Environment regime influence on Chlorophyll-a abundance and distribution in North Indian Ocean
- Published
- Accepted
- Subject Areas
- Ecology, Ecosystem Science, Statistics, Climate Change Biology, Biological Oceanography
- Keywords
- Wavelet analysis, Boosted Regression Trees, Bay of Bengal, Arabian Sea, Sri lanka Exclusive Economic Zone, Phytoplankton, Monsoon, Upwelling, Predictor variables, Seasonal
- Copyright
- © 2019 Elepathage et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. Environment regime influence on Chlorophyll-a abundance and distribution in North Indian Ocean. PeerJ Preprints 7:e27662v1 https://doi.org/10.7287/peerj.preprints.27662v1
Abstract
North Indian Ocean region around India and Sri Lanka is a complex and rich coastal ecosystem undergoing various seasonal and inter-annual changes and various pressures. Hence the objective of this study was to assess the scales of coupling between chlorophyll-a concentration (chl-a) and the influencing variables and explore the nature of the spatiotemporal variability of them. The seasonal and annual variations of chl-a along the Bay of Bengal (BoB), Arabian sea (AS) and ocean region around Sri Lanka in relation to the physical and chemical oceanographic variables were analyzed using satellite observations covering the period of 2002-2018. The effects of diffuse attenuation coefficient, photosynthetically available radiation (PAR), sea surface temperature (SST), Wind speed, Eastward wind component, Nitrate, Black carbon column mass density, Sea Salt Surface Mass Concentration, Open water net downward longwave flux, Surface emissivity were considered on a monthly time scale. Wavelet analysis and the Boosted Regression Trees (BRT) were used as the main analysis and modeling methods. The peaks of chl-a, diffuse attenuation coefficient, and nitrate were observed in September. In wind speed and eastward wind it was July and in black carbon column mass density, and PAR in March. In Sea Salt Surface Mass Concentration, Open water net downward longwave flux, Surface emissivity, Diffuse attenuation coefficient for downwelling irradiance, and SST mean maximums were found in June, February, November, September, April respectively. In BRT model the estimated cross validation (cv) deviance, standard error (se), training data correlation, cv correlation, and D2 were 0.003, 0.002, 0.932, 0.949, and 0.846 respectively. According to the results, diffuse attenuation coefficient (90%), eastward wind component (3.7%) and nitrate (3%) were the most positively correlated variables with Chl-a occurrence. SST evidenced an inverse relationship with Chl-a. According to the model built <42 Einsteinm-2day-1 PAR, <0.986 surface emissivity, <70 Wm-2 open water net downward long wave flux, 28.2 -28.5 0C SST , 2 ms-1 Wind speed, 5 ms-1 - 6 ms-1 eastward wind, 4.8 x10-8 -7x10-8 kgm-3 sea salt surface mass concentration, and 0.1-0.5micromoleL-1 nitrate are favourable for the optimum level of phytoplankton occurrence. Since BRT deals robustly with non-linear relationships of the environmental variables it can be used in further studies of ecological modeling.
Author Comment
This is a submission to PeerJ for review. This paper explored the nature of the spatiotemporal variability of the studied variables and assess the scales of coupling between Chl-a and the influencing variables. The methods used in this study; wavelet analysis and Boosted Regression Trees performed well with the non-linear relationships of the ecological variables.This paper will contribute a good knowledge on the primary production and the influence of environment on it in Bay of Bengal, Arabian sea and Sri Lanakan ocean region.