Diversity and ecological significance of seaweeds along the Saurashtra coast of Gujarat, India: a five-year study (2019–2023)

Abstract. Widyartini DS, Hidayah HA, Insan AI. 2023. Diversity and distribution pattern of bioactive compound potential seaweed in Menganti Beach, Central Java, Indonesia. Biodiversitas 24: 1125-1135. Seaweed has great potential in the pharmaceutical industry. This study aims to obtain data on the diversity and distribution of bioactive compound potential seaweeds collected from Menganti Beach. The research applied the survey and random transect sampling in each substrate type. The variables were species diversity, distribution pattern, and bioactive compound potential. The main parameters were the number of species and the number of individuals of each species of seaweed. The supporting parameters were depth, salinity, temperature, and pH. The data were analyzed by calculating the Shannon-Wiener diversity, Simpson dominance, and Morisita indices using Primer 7 software. The results showed that 21 species have bioactive compound potential. There were seven species (33 %) of Chlorophyta, including Caulerpa leltilifera, C. racemosa, C. taxifolia, Ulva lactuca, U. intestinalis, Valonopsis pacynema, and Codium tomentosum. Twelve species (57%) of Rhodophyta, including Callophyllis crispata, Chondrus crispus, Eucheuma spinosum, Halymenia harveyana, Gigartina stellata, Gracilaria gigas, G. canaliculata, G. lichenoides, G. verrucosa, Palmaria palmata, Portieria hornemannii, and Rhodymenia palmata; and two species (10%) of Phaeophyta, including Padina australis and Sargassum crassifolium. The diversity of seaweed was moderate (0.903-1.153). The dominance index (0.559-0.68) indicates no dominance, and the seaweed distribution pattern is grouping.

Climate Change and Tropical Cyclone Activity.- A Climatology of Intense Tropical Cyclones in the North Indian Ocean Over the Past Three Decades (1980-2008).- Tropical Cyclones in a Hieararchy of Climate Models of Increasing Resolution.- Modeling Climate Change: Perspective and Applications in the Context of Bangladesh.- Changes in Tropical Cyclone Precipitation Over China.- Toward Improved Projection of the Future Tropical Cyclone Changes.- Global Warming and Tropical Cyclone Activity in the Western North Pacific.- Tropical Cyclones and Climate Change: An Indian Ocean Perspective.- Recent Trends in Tropical Cyclone Activity in the North Indian Ocean.- Progress on Tropical Cyclogenesis.- Generating Synthetic Tropical Cyclone Databases for Input to Modeling of Extreme Winds, Waves, and Storm Surges.- Numerical Simulation of the Genesis of Cyclone Nargis Using a Global Cloud-System Resolving Model, NICAM.- Simulation of the North Indian Ocean Tropical Cyclones Using the Regional Environment Simulator: Application to Cyclone Nargis in 2008.- Simulation of Track and Intensity of Gonu and Sidr with WRF-NMM Modeling System.- Operational Tropical Cyclone Forecasting & Warning Systems.- Monitoring and Prediction of Cyclonic Disturbances Over North Indian Ocean by Regional Specialised Meteorological Centre, New Delhi (India): Problems and Prospective.- Evaluation of the WRF and Quasi-Lagrangian Model (QLM) for Cyclone Track Prediction Over Bay of Bengal and Arabian Sea.- Simulation of Tropical Cyclones Over Indian Seas: Data Impact Study Using WRF-Var Assimilation System.- Impact of Rain-Affected SSM/I Data Assimilation on the Analyses and Forecasts of Tropical Cyclones, and Study of Flow-Dependent Ensemble Background Errors, Over the Southwest Indian Ocean.- Statistical Forecasting of Tropical Cyclones for Bangladesh.- THORPEX and Its Application for Nargis by Ensemble Prediction.- Cyclone Gonu: The Most Intense Tropical Cyclone on Record in the Arabian Sea.- Real-Time Prediction of Cyclone Over Bay of Bengal Using High-Resolution Mesoscale Models.- Performance Evaluation of DGMANs NWP Models During Gonu.- Capabilities of Using Remote Sensing and GIS for Tropical Cyclones Forecasting, Monitoring, and Damage Assessment.- Assessment of Risk and Vulnerability from Tropical Cyclones, Including Construction, Archival and Retrieval of Best-Track and Historic Data Sets.- On Developing a Tropical Cyclone Archive and Climatology for the South Indian and South Pacific Oceans.- Improving the Australian Tropical Cyclone Database: Extension of the GMS Satellite Digital Image Archive.- Coastal Vulnerability Assessment Based on Historic Tropical Cyclones in the Arabian Sea.- The International Best Track Archive for Climate Stewardship (IBTrACS) Project: Overview of Methods and Indian Ocean Statistics.- Remote Sensing Imagery Assessment of Areas Severely Affected by Cyclone Gonu in Muscat, Sultanate of Oman.- Urban Sprawl and City Vulnerability: Where Does Muscat Stand?.- Flood Studies in Oman and the Difficulties in Using Rainfall-Runoff Analysis.- Disaster Preparedness, Management and Reduction.- Cyclone Gonu Storm Surge in the Gulf of Oman.- How the National Forecasting Centre in Oman Dealt with Tropical Cyclone Gonu.- Cyclone Disaster Management: A Case Study of MODES Experience with Cyclone Gonu.- Recent High Impact Tropical Cyclone Events in the Indian Ocean: Nargis, SIDR, Gonu and Other Events.- The Impact of Cyclone Gonu on Selected Coral Rich Areas of the Gulf of Oman Including Indications of Recovery at the Daymanyiat Islands.- Cyclone Nargis Storm Surge Flooding in Myanmar's Ayeyarwady River Delta.- The First Ever Super Cyclonic Storm GONU over the Arabian Sea During 1-7 June 2007: A Case Study.- Characteristics of Very Severe Cyclonic Storm NARGIS over the Bay of Bengal During 27 April to 3 May 2008.- Characteristics of Very Severe Cyclonic Storm SIDR over the Bay of Bengal During 11-16 November 2007.- Influence of a Tropical Cyclone Gonu on Phytoplankton Biomass (Chlorophyll a) in the Arabian Sea.- Recent Outbreaks of Harmful Algal Blooms Along the Coast of Oman: Possible Response to Climate Change?.- Understanding the Tropical Cyclone Gonu.

Long-term effects of global climate change and increased anthropogenic CO2 uptake make the Indian Ocean susceptible to ocean acidification. Several studies have projected a decline of upper ocean pH by 0.3-0.4 by the end of the 21st century, which has the potential to reduce oceanic biological production considerably. There is a critical need to understand the present status of Indian Ocean acidification and identify its key drivers. However, the number of spatially and temporally varying available observations to examine the present state of Indian Ocean acidification is limited. The numerical ocean models have a unique ability to integrate our empirical and theoretical understanding of the marine environment. Therefore, the changes in the Indian Ocean seawater pH in response to the changes in sea-surface temperature (SST), sea-surface salinity (SSS), dissolved inorganic carbon (DIC), and total alkalinity (ALK) over the period 1980-2019 and its driving mechanisms has been carried out using a high-resolution regional ocean-ecosystem model outputs. The analysis indicates that the rate of change of declining pH in the Arabian Sea (AS), the Bay of Bengal (BoB), and the Equatorial Indian Ocean (EIO) is -0.014 ± 0.002, -0.014 ± 0.001, and -0.015 ± 0.001 unit dec-1, respectively. In the AS (BoB), the highest decadal DIC trend is found in 2000-2009, whereas it is lower in 1990-1999 and 2010-2019, but, in the case of EIO, we find it opposite. Ocean acidification is seen to have accelerated throughout the IO region during 2010-2019 as opposed to the previous decades. Further, our analysis indicates that El Niño, followed by a positive Indian Ocean Dipole, increases acidification in the Indian Ocean. The increasing anthropogenic CO2 uptake by the ocean dominantly controls 79.97% (94.54% and 85.72%) of the net pH trend (1980-2019) in AS (BoB and EIO), whereas ocean warming controls 14.39% (13.38% and 7.02%) of pH trends in AS (BoB and EIO). The changes in ALK contribute to enhancing the pH trend of AS by 5.0%. ALK dominates after DIC in the EIO and, similar to AS, contributes to enhancing ocean acidification by 10.67%. In contrast, it has a buffering effect in the BoB, suppressing the pH trend by -5.4%. In summary, this research work consolidates the current level of understanding about the Indian Ocean acidification based on the available field observations, reconstructed data sets, and model simulations.

Variability of Indian summer monsoon rainfall is examined with respect to variability of surface wind stresses over Indian Ocean. The Indian Ocean region extending from 40°–120° E, and 30° S–25° N, has been divided into 8 homogeneous subregions, viz (1) Arabian Sea (AS), (2) Bay of Bengal (BB), (3) West-equatorial Indian Ocean (WEIO), (4) Central-equatorial Indian Ocean (CEIO), (5) East-equatorial Indian Ocean (EEIO), (6) South-west Indian Ocean (SWIO), (7) South-central Indian Ocean (SCIO), and (8) South-east Indian Ocean (SEIO). The period of study extends for 13 years from 1982–1994. Monthly NCEP surface wind stress data of five months – May through September, have been used in the study. The spatial variability of seasonal and monthly surface wind stresses shows very low values over CEIO and EEIO and very high values over AS, SWIO, and SEIO regions. On the seasonal scale, all India summer monsoon rainfall (AISMR) shows concurrent positive relationships with the surface wind stresses over AS, BB, WEIO, SWIO and SCIO and negative relationships with the surface wind stresses over EEIO and SEIO. The relationships of AISMR with the surface wind stresses over AS and WEIO are significant at 5% level. The concurrent relationships between monthly surface wind stresses over these 8 oceanic sub-regions and monthly subdivisional rainfalls over 29 sub-divisions have been studied. The rainfalls over the subdivisions in the central India and on the west coast of India are found to be significantly related with surface wind stresses over AS, SWIO, SCIO. Monthly subdivisional rainfalls of four subdivisions in the peninsular India show negative relationship with BB surface wind stresses. May surface wind stresses over AS, BB, WEIO, CEIO and SWIO have been found to be positively related with ensuing AISMR. The relationship with AS wind stresses is significant at 5% level and hence may be considered as a potential predictor of AISMR.

Water, heat and freshwater flux out of the northern Indian Ocean in September–October 1995

This paper aims to study the changes in the Indian Ocean seawater pH in response to the changes in sea‐surface temperature, sea‐surface salinity, dissolved inorganic carbon (DIC), and total alkalinity (ALK) over the period 1980–2019 and its driving mechanisms using a high‐resolution regional model outputs. The analysis indicates that the rate of change of declining pH in the Arabian Sea (AS), the Bay of Bengal (BoB), and the Equatorial Indian Ocean (EIO) is −0.014 0.002, −0.014 0.001, and −0.015 0.001 unit dec−1, respectively. Both in AS and BoB (EIO), the highest (lowest) decadal DIC trend is found during 2000–2009. The surface acidification rate has accelerated throughout the IO region during 2010–2019 compared to the previous decades. Further, our analysis indicates that El Ninõ and positive Indian Ocean Dipole events lead to an enhancement of the Indian Ocean acidification. The increasing anthropogenic CO2 uptake by the ocean dominantly controls 80% (94.5% and 85.7%) of the net pH trend (1980–2019) in AS (BoB and EIO), whereas ocean warming controls 14.4% (13.4% and 7.0%) of pH trends in AS (BoB and EIO). The changes in ALK contribute to enhancing the pH trend of AS by 5.0%. ALK dominates after DIC in the EIO and, similar to the AS, contributes to increasing the negative pH trend by 10.7%. In contrast, it has a buffering effect in the BoB, suppressing the pH trend by −5.4%.

The intraseasonal oscillations (ISOs) activate in the tropical Indian Ocean (IO), exhibiting distinct seasonal contrasts in active regions and propagating features. The seasonal northward migration of the ISO activity initiates in spring–early summer, composed of two stages. Strong ISO activity first penetrates into the northern Bay of Bengal (BoB) around mid-April, and then extends to the northern Arabian Sea (AS) by mid-May. The northward-propagating ISOs (NPISOs) during their initiation periods, which are referred to as the primary northward-propagating (PNP) events, are analyzed with regard to the BoB and the AS, respectively. In terms of the BoB PNP event, the northward branch could be observed only in the BoB, and the eastward movement is still clear as the winter ISOs. For the AS PNP event, a strong northward branch spreads across the wider northern IO, as obvious as the summer ISOs. The relative roles of the seasonal environmental fields in modulating the PNP events are diagnosed based on a 2.5-layer atmospheric model. The results indicate that the seasonal variations of the surface moisture dominantly regulate the BoB PNP event, while both the surface moisture and the vertical wind shear are necessary for the AS PNP event. Additionally, the leading BoB PNP event is hypothesized to potentially act as a precondition of the following AS PNP event in terms of their internal ISO reinitiation processes and in terms of creating a favorable easterly shear environment in the northern IO.

ABSTRACTThe present study was carried out to find the variability of chlorophyll-a (chl-a) concentration, sea surface temperature (SST), and sea surface height anomalies (SSHa) during 2003–2014, covering the Bay of Bengal (BoB) and Arabian Sea (AS) waters. These parameters were linked with El Niño, La Niña, and Indian Ocean Dipole (IOD) years. The observed results during 2003–2014 were evaluated and it was found that the monthly mean value for 12-year data ranged as follows: chl-a (0.11–0.46 mg m−3), SST (27–31 °C), and SSHa (−0.2 to 20 cm). The annual mean range of chl-a for 12-year data was 0.1–0.23 mg m−3, the SST range was 27–28 °C, and the SSHa range was 2.14–13.91 cm. It has been observed that with the SST range of 27–28 °C and the SSHa range of 7–9 cm, the chl-a concentration enhanced to 0.20–0.23 mg m−3. With a higher SST range of 28–29 °C and with a positive SSHa range of 11–14 cm, the chl-a concentration appeared to be low (0.17–0.18 mgm−3). During normal years, SSHa was positive with the >5 to <10 cm range during the months of April–June, which coincided with an increase in SST, >2 to <4 °C. During the normal years, SSHa (>−0.2 to <−10 cm) was observed to be negative during October–December, with a decrease in SST (<3 °C) observed. The high monthly mean chl-a concentration (>0.3 to <0.5 mg m−3) was noticed during December–February in the BoB and AS. Compared to the BoB chl-a range (<0.4 mg m−3), a high chl-a concentration was observed in AS (>0.4 mg m−3). However, during the phenomenon years, the study area had experienced low chl-a (<0.2 mg m−3), high SST (>5 °C), and more positive SSHa (>10 to <20 cm) during January–March and October–December in AS and BoB. The present study infers that a positive IOD leads to low chl-a concentration (<2 mg m−3) and low primary productivity in AS. El Niño caused the down-welling process, it results in a low chl-a concentration (<1 mg m−3) in BoB and AS. La Niña caused the upwelling process, and it results in a high chl-a concentration (>2.0 mg m−3) in BoB and AS. In the recent past years (2003–2014), the intensity and frequency of El Niño, La Niña, and IOD have been increasing, evidenced with few studies, and have impacts on the Indian Ocean climate. Therefore, the influences of the relative changes of these phenomena on the BoB and AS need to be understood for productivity assessment and ocean state monitoring.

Impact of anthropogenic Pb and ocean circulation on the recent distribution of Pb isotopes in the Indian Ocean

This observational study mainly examines the impacts of short- and long-time fluctuations of surface wind fields over the Arabian Sea (AS), the Bay of Bengal (BoB), and the southern Indian Ocean (SIO) on Indian Summer Monsoon Rainfall (ISMR), with special reference to strong and weak Indian summer monsoons (ISM). Two datasets over 1991–2014 are used: (1) the daily gridded rainfall produced by India Meteorological Department (IMD), and (2) the Cross-Calibrated Multi-Platform (CCMP) wind product version 2.0 created by Remote Sensing Systems. Monthly mean surface wind speed, convergence, and curl in the AS, BoB, and SIO are overall not significantly different between strong and weak ISMRs except for wind speed in the AS in September. However, the probability density function (PDF) distribution of daily values over the AS, BoB, and SIO during strong ISMRs is different from during weak ISMs, suggesting that sub-monthly surface wind characteristics could be useful in diagnosing rainfall characteristics. Except for rainfall in the northeast part of India, Indian regional rainfalls are closely linked with surface wind speeds over the AS, and wind convergence and curl over the BoB on short timescales of up to 1 week. The daily area-averaged wind convergence over the BoB is better correlated with regional rainfall during strong ISMs than during weak ISMRs. Multiple linear regression analysis shows that the fluctuations of monthly wind fields in the AS and BoB can affect monthly rainfall in some regions but are not related to a significant change in rainfall over the whole India. It is the short-time fluctuations of wind speed over the AS as well as wind convergence and curl over the BoB rather than their long (monthly) timescale fluctuations that are related to the strength of ISMR. Surface winds over the SIO on weather timescales have little influence on ISMR.

The northern Indian Ocean, comprising of two marginal seas, the Arabian Sea (AS) and the Bay of Bengal (BoB), is known for the occurrence of tropical cyclones. The simultaneous occurrence of the cyclones Luban in the AS and Titli in the BoB is a rare phenomenon, and, in the present study, we examined their contrasting upper ocean responses and what led to their formation in October 2018. Being a category-2 cyclone, the maximum cooling of sea surface temperature associated with Titli was 1°C higher than that of Luban, a category-1 cyclone. The higher tropical cyclone heat potential in the BoB compared with the AS was one of the reasons why Titli was more intense than Luban. The enhancement of chlorophylla(Chl-a) and net primary productivity (NPP) by Luban was 2- and 3.7-fold, respectively, while that by Titli was 3- and 5-fold, respectively. Despite this, the magnitudes of both Chl-aand NPP were higher in the AS compared with the BoB. Consistent with physical and biological responses, the CO2outgassing flux associated with Titli was 12-fold higher in comparison to the pre-cyclone value, while that associated with Luban was 10-fold higher. Unlike the Chl-aand NPP, the magnitude of CO2flux in the BoB was higher than that in the AS. Although the cyclones Luban and Titli originated simultaneously, their generating mechanisms were quite different. What was common for the genesis of both cyclones was the pre-conditioning of the upper ocean in 2018 by the co-occurrence of El Niño and the positive phase of Indian Ocean dipole along with the cold phase of the Pacific decadal oscillation, all of which worked in tandem and warmed the AS and parts of the BoB. What triggered the genesis of Luban in the AS was the arrival of the Madden–Julian oscillation (MJO) and the mixed Rossby-gravity wave during the first week of October. The genesis of Titli in the BoB was triggered by the eastward propagation of the MJO and the associated enhanced convection from the AS into the region of origin of Titli along with the arrival of the downwelling oceanic Rossby wave.

Physical forcing controls spatial variability in primary production in the Indian Ocean

The present study explored the dynamics of total dissolved Cobalt (dCo) in the Indian Ocean, revealing different distribution patterns in the different sub‐basins, nutrient‐type in the southern sector, hybrid‐type in the Arabian Sea to scavenged‐type in the Bay of Bengal (BoB). The dCo in the coastal water of the Arabian Sea displays elevated (0.12–0.13 nmol L−1) abundance and diminishes gradually toward the central Arabian Sea. Similarly, in the BoB, dCo concentrations are notably higher in the northern region (0.11 nmol L−1) and gradually decrease toward the south (0.03 nmol L−1 at 5°N). The Arabian Sea with higher biological uptake and remineralization in the oxycline supports a higher abundance of dCo in the intermediate oxygen minimum zone (OMZ), much a like the OMZs of the Atlantic and the Pacific Oceans. The influence of the phytoplankton community shift and uptake on the dCo distribution in the Indian Ocean could be inferred from the association between Co and phosphate in the photic waters. Our observation demonstrates a scavenging type dCo profile in the BoB due to its higher riverine as well as dust inputs in addition to its supply from continental shelf sediments. Such a higher concentration of dCo in the surface waters of the northern BoB masks the dCo signal associated with nitrite maxima. dCo gets removed by its scavenging with Mn oxides at deeper depths, as reflected by higher particulate Co in the BoB. Subduction fluids contribute significantly to the dCo inventory of the deep water in the Indian Ocean near the Java‐Sumatra subduction zone.

Spatial variability in plankton metabolic balance in the tropical Indian Ocean during spring intermonsoon

The intermediate water circulation and ventilation of the Indian Ocean is somewhat unique among the world oceans (in terms of the source waters). This has been studied with historical and recently obtained hydrographic data including potential temperature, salinity, dissolved oxygen, phosphate and silicate in a mixing model of applying optimum multiparameter analysis (OMP). The mixing model comprises three source water masses, Antarctic Intermediate Water (AAIW) (applied the transformed AAIW north of the Antarctic frontal zone and central South Indian Ocean), Indonesian Intermediate Water (IIW) and Red Sea Intermediate Water (RSIW) (including the influence of Persian Gulf Intermediate Water). A possible source from south of Australia has also been considered and accommodated into the water type definition of AAIW. This study was performed on six closely spaced neutral density surfaces which encompass the intermediate layer of the Indian Ocean from 500 m (in the northern Indian Ocean) to 1500 m (in the subtropical latitudes) with a distance of about 100-150 m between a pair of surfaces. Water-mass mixing contributions were plotted on the neutral surfaces and in three cross sections, the western Indian Ocean along 60E, the eastern Indian Ocean along 90E, and a zonal section along 10S. The intermediate water circulation and ventilation of the Indian Ocean can thus be inferred from the spreading paths and mixing patterns of these source water masses. A schematic intermediate water circulation of the Indian Ocean therefore emerges from the water-mass and dynamical information. The latter is derived from the acceleration potential (10 m 2 s -2 ) mapped on the neutral surfaces. The equatorward AAIW enters the Indian Ocean from the mid-ocean of the southern Indian Ocean, is advected with the subtropical gyre and transits to the north through the western boundary. In the western equatorial Indian Ocean, AAIW flows northeastward to eastward. At about 80E, AAIW bifurcates into northward and southward flows. The former continues into the Bay of Bengal through the western boundary (east of Sri Lanka) with up to 10% of the contribution. It returns southward in the eastern Bay of Bengal and along the Sumatra and Java Islands, zonally westward with IIW. The latter recirculates southward and then westward, forming a cyclonic gyre. The AAIW then turns southward into the Agulhas Current system through either side of Madagascar. AAIW contributes about 10-20% of its water into the equatorial Indian Ocean. Its northward flow in the western Indian Ocean is limited to 5N. IIW flows zonally westward and bifurcates into a northward and a southward flow in the western Indian Ocean. The direction of the latter is southward into the Agulhas Current system through either side of Madagascar. The former lows northward by the way of AAIW. Although AAIW does not flow into the Arabian Sea, IIW is found flowing into the Arabian Sea via the west coast of India. The main flow path of IIW into the Bay of Bengal is through the south of Sri Lanka. IIW largely contributes about 50-60% of its water into the Bay of Bengal. The northward flow of IIW is interrupted at the central equatorial region by the eastward AAIW. These circulations form two cyclonic gyres in the western and eastern equatorial Indian Ocean.