Burrowing behaviour of signal crayfish, Pacifastacus leniusculus (Dana), in the River Great Ouse, England

Observations were made on crayfish burrows in five locations on the Great Ouse River. The burrow densities and the relative abundance of crayfish were observed. Also, laboratory experiments were carried out in order to study the characteristics and mechanisms of burrowing.

The home range of the signal crayfish in a British lowland river

The signal crayfish Pacifastacus leniusculus (Dana), a native of north-western North America, is now a common resident in some British fresh waters following its introduction to England in 1976 (Lowery & Holdich 1988). In 1984, signal crayfish were introduced into the River Great Ouse, the major lowland river in southern central England, where they have established a large breeding population. This study examines two sites near Thornborough Weir. For the measurement and description of home range a new eletronic microchip system and a modified capture-mark-recapture method were employed. Signal crayfish were marked or tagged to see if they gradually moved away from their burrows. This method proved to be successful for estimating population densities when a section of river is divided into several equidistant linear ”locations”.

The current distribution of signal and native crayfish in the Broadmead Brook, Wiltshire

Signal crayfish (Pacifastacus leniusculus) have existed in the upper reaches of Broadmead Brook in Wiltshire since 200 individuals were introduced at West Kington in 1981. The population has expanded upstream and downstream since this introduction, however, giving rise to concerns that it may potentially threaten the native crayfish population further downstream. Signal crayfish can act as a vector of crayfish plague - a disease caused by the fungus Aphanomyces astaci Schikora which results in almost complete mortality to the native, white-clawed crayfish Austropotamobius pallipes. The native crayfish in Broadmead Brook have not yet succumbed to crayfish plague and are currently free of the disease. However, as signal crayfish appear to out-compete the native species, the native population could still be under threat. In this article, we highlight the findings of previous crayfish surveys on Broadmead Brook and describe work undertaken in summer 2001 to map the current distribution of native and signal crayfish. Finally, options for controlling the spread of signal crayfish are discussed.

Estimating relative abundance from catch and effort data, using neural networks

We develop and test a method to estimate relative abundance from catch and effort data using neural networks. Most stock assessment models use time series of relative abundance as their major source of information on abundance levels. These time series of relative abundance are frequently derived from catch-per-unit-of-effort (CPUE) data, using general linearized models (GLMs). GLMs are used to attempt to remove variation in CPUE that is not related to the abundance of the population. However, GLMs are restricted in the types of relationships between the CPUE and the explanatory variables. An alternative approach is to use structural models based on scientific understanding to develop complex non-linear relationships between CPUE and the explanatory variables. Unfortunately, the scientific understanding required to develop these models may not be available. In contrast to structural models, neural networks uses the data to estimate the structure of the non-linear relationship between CPUE and the explanatory variables. Therefore neural networks may provide a better alternative when the structure of the relationship is uncertain. We use simulated data based on a habitat based-method to test the neural network approach and to compare it to the GLM approach. Cross validation and simulation tests show that the neural network performed better than nominal effort and the GLM approach. However, the improvement over GLMs is not substantial. We applied the neural network model to CPUE data for bigeye tuna (Thunnus obesus) in the Pacific Ocean.

Exploring the use of neural networks for biomass forecasts in the Peruvian upwelling ecosystem

A pilot study was conducted to study the ability of an artificial neural network to predict the biomass of Peruvian anchoveta Engraulis ringens, given time series of earlier biomasses, and of environmental parameters (ocenographic data and predator abundances). Acceptable predictions of three months or more appear feasible after thorough scrutiny of the input data set.