Abstract
The identification of appropriate spatial distribution patterns for the observation, analysis and management of stocks with a persistent spatial structure, such as sea urchins, is a key issue in fish ecology and fisheries research. This paper describes the development and application of a geostatistical approach for determining the spatial distribution and resilience of the population of the sea urchin Paracentrotus lividus in a fishing ground of western Sardinia (western Mediterranean). A framework combining field data collection, experimental modelling and mapping was used to identify the best-fit semivariogram, taking pre-fishing and post-fishing times into consideration. Variographic analyses indicate autocorrelation of density at small distances, while the isotropic Gaussian and spherical models are suitable for describing the spatial structure of sea urchin populations. The point kriging technique highlights a generally patchy population distribution that tends to disappear during the fishing season. Kriging maps are also useful for calculating predictable stock abundances, and thus mortality rates, by class diameters within six months of fishing. We conclude that the framework proposed is adequate for biomass estimation and assessment of sea urchin resources. This framework can therefore be regarded as a useful tool for encouraging a science-based management of this fishery.
Highlights
This paper describes the development and application of a geostatistical approach for determining the spatial distribution and resilience of the population of the sea urchin Paracentrotus lividus in a fishing ground of western Sardinia
Variographic analyses indicate autocorrelation of density at small distances, while the isotropic Gaussian and spherical models are suitable for describing the spatial structure of sea urchin populations
We conclude that the framework proposed is adequate for biomass estimation and assessment of sea urchin resources
Summary
Over the past decade there has been increasing interest in modelling and measuring spatial patterns (e.g. gradients and patches) in biotic variables as a means of understanding the mechanisms that control critical aspects of the ecology of species, such as spatial distribution (Legendre and Legendre 1998).With the advancement of computer science, geostatistics has become a powerful tool for estimating the spatial distribution of marine populations (Conan 1985, Maynou 1998), for predicting stock abundances (Petitgas 1993, 2001, Megrey and Moksness 2009) and for assessing marine reserve benefits (Stelzenmüller et al 2007). Among human-related impacts, site accessibility during harvesting by diver fishermen significantly affects the structuring of sea urchin populations in a fishing ground in northern Sardinia (Ceccherelli et al 2011) Such studies, like other significant ones on sea urchin predation (Sala and Zabala 1996, Guidetti et al 2004), recruitment (Tomas et al 2004), migration (Palacín et al 1997, Crook et al 2000), competition (Guidetti 2004) and harvesting (Pais et al 2011), employ conventional approaches that assume spatial independence of a measured variable ( abundance indices), i.e. values at one location are independent of values at neighbouring locations. Geostatistical techniques are more powerful tools for estimating the spatial distribution of marine benthic communities than conventional statistical methods because they explicitly consider spatial correlation between observations (Warren 1998, Rueda 2001)
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