Exploring gillnet catch efficiency of sardines in the coastal waters of Sri Lanka by means of three statistical techniques: a comparison of linear and nonlinear modelling techniques

Abstract

The present investigation was undertaken to study the gillnet catch efficiency of sardines in the coastal waters of Sri Lanka using commercial catch and effort data. Commercial catch and effort data of small mesh gillnet fishery were collected in five fisheries districts during the period May 1999–August 2002. Gillnet catch efficiency of sardines was investigated by developing catch rates predictive models using data on commercial fisheries and environmental variables. Three statistical techniques [multiple linear regression, generalized additive model and regression tree model (RTM)] were employed to predict the catch rates of trenched sardine Amblygaster sirm (key target species of small mesh gillnet fishery) and other sardines (Sardinella longiceps, S. gibbosa, S. albella and S. sindensis). The data collection programme was conducted for another six months and the models were tested on new data. RTMs were found to be the strongest in terms of reliability and accuracy of the predictions. The two operational characteristics used here for model formulation (i.e. depth of fishing and number of gillnet pieces used per fishing operation) were more useful as predictor variables than the environmental variables. The study revealed a rapid tendency of increasing the catch rates of A. sirm with increased sea depth up to around 32 m.

Keywords:

• fisheries
• modelling
• multiple linear regression
• generalized additive models
• regression tree models

Acknowledgements

We are grateful to Dr Aruna Jayawardane and Mr Tinil Fernando of the National Aquatic Resources Research and Development Agency (NARA) of Sri Lanka for their invaluable support in completing this study successfully. The first author would like to thank the Asian Development Bank for granting a scholarship to follow a PhD programme at Ecole Nationale Supérieure Agronomique de Toulouse, France. We also thank the Meteorological Department of Sri Lanka for providing rainfall data and NASA for granting the permission to download the sea surface temperature data for this study.