Efficient Estimators of Abundance, for Fish and Plankton Surveys

Pennington, Michael

doi:10.2307/2530830

Cited by 292 publications

(179 citation statements)

References 3 publications

Supporting

Mentioning

169

Contrasting

Unclassified

Order By: Relevance

“…If we still have a large proportion of sampling units with zero changes, then we could try more precise estimators of the total change. Pennington (1983) describes such estimators, but these estimators cannot be used applied as simply to pps-samples.…”

Section: Discussionmentioning

confidence: 99%

Can we gain precision by sampling with probabilities proportional to size in surveying recent landscape changes in the Netherlands?

Brus

Nieuwenhuizen

Koomen

2006

Environ Monit Assess

View full text Add to dashboard Cite

Abstract. Seventy-two squares of 100 ha were selected by stratified random sampling with probabilities proportional to size (pps) to survey landscape changes in the period 1996-2003. The area of the plots times the urbanization pressure was used as a size measure. The central question of this study is whether the sampling with probabilities proportional to size leads to gain in precision compared to equal probability sampling. On average 1.03 isolated buildings per 100 ha have been built, while 0.90 buildings per 100 ha have been removed, leading to a net change of 0.13 building per 100 ha. The area with unspoiled natural relief has been reduced by 2.3 ha per 100 ha, and the length of linear relicts by 137 m per 100 ha. On average 74 m of linear green elements have been planted per 100 ha, while 106 m have been removed, leading to a net change of −31 m per 100 ha. For the state variables 'unspoiled natural relief', ' linear relicts', 'removed linear green elements', and 'new -removed linear green elements' there is a gain in precision due to the pps-sampling. For the remaining state variables there is no gain or even a loss of precision ('new buildings', 'removed buildings', 'new -removed buildings', 'new linear green elements'). Therefore, if many state variables must be monitored or when interest is not only in the change but also in the current totals, we recommend to keep things simple, and to select plots with equal probability.

show abstract

Section: Discussionmentioning

confidence: 99%

Can we gain precision by sampling with probabilities proportional to size in surveying recent landscape changes in the Netherlands?

Brus

Nieuwenhuizen

Koomen

2006

Environ Monit Assess

View full text Add to dashboard Cite

show abstract

“…For a global estimate of abundance, an estlmator based on the delta distribution is efficient (Pennington 1983). …”

Section: Discussionmentioning

confidence: 99%

Analysis of spatial variability of Georges Bank haddock (Melanogrammus aeglefinus) from trawl survey data using a linear regression model with spatial interaction

Polacheck

Vølstad

1993

ICES Journal of Marine Science

View full text Add to dashboard Cite

A regression model with spatially autocorrelated errors is used to test if there is systematic spatial variation across the study area in haddock catch per tow data from the NEFC annual fall survey from 1963 -1987. A maximum likelihood estimation procedure is used, and the likelihoodratio method is used for testing the significance of the model.The results suggest that for many of the years analyzed information on the depth and location of sampling stations can be used to explain part of the spatial variability of the trawl catches.If model-based inferences are made from the survey data, the depth related trend and the autocorrelation for the errors of the model should be taken into account.The potential use of a spatial prediction method for geographic mapping of density is discussed.

show abstract

“…The fact that a portion of the observed catches are zero, while the rest take positive values and are right-skewed, leads to the proposal of using the so-called ∆ distribution in estimating the abundance index. This distribution assigns a positive parameter for the probability of having zero catch, and often a lognormal distribution for the nonzero catches (Pennington, 1983;Smith, 1988). The mean parameter of the ∆ distribution is treated as the abundance index and is estimated through the maximum likelihood method.…”

Section: The Design-based and The Model-based Approachesmentioning

confidence: 99%

Estimation of Fish Abundance Indices Based on Scientific Research Trawl Surveys

Chen¹,

Thompson

2004

Biometrics

View full text Add to dashboard Cite

Summary. The fish abundance index over an ocean region is defined here to be the integral of expected catch per unit effort (CPUE), approximated by the sum of expected CPUE over grid squares. When trawl surveys are done within grid squares selected according to a probability sampling design, several other sources of variation such as the fish population dynamics and the catching process are also involved. In such situations model-assisted methods for estimating abundance, assessed under both design and model perspectives, have some advantages over purely design-based methods such as the Horvitz-Thompson (HT) estimator or purely model-based prediction approaches. This article develops model-assisted empirical likelihood (EL) methods via loglinear regression and nonparametric smoothing. The methods are applied to grid surveys of the Grand Bank region carried out annually by Fishery Products International from 1996 through 2002. The HT and EL methods produce similar point estimates of abundance indices. Simulation results, however, indicate that the EL estimator under local linear smoothing is associated with smaller standard errors.

show abstract

Efficient Estimators of Abundance, for Fish and Plankton Surveys

Cited by 292 publications

References 3 publications

Can we gain precision by sampling with probabilities proportional to size in surveying recent landscape changes in the Netherlands?

Can we gain precision by sampling with probabilities proportional to size in surveying recent landscape changes in the Netherlands?

Analysis of spatial variability of Georges Bank haddock (Melanogrammus aeglefinus) from trawl survey data using a linear regression model with spatial interaction

Estimation of Fish Abundance Indices Based on Scientific Research Trawl Surveys

Contact Info

Product

Resources

About