Geostatistics in fisheries survey design and stock assessment: models, variances and applications

Abstract

Over the past 10 years, fisheries scientists gradually adopted geostatistical tools when analysing fish stock survey data for estimating population abundance. First, the relation between model‐based variance estimates and covariance structure enabled estimation of survey precision for non‐random survey designs. The possibility of using spatial covariance for optimising sampling strategy has been a second motive for using geostatistics. Kriging also offers the advantage of weighting data values, which is useful when sample points are clustered. This paper discusses, with fisheries applications, the different geostatistical models that characterise spatial variation, and their variance formulae for many different survey designs. Some anticipated developments of geostatistics related to multivariate structures, temporal variability and adaptive sampling are discussed.

Small-scale non-linear geostatistical analysis of Liocarcinus depurator (Crustacea: Brachyura) abundance and size structure in a western Mediterranean population

“…Most applications of geostatistics to marine organisms and fisheries are focused on fish species and were recently reviewed by Petitgas (1993Petitgas ( , 2001. However, the application of geostatistics to crustacean populations is still limited (Freire et al 1991b, Simard et al 1992, Maynou et al 1996, Maynou 1998.…”

The spatial distribution of the subtidal benthic macrofauna and its relationship with environmental factors using geostatistical tools: a case study in Trapandé Bay, southern Brazil

“…Although it is most frequently applied to marine fisheries (PETITGAS 2001, MAYNOU et al 1998, SIMARD et al 1992, RUFINO et al 2004, it has also been used to assess the patterns of spatial distribution of the benthic macrofauna (BERGSTROM et al 2002, COLE et al 2001.…”

Análisis espacial de la concentración de metales en el camarón gris <i>Crangon crangon</i> (Linnaeus, 1758) en el Mar del Norte meridional

“…Only limited information on ecology and biomonitoring of marine invertebrates explicitly taking spatial autocorrelations into account is available (Jung et al, 2006). application of classical statistical procedures assumes stochastic independence of the data (Petitgas, 2001). Our results indicate at least weak spatial autocorrelations at the scale of investigation for ni and Pb and, to a lesser extent, for mbww and Cd, as can be inferred from increasing semivariograms (Fig.…”

“…sampling points randomly distributed over an area can yield unbiased estimates of the variable of interest only if the sampling-point observations are independent (Petitgas, 2001). When random sampling is carried out at an appropriate spatial scale, it effectively extinguishes any underlying spatial structure in the distribution of organisms.…”