A regional algorithm to model mesozooplankton biomass along the southwestern Bay of Bengal
- 65 Downloads
A three-dimensional regression analysis attempted to model mesozooplankton (MSP) biomass using sea surface temperature (SST) and chlorophyll-a (Chl-a). The study was carried out from January 2014 to July 2015 in the southwestern Bay of Bengal (BoB) and sampling was carried out on board Sagar Manjusha and Sagar Purvi. SST ranged from 26.2 to 33.1 °C while Chl-a varied from 0.04 to 6.09 μg L−1. During the course of the study period, there was a weak correlation (r = 0.32) between SST and Chl-a statistically. MSP biomass varied from 0.42 to 9.63 mg C m−3 and inversely related with SST. Two kinds of approaches were adopted to develop the model by grouping seasonal datasets (four seasonal algorithms) and comprising all datasets (one annual algorithm). Among the four functions used (linear, paraboloid, the Lorentzian and the Gaussian functions), paraboloid model was best suited. The best seasonal and annual algorithms were applied in the synchronous MODIS-derived SST and Chl-a data to estimate the MSP biomass in the southwestern BoB. The modelled MSP biomass was validated with field MSP biomass and the result was statistically significant, showing maximum regression coefficient for the seasonal algorithms (R2 = 0.60; p = 0.627; α = 0.05), than the annual algorithm (R2 = 0.52; p = 0.015, α = 0.05).
KeywordsMesozooplankton Sea surface temperature Chlorophyll-a MODIS Bay of Bengal
We thank the Director and Dean, CAS in Marine Biology, Faculty of Marine Sciences and authorities of Annamalai University for providing with the necessary facilities. We also thank the Director, Space Application Centre (SAC), Indian Space Research Organization (ISRO), Govt. of India, Ahmedabad, and the Deputy Director, RESPOND, ISRO HQ, Department of Space, Govt. of India, Bangalore. We are thankful to the Coordinator of RESPOND programme (Project Ref. No. ISRO/RES/4/606/2012–2015). The authors are also thankful to the Indian Space Research Organisation (ISRO) and two anonymous reviewers.
- Antony, G., Kurup, K. N., & Naomi, T. S. (1997). Zooplankton abundance and secondary production in the seas around Andaman-Nicobar islands. Indian Journal of Fisheries, 44(2), 141–154.Google Scholar
- Bruno, A. W., & Joslin, L. M. (2005). The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance. Ecography, 28, 815/829, 2005.Google Scholar
- Chaturvedi, N. (2003). Intrannual and interannual chlorophyll variability in the Arabian Sea and Bay of Bengal as observed from SeaWifs data from 1997–2000 and its interrelationship with Sea Surface Temperature (SST) derived from NOAA AVHRR, 2005. International Journal of Remote Sensing, 26(17), 3695–3706.CrossRefGoogle Scholar
- Chust, G., Allen, J. I., Bopp, L., Schrum, C., Holt, J., Tsiaras, K., Zavatarelli, M., Chifflet, M., Cannaby, H., Dadou, I., Daewel, U., Wakelin, S. L., Machu, E., Pushpadas, D., Butenschon, M., Artioli, Y., Petihakis, G., Smith, C., Garçon, V., Goubanova, K., le Vu, B., Fach, B. A., Salihoglu, B., Clementi, E., & Irigoien, X. (2014). Biomass changes and trophic amplification of plankton in a warmer ocean. Global Change Biology, 20, 2124–2139.CrossRefGoogle Scholar
- Das, S., Chanda, A., Dey, S., Banerjee, S., Mukhopadhyay, A., Akhand Akhand A., Ghosh A., Ghosh S., Hazra S., Mitra D., Lotliker A.A., Rao K.H., Choudhury S.B., Dadhwal V.K., (2016). Comparing the spatio-temporal variability of remotely sensed oceanographic parameters between the Arabian Sea and Bay of Bengal throughout a decade. Current Science, 110 (4), 627–639. doi: https://doi.org/10.18520/cs/v110/i4/627-639.
- Fernandes, V., & Ramaiah, N. (2014). Distributional characteristics of surface-layer mesozooplankton in the Bay of Bengal during the 2005 winter monsoon. Indian Journal of Geo-Marine Sciences, 43(1), 1–12.Google Scholar
- Goes, J. I., Caeiro, S., & Gomes, H. R. (1999). Phytoplankton-zooplankton inter-relationships in tropical waters—grazing and gut pigment dynamics. Indian Journal of Marine Sciences, 28, 116–124.Google Scholar
- Hirst, A. G., & Bunker, A. J. (2003). Growth of marine planktonic copepods: global rates and patterns in relation to chlorophyll a, temperature and body weight. Indian Journal of Marine Sciences, 48, 1988–2010.Google Scholar
- IOCCG. (2000). Remote sensing of ocean colour in coastal, and other optically-complex waters. Reports of the International Ocean Colour Coordinating Group, Sathyendranath, S. (ed.), No. 3, IOCCG, Dartmouth, Canada.Google Scholar
- Jeffrey, S. W., Mantoura, R. F. C., & Wright, S. W. (1997). Phytoplankton pigments in oceanography: guidelines to modern methods. Paris: UNESCO Publishing.Google Scholar
- Jutla, A.S., Akanda, S., & Islam, S. (2009). Relationship between phytoplankton, sea surface temperature and river discharge in Bay of Bengal. Geophysical Research abstracts; EGU 2009-1091-2, EGU General Assembly 2009; Vienna, Austria.Google Scholar
- Lalli, C. M., & Parsons, T. R. (1997). Biological oceanography: an introduction (2nd ed.). Oxford, U. K.: Elsevier Butterworth-Heinemann.Google Scholar
- Madhu, N. V., Maheswaran, P. A., Jyotibabu, R., Sunil, V., Ravichandran, C., Balasubramaniam, T., Gopalakrsishnaan, T. C., & Nair, K. K. C. (2002). Enhanced biological production off Chennai triggered by October 1999 super cyclone (Orissa). Current Science, 82(12), 1472–1479.Google Scholar
- Madhu, N. V., Jyothibabu, R., Mheshwaran, P. A., Gerson, V. J., Gopalakrishnan, T. C., & Nair, K. K. C. (2006). Lack of seasonality in phytoplankton standing stock (chlorophyll a) and production in the western Bay of Bengal. Continental Shelf Research, 26(16), 1868–1883p. https://doi.org/10.1016/j.csr.2006.06.004.CrossRefGoogle Scholar
- Madhupratap, M., Nair, V. R., Nair, S. R. S., & Achuthankutty, C. T. (1981). Zooplankton abundance of the Andaman Sea. Indian Journal of Marine Sciences, 10, 258–261.Google Scholar
- Madhupratap, M., Gauns, M., Ramaiah, N., Prasanna Kumar, S., Muraleedharan, P. M., de Douza, S. N., Sardesai, S., & Usha, M. (2003). Biogeochemistry of Bay of Bengal: physical, chemical and primary productivity characteristics of the central and western Bay of Bengal during summer monsoon 2001. Deep-Sea Research II, 50, 881–886.CrossRefGoogle Scholar
- Narayanan, M., Vasan, D. T., Bharadwaj, A. K., Thanabalan, P., & Dhileeban, N. (2013). Comparison and validation of sea surface temperature (SST) using MODIS and AVHRR sensor data. International Journal of Remote Sensing and Geoscience, 2(3), 1–7.Google Scholar
- Park, W.-G. (2007). Spatial and monthly changes of sea surface temperature, sea surface salinity, chlorophyll a, and zooplankton biomass in southeastern Alaska: implications for suitable conditions for survival and growth of dungeness crab Zoeae. Journal of the Fisheries Science and Technology I, 10(3), 133–142.Google Scholar
- Poornima, D. P., Sarangi, R. K., Shanthi, R., Thangaradjou, T., & Chauhan, P. (2015). Seasonal nitrate algorithms for nitrate retrieval using OCEANSAT-2 and MODIS-AQUA satellite data. Environmental Monitoring and Assessment, 187(4), 176. https://doi.org/10.1007/s10661-015-4340-x.CrossRefGoogle Scholar
- Pretorius, M., Huggett, J.A., & Gibbons, M.J. (2016). Summer and winter differences in zooplankton biomass, distribution and size composition in the KwaZulu-Natal Bight, South Africa. In: Roberts MJ, Fennessy ST, Barlow RG (eds), Ecosystem processes in the KwaZulu-Natal Bight. African Journal of Marine Science, 38 (Supplement): S155–S168 .Google Scholar
- Ramage, C. S. (1984). Climate of the Indian Ocean north of 13° S. In world Survey of climatology, 15: Climates of the Oceans, H. Van loon (ed). Amsterdam: Elsevier Scientific, 603–659.Google Scholar
- Sarangi, R. K., Thangaradjou, T., Poornima, D., Shanthi, R., Saravana Kumar, A., & Balasubramanian, T. (2015). Seasonal nitrate algorithms for the Southwest Bay of Bengal water using in situ measurements for satellite remote-sensing applications. Journal of Coastal Research, 31(2), 398–406.CrossRefGoogle Scholar
- Solanki, H. U., Dwivedi, R. M., Nayak, S. R., Somvanshi, V. S., Gulati, D. K., & Pattnayak, S. K. (2003). Fishery forecast using OCM chlorophyll concentration and AVHRR SST: validation results off Gujarat coast, India. International Journal of Remote Sensing, 24(18), 3691–3699.CrossRefGoogle Scholar
- Solanki, H. U., Chauhan, R., George, L. B., & Dwivedi, R. M. (2015). Development of bio-physical model for the estimation of zooplankton biomass production in the Arabian Sea using remotely sensed oceanographic variables. Indian Journal of Marine Sciences, 44(3), 348–353.Google Scholar
- Steinberg, D. K., Lomas, M. W, & Cope, J.S. (2012). Long-term increase in mesozooplankton biomass in the Sargasso Sea: linkage to climate and implications for food web dynamics and biogeochemical cycling, Global Biogeochemical Cycles, 26, GB1004 https://doi.org/10.1029/2010GB004026.
- Strickland, J.D.H., & Parsons, T.R. (1972). A practical handbook of sea water analysis, 2nd ed. Bulletin Fisheries Research of Board of Canada, 167, p. 310.Google Scholar
- Williams, G. N., Dogliotti, A. I., Zaidman, P., Solis, M., Narvarte, M. A., Gonzalez, R. C., Estevez, J. L., & Gagliardini, D. A. (2013). Assessment of remotely sensed sea-surface temperature and chlorophyll-a concentration in San Matías Gulf (Patagonia, Argentina). Cont Continental Shelf Research, 52, 159–171.CrossRefGoogle Scholar