Linear Discriminant Function Research Articles

Determination of Sex in Ungulate Herbivores via near Infrared Spectroscopy of Hair: Growing Cattle as a Surrogate Model

Abstract Near infrared spectroscopy (NIRS) has been used in a variety of medical and veterinary science applications. In particular, NIRS calibrations have been developed in livestock for steroid content in cattle hair, and wound age or stage of healing in hot iron cattle brands. These NIRS applications also have potential utility in forensic science. Portable NIRS instruments facilitate measurements on live animals and or animal samples in the field. The objective of this study was to evaluate the efficacy of determining sex in growing cattle via NIRS of hair utilizing a portable spectrometer. In two consecutive years, log 1/R spectra (350–2500 nm) were collected using an ASD Field Spec fitted with a contact probe. Experimental subjects were Bos taurus cross calves (n = 12, yr 1; n = 14, yr 2) born to cows grazing central Arizona rangeland. Calf age was approximately 60, 90 and 210 d at branding, estrus synchronization, and weaning, respectively. As cattle were gathered for these routine working events, a total of 7 M and 19 F calves were scanned 3 times each over the left ribcage. A linear discriminant function was applied to spectral data in order to determine sample membership in M or F groups at each collection date. Chi-square procedures were used to determine differences (P < 0.05) in proportion of correct identifications per group and collection date. Overall, 86% of F and 72% of M were correctly (P < 0.05) identified. Corresponding values were 82% for F and 71% for M at branding, 100% for F and 89% for M at estrus synchronization, and 86% for F and 64% for M at weaning. Calf sex was successfully determined using portable NIRS in this proof of concept study. Efficacy of this method should be evaluated for different ungulate herbivores and under additional collection scenarios.

Grain Size Analysis and Characterization of Sedimentary Process in Tidal Flat of Chandipur Region, East Coast of India

Sediment characteristics and distribution analysis along the Chandipur Coast in Odisha, India was performed using a variety of techniques that include sedimentary characteristics including variation in grain size, shape and character of quartz grain surfaces (determined in light microscope and electron microscope scanning). Samples were taken from various locations representing high-tide areas and low-tide areas for analysis of the grain size, intended to determine physical properties and textural parameters such as mean, sorting, skewness and kurtosis. Grain size distribution research was conducted to characterize sedimentary transportation behaviours. CM diagram defines the rolling, saltation and graded suspension process for sediment deposition at the beach. Linear Discriminant Function (LDF) indicates the superiority of marine environment with lower deposition areas. Scanning Electron Microscope (SEM) was used to examine the quartz grains to determine the origin of the sediments and to establish the physical and chemical processes that occurred during their period of deposition in recent coastal sediments.

Generating a Model to Predict Secondary School Students at Risk in Mathematics

Mathematical courses aid individuals to deal with any problem that they might encounter in their life on the ground that Mathematics familiarize them with a problem-solving process. Additionally, people who have stood out in their profession are deemed to be those who have mastered their mathematical skills. Though, a lot of students encounter insurmountable difficulties and as a consequence they fail their Mathematical courses. That holds true particularly on the case of secondary school students. Thereby, controlling the risk of students’ failure in Mathematics is of utmost importance. This paper proposes a way to predict secondary school students’ critical performance in Mathematics through the generation of a potent model by means of a proper analysis of students’ engagement data. A discriminant function analysis has been carried out on the respective data and the generated linear discriminant functions constitute the prediction model. The prediction model generation process is demonstrated through a case study on a specific Mathematical course delivered at a Greek private secondary school. The research outcome is very promising given that our model could potentially be used to develop an early warning system for secondary school students at risk in Mathematics.

Genomic and acoustic differences separate Lilian’s Meadowlark (Sturnella magna lilianae) from Eastern (S. magna) and Western (S. neglecta) meadowlarks

AbstractExamining differences among recently diverged populations can provide insight into the traits and evolutionary mechanisms that drive or maintain divergence. The genus Sturnella includes 2 recently diverged species, Sturnella magna (Eastern Meadowlark) and S. neglecta (Western Meadowlark), the former of which has a complex of subspecies distributed across the Americas. Of the S. magna subspecies that occur in the United States, S. m. lilianae is the only one with a disjunct range, occurring in the southwestern United States and central Mexico. It also has markedly different song patterns than all other S. magna subspecies. In order to assess population differentiation, we performed whole-genome sequencing of 35 birds and analyzed song characteristics from 85 birds. Songs from each species and S. m. lilianae were diagnosable using linear discriminant function analysis and support divergence in song between all taxa. Phylogenetic analysis and admixture proportions support 3 distinct clades within North American meadowlarks, and tests of introgression failed to detect a significant signal. Overall, our results indicate that S. m. lilianae exhibits high levels of genetic and vocal differentiation from both S. magna and S. neglecta, with no evidence of introgression between any group, and forms a distinct evolutionary lineage. We thus recommend the elevation of S. m. lilianae to species status.

Range Extension of Northern Form Dolly Varden (<i>Salvelinus malma malma</i>) to the Upper Arctic Red River Watershed, Northwest Territories, Canada

Northern form Dolly Varden is an anadromous char with significant ecological value found in high-gradient rivers of the Western Arctic. Because of declines in population abundance, Dolly Varden was recently designated as “Special Concern” under the federal Species at Risk Act. This species is also of great cultural and dietary significance to Indigenous Peoples of many communities in the Western Arctic; thus, expanding knowledge of the distribution, biology, and essential habitat is an important priority. We present results of a fisheries survey in the headwaters of the Arctic Red River, Northwest Territories, that focused on confirming the presence of Dolly Varden. Of 143 fish captured among 12 sampling locations, two were Arctic grayling (Thymallus arcticus), 33 were slimy sculpin (Cottus cognatus), and 108 were char identified using qualitative and quantitative morphological features. A subsample of 44 char voucher specimens were frozen whole and later identified using a linear discriminant function (LDF) based on meristic counts and morphological measurements, and a mitochondrial DNA genetic marker. LDF scores indicated that char collected in the Arctic Red River were northern form Dolly Varden. Genetic analysis showed that all but one char possessed mitochondrial DNA sequences common in northern form Dolly Varden from Canada. Our results confirm the presence of Dolly Varden in the Arctic Red River headwaters, extending the confirmed known distribution of this taxon in the Northwest Territories approximately 450 km south and 100 km east of previously delimited areas.

Life history through the eyes of a hogfish: trophic growth and differential juvenile habitat use from stable isotope analysis

Understanding ontogenetic linkages among fish habitats is critical for conservation of fish populations and the ecosystems on which they rely. Natural tags such as stable isotopes are effective for investigating ecological questions regarding fish movement and habitat use. We analyzed stable isotopes from sequentially deposited laminae of hogfish Lachnolaimus maximus eye lenses from the eastern Gulf of Mexico (eGOM) to investigate trophic and geographic changes across individual life histories. We documented evidence of 1 to 2 step trophic level increases through δ15N increases. We also observed depth separation at the juvenile stage, evidenced by δ13C variation early in life. These results suggest that adult hogfish in deeper habitats likely inhabited deeper juvenile habitats (i.e. nearshore reefs), while adult hogfish inhabiting shallower adult habitats likely used shallower juvenile habitats (i.e. estuaries). This novel finding for eGOM hogfish contradicts prior literature that solely discusses seagrass as juvenile habitat. We used muscle tissue isotopes to characterize juvenile hogfish habitats and linear discriminant function analysis (LDA) to determine the habitats previously inhabited by adults in this study. The LDA revealed Cedar Key as the most used juvenile hogfish habitat in this study, but more evidence is needed to determine its status as a nursery. This study provides the first evidence for ontogenetic migration of individual hogfish using natural tags as tracers and demonstrates a mechanism for identifying juvenile habitats based on eye lens stable isotope analysis. Identifying ontogenetic patterns and habitat use can help to better conserve stocks and essential fish habitats.

A Comparison Of Regularized Linear Discriminant Functions For Poorly-Posed Classification Problems

For statistical classification problems where the total sample size is slightly greater than the feature dimension, regularized statistical discriminant rules may reduce classification error rates. We review ten dispersion-matrix regularization approaches, four for the pooled sample covariance matrix, four for the inverse pooled sample covariance matrix, and two for a diagonal covariance matrix, for use in Anderson’s (1951) linear discriminant function (LDF). We compare these regularized classifiers against the traditional LDF for a variety of parameter configurations, and use the estimated expected error rate (EER) to assess performance. We also apply the regularized LDFs to a well-known real-data example on colon cancer. We found that no regularized classifier uniformly outperformed the others. However, we found that the more contemporary classifiers (e.g., Thomaz and Gillies, 2005; Tong et al., 2012; and Xu et al., 2009) tended to outperform the older classifiers, and that certain simple methods (e.g., Pang et al., 2009; Thomaz and Gillies, 2005; and Tong et al., 2012) performed very well, questioning the need for involved cross-validation in estimating regularization parameters. Nonetheless, an older regularized classifier proposed by Smidt and McDonald (1976) yielded consistently low misclassification rates across all scenarios, despite the shape of the true covariance matrix. Finally, our simulations showed that regularized classifiers that relied primarily on asymptotic approximations with respect to the training sample size rarely outperformed the traditional LDF, and are thus not recommended. We discuss our results as they pertain to the effect of high dimension, and offer general guidelines for choosing a regularization method for poorly-posed problems.

Sexing the bony labyrinth: A morphometric investigation in a subadult and adult Belgian identified sample.

In forensic anthropology, sex estimation is a fundamental step in assessing individual biological profiles when analyzing human skeletons. Yet, current methods are not reliable enough to allow an accurate sex identification of highly fragmented, burnt, or subadult remains. This paper aims to investigate sexual dimorphism of the bony labyrinth on both identified subadult and adult individuals. The bony labyrinth is of particular interest for sex estimation since it is alleged to complete size and maturation pre-pubertally and is located inside the petrous part of the temporal bone which protects it from taphonomic processes. The study was performed on 93 CT scans of identified individuals from two Belgian osteological collections (19-20th century) and from current pediatric images (Erasme hospital, Brussels). Linear and angular measurements were taken on 2D slices of right bony labyrinths. Intra- and interobservers error measurements were calculated. Statistical tests were used to unravel any morphological variations between subadult and adult bony labyrinths and to highlight differences between females and males, separately in subadults and adults. Linear discriminant functions were established by cross-validation and tested on an independent sample from Belgium. Some measurements were significantly different between subadults and adults, and between females and males within both subadult and adult samples. Univariate functions achieved 72.7% in subadults and 68.4% in adults whereas multivariate equations increased accuracy respectively up to 84.9% and 78.4%. This study entails promising results to design a sex estimation method suitable for fragmented and/or subadult remains. Further metric approaches are needed to explore bony labyrinth sexual dimorphism.

Insider Threat Detection and Prevention Protocol: ITDP

Insider threat is a severe problem of many computer department since they have an authorized to do some assigned tasks. They can easily seek for any organizational computer security vulnerability. Protocol “Insider threat detection and prevention Protocol: ITDP” is designed to detect if a requesting “IT user” is authentic IT user whom has been allocated rights to particular application. User’s knowledge and his behavior are used to classify whether he is a real identified IT user or not. Statistical classification technique is used to predict if the guest is authentic. Best classification technique is linear binary discriminant function analysis with 98.3 percentage of accuracy in insider threat detection classification.

Botanical origin identification of Rhei Radix et Rhizoma based on color digitalization

The color characteristic information of Rhei Radix et Rhizoma powder was obtained by spectrophotometer, the feasibility of rapid identification of Rhei Radix et Rhizoma origin based on chromaticity value was studied by statistical analysis. The results of rank correlation analysis showed that a~*(P<0.01), b~*(P<0.01) had significantly correlation with the origin of medicinal herbs, which could be used as two important parameters to distinguish the origin of Rhei Radix et Rhizoma, the larger the a~* value, the more red the powder color,and the greater the b~* value, the more yellow the powder color. Meanwhile, through Fisher discriminant analysis, the linear discriminant functions of different genus Rhei Radix et Rhizoma were established, which was Rheum tanguticum=40.666a~*+0.019b~*-213.303, Rh. palmatum=34.121a~*+0.061b~*-151.770, Rh. officinale=28.071a~*+0.113b~*-104.604 3, the coincidence rate of cross-validation was over 95%, among them, the discriminant rate of Rh. tanguticum and Rh. officinale reached 100%;In addition, using the percentile method to analyze the 90% reference value range of three different origin of Rhei Radix et Rhizoma, as a result, Rh. tanguticum a~*(10.236 5-10.604 7), b~*(32.294 8-34.841 7); Rh. palmatum a~*(8.602 7-8.770 0), b~*(27.534 8-28.968 6), and Rh. officinale a~*(6.825 7-7.464 3),b~*(21.001 6-27.716 4). According to this study, rank correlation analysis and Fisher discriminant analysis are feasible to distinguish the base of Rhei Radix et Rhizoma in a certain range, and provide some theoretical basis for the identification of Rhei Radix et Rhizoma. It also provides a new method and idea for the identification of other multi-base Chinese medicine.

Discriminant analysis of distributional data via fractional programming

We address classification of distributional data, where units are described by histogram or interval-valued variables. The proposed approach uses a linear discriminant function where distributions or intervals are represented by quantile functions, under specific assumptions. This discriminant function allows defining a score for each unit, in the form of a quantile function, which is used to classify the units in two a priori groups, using the Mallows distance. There is a diversity of application areas for the proposed linear discriminant method. In this work we classify the airline companies operating in NY airports based on air time and arrival/departure delays, using a full year flights.

Straying of brown trout in the catchment of a large New Zealand river evaluated by otolith microchemistry

AbstractNatal homing and straying are behavioural tactics of migratory animals that return to nursery habitats to breed as adults. Straying has evolutionary and ecological importance for species flexibility, interpopulation connectivity and success of invasive species in new environments. This study aimed to estimate the relative importance of straying and homing in introduced brown trout Salmo trutta spawning in a model coastal tributary (Silverstream) of a large New Zealand river using otolith microchemistry. The data on natal homing and straying are important for the understanding of the ecological resilience of salmonid populations and metapopulations, and the development of effective management measures at stream or catchment scales. To examine brown trout homing and straying in a river catchment, otolith microchemical analysis was applied. We used linear discriminant function analysis to compare the trace element composition in the otoliths of YoY (young‐of‐the‐year) trout collected at spawning streams across the catchment and adult trout reproducing in Silverstream. The results showed that only six from a sample of 30 spawning adults in Silverstream originated from Silverstream. Another 14 fish likely originated from other Taieri tributaries further upstream, but the origin of the remaining 10 could not be determined. Contrary to published studies based on wild populations of brown trout in Europe, and several other species of salmonids, a large proportion of brown trout spawners collected in Silverstream were strays. The large number of strayers identified in Silverstream may be a consequence of the geomorphology of the Taieri catchment and highlights the adaptability of brown trout to different environmental contexts.

Discrimination of earthquakes and quarries in the Edirne district (Turkey) and its vicinity by using a linear discriminate function method and artificial neural networks

In this study, seismic events in the Edirne district (Turkey) and its vicinity have been investigated in order to discriminate earthquakes from quarry blasts. A total of 150 seismic events with Md ≤ 3.5 duration magnitude from a seismic activity catalog between 2009 and 2014 recorded by the Enez (ENEZ), Erikli (ERIK) and Gelibolu (GELI) broadband stations operated by Bogazici University, Kandilli Observatory and Earthquake Research Institute Regional Earthquake-Tsunami Monitoring Center were used in this study. The maximum S-wave and maximum P-wave amplitude ratio of vertical component velocity seismograms, power ratio (Complexity) and total signal duration of the waveform were calculated. Earthquakes and quarry blasts were discriminated using the linear discriminate function (LDF) and back propagation feed forward neural networks, an artificial neural network (ANN) learning algorithm, taking the determination coefficient and variance account values between these parameters into consideration. Eighty-one (54%) of the total 150 seismic events studied were determined to be earthquakes, and sixty-nine (46%) of them were determined to be quarry blasts. The LDF and ANNs methods were applied to the data in Edirne and its vicinity using a pair of parameters and were compared to each other for the first time. The accuracy of the methods are 95% and 99% for LDF and ANNs, respectively.

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An alternative approach to the processing of elements of linear discriminant functions for the classification of objects is proposed in the paper. This approach is based on the method of processing a vector array of data by differential slices which is distributed in the column of matrix representation of elements of discriminant functions. In its turn, it indicates the spatially distributed nature of the processing of matrix elements with dimension m ? n, where m is the number of classes, n is the dimension of the input data vector. The linearity of the representation of each discriminant function in a set of discriminant functions allows not only to represent their terms in the form of matrix elements, but also apply to their processing some rules concerning such properties as associativity and commutativity of components (terms) of the sum. As a result, such complex procedures as multi-operand summation in the formation of each discriminant function are replaced. Moreover, it is followed by their comparison with each other with the aim of identifying the one with the maximum value. In each processing cycle of the proposed method, the basic operation is a simultaneous reduction in the magnitude of the elements similarity of the same name in the columns of the matrix, which allows determining the maximum value of the discriminant function. The operation of transposition (advancement) to the edge of all current zero elements in each row of the matrix is also involved. This approach is applied to linear discriminant functions as an apparatus of the statistical method of object classification. Consequently, according to the known decision rule, the maximum discriminant function determines the corresponding class to which the input object, represented as an n-dimensional feature vector, belongs. One of the options for the hardware application of such classifier is the decision support subsystem for medical diagnosis.

ATTRACTIVE – An Auto-Updating Database for Experimental Protocols in Regenerative Medicine

Many research articles are published on regenerative medicine every year. However, only a small proportion of these articles provide experimental methods on organ/tissue differentiation. Therefore, we developed a database – ATTRACTIVE (An auTo-updating daTabase foR experimentAl protoCols in regeneraTIVe mEdicine) – that collects journal articles with differentiation methods in regenerative medicine and updates itself automatically on a regular basis. Since the number of articles in regenerative medicine was insufficient and unbalanced, which limited the performance of the supervised learning algorithms, we proposed an algorithm that combines cosine similarity and linear discriminant functions to classify articles based on their titles and abstracts more efficiently. The results show that our proposed methods out-performed other machine learning algorithms such as k-nearest neighbors, support vector machine, and long short-term memory methods. The classification accuracy reached 94.62%, even with a small and unbalanced dataset. Lastly, we incorporated our classifier into the database for automatic updates. The database is available at http://attractive.cgm.ntu.edu.tw/ .

Study on Discrimination Model of Water Inrush Source in Binhuang Mining Area of China

Groundwater flow into the mine by coal mining, which threatens the safety of coal mine. Therefore, it is necessary to identify the source of the water flowing into the coal mine. Taking binhuang mining area in Shaanxi Province as the research background, the discrimination models of six water inrush sources had been studied. The composition of 256 water samples was determined and the water quality characteristics of 6 water sources were analyzed. Fisher linear discriminant function model was established by selecting 9 indexes of water quality components. By testing the discriminant effect, it is considered that the probability of K1L aquifer misjudging as Q aquifer and surface water is high, and the misjudgment rate is 30.4%. Combined with the analysis of geological conditions, it is considered that K1L aquifer has a good hydraulic connection with Q aquifer and surface water. Aiming at the problem of misjudgment, the neural network analysis model is used, and the misjudgment rate is reduced to 0%.

Linear Classifiers Under Infinite Imbalance

We study the behavior of linear discriminant functions for binary classification in the infinite-imbalance limit, where the sample size of one class grows without bound while the sample size of the other remains fixed. The coefficients of the classifier minimize an expected loss specified through a weight function. We show that for a broad class of weight functions, the intercept diverges but the rest of the coefficient vector has a finite limit under infinite imbalance, extending prior work on logistic regression. The limit depends on the left tail of the weight function, for which we distinguish three cases: bounded, asymptotically polynomial, and asymptotically exponential. The limiting coefficient vectors reflect robustness or conservatism properties in the sense that they optimize against certain worst-case alternatives. In the bounded and polynomial cases, the limit is equivalent to an implicit choice of upsampling distribution for the minority class. We apply these ideas in a credit risk setting, with particular emphasis on performance in the high-sensitivity and high-specificity regions.

A prototypical MMPI-2 configuration of Obsessive-Compulsive Disorder

Background: Obsessive-Compulsive Disorder is often measured through self-report questionnaires focused on measuring symptomatology (Y-BOCS, OCI-R, and PI). The scientific literature identifies the Pt scale as a distinctive marker, but does not report data related to a specific OCD profile of the MMPI-2 test. Therefore, based on some clinical considerations and preliminary investigations, the following study aims to identify a specific MMPI-OCD configuration marked by the presence of significant indicators able to describe its psychological functioning and its key features in cognitive, emotional and affective terms. Method : In order to explore the expected relationships, 395 participants were involved (average age: 34.20 years) and evaluated through the MMPI-2 test and the Yale-Brown interview. Results : MANOVA, Multiple Linear Regression Analysis and Discriminant Function Analysis confirm that the OCD is marked by the recurrence of three elevated clinical scales (Pt, D, Sc). Hyper-prudential reasoning and some reasoning bias, typically employed by these subjects, could justify such results. Conclusions: This specific configuration could therefore support the clinician during the assessment phases and lead him to identify – as well as differentiate – his characteristics from those of other psychological disorders, thus stimulating him to deepen during the interpretation of the results.

Combined application of MRS and DWI can effectively predict cell proliferation and assess the grade of glioma: A prospective study

In order to assess combined application of MRS and DWI for prediction cell proliferation and grade diagnosis of glioma, We prospectively collected the Cho/Cr, Cho/NAA, Cr/NAA of MRS and tumor parenchyma ADC (ADCT), contralateral mirror brain tissue ADC (ADCH), rADC (rADC = ADCT/ADCH). According to postoperative pathology, the patients were divided into two groups: LGG group and HGG group, compared differences of age, gender, Ki67, MRS, DWI between two groups. Next, we analyzed the correlation between MRS, DWI and Ki67. On this basis, the sensitivity and specificity of MRS, DWI and MRS combined with DWI (MRS + DWI) in diagnosis of glioma grade were evaluated. The differences of Ki67, Cho/Cr, Cho/NAA, Cr/NAA, ADCT, rADC between LGG group and HGG group were statistically significant (p = 0.000, 0.000, 0.000, 0.008, 0.000, and 0.000 respectively). From ROC curve, area under the curve (AUC), sensitivity and specificity of Cho/Cr, Cho/NAA, Cr/NAA, ADCT, rADC, PRE (MRS + DWI) were (0.901, 86.7%, 85.7%), (0.876, 80.0%, 82.1%), (0.704, 63.3%, 71.4%), (0.862, 82.1%, 83.3%), (0.820, 75.0%, 76.7%), (0.920, 86.7%, 89.3%), respectively. Fisher's linear discriminant functions results suggest: Y1 = -20.447 + 3.46•X1 + 17.141•X2 (LGG), Y2 = -19.415 + 4.828•X1 + 14.543•X2 (HGG). Our study suggested that MRS and DWI can effectively predict cell proliferation preoperative. MRS combined with DWI can further improve sensitivity and specificity in assessing the grade of glioma.

Effect of eco-physiological factors on biometric traits of green mussel Perna viridis cultured in the south-east coast of the Bay of Bengal, Bangladesh

Information on allometry, condition indices (CIs), and biometric traits (BTs) of marine bivalves are necessary for determining the growth pattern and the size at which harvest can be intensified to maximize production in aquaculture. These parameters of marine bivalves are largely regulated by the complex interactive effects of various ecological and physiological factors. Therefore, the present study aimed to apply an integrated multivariate approach to explore a comprehensive knowledge about how allometry, condition indices (CIs) and BTs are interlinked with various eco-physiological factors of cultured green mussel (Perna viridis), with a particular focus on the effect of seasonality. Six selected length-weight dimensions (shell length, shell width and shell height against total weight and soft tissue weight) were proved to be positively correlated with negative allometric relation (slope<3) by regression analysis in male, female and sexually undifferentiated individuals. Linear discriminant function analysis (LDFA) displayed that CIs and BTs of green mussel were not differentiated by the sex, rather evidenced a pronounced discrimination by the seasonality and reproductive cycle. The correlation analysis demonstrated that different CIs and BTs were significantly correlated with different ecological factors, ingested plankton and gonadosomatic index (GSI) of the green mussel. Taken together all datasets, the principal component analysis (PCA) showed that seasonality and reproductive cycle explained>60 % of the variability. In both cases, PCA analysis further revealed that CIs and BTs increased with the increase of salinity, dissolved oxygen, food availability, plankton ingestion and GSI value, while they decreased with the increase of temperature, current speed and turbidity. Thus, our results clearly demonstrated that CIs and BTs of the green mussel were influenced by seasonality, ecological factors, plankton ingestion and reproductive cycle of the species. The findings of the present study represent a step toward a better comprehension of the CIs and BTs of P. viridis in relation to different eco-physiological factors and would be helpful to determine the ideal period for commercial exploitation, biodiversity conservation and future potential aquaculture of this commercially important species to a greater extent.