The Minimum Capture Proportion for Reliable Estimation in Capture–Recapture Models

Li, Xi; Watson, Ray; Yip, Paul S. F.

doi:10.1111/j.1541-0420.2007.00876.x

Cited by 7 publications

(10 citation statements)

References 13 publications

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“…with the mean detectability and the level of replication (Xi, Watson & Yip 2008). Our simulations point out that for very low detection probabilities (i.e.…”

Section: E S T I M a T O R S O F N A C C O U N T I N G F O R Pmentioning

confidence: 73%

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When can we ignore the problem of imperfect detection in comparative studies?

Archaux¹,

Henry²,

Giménez

2011

Methods Ecol Evol

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Summary1. Numbers of individuals or species are often recorded to test for variations in abundance or richness between treatments, habitat types, ecosystem management types, experimental treatments, time periods, etc. However, a difference in mean detectability among treatments is likely to lead to the erroneous conclusion that mean abundance differs among treatments. No guidelines exist to determine the maximum acceptable difference in detectability. 2. In this study, we simulated count data with imperfect detectability for two treatments with identical mean abundance (N) and number of plots (n plots ) but different mean detectability (p). We then estimated the risk of erroneously concluding that N differed between treatments because the difference in p was ignored. The magnitude of the risk depended on p, N and n plots . 3. Our simulations showed that even small differences in p can dramatically increase this risk. A detectability difference as small as 4-8% can lead to a 50-90% risk of erroneously concluding that a significant difference in N exists among treatments with identical N = 50 and n plots = 50. Yet, differences in p of this magnitude among treatments or along gradients are commonplace in ecological studies. 4. Fortunately, simple methods of accounting for imperfect detectability prove effective at removing detectability difference between treatments. 5. Considering the high sensitivity of statistical tests to detectability differences among treatments, we conclude that accounting for detectability by setting up a replicated design, applied to at least part of the design scheme and analysing data with appropriate statistical tools, is always worthwhile when comparing count data (abundance, richness).

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“…with the mean detectability and the level of replication (Xi, Watson & Yip 2008). Our simulations point out that for very low detection probabilities (i.e.…”

Section: E S T I M a T O R S O F N A C C O U N T I N G F O R Pmentioning

confidence: 73%

“…Burnham & Overton 1979;Chao 1987;Hellmann & Fowler 1999;Walther & Moore 2005;Xi, Watson & Yip 2008). Indeed, information about actual differences in N may be more reliable when detectability is low but varies little among treatments than it is when detectability is globally higher but varies more among treatments.…”

Section: Introductionmentioning

confidence: 99%

When can we ignore the problem of imperfect detection in comparative studies?

Archaux¹,

Henry²,

Giménez

2011

Methods Ecol Evol

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“…The only exception to this rule is given by methods in Lloyd and Chaiyapong (), which is restricted to model

M_{b}

, and Xi et al. (). The paper by Xi et al.…”

Section: Introductionmentioning

confidence: 99%

“…The paper by Xi et al. () covers

M_{t}, M_{b},

and

M_{h}

, but is devoted mostly to planning the sampling fraction

n / N

, which can not be fixed before the experiment. In fact, in capture‐recapture studies the sample size n is random, and can not be fixed by design.…”

Section: Introductionmentioning

confidence: 99%

On the design of closed recapture experiments

Fegatelli

Farcomeni

2016

Biometrical J

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We propose a method to plan the number of occasions of recapture experiments for population size estimation. We do so by fixing the smallest number of capture occasions so that the expected length of the profile confidence interval is less than or equal to a fixed threshold. In some cases, we solve the optimization problem in closed form. For more complex models we use numerical optimization. We detail models assuming homogeneous, time-varying, subject-specific capture probabilities, behavioral response to capture, and combining behavioral response with subject-specific effects. The principle we propose can be extended to plan any other model specification. We formally show the validity of the approach by proving distributional convergence. We illustrate with simulations and challenging examples in epidemiology and ecology. We report that in many cases adding as few as two sampling occasions may substantially reduce the length of confidence intervals.

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“…Recent studies have focused on how to deal with some of the problems encountered when using the capture–recapture method to estimate the size of a population. Such problems include density dependence, sample independence, population movement, population definition and sample heterogeneity (Bloor, 2005; Pledger, 2005; Xu et al, 2007; Bartolucci and Lupparelli, 2008; Böhning, 2008; Malec and Maples, 2008; Schofield and Barker, 2008; Xi et al, 2008). Cadwell et al (2005) propose a procedure to deal with cases where the number of individuals appearing in both lists of the capture–recapture data lack personal identifiers.…”

Section: Introductionmentioning

confidence: 99%

A Capture–Recapture Approach to Estimation of Refugee Populations

Gold

Wibert

Bondartsova

et al. 2012

International Migration

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The debate over immigration continues to be one of the most politically charged policy issues in the United States (US). Given the charged nature of this topic, it is vitally important to have reliable data on not only the number of US foreign nationals but also the characteristics of this extremely heterogeneous group – a population comprised not only of immigrants but also of refugees. There exist a small number of data sources for informing policy and practice at the national level. However, such data are often lacking for smaller geographical areas. This paper describes a recent effort to generate serviceable data on the immigrant and refugee population for a medium‐sized metropolitan area in the US. The objectives of this research were twofold. Our first goal was to provide local stakeholders with information to assist them with resettling and obtaining funding for immigrants and refugees. The second aim was to develop better techniques for tabulating diverse refugees and immigrants in a medium‐sized community. By comparing and contrasting three data sources – that is, refugee services, public schools and a local health plan – we are able to generate estimates of the local refugee and immigrant population. During the period from 2005 to 2007, we estimate the total number of immigrants and refugees in the community to be somewhere between 10,938 and 13,282. Although perhaps a bit on the high end due to methodological assumptions, these estimates seem plausible, based on previously cited figures for the region. While such estimates are valuable, a number of shortcomings related to the data prevent us from painting a more complete picture of these populations. We conclude this paper with a number of recommendations that will assist others in planning research designed to inform migration policy and practice in medium‐sized metropolitan areas.

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The Minimum Capture Proportion for Reliable Estimation in Capture–Recapture Models

Cited by 7 publications

References 13 publications

When can we ignore the problem of imperfect detection in comparative studies?

When can we ignore the problem of imperfect detection in comparative studies?

On the design of closed recapture experiments

A Capture–Recapture Approach to Estimation of Refugee Populations

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