Acknowledgements

The authors like to acknowledge the Chilika Development Authority (CDA), Bhubaneswar, Odisha, India, for providing time series fish catch data of Chilika lagoon for the present study. We are thankful to ICAR-CIFRI, Barrackpore,

temperature and salinity for the same period of April 2000–2001 to March 2014– 2015 of the Chilika lagoon). The data sets for the period April 2001–March 2011 were used for training sets and April 2011 to March 2015 were taken as validation of the model. SARIMAX (1,0,0)(2,0,0)12 was found to be the best fitted model. Here, SARIMA (1,0,0)(2,0,0)12 model is also compared with the SARIMAX (1,0,0) (2,0,0)12 (SARIMA (1,0,0)(2,0,0)12 with regressors as factor) and found that the R square value is greater for SARIMAX than SARIMA and root mean square error is less for SARIMAX than the SARIMA model, which reveals that SARIMAX model is better performer than the SARIMA model for the forecasting of the total fish catch of the lagoon. The regressor, i.e., temperature and salinity used for SARIMAX development, showed significantly positive influence (p < 0.05) on the fish catch in the lagoon. Using SARIMAX model, forecasting is done for the fish production within the error catch less than 10% (for details please see [14]; Figure 5). Total fish catch forecasting also showed an increasing catch in the upcoming year till 2018

In Chilika lagoon, fisheries in general and shrimp catch in particular play an important role in support of livelihoods of fishermen around it. The quarterly catch variation in shrimp landings in lagoon could be due to direct or indirect influence by hydrology and environmental condition of the lagoon system [27–29]. Average shrimp landings during first quarter Q1 were maximum, which reflects the summer

representing the post-monsoon season (September, October and November). The maximum variation in shrimp catch was observed during second quarter Q2 representing monsoon season and lowest variation in fourth quarter Q4 winter season (December, January and February). Seasonal and environmental variation influences the special trends of fisheries in the estuaries system [30]. Physicochemical parameters such as salinity and water temperature influence the distribution of various Penaeid shrimp [31]. The physicochemical parameters of the Chilika lagoon are very much influenced by the seasonal variation as it is connected with the fresh water river by one side and the Bay of Bengal on the other side. The fresh water flow showed positive, negative and inconsistent influence on fish production in the coastal lagoon system for different fish species across different countries [32]. The major commercial fishes in the lake are mostly migrant species (between sea and lake) and account more than 86% of the total fish diversity [33]. For feeding and breeding purpose, the migrant species enter the lake from the sea. The lagoon environment becomes favourable in post winter through summer due to the more availability of food. The higher temperature and salinity phase triggers spawning activity of many clupeid species which contribute more than 26% of total annual catch. Hence, in general, more number of fish species abundance is influenced by the temperature and salinity of the lagoon. As reported, the total fish production showed an increase trend in catch with the increase in temperature and salinity [14]. Similar observation was made as marine-dependent species increased from February month to onset of monsoon (June) [19] in Chilika lagoon. Moreover, during monsoon season, the nutrient inflow in the lake comes through catchment runoff and fresh water, which induces plankton production during post winter and

The information on fisheries catch prediction in advance plays a crucial role for managing fisheries resources in the lagoon ecosystem. In fisheries, several time series models were developed and forecasted for fisheries catch data such as ARIMA

season (March, April and May) catch and the lowest in third quarter Q3

summer (high salinity and temperature) period.

92

with respect to the base year 2015.

Time Series Analysis - Data, Methods, and Applications

4. Discussion

Kolkata, India, for providing laboratory facilities and other logistics for completion of this research work.

References

157-170

Science; 2002. p. 636

12-801948-1.00013-6

2003;4:24-26

81-85589-51-0

[1] Elliot M, Hemingway KL. Fishes in Estuaries. 2nd ed. Oxford: Blackwell

DOI: http://dx.doi.org/10.5772/intechopen.85458

Forecasting Shrimp and Fish Catch in Chilika Lake over Time Series Analysis

[9] Saila SB, Wighbout M, Lermit RJ. Comparison of some time series models for the analysis of fisheries data. Journal

[10] Sathianandan TV, Srinath M. Time series analysis of marine fish landings in India. Journal of the Marine Biological Association of India. 1995;37:171-178

[11] Prista N, Diawara N, Costa MJ, Jones C. Use of SARIMA models to assess data-poor fisheries: A case study with a sciaenid fishery of Portugal. Fishery

du Conseil. 1980;39:44-52

Bulletin. 2011;109:170-185

2017;40(6):393-397

49(1):55-63

2018;26(4):677-687

India; 2005. p. 40

[12] Raman RK, Sathianandan TV, Sharma AP, Mohanty BP. Modelling and forecasting marine fish production in Odisha using seasonal ARIMA model. National Academy Science Letters.

[13] Raman RK, Sahoo AK, Mohanty SK, Das BK. Forecasting of commercial fish (Beloniformes: Order) catch in Chilika lagoon, Odisha, India. Journal of the Inland Fisheries Society of India. 2017;

[14] Raman RK, Mohanty SK, Bhatta KS, Karna SK, Sahoo AK, Mohanty BP, et al. Time series forecasting model for fisheries in Chilika lagoon (a Ramsar site, 1981), Odisha, India: A case study. Wetlands Ecology and Management.

[15] World Bank. Scenario assessment of provision of environmental flows to Lake Chilika from Naraj Barrage, Orissa, India. Reports from the environmental flows window of the bank Netherlands water partnership programme (World Bank) to the Government of Orissa,

[16] Mohapatra ARK, Mohanty SK, Mohanty, Bhatta KS, Das NR. Fisheries

enhancement and biodiversity

[2] George B, Kumar JIN, Rita NK. Study on the influence of hydro-chemical parameters on phytoplankton

distribution along Tapi estuarine area of Gulf of Khambhat, India. Egyptian Journal of Aquatic Research. 2012;38:

[3] Mohapatra A, Mohanty SK, Mishra SS. Fish and Shellfish Fauna of Chilika Lagoon: An updated checklist. In: Venkataraman K, Sivaperuman C, editors. Marine Faunal Diversity in India. Elsevier Inc. Publications; 2015. pp. 195-224. DOI: 10.1016/B978-0-

[4] Ritesh K. Economic valuation of Chilika lagoon. Chilika Newsletter.

[5] Mohanty SK, Bhatta KS, Mohanty RK, Mishra S, Mohapatra A, Pattnaik AK. Eco-Restoration Impact on Fishery Biodiversity and Population Structure in

[6] Rothschild BJ, Smith SG, Li H. The application of time series analysis to fisheries populations assessment and

Quantitative Methods and Applications for Small-Scale Fisheries, Florida: CRC

[7] Box GEP, Jenkins GM. Time Series Analysis: Forecasting and Control. San

[8] Roy M. Using Box-Jenkins models to

Identification, estimation and checking. Fishery Bulletin. 1981;78(4):887-896

modelling Stock Assessment:

Francisco: Holden-Day; 1976

forecast fishery dynamics:

95

Press; 1996. pp. 354-402

Chilika Lake. Lakes and Coastal Wetlands Conservation, Restoration and Management. New Delhi: Capital Publishing Company; 2007. ISBN-
