um número fixo ou aleatório de indivíduos e aqueles que não foram capturados na(s) amostra(s) anterior(es) recebem uma marca, antes de todos (ou uma parte deles) serem devolvidosà população. O objetivoé, então, estimar o tamanho populacional, N .Uma dificuldade que envolve o uso da inferência clássica na estimação do tamanho de uma populacãoé o fato de que a estimativa de máxima verossimilhança de Né infinita quando todas as capturas são de animais distintos ([8] e [16]). Uma forma de contornar este problemaé usar modelos Bayesianos que produzem estimativas finitas para N .

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The Estimation of Biological Populations

Cited by 114 publications

References 17 publications

Paradigms in isotope dilution mass spectrometry for elemental speciation analysis

Paradigms in isotope dilution mass spectrometry for elemental speciation analysis

Change-In-Ratio Methods for Estimating Population Size

O Uso de Prioris Não Informativas para Estimação do Tamanho Populacional

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