A non-destructive morphometric technique to predict Ligula intestinalis L. plerocercoid load in roach (Rutilus rutilus L.) abdominal cavity

The aim of the present work was to propose a model for the estimation of the endoparasitic load using morphological descriptors easily accessible without killing the animal i.e. non-destructive method. The study was conducted using plerocercoid forms of Ligula intestinalis in its second intermediate host, the roach (Rutilus rutilus). The Kohonen Self-Organizing Map (non-supervised neural network) made it possible to present the complex data matrix in a two-dimensional space, with individual clusters visualised by the U-matrix method. The six main descriptors were selected and used to build the predictive model, four lateral and two thickness measures. The generalisation ability of the backpropagation algorithm (supervised neural network) is confirmed by a determination coefficient higher than 0.90 between observed and predicted values. The study for the first partial derivatives of the parasitic load with respect to the six morphological variables is used to identify the factors influencing the parasitic load and the mode of action of each factor.