903-907The aim of this study was to assess the impact of environmental factors influencing the spatio-temporal distribution of Johnius belangerii. Fishery independent fortnightly resource surveys were conducted in Mumbai waters from September, 2017 to May, 2018 to determine the effect of environmental variables on spatio-temporal distribution. A Generalized Additive Modelling (GAM) approach showed that distribution and abundance of J. belangerii varied spatio-temporally, and environmental factors were found to be influential. GAM models demonstrated that higher catch was located in waters of Sea Surface Temperature (SST) from 25.1 - 31.1  °C with Sea Bottom Temperature (SBT) values ranging from 26-31 °C and salinity of more than 34 0/00. Correlation of environment variables with Catch per Unit Effort (CPUE) suggests significant relationship with SST (0.81), SBT (0.85) and Sea Bottom Chlorophyll-a (0.73). The result of this study will be helpful for clearly understanding the intricacies of spatial distributions of fish in relation to changing habitat condition which will be useful in the sustainable management of aquatic resources

Deshmukhe, G

Inamdar, A B

Nayak, B B

Ramteke, K K

Shenoy, L

Singh, V V

NOPR

  Indian Journal of Geo Marine Sciences Vol. 49 (05), May 2020, pp. 903-907   Short communication    Assessing the impact of environmental factors influencing the spatio-temporal distribution of Johnius belangerii  (Cuvier, 1830) Belanger's croaker along Mumbai, Northwest Coast of India K K Ramtekea, L Shenoy*,a, B B Nayaka, G Deshmukhea,  A B Inamdarb & V V Singhc aICAR-Central Institute of Fisheries Education,  Mumbai – 400 061, India bCentre of Studies in Resource Engineering (CSRE), IIT Bombay, Powai, Mumbai – 400 076, India cICAR-Central Marine Fisheries Research Institute (CMFRI), Kochi – 682 018, India *[E-mail: lathashenoy@cife.edu.in] Received 19 November 2018; revised 14 January 2019 [Keywords: Environment, GAM, Johnius belangerii, Spatio- Temporal distribution] The aim of this study was to assess the impact of environmental factors influencing the spatio-temporal distribution of Johnius belangerii. Fishery independent fortnightly resource surveys were conducted in Mumbai waters from September, 2017 to May, 2018 to determine the effect of environmental variables on spatio-temporal distribution. A Generalized Additive Modelling (GAM) approach showed that distribution and abundance of  J. belangerii varied spatio-temporally, and environmental factors were found to be influential. GAM models demonstrated that higher catch was located in waters of Sea Surface Temperature (SST) from 25.1 - 31.1 C with Sea Bottom Temperature (SBT) values ranging from 26-31 °C and salinity of more than 34 0/00. Correlation of environment variables with Catch per Unit Effort (CPUE) suggests significant relationship with SST (0.81), SBT (0.85) and Sea Bottom Chlorophyll-a (0.73). The result of this study will be helpful for clearly understanding the intricacies of spatial distributions of fish in relation to changing habitat condition which will be useful in the sustainable management of aquatic resources.  Introduction The Belanger's croaker Johnius belangerii (Cuvier, 1830) belongs to family Sciaenidae, also known as jew fish, croakers or grunters and has wide geographical distribution in Indo-West Pacific, west of the Persian Gulf, east of southern China and Indonesia. J. belangerii inhabits shallow coastal waters upto 40 m depth and is known for its carnivorous feeding habit which mainly consists of invertebrates particularly stomatopods, molluscs and isopods. Majority landing of sciaenid fishes comes as by-catch from shrimp trawler in Maharashtra. Quantity-wise landing of sciaenid occupies third position after prawn and mackerel in Maharashtra. Total marine fish landing of India is estimated at  3.63 mt1; Sciaenids contribute 6 % to the total marine fish landing and 20 % to demersal landing. Majority of marine landing is contributed by modern mechanized craft (99.2 %) and remaining from motorized (0.45 %) and artisanal craft (0.35 %). Many studies are available on food and feeding, reproductive biology, growth and mortality aspects of sciaenid fishes. However, the change in spatiotemporal distribution in relation to environmental factors has not been examined. In a situation where increasing fishing pressure due to capital intensive fishing shows declining pattern in rate of fish catch from known fishing ground, prediction based on environmental variables becomes important because of its accuracy and reliability. Environmental variables control the spatio-temporal distribution of fish population and therefore, it is crucial to find out relationship between the environment and catch rate2. Environmental variables vary according to location and in turn are responsible for formation of meso-scale events (for instance ocean circulation, temperature and chlorophyll front which, in turn determines the food availability). Many fisheries independent and dependent surveys on this complex pattern of environment and aquatic organism have revealed that distribution change is influenced by the combined activities of abiotic and  biotic variables3. Change in distribution is the initial response received by species from their surrounding4,5. Understanding of adaptive behavior in response to short and long term changes in environment aids to resolve the complexity in spatio-temporal distribution, which is critical of successful stock assessment, lack INDIAN J GEO-MAR SCI, VOL 49, NO 05, MAY 2020   904 of understanding may result in complete collapse of overexploited fish resources6. Ecosytem approach to fisheries management is based on understanding the effects of environment variables on spatio-temporal distribution of fishes. In this context, an attempt was made to study spatio-temporal distribution of  J. belangerii in relation to environmental variables.  Material and Methods Fishery independent fortnightly trawl surveys were conducted on M.F.V NARMADA (IV), Training cum research vessel of ICAR-Central Institute of Fisheries Education, Mumbai from September, 2017 to May, 2018 to collect fish catch data from coastal waters off Mumbai (Fig. 1). Identical net setting and hauling procedure was followed, 46 m bottom trawl of 35 mm mesh size cod end was used during survey and sorted samples were analyzed in the laboratory and identified up to species level. To predict the influence of environmental conditions on the catch rate of J. belangerii, Generalized Additive Models (GAMs) were used. Models considered for study were fitted using ‘mgcv’ package7 in the R version 3.5.1 environment. Spatial data collected during independent survey overlay on the India geo referenced base map with World Geodetic System (WGS) coordinate reference system was used for generation of theme map. After confirming dates and location for remotely sensed data extraction, maps were generated in ArcGIS. Parameters included in the study were surface chlorophyll-a (chl-a), Sea Surface Temperature (SST), Bottom Temperature (SBT), Sea Surface Height (SSH), Sea Bottom Chlorophyll-a (SBC), Ocean Mixed Layer (OML) and Salinity (Table 1). These variables were selected based on the coverage and importance. Results and Discussion The spatio-temporal distribution map from surveyed catch data was prepared and processed in GIS Software (Fig. 2). For each pair of column matrix function pair drawn by scatter plots showed clear relationship between catch rate and environmental variables. Upper diagonal panel of the plot shows correlation coefficient while lower diagonal panel shows smoothing line. Preliminary correlation for environment variables with Catch per unit effort (CPUE) was performed. Analysis suggests collinearity (Fig. 3) for Surface chlorophyll-a (0.68), SST (0.81), SBT (0.85) and SBC (0.73). Inference test   Fig. 1 —  Sampling locations map, off Mumbai coast, India Table 1 — Environmental data sources for assessment Parameter Deviance explained % GCV s(Chla)    46.6 0.012433   s(Chla)+s(sst) 69.4 0.016478   s(Chla)+s(sst)+s(Salinity) 72.5 0.017709   s(Chla)+s(sst)+s(Salinity)+s(bottomT) 76.7 0.0152   s(Chla)+s(sst)+s(Salinity)+s(bottomT)+s(OMixedLayer) 78 0.0152   s(Chla)+s(sst)+s(Salinity)+s(bottomT)+s(OMixedLayer)+  s(SeaSurfaceHeight) 90 0.014586 s(Chla)+s(sst)+s(Salinity)+s(bottomT)+s(OMixedLayer)+s(SeaSurfaceHeight)+s(DO) 94.3 0.010744   s(Chla)+s(sst)+s(Salinity)+s(bottomT)+s(OMixedLayer)+s(SeaSurfaceHeight) +s(DO)+s(bottom_chla) 94.8 0.012433   Total variation % explained 94.8 RAMTEKE et al.: ENVIRONMENTAL FACTORS AND SPATIO-TEMPORAL DISTRIBUTION OF J. BELANGERII   905 for fitting the variable in GAM model was similar for all regression model mainly, the Chi-square (χ2 ) tests8,9. The χ2 –test used for estimation of significant level for added predictor for that level of significance was set at 95 %. Cross validation was used to estimate optimal degrees of freedom. Best fit model CPUE for J. belangerii was selected on the basis of deviance level (94.8 %).The best GAM with the lowest Akaike's Information Criteria (AIC) was of the following form (Table 2):  CPUE ~ α + S1(Chl-a) + S2(SST) + S3(SBS) + S4(SBT) + S5(OML) + S6(SSH) + S7(DO) + S8(SBC)  In the model, all smoothing functions S1, S2, S3, S4, S5, S6, S7 and S8 are penalized cubic regression splines. The spline of GAM plot showed significant relationship between the environmental variables and catch rate of J. belangerii. GAM plot of Chlorophyll-a indicated its impact on locations where catch rate was 2.4 - 4 kg and bottom Chlorophyll as significant for catch abundance (Fig. 4). The concentration of Chlorophyll-a had been used to identify the degree of primary productivity and support good fisheries10. SST and SBT showed positive relationship at  25.1 - 31.1 °C and 26 - 31 °C respectively, similar to findings by other researchers11. They also observed that tropical sciaenid fishes prefer warmer temperature. Similarly, distribution pattern of fish depends on changes in environmental temperature12,13. Ocean Mixed Layer Response plots showed high CPUE at 9-12 m which affect distribution by determining the average level of light seen. SSH   Fig. 2 —  Spatial distribution of Catch per unit effort (CPUE) during study period   Fig. 3 — Pairplot of Johnius belangerii and selected explanatory variables. In the pair plot, the upper diagonal panel shows the (absolute) correlations coefficient and the lower diagonal part the pair-wise scatterplots with a smoothing line. INDIAN J GEO-MAR SCI, VOL 49, NO 05, MAY 2020   906 controls the mass movement of water and nutrient flow which is basic for productivity of marine environment. Sea Surface Height plot indicated high catch at 0.35 - 0.41 m. SSH  used to delineate current patterns and mesoscale eddies formed by warm cold rings14-16. In addition, salinity and dissolved oxygen has shown influence on catch rate at 34 - 36.5 0/00 and 200-215 mmol/L, respectively. Salinity is crucial in alteration of species composition and spatiotemporal distribution17,18. Fisheries-independent data along with environmental parameters are valuable for assessing spatio-temporal distribution. Results of the study clearly demonstrate that environmental features impact the distribution fish, and it is strongly linked to physicochemical variables of the habitat in which they live.  Conclusion The findings of the present study are based on the fishery independent fortnightly resource surveys and remotely-sensed environmental variables from Mumbai waters, India.  Analysis of environmental variables showed that they were influential in the spatio-temporal distribution and abundance of  J. belangerii.   Acknowledgements Authors are grateful to the Director, ICAR-Central Institute of Fisheries Education, Mumbai for providing the facilities to carry out the above  research work.  Conflict of Interest There is no conflict of interest in relation to this work.  Author Contributions KKR and LS: Conceived and designed the experiments and wrote the manuscript; KKR and BBN: Performed the experiments; KKR, GD and ABI: Analyzed the data; and KKR, VVS and LS: Contributed to reagents/materials/analysis tools. Table 2 — Analysis of deviance for GAM covariates and their interactions of the best GAM fitted Parameter Data Source Resolution Surface chlorophyll MODIS-Aqua 4km Satellite –NASA 4km Sea surface temperature MODIS-Aqua 4km Satellite –NASA 4km Bottom temperature E.U. Copernicus Marine Service Information 0.0830x 0.0830 Dissolved oxygen E.U. Copernicus Marine Service Information 0.50 x 0.50 Sea surface height E.U. Copernicus Marine Service Information 0.0830x 0.0830 Bottom Chlorophyll E.U. Copernicus Marine Service Information 0.50 x 0.50 Ocean Mixed Layer E.U. Copernicus Marine Service Information 0.0830x 0.0830 Salinity E.U. Copernicus Marine Service Information 0.0830x 0.0830    Fig. 4 — Coefficients of the Generalized Additive Model (GAM) for Johnius belangerii against additive terms. Dashed lines indicate the value of GAM coefficient, Shaded area are the 95 % confidence intervals at P < 0.05. RAMTEKE et al.: ENVIRONMENTAL FACTORS AND SPATIO-TEMPORAL DISTRIBUTION OF J. BELANGERII   907 References 1 CMFRI: Annual Report 2016-17. Central Marine Fisheries Research Institute, Kochi. 2017, pp. 292. 2 Planque B, Loots C, Petitgas P, Lindstrom U & Vaz S, Understanding what controls the spatial distribution of fish populations using a multi-model approach, Fish Oceanogr, 20 (2011) 1–17. 3 Zheng X, Pierce G J, Reid, D G & Jolliffe I T, Does the North Atlantic current affect spatial distribution of whiting? 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Assessing the impact of environmental factors influencing the spatio-temporal distribution of Johnius belangerii  (Cuvier, 1830) Belanger's croaker along Mumbai, Northwest Coast of India

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Assessing the impact of environmental factors influencing the spatio-temporal distribution of Johnius belangerii (Cuvier, 1830) Belanger's croaker along Mumbai, Northwest Coast of India

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