Ecosystem response in temperature fronts in the northeastern Arabian Sea

Sarma, V. V. S. S.; Desai, Dattesh V.; Patil, Jagadish S.; Khandeparker, Lidita; Aparna, S G; Shankar, D.; D'Souza, Selrina; Dalabehera, H.B.; Mukherjee, Jayati; Sudharani, P.; Anil, Arga Chandrashekar

doi:10.1016/j.pocean.2018.02.004

Cited by 22 publications

(16 citation statements)

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“…Significant negative relationship between zooplankton biomass and POC ( r 2 = −0.41; p < 0.001) suggests that zooplankton may be feeding on POC. A linear relationship of TBC with pCO 2 ( r 2 = 0.43; p < 0.001) and an inverse relation with the dissolved oxygen ( r 2 = −0.45; p < 0.001) within fronts of this region (Sarma et al, , ) indicate an intense bacterial decomposition of organic matter within the fronts compared to the surrounding NF regions that lead to conversion of POC to DOC is possible. However, no DOC data are available to compare.…”

Section: Discussionmentioning

confidence: 99%

“…As a result, higher integrated phytoplankton biomass was found within the frontal than the corresponding NF regions, except T1 (Table ). Further, phytoplankton cells were found to be healthy and active within the frontal than the NF regions from higher Fv/Fm ratios in the former than the latter (Sarma et al, ).…”

Section: Discussionmentioning

confidence: 99%

“…Although the fronts were identified by the difference in SST at the time of our sampling, however, characterization of the frontal and NF region is a difficult task because the front is dynamic in nature and laterally advects in the direction of the mean flow. Since the prevailing currents in the NEAS during our study period were in the northwestward, the fronts move laterally toward northwest direction (Sarma et al, 2017). The open ocean fronts T1 and T2 were sampled from northwest to southeast direction, while the coastal fronts T3 and T5 were sampled from east to west except T4, which was sampled from west to east (Figure 1).…”

Section: Variable Hydrographic Conditions and Sources Of Nutrients Inmentioning

confidence: 93%

“…The temperature fronts in the NEAS extend from 10 to 20 km in width and about 100 km in length (Vipin et al, ). In the present study, the fronts of different ages at the time of their sampling, ranging from as young as 0.5 to as old as 11 days (Sarma et al, ), in the NEAS were investigated. Among the five fronts sampled, front T5 is the youngest (0.5 day) following T1 (3.3 days), T2 (6 days), whereas T3 and T4 are the oldest (9–11 days) at the time of our sampling (Sarma et al, ).…”

Section: Study Regionmentioning

confidence: 99%

“…The authors suggested that the strength of the mixing process determines the front whether it is a source or sink of atmospheric CO 2 . Very recently, it was also found that the depth-integrated total bacterial count (TBC) and zooplankton biomass in the upper 50 m of the water column were higher within the fronts than those outside the fronts in the NEAS (Sarma et al, 2017). All these studies were focused on the vertical mixing of nutrients and associated changes in autotrophic and heterotrophic activities within the frontal regions.…”

Section: Introductionmentioning

confidence: 99%

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Particulate Organic Carbon Composition in Temperature Fronts of the Northeastern Arabian Sea during Winter

et al. 2018

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In order to understand the major sources of particulate organic carbon (POC) in the frontal zones and to examine their variability with space and time, a total of five temperature fronts of different ages was sampled in the northeastern Arabian Sea during winter. Compared to the nonfrontal regions, POC and chlorophyll‐a were higher within the coastal fronts, whereas chlorophyll‐a was lower within the open ocean front (T1). The variation of POC between coastal and open ocean fronts is attributed to the combined influence of variable vertical mixing, heterotrophic transformation and age of the front. Relatively depleted δ13CPOC and δ15NPN were observed within the fronts, suggesting that POC pool is contributed by in situ production supported by upwelling of nutrient‐rich water and zooplankton biomass. Elemental C:N ratios, POC:Chl‐a, δ13CPOC, and δ15NPN suggest that POC is mainly contributed from primary producers and heterotrophs in the study region. However, relative contributions from these two sources vary spatially from coastal to open ocean and with the age of the front. Stable Isotope Analysis in R (SIAR) model revealed that zooplankton biomass largely contributed to POC in the open ocean (60–80%) than phytoplankton (20–40%) and nearly equal contribution was observed in the coastal fronts (50–60% and 40–50%, respectively). This study, thus, demonstrates that dominant heterotrophy and autotrophy in the open ocean and coastal fronts and it is consistent with their biomasses. Predominant heterotrophy in the open ocean is attributed to deeper mixed layer resulting in upwelling of bacteria‐rich and phytoplankton‐poor water to surface leading to existence of microbial loop.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Variable Hydrographic Conditions and Sources Of Nutrients Inmentioning

confidence: 93%

Section: Study Regionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Particulate Organic Carbon Composition in Temperature Fronts of the Northeastern Arabian Sea during Winter

et al. 2018

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Predicting the foraging patterns of wintering Auks using a sea surface temperature model for the Barents Sea

Hodges

Erikstad

Reiertsen

2022

Ecol Sol and Evidence

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The conservation of seabirds is increasingly important for their role as indicator species of ocean ecosystems, which are predicted to experience increasing levels of exploitation this century. Safeguarding these ecosystems will require predictive, spatial studies of seabird foraging hotspots. Current research on seabird foraging hotspots has established a significant relationship between probability of presence and several environmental variables, including Sea Surface Temperature (SST). However, inter‐annual, basin‐wide variation has the potential to invalidate these models, which depend on seasonal mesoscale variability. In this study, we present a novel solution to predict presence from spatially and temporally variable environmental predictors, while reducing the influence of large‐scale basin‐wide variation. We model the Maximum Entropy (MaxENT) Model‐derived relationship between Standardized Monthly SST (StdSST) and Habitat Suitability using Gaussian curve models, and then apply these models to independent StdSST data to produce heatmaps of predicted seabird presence. In this study, we demonstrate StdSST to be a functional environmental predictor of seabird presence, within a Gaussian curve model framework. We demonstrate accurate predictions of the model's training data and of independent seabird presence data to a high degree of accuracy (area under the receiver operator characteristic curve > 0.65) for four species of Auk: Common Guillemots (Uria aalge), Razorbills (Alca torda), Atlantic Puffins (Fratercula arctica) and Brunnich's Guillemots (Uria lomvia). We believe that the methodology we have developed and tested in this study can be used to guide ecosystem management practices by converting coupled‐climate model predictions into predictions of future presence based on Habitat Suitability for the species, allowing us to consider the possible effects of climate change and yearly variation of SST on foraging seabird hotspots in the Barents Sea.

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