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A two-dimensional geostatistic method to simulate the precision of abundance estimates

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Abstract:

In this paper, we outline a geostatistic method to simulate the relative precision (coefficient of variation, CV) of total abundance estimates of one species in a predetermined, stratified area when it is appropriate to treat the observations within each stratum as realizations of a second-order homogenous and ergodic random process. To model the spatial correlations, a variogram is fitted to normal-transformed values of the original observations. Based on the variogram and its corresponding covariance matrix, extensive simulations on a fine grid that includes the sample locations provide random realizations of the process. The normal values are back-transformed to original observation space by nonparametric reversed bootstrap, as well as by a parametric Weibull approach. The method is applied to a total of 1069 shrimp (Pandalus borealis) abundance observations from 11 annual surveys in the Barents Sea (1992–2002) where a 20 nautical mile sampling grid has been applied. On average, the CV was estimated to be 6.4% for the applied regular grid when the simulations were conditional on the observations, compared with 8.1% when the sampling locations within each of the six strata were random.

On trouvera ici la description d'une méthode géostatistique pour simuler la précision relative (coefficient de variation, CV) des estimations d'abondance totale d'une espèce dans une aire prédéterminée et stratifiée, lorsqu'il est approprié de traiter les observations dans chacune des strates comme les résultats d'un processus aléatoire de second degré homogène et ergodique. Afin de modéliser les corrélations spatiales, nous ajustons un variogramme aux valeurs normalisées des observations originales. Des simulations répétées, basées sur le variogramme et sa matrice de covariance correspondante et faites sur une grille fine qui comprend les stations d'échantillonnage, fournissent des résultats aléatoires du processus. Les valeurs normalisées sont retransformées dans l'espace original d'observation au moyen d'un bootstrap non paramétrique inversé, ainsi qu'au moyen d'une approche paramétrique de Weibull. La méthode est utilisée sur un total de 1069 observations d'abondance de crevettes (Pandalus borealis) provenant de 11 inventaires dans la mer de Barents (1992–2002) qui emploient une grille d'échantillonnage de 20 milles marins. En moyenne, le CV est estimé à 6,4 % pour la grille utilisée lorsque les simulations sont dépendantes des observations, alors qu'il est de 8,1 % lorsque les sites d'échantillonnage dans chacune des six strates sont aléatoires.[Traduit par la Rédaction]

Document Type: Research Article

Publication date: December 1, 2003

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  • Published continuously since 1901 (under various titles), this monthly journal is the primary publishing vehicle for the multidisciplinary field of aquatic sciences. It publishes perspectives (syntheses, critiques, and re-evaluations), discussions (comments and replies), articles, and rapid communications, relating to current research on cells, organisms, populations, ecosystems, or processes that affect aquatic systems. The journal seeks to amplify, modify, question, or redirect accumulated knowledge in the field of fisheries and aquatic science. Occasional supplements are dedicated to single topics or to proceedings of international symposia.
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