A Review of Linear Geostatistics for Fisheries Survey Design and Stock Assessment

Abstract

In the past ten years, fisheries scientists have shown great interest in geostatistical tools when analysing survey data of fish stocks for estimating population abundance. Fisheries scientists have first paid attention to the relation between a model based variance estimate and a covariance structure in their data as this enabled to reconsider survey design. The possibility to use the spatial covariance for optimizing sampling strategy has been a second motive for using geostatistics. Last, kriging has also raised interest as it provides a weighing of the data values which can be usefull when sample points are clustered.

Monitoring surveys at sea provide time series of population abundance indices and maps of distributions independently from commercial catches. Survey indices are used for calibration of dynamic population models. Second, series of yearly spatial distributions enable to undertake ecological studies, for example on habitat preferendum, species communities or density dependence of fish spatial distributions. Population abundance is generally estimated from one annual survey convering the entire area of distribution of the stock. In certain cases, the stock is estimated by the abundance of the eggs spawned in the plankton and this mehod may require several egg surveys during the spawning season. The estimation is then spatio-temporal.

The present review focuses on the use of geostatistics to estimate population abundance over a given area for the sampled realization (ie global estimation and its estimation variance). First, we consider the relation between variogram and variance. Second, different aspects of survey designs are discussed. A geostatistical software, EVA: Estimation Variance (Petitgas and Lafont 1997) enables to perform all computations discussed followingly.