Estimating Equations for Removal Data Analysis

Wang, You‐Gan

doi:10.1111/j.0006-341x.1999.01263.x

Cited by 9 publications

(4 citation statements)

References 22 publications

(24 reference statements)

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“…There is also a range of methods for obtaining interval estimates for the population size (e.g., Schnute 1983;Harding et al 1984;Hirst 1994). When large numbers of passes are undertaken, it is possible to apply more realistic statistical models that allow for a systematic decline in capture probability between passes (Schnute 1983), random variation in capture probability between passes (Wang and Loneragan 1996;Wang 1999), and random variation in capture probability between individual fish (Lee and Chao 1994;Chao and Chang 1999).…”

Section: Introductionmentioning

confidence: 99%

Estimating riverine fish population size from single- and multiple-pass removal sampling using a hierarchical model

Wyatt¹

2002

Can. J. Fish. Aquat. Sci.

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A hierarchical model is described for estimating population size from single-and multiple-pass removal sampling. The model is appropriate for two-stage sampling schemes, typified by surveys of riverine fish populations, in which multiple sites are surveyed, but a low number of passes are undertaken at each site. The model estimates the average population size within the target area from the raw catch data, and thus allows for differences in the sampling procedure at each site, such as including single-pass sampling. The model also uses the data from all sites to estimate the population size at each individual site. This results in generally improved precision for multiple-pass sites and provides comparable estimates from single-pass sites. A Bayesian approach is described for estimating the parameters of the hierarchical model using sampling importance resampling (SIR). An empirical Bayesian approach, which ignores prior uncertainty but is simpler to implement, is also described. Application of the hierarchical model is illustrated with electrofishing data for 0+ trout (Salmo trutta) in the River Inny, U.K.Résumé : On trouvera ici un modèle hiérarchique qui permet d'estimer la taille d'une population à partir d'un échantil-lonnage par retraits uniques ou répétés. Le modèle est approprié à un plan d'échantillonnage à deux étapes, comme, par exemple, dans les inventaires de poissons de rivière où de nombreux sites sont inventoriés, mais avec un petit nombre d'échantillonages à chaque site. Le modèle estime la taille moyenne de la population dans la zone ciblée à partir des données brutes de capture et ainsi s'accommode de différences de stratégie d'échantillonnage d'un site à l'autre et peut, en particulier, inclure les données d'un échantillonnage unique. Le modèle utilise aussi les données recueillies à tous les sites pour estimer la taille de la population à chacun des sites. Il en résulte généralement une précision accrue pour les sites échantillonnés à plusieurs reprises et une précision comparable pour les sites échantillonnés une seule fois. Une approche bayésienne, décrite ici, permet d'estimer les paramètres du modèle hiérarchique à l'aide d'un échantillonnage d'importance et d'un rééchantillonnage (modélisation SIR). On trouvera aussi une approche empirique bayésienne qui ignore l'incertitude a priori et qui est plus simple à utiliser. L'étude de données obtenues par pêche électrique sur des truites (Salmo trutta) de l'Inny, au Royaume-Uni, illustre le genre d'application qu'on peut faire du modèle hiérarchique.[Traduit par la Rédaction] Wyatt 706

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Section: Introductionmentioning

confidence: 99%

Estimating riverine fish population size from single- and multiple-pass removal sampling using a hierarchical model

Wyatt¹

2002

Can. J. Fish. Aquat. Sci.

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“…Principal methods for analyzing removal data include regression of the catch against the cumulative catch (Leslie & Davis 1939; DeLury 1947; Ricker 1975), a multinomial model with maximum likelihood estimation (Carle & Strub 1978; Schnute 1983; Gould & Pollock 1997) and estimating functions/equations (e.g. Chao & Chang 1999; Wang 1999), using both frequentist and Bayesian approaches (e.g. Wyatt 2002).…”

Section: Methods and Resultsmentioning

confidence: 99%

Linking modelling, monitoring and management: an integrated approach to controlling overabundant wildlife

Chee

Wintle

2010

Journal of Applied Ecology

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Summary1. Overabundant wildlife can cause economic and ecological damage. Therefore population control typically seeks to maintain species' abundance within desired control limits. Efficient control requires targets, methods for estimating population size before and after control, and reliable means of forecasting population size. Demographic stochasticity, environmental variability and model uncertainty complicate these tasks. Monitoring provides critical feedback in the control process, yet examples of integrated monitoring and management are scarce. 2. We developed an integrated Bayesian population modelling and monitoring algorithm to assist with dynamic cull control of an overabundant population. We describe components of the control algorithm and their combination to produce a structured, sequential prescription for implementing control of a kangaroo population. We demonstrate its application within a single management year and evaluate its performance over a multi-year horizon under a range of scenarios reflecting uncertainties about population dynamics. 3. Simulation testing of the algorithm demonstrates that it provides a coherent, flexible, efficient and robust basis for managing population control. It is coherent in that connections between management objectives, models and operating rules are explicit and logically integrated. It is flexible in that the management objectives can be freely varied. It is both cost and operationally efficient because: (i) it avoids the need for an expensive, dedicated sampling process to estimate population size prior to culling; (ii) a relatively small number of culls produces reasonable population size estimates and (iii) the estimation by removal process enables direct assessment of whether control has been achieved. Lastly, it is robust because even when there is substantial uncertainty about system state and dynamics, the algorithm performs well at keeping the population under control over the duration of the management horizon. 4. Synthesis and applications. We provide a general and flexible framework for integrated monitoring and culling when the objective is to keep a species' abundance within control limits. Our framework explicitly deals with uncertainty arising from demographic stochasticity, ecological complexity and lack of knowledge, and provides the foundation for maximizing efficiency and costeffectiveness of control operations. Our approach could be applied in any instances where control is effected via culling.

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“…1 and estimating equations were used in Ref. 46. Sampling probabilities have also been modeled [47] as linear logistic functions of covariates (see Logistic regression) in addition to effort.…”

Section: Catch-effortmentioning

confidence: 99%

Abundance: Population Size and Density Estimation

Udevitz

Gould

2012

Encyclopedia of Environmetrics

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Estimates of population size (total number of individuals) or density (number of individuals per unit area) are some of the most basic requirements for wildlife research and management. This article provides a brief overview of approaches for wildlife population estimation. These include habitat‐based approaches such as quadrat, line intercept, distance, and repeated count methods, as well as animal‐based approaches such as capture–recapture and harvest methods. In contrast to methods that only index changes in these parameters, estimates of size or density must account for the probability that an individual will be included in the sample used for estimation. The essential differences in the approaches are in how the samples are obtained and how the sampling probabilities are estimated.

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Estimating Equations for Removal Data Analysis

Cited by 9 publications

References 22 publications

Estimating riverine fish population size from single- and multiple-pass removal sampling using a hierarchical model

Estimating riverine fish population size from single- and multiple-pass removal sampling using a hierarchical model

Linking modelling, monitoring and management: an integrated approach to controlling overabundant wildlife

Abundance: Population Size and Density Estimation

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