Seasonal variations in a population of the monogenean Ancyrocephalus mogurndae Gussev, 1955 were investigated on gills of cage-cultured mandarin fish, Siniperca chuatsi (Basilewsky), during the period from April 1994 to April 1995. The abundance of A. mogurndae peaked in late spring and summer. Prevalence was high (75-100 %) throughout the study period, and did not vary significantly between months. More than 50 % of all monogeneans were found on the first and second gill arches, except one occasion when the fourth gill arch had the majority in April 1995. The niche breadths were significantly correlated with the population abundance. A coexistent parasitic myxosporean, Henneguya weishanensis Hu, 1965, on the gills of the fish was found to have little influence on the gill-arch preference of the monogenean, although the monogenean abundance was higher in those fish infected with the myxosporean.
The process of manual species identification is a daunting task, so much so that the number of taxonomists is seen to be declining. In order to assist taxonomists, many methods and algorithms have been proposed to develop semi-automated and fully automated systems for species identification. While semi-automated tools would require manual intervention by a domain expert, fully automated tools are assumed to be not as reliable as manual or semi-automated identification tools. Hence, in this study we investigate the accuracy of fully automated and semi-automated models for species identification. We have built fully automated and semi-automated species classification models using the monogenean species image dataset. With respect to monogeneans’ morphology, they are differentiated based on the morphological characteristics of haptoral bars, an-chors, marginal hooks and reproductive organs (male and female copulatory organs). Landmarks (in the semi-automated model) and shape morphometric features (in the fully automated model) were extracted from four monogenean species images, which were then classified using k-nearest neighbour and artificial neural network. In semi-automated models, a classification accuracy of 96.67 % was obtained using the k-nearest neighbour and 97.5 % using the artificial neural network, whereas in fully automated models, a classification accuracy of 90 % was obtained using the k-nearest neighbour and 98.8 % using the artificial neural network. As for the cross-validation, semi-automated models performed at 91.2 %, whereas fully automated models performed slightly higher at 93.75 %. and Corresponding author: Sarinder Kaur A/p Kashmir Singh