ABSTRACT

Present study was carried out to identify six important species (Nemipterus japonicus, N. randalli, N. bipunctatus, Scolopsis vosmeri, S. bimaculatus and Parascolopsis aspinosa) under the family Nemipteridae based on their morphometric characters. A total of 360 samples were collected randomly from east and west coast of India. Twenty one morphometric distances, measured from each individual, were subjected to factor analysis where body depth and fin base measurements explained 53% variations while head related measurements explained 20% of the variations among the six species. Artificial neural network analysis and discriminant function analysis was able to classify, 87.8% and 93.3% of the samples, correctly to the respective species. Results underlined the utility of the morphometric characters as a basis of fish identification when there is an overlap of characters among the species.

Keywords: Nemipteridae; Species; Morphometric Character; Factor Analysis; Discriminant Function Analysis

Introduction

Many authors have described external morphology as an integral part of ichthyology of many fishes Hubbs and Lagler [1- 5]. External features are used in species differentiation ever since ichthyology can be traced back Strauss and Bond [6]. Morphometry is reliable in case of aged specimen also because long term preservation leads to loss of weight but limited effects on length measurements Shields and Carlson [7]. Nemipterids are one of the most economically important fish groups of tropical Indo- West pacific and taxonomically difficult group to distinguish them Russell [8]. Nemipterids of Indian waters are limited to three genera namely Nemipterus, Scolopsis & Parascolopsis Barman and Mishra [9]. Fishes from family Nemipteriidae contribute significantly towards Indian marine fisheries, with a share of 5.3% towards total marine fish production and 18.9% of demersal fish production CMFRI [10]. Nevertheless, in view of importance of nemiptreids the present communication is an attempt to distinguish the species of family Nemiteridae from Indian waters.

Material and Methods

Sampling

Samples of six species of the family Nemipteridae (Nemipterus japonicus, N. randalli, N. bipunctatus, Scolopsis vosmeri, and Parascolopsis aspinosa) were collected from Versova fish landing centre, Mumbai and also from the trawl catch of research vessel ‘MFV Narmada’ of Central Institute of Fisheries Education. Specimens of S. bimaculatus were collected from Pamban landing centre of Mandapam, Tamil Nadu. A total of 360 fish samples weight were collected randomly between October 2010 and December 2010 (Table 1).

Morphometric Measurements

Digital images can be used for measuring the morphometric variables along the surface of the fish after transforming them into readable formats on computer Sajina, et al. [11]. Hence the image of each fish specimen was first captured using a cyber-shot DCS-S500 digital camera (Sony, Japan). Twenty one morphometric distances were measured along the entire body surface on left side of the fish i.e., head, trunk and tail. These distances were measured using the software “tpsdig2 V2.17” Rohlf [12].

Transformation for Removing Size Dependent Effects

The size dependent variation in the whole data may discriminate the population based on size Humphries, et al. [13]. Significant correlations were observed between body size and the morphometric variables. Therefore, the absolute morphometric variables were transformed into size independent shape variables. The distances measured were first tested for outliers and they were removed based on Cook’s distance estimates using PROC ROBUSTREG procedure of SAS, as they may distort the general tendency in the size distribution SAS Institute [14]. Further the size dependent effects were removed using an allometric approach by Reist [15]. Data were transformed using the formula:

Where,

Y’ij : Transformed morphometric measurement

Yij : Original morphometric measurement

SLi : Standard length of the fish

: Overall mean standard length of the fish

bj : Regression coefficient of the log Yij on loglogSLi.

Multivariate Analysis

The multinomial test, Mardia’s test was used to check whether the data follows a multivariate normal distribution Cox and Small [16]. This was done by using PROC MODEL procedure on the transformed morphometric measurements in SAS SAS Institute [14]. Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of potentially lower number of unobserved variables named factors. For example, it is possible that variations in four observed variables can reflect the variations in two unobserved variables. Factor analysis searches for such joint variations in response to unobserved latent variables. Observed variables are modeled as linear combinations of the potential factors, plus “error” terms. The information gained about the interdependencies between observed variables can be used later to reduce the set of variables in a dataset. Computationally this technique is equivalent to low rank approximation of the matrix of observed variables.

In the present study, twenty one morphometric measurements were subjected to factor analysis by using PROC FACTOR procedure of SAS (SAS Institute, 2010), to test whether morphometric characters are effective in discriminating the species. In the factor analysis, the transformed morphometric distances which loaded high on the first and second factors were identified. Variables loaded on different factors were selected based on the Hatcher’s scratching procedure Hatcher [17], and Artificial Neural Network (ANN) analysis was employed by NEURAL MODEL option in JMP 8 (SAS Institute, 2010) to find out the accuracy of classifications of observations among different species on the basis of the first two factor scores. ANN is a nonlinear mathematical structure capable of representing complex nonlinear process that relates the inputs to the outputs of a system Pulido - Calvo and Portela [18]. The structure of a neural net consists of connected units referred to as “nodes” or “neurons”. Each neuron performs a portion of the computations inside the net. A neuron takes some numbers as inputs, performs a relatively simple computation on these inputs, and returns as an output. Output value of a neuron is passed on as one of the inputs for another neuron, except for neurons that generate the final output values of the entire system.

In the present study, we used a single layered feed forward network (SLFN) of 3 hidden nodes with the following specifications (Figure 1).

1. Input layer: the scores of first and second factors

2. Hidden layer: 3 hidden nodes; H1, H2 and H3

3. Output: species category

4. Over all penalty =0.01

5. Number of tours = 4

6. Iterations = 500

7. Training data set = 65% of the observations

8. Cross validation = Random Hold back

For a set of observations containing more than one quantitative variables and a classification variable defining groups of observations, the discriminant function analysis can develop a discriminant criterion to classify each specimen into one of the groups which can be locations or coasts based on the context of analysis Rao [19].

Results

The factor analysis revealed that there is a significant morphometric variation between the species. The variables with high loadings on the first two factors were found useful in distinguishing these species.

Differentiation of Different Species Under Nemipteridae Collected Along Indian Coasts

The plot between first and second factor scores indicates the morphometric discrimination of different species (Figure 2). The measurements loaded on the first factor were associated to vertical body depth and fin base measurements. The distances; body depth, dorsal fin base length, pectoral fin base length, caudal fin base length, caudal peduncle depth and second anal fin length were found to have significant loadings on the first factor which explained 53% of the total variation. The second factor represented the measurements associated with the head like eye diameter, head length, pre pelvic fin length and pre pectoral fin length. The analysis revealed a larger dimension of body depth and fin base measurements in S. vosmeri and a larger proportion of head measurements in P. aspinosa.

Classification by Discriminant Function Analysis

The first and second factor scores obtained in the factor analysis were taken for classifying samples among different species. Species wise discriminant function analysis has given 87.78% of accuracy in classifying the observations to various species (Table 1). The results of analysis indicate clear discrimination of the samples among different species.

Classification by Artificial Neural Network

The first two factor scores were taken for classifying samples among different species. Species wise neural network analysis of one hidden layer with three hidden nodes was carried out with the selected variables as the input and species category as the output. As a training data, 65% of the observations were fed to the network and the remaining data set was kept for validation. The results of this analysis have clearly discriminated the samples among different species with 93.3% of accuracy in classifying the observations to various species (Table 2).

Discussion

The methods employed (factor analysis, discriminant function analysis and artificial neural network) have effectively proved that morphometrics alone is accountable for discriminating fish species. Body depth and fin measurements are loaded on first factor shows swimming is attributed in the variation primarily, whereas second factor loaded with head length and head associated measurements (eye diameter, pre pelvic fin length, pre pectoral fin length). Genus Scolopsis and Parascolopsis were notably varied based on the body depth and head measurements, respectively, as they were slow swimming and exclusively inhabit coral reefs Aleyev [20,21]. N. japonicas, N. bipunctatus and N. randalli being fast swimmers, has thin caudal peduncle depth compared to Scolopsis and Parascolopsis because thin caudal peduncle aids in fast swimming Assumpcao, et al. [22]. Fishes belonging to the genus Scolopsis and Parascolopsis were having deep body than fishes of genus Nemipterus. Being a benthic species of inshore waters inhabiting usually on sand or mud bottoms close to reefs, S. vosmeri, S. bimaculatus and Parascolopsis aspinosa possess more deep body than other fishes. Deeper and shorter caudal peduncle are characteristics of fast swimming fishes Gatz [23], and an increased body depth may also impart the same Watson and Balon [24]. The robust peduncle of deep water fishes may allow burst swimming as a part of successful ambush behavior needed for more mobile prey Bronte, et al. [25]. Multivariate analysis of morphological measurement in fishes is useful method to support inference of patterns of interspecific ecological diversification Winemiller & Taylor [26,27]. Discriminant function analysis found to be a successful to discriminate among species. Turan, et al. [28] were able to classify overall 78% among six populations of Clarias gariepinus. Similarly, Pollar, et al. [29] classified 95.6% for Tor tambroides. Artificial Neural Networks has proven fit for identification of fish schools based on acoustic data Haralabous and Georgakarakos [30] and later applied in species identification based on acoustic data Cabreira, et al. [31]. Morphometric measurements are another input for species identification Morimoto, et al. [32,33].

Acknowledgement

The study was conducted at Central Institute of Fisheries Education (CIFE, Indian Council of Agricultural Research), India. We are thankful to Dr. W.S. Lakra, Director, ICAR-CIFE for providing financial and technical support to conduct the study. We are also thankful to ICAR-Central Marine Fisheries Research Institute for all the help in sample collection.

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ABSTRACT

Present study was carried out to identify six important species (Nemipterus japonicus, N. randalli, N. bipunctatus, Scolopsis vosmeri, S. bimaculatus and Parascolopsis aspinosa) under the family Nemipteridae based on their morphometric characters. A total of 360 samples were collected randomly from east and west coast of India. Twenty one morphometric distances, measured from each individual, were subjected to factor analysis where body depth and fin base measurements explained 53% variations while head related measurements explained 20% of the variations among the six species. Artificial neural network analysis and discriminant function analysis was able to classify, 87.8% and 93.3% of the samples, correctly to the respective species. Results underlined the utility of the morphometric characters as a basis of fish identification when there is an overlap of characters among the species.

Keywords: Nemipteridae; Species; Morphometric Character; Factor Analysis; Discriminant Function Analysis