Does the size of subsamples taken from multispecies trawl catches affect estimates of catch composition and abundance?

Although subsampling is a common method for describing the composition of large and diverse trawl catches, the accuracy of these techniques is often unknown. We determined the sampling errors generated from estimating the percentage of the total number of species recorded in catches, as well as the abundance of each species, at each increase in the proportion of the sorted catch. We completely partitioned twenty prawn trawl catches from tropical northern Australia into subsamples of about 10 kg each. All subsamples were then sorted, and species numbers recorded. Catch weights ranged from 71 to 445 kg, and the number of fish species in trawls ranged from 60 to 138, and invertebrate species from 18 to 63. Almost 70% of the species recorded in catches were “rare” in subsamples (less than one individual per 10 kg subsample or less than one in every 389 individuals). A matrix was used to show the increase in the total number of species that were recorded in each catch as the percentage of the sorted catch increased. Simulation modelling showed that sorting small subsamples (about 10% of catch weights) identified about 50% of the total number of species caught in a trawl. Larger subsamples (50% of catch weight on average) identified about 80% of the total species caught in a trawl. The accuracy of estimating the abundance of each species also increased with increasing subsample size. For the “rare” species, sampling error was around 80% after sorting 10% of catch weight and was just less than 50% after 40% of catch weight had been sorted. For the “abundant” species (five or more individuals per 10 kg subsample or five or more in every 389 individuals), sampling error was around 25% after sorting 10% of catch weight, but was reduced to around 10% after 40% of catch weight had been sorted.

Modelling the dynamics of phytoplankton with the needs of the end user in mind

How to regulate phytoplankton growth in water supply reservoirs has continued to occupy managers and strategists for some fifty years or so, now, and mathematical models have always featured in their design and operational constraints. In recent years, rather more sophisticated simulation models have begun to be available and these, ideally, purport to provide the manager with improved forecasting of plankton blooms, the likely species and the sort of decision support that might permit management choices to be selected with increased confidence. This account describes the adaptation and application of one such model, PROTECH (Phytoplankton RespOnses To Environmental CHange) to the problems of plankton growth in reservoirs. This article supposes no background knowledge of the main algal types; neither does it attempt to catalogue the problems that their abundance may cause in lakes and reservoirs.

A modelling approach to the development of an active management strategy for the Queen Elizabeth II reservoir

This article outlines the outcome of work that set out to provide one of the specified integral contributions to the overarching objectives of the EU- sponsored LIFE98 project described in this volume. Among others, these included a requirement to marry automatic monitoring and dynamic modelling approaches in the interests of securing better management of water quality in lakes and reservoirs. The particular task given to us was to devise the elements of an active management strategy for the Queen Elizabeth II Reservoir. This is one of the larger reservoirs supplying the population of the London area: after purification and disinfection, its water goes directly to the distribution network and to the consumers. The quality of the water in the reservoir is of primary concern, for the greater is the content of biogenic materials, including phytoplankton, then the more prolonged is the purification and the more expensive is the treatment. Whatever good that phytoplankton may do by way of oxygenation and oxidative purification, it is eventually relegated to an impurity that has to be removed from the final product. Indeed, it has been estimated that the cost of removing algae and microorganisms from water represents about one quarter of its price at the tap. In chemically fertile waters, such as those typifying the resources of the Thames Valley, there is thus a powerful and ongoing incentive to be able to minimise plankton growth in storage reservoirs. Indeed, the Thames Water company and its predecessor undertakings, have a long and impressive history of confronting and quantifying the fundamentals of phytoplankton growth in their reservoirs and of developing strategies for operation and design to combat them. The work to be described here follows in this tradition. However, the use of the model PROTECH-D to investigate present phytoplankton growth patterns in the Queen Elizabeth II Reservoir questioned the interpretation of some of the recent observations. On the other hand, it has reinforced the theories underpinning the original design of this and those Thames-Valley storage reservoirs constructed subsequently. The authors recount these experiences as an example of how simulation models can hone the theoretical base and its application to the practical problems of supplying water of good quality at economic cost, before the engineering is initiated.