Climate change impact on the feeding habits of Indian mackerel observed along the Kerala coast

While most food and feeding research in fisheries emphasize the feeding habits and diet components of the fish, the aim of the present work was to assess any change in the diet composition of Indian mackerel Rastrelliger kanagurta (Cuvier, 1816) and identify the role of climate change in effecting it. Non-parametric statistical technique, the Generalized Additive Model (GAMs) was used for modeling the causal link of diet changes in relation to climate change using mgcv package of R software. Monthly Index of Relative Importance (IRI) was calculated for 36 months from January 2013 to December 2015 and major prey items in the diet were identified. Climatic variables - Sea Surface Temperature (SST), precipitation (Pr), Chlorophyll a (Chl a), salinity and Ekman transport or coastal upwelling index (CUI)) pertaining to the study area were extracted for the period. Length-wise IRI was compared to check the existing patterns in the feeding habits of the fish. The dominant prey items observed in the gut of Indian mackerel during the study period were diatoms ( Fragillaria sp., Nitzchia sp., Thalassiothrix sp., Thalassiosira sp. and Coscinodiscus sp.), dinoflagellates (Ceratium sp., Ornothocercus sp., Dinophysis sp. and Prorocentrum sp.), copepods (Calanoida, Cyclopoida and Harpacticoida), decapods (Acetes) and tintinnids (Tintinnopsis sp., Codenellopsis sp. and Flavella sp). GAM models were fitted with monthly IRI of the major prey items (copepods, diatoms, dinoflagellates, Acetes and tintinnids) in the gut and climatic variables. Model selection was done in terms of sharpening the relation between the predictors and the response variable using Akaike information criterion, R-squared and F-statistics. GAM model results revealed that occurrence of prey items in the diet of Indian mackerel were influenced by environmental variables. An increase in the relative importance of dinoflagellates, Acetes and tintintids in the diet over the historic period was observed. The study also revealed a shift in the diet composition of younger fishes. The results of the current study provide a more in-depth assessment of the nonlinear relationship between climatic variables and diet composition of Indian mackerel

Adaptations to Climate Change Impacts on Coastal Fisheries and Aquaculture

Adaptations to Climate Change Impacts on Coastal Fisheries and Aquacultur

Marine climate and fisheries scenario of Kerala Climcard-3

Marine climate and fisheries scenario of Kerala Climcard-

Preliminary assessment, restoration and aquaculture support for a small wetland

In line with the strategy of regional wetland datasets integration to a common national digital platform, map of small wetlands less than 2.2 ha in Kochi Taluk was prepared. A representative small wetland at Edakochi village of Kerala was selected through maps and field visits for preliminary assessment and restoration. Shuttle Radar Topography Mission’s Digital Elevation Model (DEM) was used to assess the general elevation, slope and flow accumulation pattern of the selected wetland along with assessment of the catchment area and drainage pattern. Restoration works of the selected wetland was carried out vis-a-vis side bund strengthening and sluice gate fortification. The comparative analysis of water quality assessment of wetland before and after restoration revealed improvement in water quality parameters as well as increase in water level. The Dissolved Oxygen level of the aquatic system was found to have increased substantially along with other several favourable changes in water parameters due to the restoration activities. The restored wetland at Edakochi was further utilised for multispecies farming of prawns, Pearl spot, Milk fish and Grey mullet and the harvest indicated sustainable yield. Aquaculture practice in wetlands with real time scientific advisories could ensure continuous data generation and village level climate resilience

Effect of Climatic Variability on the Fishery of Indian Oil Sardine Along Kerala Coast

Indian oil sardine (IOS), the commercially and ecologically important pelagic fish of the Kerala coast is susceptible to climatic variation. The study analyzes the impact of climate change on the catch of Sardinella longiceps along the Kerala coast and tries to predict the catch trend under the two RCP scenarios 4.5 and 6.0 for the period 2020-2100. Monthly catch of IOS by major gears for the period 1990-2016 was collected and Relative effort (Effort) and Weighted CPUE (cpue) were accordingly estimated. The climatic variables Sea Surface Temperature (SST), Precipitation (Pr), Chlorophyll a (SSC) and Salinity (SSS) were obtained from NOAA/NASA. The relationship of cpue and Effort of IOS to environmental variables were explored by Generalized Additive Model. The best fit model was selected using lowest Akaike information criterion (AIC) value, Deviance and F statistic. Predictions of cpue and Effort under RCP 4.5 and RCP 6.0 were done and the catch of IOS was estimated. The GAM model revealed the variations in the catch of IOS in relation to climate change. The SST, SSS and Pr showed a negative relation whereas SSC was found to be positively related to the catch of IOS. The results of the study indicate a decreasing trend of cpue and catch and an increasing trend of Effort towards 2100 under both climate change scenarios

Climate change drivers influencing Indian mackerel fishery in south-eastern Arabian Sea off Kerala, India

The Indian mackerel Rastrelligerkanagurta(Cuvier, 1816) is one of the most important marine fishery resources along the south-eastern Arabian Sea along the coast of Kerala, south India. The effect of selected environmental variables on the Relative effort (Effort) and weighted catch per unit effort (cpue)of the fish were investigated using simple correlation and Path analysis. Six major oceanographic variables, namely sea surface temperature (SST), sea surface chlorophyll-a concentration (SSC), sea surface salinity (SSS), Precipitation (Pr) Indian Ocean Dipole (IOD) and Southern Oscillation Index (SOI) (ENSO index) were selected for the present study. Among these SST had the highest direct negative effect (-0.282, p SSC >SSS

Application of Information Communication Technology in Coastal Resilience through Income Improvement

Application of Information Communication Technology in Coastal Resilience through Income Improvemen

കേരളത്തിലെ കടൽ കാലാവസ്ഥയുടെയും മത്സ്യബന്ധന മേഖലയുടെയും സംക്ഷിപ്തരൂപം Climcard-3

കേരളത്തിലെ കടൽ കാലാവസ്ഥയുടെയും മത്സ്യബന്ധന മേഖലയുടെയും സംക്ഷിപ്തരൂപ

Giant devil manta rays landed by purse seiner at Cochin fisheries harbour

Two specimens of giant devil ray, Manta birostris locally known as ‘Aanathirandi’ measuring 307 and 194.5 cm in TL, 534 and 416 cm in disc width and weighing about 780 and 570 kg respectively were landed at Cochin fisheries harbour on 19.05.14 and 20.05.1

Spatio-temporal variations of chlorophyll from satellite derived data and CMIP5 models along Indian coastal regions

Comparison of chlorophyll data of three sets of CMIP5 models for RCP 4.5 (MPI-ESM-MR, HadGEM2-ES and GFDL-ESM2M) and RCP 6.0 (IPSL-CM5A-LR, HadGEM2-ES and GFDL-ESM2M) were done with satellite derived data (OC-CCI) for the period of 1998–2017 along four Indian coastal regions. The monthly, yearly and zone-wise seasonal comparison between model and satellite data were carried out. Analysis of monthly variations of chlorophyll during 1998–2017 reveals that the satellite data show maximum value of 0.53 mg/m3 in September, whereas all other models show maximum in August. Yearly analysis indicates maximum satellite data in the year 2004, while minimum was observed in 2015. HadGEM2-ES exhibited maximum model value and the lowest was found for IPSL-CM5A-LR. It was observed that the maximum chlorophyll value of 2.56 mg/m3 for satellite data was in the monsoon season and the lowest value of 0.14 mg/m3 was in the pre-monsoon. Seasonal analysis reveals no clear match among model and satellite values in any of the coastal regions. In northwest and northeast regions, the satellite values were found higher than the model values in most of the years, whereas in other regions, the model values were found fluctuating with the satellite values. Owing to the mismatch of the model and the satellite values, the work cautions to apply biases or corrections on usage of RCP model data for regional marine climate change research