This overview compares algal blooms and pelagic fisheries of the Arabian Gulf with the Sea of Oman. The data consist of remotely sensed characteristics, directly sampled and modeled. Elucidated seasonal trends were based on 15-year seasonal means, as well as weekly time series of physical parameters. The environmental characteristics (namely photosynthetically available radiation, atmospheric pressure, temperature, wind, aerosol optical thickness, surface currents, surface temperature, salinity, concentration of dissolved oxygen, nitrates, phosphates, chlorophyll-a, net primary production, phytoplankton, zooplankton biomass, fish larvae abundance, small and large pelagic fish catches) were compared between regions. In Sea of Oman, high concentrations of chlorophyll-a were associated with relatively high concentrations of nitrates and phosphates, as well as kinetic energy of surface currents which exceeded that in Arabian Gulf. The dinoflagellate Noctiluca scintillans is one dominat alga in the Sea of Oman, whereas diatom species are more common in the Arabian Gulf blooms. In general, the phytoplankton and zooplankton species diversity during winter was higher than in summer periods. Catches of small pelagic fish (in particular sardines) in the Sea of Oman exceeded that in the Arabian Gulf. This might be associated, in part, with differences in trophic levels interactions. The turnover rate of the net primary production through zooplankton in Sea of Oman was found to be much higher than in Arabian Gulf waters.

Introduction

While the Arabian (also known as Persian) Gulf and the Sea of Oman (Gulf of Oman) belong to the same region, they are affected by different weather systems and differ in their bathymetry, topography, atmospheric forcing regime and thermo-haline structure of waters. The Arabian Gulf (AG) is affected by the extra-tropical weather systems from the northwest, while the Sea of Oman (SO) is located at the northern edge of the tropical weather system mediating the Arabian Sea and Indian Ocean (https://earth.nullschool.net/#current/wind/isobaric/1000hPa/orthographic=-315.54,23.28,1102). The Strait of Hormuz acts as a boundary between these two systems (Hastenrath and Greischar, 1991; Reynolds, 1993, 2002).

The “Shamal” winds (in summer and winter), monsoonal winds, sea and land breezes prevail in the region. “Shamal” winds are caused by the atmospheric pressure gradients developing at the passages of cold fronts. The winter “Shamal” can last up to five days during which the wind can attain a speed of 20 m s−1 (Thoppil and Hogan, 2010). Monsoonal winds form four seasons: the southwest monsoon (from June through September); the fall inter-monsoon (October and November); the northeast monsoon (December through March), and the spring inter-monsoon period (April and May; Fett et al., 1983). On the Iranian side, seasonal variation of breezes is affected by the proximity of mountains, which are located closer to the coast in the eastern part of the AG. Mountain hillside steepness amplifies the intensity of breezes, although in general, wind intensity decreases from the west toward the east of the AG (Komijani et al., 2012).

Seasonal fluctuations of surface water temperature are high and could reach 35 °C in August, cooling down to 12 °C in February (Hassan and Gerges, 1994; Rezai et al., 2004; Bento, 2010). Waters of the AG, especially in the region of confluence with the SO waters, are extremely interesting for marine ecologists due to sharp spatial-temporal gradients of physical and chemical parameters. Periodic reviews highlight a wide range of environmental issues associated with the AG (Sheppard, 1993; Hamza and Munawar, 2009; Sheppard et al., 2010; ROPME, 2013).

The AG basin bathymetry is asymmetric, with a mean depth of 36 m and maximal depths of ∼90 m shifted to the north-eastern part along the Iranian coast, while a shallow broad southern margin (with depths of 10-30 m) stretches across it, along the coast of the United Arab Emirates. The water exchange between AG and SO is rather limited, so the water residence time is characterized by the range of 2-5.5 years (Al-Rabeh et al., 1992; Reynolds, 1993; Sheppard, 1993). Key river inflows (formed by the Shatt Al Arab, Mand, Hilleh and Hendijan rivers) are located in the northern part of the AG, primarily on the Iranian side, and they do not contribute significantly to the water structure formation in the basin (Alosairi et al., 2011).

SO is a deeper and more oceanic deep basin, in comparison to AG. Approximately 50% of SO is 1000 m deep with maximum depths reach 3000 m (Ross and Stoffers, 1978; Curtis et al., 1999). The large deficit of precipitation over evaporation, along with a weak river inflow and pronounced seasonality of winds results in the formation of one of the most saline oceanic water masses. This water mass (known in literature as the Persian Gulf Water Mass), with salinity over 40 psu, flows into SO from AG via the Strait of Hormuz (Reynolds, 1993; L’Hégaret et al., 2016). The flow makes its way from the shallow eastern part of AG, in which the salinity can attain 70 psu to a deep SO (Curtis et al., 1999). In both regions, surface currents form a general cyclonic circulation complemented by the system of mesoscale and sub-mesoscale eddies. The intensity of water mass advection, and mesoscale eddy formation are both subjected to a seasonal variation mediated by the atmospheric pressure gradient (primarily through the wind field) and water density gradients (Thoppil and Hogan, 2010).

It terms of the thermo-haline structure, the Indian Ocean Water Mass forms the upper layer of the SO. The high salinity dense waters from the AG form the layer beneath, in which the salinity varies over seasons in the range of 39.3-40.8 psu. The Iranian Coastal Current acting in the SO along the Iranian coast mediates the basin scale regional circulation. Entering the AG, this current reverses in the region of Qatar, due to the persistence of the frontal zone stretched across the AG and heads back through the Strait of Hormuz along the Omani coast, toward the Arabian Sea, in the form of the East Oman Current (Figure 1). Interactions between the orography of regions and coastal currents form three frontal zones: the westernmost one stretches across the AG off the Qatar peninsular; the middle one is bounded by the Musandam Peninsular and the Strait of Hormuz; and the eastern one develops seasonally across the easternmost part of the SO (Reynolds, 1993; Böhm et al., 1999; Johns et al., 1999). The eastern front is induced by the confluence of the East Oman Current and the Oman Coastal Current.

Figure 1.

Regional circulation (modified from Piontkovski and Chiffings, 2014).

Figure 1.

Regional circulation (modified from Piontkovski and Chiffings, 2014).

The speed of the surface currents changes dramatically, from over 10 cm s−1 along the Iranian coast to less than 3 cm s−1 when the flow reverses its direction to the east (Chao et al., 1992; Reynolds, 1993). The AG Water outflow spreads in the form of a subsurface salinity maximum, occupying (in SO and the western Arabian Sea) the range of depths between 200 and 350 m. The water mass transport varies from 0.08 sverdrups in December to 0.18 sverdrups in March (Yao, 2008), although the seasonality of this outflow is not well pronounced (Johns et al., 2003). A basin scale circulation of the AG and SO is induced by the wind-driven and thermohaline-driven flows, both affected by mesoscale eddies and water exchange with the Indian Ocean (Yao and Johns, 2010).

In taking into account the high spatial gradients of physico-chemical parameters and their seasonal variability, this overview gives a comprehensive comparison of two regions –the southeastern part of AG and SO. It capitalizes on the analysis of dynamic situations observed in shallow waters of the AG versus the deep SO, which should be footprinted in biological properties of a pelagic ecosystem.

Physical variability

In this section, we analyze the parameters which are believed to be ecologically important drivers of the seasonal variability of a pelagic ecosystem.

Atmosphere characteristics

Data retrieved from monthly 4km MODIS-Aqua _L3km-PAR-v2018 products were used to characterize the photosynthetically available radiation (PAR) in the 400-700 nm wavelength range, which, along with nutrients, mediate primary productivity in the ocean (Frouin and Pinker, 1995). The PAR seasonal variations were similar over the AG and SO regions (ranging from 24 to 61 Einstein m−2 d−1 and from 30to 60 Einstein m−2 d−1).

The comparison of minimal and maximal daily atmospheric temperatures from coastal meteorological stations implied similar seasonal cycles in the AG and the SO. In 2000-2018, summer temperatures varied between 45-48 °C over regions, while the winter temperatures ranged from 12 to 16 °C. According to the NCEP/NCAR reanalysis database (Kistler et al., 2001), the mean temperature over the AG was 20 ± 1 °C in January and 38 ± 0.7 °C in July versus 20 ± 0.8 °C and 34 ± 0.7 °C, in the SO (Muscat region). In general, summer temperatures over the AG were 1.1 °C higher that over the SO. Values over the past 18 years were higher in comparison to earlier records from 1950 to 2000, which is associated with the decadal regional trend of climate change.

Seasonal variations of the atmospheric temperature create atmospheric pressure anomalies. In turn, the spatial gradient of atmospheric pressure could be one of the factors mediating (through the wind field) the water mass transport between the AG and SO. According to the global circulation model assembled by NCEP/NCAR, the gradient of atmospheric pressure between the AG and SO is well pronounced throughout the annual cycle. The 15-year average pattern for 2002-2017 shows the level of atmospheric pressure to be generally lower over the AG compared to the SO. The SO is located closer to the Siberian High atmospheric pressure system, which affects both regions, especially during winter (Kim et al., 2005). The anomaly is defined by values above 1028 hPa over the middle to higher Asia continent.

Spatial gradients of atmospheric pressure affect characteristics of the wind field and water mass transport. Annually averaged wind direction shows a reversal of the dominant direction of wind over regions. In Abu Dhabi (AG), the prevailing wind was north-western. This direction shifts clockwise, from Abu Dhabi through Dubai, Khasab to the Muscat region (SO), in which the prevailing wind exhibits the northeast direction. As far as wind speed is concerned, annually averaged values based on meteorological stations along the coast did not show much difference; 92% of seasonal values over named regions fit the range of 4 to 5 ms−1. However, more detailed characteristics of the wind field (i.e. zonal and meridional components of the wind speed), demonstrated seasonal variations. In general, remotely sensed (IFREMER/CESAT) blended winds implied the prevalence of northwesterly winds over the Arabian Peninsula and northwestern Arabian Sea, which is associated with the winter “Shamal” (Aboobacker et al., 2011). On average, 10 “Shamal” events per year take place in the Gulf region (Al-Senafi and Anis, 2015).

Thermohaline structure and currents

The plankton community drifting through the SO and AG, as well as migrating small and large pelagic fishes experience a marked spatial gradient of the surface salinity. The latter one is exemplified by two episodes featuring the winter season of 2015 and 2017 (Figure 2). Sharp gradients are commonly observed during intensive winter winds, which affect the surface salinity as well as the ratio of evaporation to precipitation.

Figure 2.

Sea surface salinity in December-2015 (a) and January-2017 (b). Maps were constructed and downloaded from the Live Access Server (https://podaac-tools.jpl.nasa.gov/las)

Figure 2.

Sea surface salinity in December-2015 (a) and January-2017 (b). Maps were constructed and downloaded from the Live Access Server (https://podaac-tools.jpl.nasa.gov/las)

The sea surface temperature distribution exhibits marked spatial gradients as well. During the summer, temperatures in a shallow AG are much higher, compared to those of a deep SO. Therefore, depth is an important factor in determining the ecological capacity of the pelagic ecosystem. Seasonal variations of sea surface temperature in the 15-year monthly time series retrieved from the MODIS-Aqua database (http://www.esrl.noaa.gov/psd/data/timeseries) are comparable by means and correlated by the timing of peaks (at r = 0.9, p < 0.05). However, these series are different in magnitude, which was three times higher (by variance) in the AG.

Surface currents affect plankton communities. Similar to the wind field inducing and affecting the surface current, the speed of the drift has its zonal and meridional components. On a basin scale, the surface currents of the AG and SO exhibit spatial gradients of velocity compared to their westward-located oceanic extensions (i.e. the western Arabian Sea). These spatial gradients are subjected to seasonal variations exemplified for the winter and summer months (Figure 3). For instance, the zonal speed exhibited seasonal variations in the AG were twice those in the SO. Interplay of zonal and meridional components of the current velocity mediates the kinetic energy of the surface flow. Our calculations point out that the kinetic energy of the current in the SO is twice that of the AG. The clue is the energy gradient of oceanic current in the deep basin compared to that of the shallow one; the water mass volume involved in the wind induced current in the SO is much larger than that in the AG. Due to this difference, the occurrence and dynamic activity of mesoscale eddies in the SO is more pronounced (L’Hégaret et al., 2015; Vic et al., 2015).

Figure 3.

Monthly variations of the zonal component (U) of current velocity anomaly in AG (dashed line) and SO (solid line). Monthly time series downloaded from http://thredds.jpl.nasa.gov/thredds/dodsC/ocean_circulation/ALL_OSCAR_L4_OC_third-deg.nc.

Figure 3.

Monthly variations of the zonal component (U) of current velocity anomaly in AG (dashed line) and SO (solid line). Monthly time series downloaded from http://thredds.jpl.nasa.gov/thredds/dodsC/ocean_circulation/ALL_OSCAR_L4_OC_third-deg.nc.

The thermo-haline structure of waters exerts seasonal changes as well. During winter, the Iranian current can be weakened by the opposite direction of “Shamal” winds, whereas in summer the current is strong and observed along the westernmost part of the AG (Reynolds, 1993). In this region, a well-mixed water mass formed in the winter transfers into the stratified two-layered system observed during summer. The upper layer stagnates mostly to the east of Qatar, which is the region of excessive evaporation and formation of the Persian Gulf Water Mass, with maximal salinity and density values (Reynolds, 1993). In the SO, the two-layer system is observed throughout the year, but the extension of the upper mixed layer varies.

In order to demonstrate statistically significant seasonal variations of the upper mixed layer, vertical profile averages of temperature retrieved from oceanographic expeditions of the past two decades were calculated (Figure 4). In the figure, the upper 300 m layer is shown, for the SO, whilst in the AG (which is shallower), the upper 80 m were investigated. About 85% of all CTD casts were located in the northern (deepest) part of this region. In the SO, the vertical extension of the layer with homogeneously distributed temperatures varied from ∼10 m in summer to ∼50 m in winter, whilst the winter mixing spanned the entire water column in the AG. In summer, the vertical stratification is well pronounced in both regions, with some differences reflecting the formation of the Persian Gulf Water mass below the seasonal thermocline. Due to limited space, only summer and winter seasons are displayed. However, these are seasons with the most pronounced temperature gradients.

Figure 4.

An averaged pattern of the vertical distribution of temperature in the SO and AG, during (a) winter and (b) summer seasons. SO: (a) winter and (b) summer. AG: (c) winter and (d) summer. Dashed curves stand for maximal and minimal temperature values (1997-2017). The number of CTD casts averaged for AG was 406 for winter and 214 for summer seasons. The number of CTD casts averaged for the SO was 1146 for winter and 1354 for summer seasons.

Figure 4.

An averaged pattern of the vertical distribution of temperature in the SO and AG, during (a) winter and (b) summer seasons. SO: (a) winter and (b) summer. AG: (c) winter and (d) summer. Dashed curves stand for maximal and minimal temperature values (1997-2017). The number of CTD casts averaged for AG was 406 for winter and 214 for summer seasons. The number of CTD casts averaged for the SO was 1146 for winter and 1354 for summer seasons.

In some way, high kinetic energy of the surface current in the SO (complemented by high seasonal variability) is reflected in the seasonal variation of the chlorophyll-a concentration. Annually averaged chlorophyll-a concentration in the SO is 1.5 times higher than that of the AG and 15 times higher than that by its variance (Figure 5). The statistical analysis of time series of chlorophyll-a averaged for the AG and SO basins showed a time lag of one to two months observed for the time range of 2002-2017, with the SO chlorophyll lagging. Indirectly, this points to the prevalence of the eastward advection of the phytoplankton biomass by the East Oman Current.

Figure 5.

Monthly variations of remotely sensed chlorophyll-a concentration in the AG (solid line) and the SO (dashed line) during 2002-2017. Monthly area averaged time series retrieved from the MODIS-Aqua database (http://www.esrl.noaa.gov/psd/data/timeseries).

Figure 5.

Monthly variations of remotely sensed chlorophyll-a concentration in the AG (solid line) and the SO (dashed line) during 2002-2017. Monthly area averaged time series retrieved from the MODIS-Aqua database (http://www.esrl.noaa.gov/psd/data/timeseries).

Nutrients

One of the factors contributing to comparatively high seasonal variations in the SO is the mean concentration of phosphates, nitrates and silica, which is reportedly higher in the SO (Curtis et al., 1999). In the western Arabian Sea, the primary productivity is known to be nitrogen-limited in all the surface waters, with the greatest limitation during the inter-monsoon seasons (Woodward et al., 1999). Unfortunately, insufficient data exist regarding the seasonal dynamics of nutrients complementing in full the 15-year time series of remotely sensed parameters. The available six-year data for the period of 2006-2011 are presented here as monthly time series of the Muscat region (Figure 6). The 25-75% quartiles of seasonal trends pointed to a high inter-annual variability of phosphates and nitrates in coastal waters, although the dominance of the Northeast (winter) monsoon was well pronounced, in the formation of nitrate peaks (Al-Hashmi et al., 2015). Unfortunately, there is no similar data for the AG region so far. Limited measurements carried out in winter 1987 reported a net increase of nitrates, phosphates and silica by the AG (Emara, 2010). In addition, direct measurements in 1995-1996, showed the concentration of nitrates and phosphates in the SO (in summer and winter) to be 1.2-3.3 times that of the AG (Shriadah, 2006). The Environmental Agency of Abu Dhabi (Al-Abdessalaam et al., 2007) carried out measurements on nitrate and phosphate concentrations in regions located away from intensive human impact. However, data on seasonal patterns of nutrients from these regions are not publicly available.

Figure 6.

Seasonal variations of the nitrate and phosphate concentration on a coastal station in the Muscat region, in 2006-2011. Black dots stand for monthly medians. Boxes stand for 25-75% quartiles. The trend curves were approximated by the Distance Weighted Least Squares method.

Figure 6.

Seasonal variations of the nitrate and phosphate concentration on a coastal station in the Muscat region, in 2006-2011. Black dots stand for monthly medians. Boxes stand for 25-75% quartiles. The trend curves were approximated by the Distance Weighted Least Squares method.

Among the other factors potentially affecting the primary productivity of plankton communities in the AG and SO regions are the mineral dust aerosols depositing on sea surface that are rich in nutrients (Measures and Vink, 1999; Hamza, 2008, Hamza et al., 2011). In this regard, we analyzed the fluctuations of the optical thickness of aerosol, which were retrieved from the GIOVANNI database (http://www.esrl.noaa.gov/psd/data/timeseries), in the form of weekly time series spatially averaged over two regions. The mean optical thickness showed no statistical differences, with weekly fluctuations correlating (r = 0.43, p < 0.05). The spectral analysis of a 15-year time series revealed two major peaks of spectral density corresponding to the annual and semi-annual fluctuation periods. Chemical analysis of the Arabian Peninsula dust showed high percentages of aluminum, iron, calcium, magnesium, and other elements –all capable of enriching aquatic environments with minerals and nutrient salts (Hamza, 2008; Hamza et al., 2011). It is believed as well, that the supply of soluble Aeolian iron through precipitation may cause additional natural iron fertilization affecting phytoplankton growth (Gao et al., 2003; Nezlin et al., 2010). Regionally, the transport of dust is mediated by atmospheric flows during monsoon periods (Tindale and Pease, 1999).

Physical-biological coupling on low trophic levels

Winds and chlorophyll-a

Wind speed, stress and direction are important factors affecting the biomass and primary productivity of the phytoplankton community. The wind-induced coastal upwellings and downwellings along with the direction of coastal currents could play a dominant role in dispersing the algal blooms (Barber et al., 2001). Over seasons and years, one can observe ecological situations in which the chlorophyll-a concentration (acting as the indicator of phytoplankton biomass) is higher in the SO than the AG, as well as in the reversed pattern. For instance, in February-2004, zonal and meridional components of the wind speed exhibited similarly high values in the SO (Figure 7). In general, the northwestern wind dominated the western part of the SO, in which high chlorophyll concentrations, penetrating through the Strait of Hormuz to the AG, were observed. Figure 8 presents an episode with an opposite direction of wind and an intensive upwelling developing in the AG, along the Iranian coast, inducing a huge phytoplankton bloom.

Figure 7.

U-wind (a, ms-1), V-wind (b, ms-1), vector of wind (c, ms-1) and chlorophyll-a concentration (d, mg m-3) in February-2004. Maps were constructed and downloaded from the Live Access Server (https://podaac-tools.jpl.nasa.gov/las)

Figure 7.

U-wind (a, ms-1), V-wind (b, ms-1), vector of wind (c, ms-1) and chlorophyll-a concentration (d, mg m-3) in February-2004. Maps were constructed and downloaded from the Live Access Server (https://podaac-tools.jpl.nasa.gov/las)

Figure 8.

U-wind (a, ms-1), V-wind (b, ms-1), vector of wind (all three at 10m) (c, ms-1) and chlorophyll-a concentration (d, mg m-3) in December-2008. Maps were constructed and downloaded from the Live Access Server (https://podaac-tools.jpl.nasa.gov/las).

Figure 8.

U-wind (a, ms-1), V-wind (b, ms-1), vector of wind (all three at 10m) (c, ms-1) and chlorophyll-a concentration (d, mg m-3) in December-2008. Maps were constructed and downloaded from the Live Access Server (https://podaac-tools.jpl.nasa.gov/las).

As far as the spatial correlation pattern is concerned, correlations between the wind stress and chlorophyll-a concentrations are pronounced in winter, in the shallow AG compared to the deep SO (Figure 9). The correlation pattern fits the tendency reported for the global ocean scale. Negative correlations between satellite-derived wind speed and surface chlorophyll concentrations have been observed over areas with deep mixed layers, while positive coupling was associated with shallow mixed layer areas (Kahru et al., 2010).

Figure 9.

The spatial correlation pattern between the wind stress components and chlorophyll-a concentration for December-2005 (a) and March-2006 (b). Monthly wind stress and chlorophyll-a data were retrieved from MERRA model MATMNXFL v5.2.0. and MODIS-Aqua MODISA_L3m_ChL v2018.

Figure 9.

The spatial correlation pattern between the wind stress components and chlorophyll-a concentration for December-2005 (a) and March-2006 (b). Monthly wind stress and chlorophyll-a data were retrieved from MERRA model MATMNXFL v5.2.0. and MODIS-Aqua MODISA_L3m_ChL v2018.

The assessment of the coefficient of variation implies the spatial distribution of chlorophyll-a concentrations over the SO is more heterogeneous, in comparison to the AG. The level of spatial heterogeneity affects the correlation between the wind stress and the chlorophyll field. The main reason of high heterogeneity in chlorophyll spatial distribution is the persistence of mesoscale eddies. The later ones are more pronounced in the SO compared to the AG. The influence of eddies is well documented in Figure 7-d. One can see the patch of high chlorophyll-a concentration in the central SO. This patch contours the location of a cyclonic eddy, in which the ascending flux of nutrients is observed (McGillicuddy et al., 1999). Mesoscale cyclonic and anticyclonic eddies contribute gradually to the kinetic energy of surface currents (Flagg and Kim, 1998). This energy exhibited the two-fold difference over two studied regions.

Spatial-temporal variability of phytoplankton

Limited research on the taxonomy and the abundance of phytoplankton in the central and south-eastern parts of the AG was carried out since the 1930s (Bohm, 1931; Al-Kaisi, 1976). The spring-1977 survey reported about 100 species of phytoplankton in which diatoms were the most diverse by taxa while coccolithophores dominated in total abundance (Grice and Gibbson, 1978). Basin scale sampling of the AG and SO regions was initiated in September-1986. Phytoplankton was collected from the surface during the hydrographic survey, which incorporated 42 stations (Dorgham and Moftah, 1989). Sample processing identified 299 phytoplankton taxa in the AG versus 146 in the SO. The diatom taxa dominated in the AG (with 175 diatom versus 124 dinoflagellate taxa), whereas the opposite pattern was observed in the SO (54 diatom versus 92 dinoflagellate taxa). In the AG, Trichodesmium sp. dominated while in the SO, its role has gradually decreased so diatoms (represented by Nitzschia seriata, Climacodium frauenfeldianum and some others) dominated. The average phytoplankton biomass of two dominant species in the AG was about nine-fold that of the SO. Along with the general ratio of species over regions, the authors denoted a marked patchiness in their spatial distribution, which they associated with regional water circulation. Presumably, they dealt with the mesoscale eddies reported by Grasshoff (1976).

The phytoplankton sampling over 24 AG stations in December 1993 and 1994 showed the dominance of diatoms, comprising 83% of the total abundance followed by dinoflagellates with 15% (Husain and Ibrahim, 1998). Jacob and Al-Muzaini (1995) reported three diatom species (Thalassionema nitzchioides, Thalassiothrix frauenfeldii, and Climacodium frauenfeldianum) dominating the phytoplankton in the AG. In this regard, Quigg et al. (2013) noted the Chaetoceros spp. were a far more important fraction of the entire community. In addition, they concluded the phytoplankton community to be nutrient limited in this region. In sampling phytoplankton in Qatari coastal waters in 2010-2011, they reported the diversity and species composition in May and July to be different from that in February. Of the 125 species identified, 82 were diatoms and 41 were dinoflagellates. Among the diatoms, the Chaetoceros species made up over 50% of the total abundance in February, and 30% in July. Similar to earlier findings of Dorgham and Moftah (1989), Trichodesmium sp. was most abundant.

The winter phytoplankton from the ROPME-2006 expedition was analyzed by Polikarpov et al., (2016) who reported the Sea of Oman and the Strait of Hormuz dominated by diatoms, but flagellates dominating the north and Arabian coast of the AG. Overall, 376 taxa were identified. Among dinoflagellates, genera Protoperidinium and Ceratium were the most dominant, while the family Chaetoceraceae dominated diatoms. Small-sized chain forming Chaetoceros sp, Pseudonitzschia sp and Skeletonema sp. dominated local blooms. The highest concentrations (over 106 cells L−1) were observed in surface waters of the central part of the AG, near Qatar. Diatoms contributed 70% to the total phytoplankton abundance followed by dinoflagellates (21%). In general, 338 taxa were reported in the AG versus 286 in the SO. This is much higher, compared to the 147 phytoplankton species reported during the summer-2001 cruise, for the same basin (ROPME, 2013).

Based on monthly sampling in the coastal region near Muscat (SO), the phytoplankton community was represented by 287 species and dominated by dinoflagellates (Al-Hashmi et al., 2014, 2015). However, on a basin scale, the dominant algae in blooms were different. For example, The species of Chaetoceros contributed markedly to the nutrient-reach waters of the SO, while Pseudo-nitschia sp. and Skeletonema sp. were the main bloom forming taxa in the central part of the AG (Polikarpov et al., 2016). Similar monthly sampling of the phytoplankton community was carried out on a coastal station in the Sohar region, in 2009-2011. Small flagellates and diatoms contributed 10 and 25% respectively, to the phytoplankton community from January to April 2010. The dominant genera of Dinophytae at that time were Prorocentrum, Gymnodinium and Dinophyta while in March-2010 the dominant genera were Chaetoceros and Thalassiosira (Al-Abri, 2013). The period from April to the end of December-2010 showed a similar trend of small flagellates and Dinophytae with the contribution ranging between 25 and 40%. However, this contribution varied markedly over years. For instance, in March 2011, Rhizosolenia represented more than 90% of the phytoplankton community followed by small flagellates.

In general, only few species form large algal blooms in the AG and SO. A periodic appearance of algal blooms was observed in these regions from the 1970s to the present. These blooms could be formed by the most abundant species in the region, as well as by exotic ones appearing episodically over years (Thangaraja et al., 2007; Richlen et al., 2010). A special reference might be made to the occurrence of dinoflagellate species Noctiluca scintillans which generates huge blooms during winter and summer monsoons, in the AG and SO (Al-Hashmi et al., 2014; Murugesan et al., 2017). During these blooms, the Noctiluca biomass gradually exceeds that of all other phytoplankton species. In the SO, winter blooms develop in the upper 10m layer. Over time, they dissipate and submerge slowly to the depth of the picnocline (between 25 and 55m) at which they were traced during the spring inter-monsoon season and up to the summer monsoon. Therefore, the subsurface algal bloom persists throughout inter-monsoon seasons, linking algal blooms induced by the South-West and North-East monsoons (Piontkovski et al., 2016). A detailed seasonality and proximity of Noctiluca subsurface blooms in the AG has not been studied.

Spatial-temporal variability of zooplankton

As for the zooplankton, community analysis of historical data showed that copepods are the most abundant. The prevalence of copepods in the AG zooplankton was reported since the first samples collected in the 1960s (Frontier, 1963). A subsequent sampling targeting a larger area implied the peak of abundance in the central part of the AG, although the zooplankton biomass in the SO exceeded that of the AG (Grice and Gibbson, 1978). Sampling in December-1993 in the central part of the AG showed the presence of 75 genera and species of zooplankton dominated by copepods, which contributed about 64% to the total zooplankton abundance. The most abundant genera were Oithona, Onceae and Paracalanus (Al-Yamani et al., 1998). Almost 10 years later, 233 zooplankton species were reported by the 2006 ROPME expedition in offshore waters of the AG and SO, which assessed the diversity for winter only (Dorgham, 2013; ROPME, 2013). In winter-2006, the AG was more diverse (with 210 species), compared to the SO (144 species), while in the SO, the total zooplankton abundance was 1.7 times higher than that of the AG (ROPME, 2010). In general, the winter zooplankton was more diverse by species, compared to 71 taxonomic entities reported by the summer-2001 ROPME expedition, sampling the same region.

Along with the ROPME expeditions, which covered large areas of the AG and SO, regular monthly sampling was carried out on coastal stations. In the coastal waters of Abu Dhabi, Dubai, Sharjah, Umm Al Quwain and Ras Al Khimah, zooplankton was represented by 70 taxa and species, of which copepods contributed 66% to the total abundance. Data on seasonality are inconsistent. Serehy (1999) observed the peak of abundance in January, with lowest abundance in September. Al-Abdessalaam et al., (2007) reported two peaks of zooplankton biomass in March and November, while minimal values were associated with July. In the Musandam region, the copepod dominance was about the same (65%) followed by Cladocera, which contributed 33% to the total zooplankton abundance (Al-Esari and Al-Mokhani, 2013). The remaining groups of zooplankton accounted for 2% (Cirripedia larvae, Decapoda, Crustacea larvae, Stomatopoda, Ostracoda, Amphipoda, and Euphausiidae). In the SO (Sohar region), copepods contributed 81% to the total abundance, followed by Cladocera (Pseudoevadne tergestina and Penilia avirostris), with 13%. Out of 28 copepod species, Temora turbinata, Centropages orsinii, and Corycaeus sp. dominated in samples.

The three-year time series carried out in 2013-2015 showed the presence of summer and winter zooplankton biomass peaks in the SO (Muscat region). In the AG, peaks were not observed in 2013, although one appeared in October-2014 and the other (less developed) was observed in April-May 2015 (Figure 12). Plots showed the absence of a stable seasonal cycle, with differences in the timing of peaks over seasons in the three-year time series. In addition, an inter-annual variation of peak magnitudes was noticed. The monthly sampling carried out in the Muscat coastal region earlier (in 2005-2006 and 2007-2011), showed no pronounced seasonal pattern in the total copepod abundance, which contributed about 80% to the total zooplankton abundance. Oithona brevirocnis, Oithona nana, Oithona sp., Temora turbinata and Parvocalanus elegans were the species contributing from 6% to 10% of the total copepod abundance. The other species contributed much less. Multiple peaks were observed in monthly fluctuations of the total copepod abundance, genera and abundant species. Amplitudes and timing of peaks were variable over years (Al-Azri et al., 2009; Piontkovski et al., 2014b). Coastal monthly time series of almost all parameters (including nutrients, phyto- and zooplankton) implied a marked interannual variability, which in part is shown in Figure 10. However, the issue of interannual and other long-term variations will be addressed elsewhere.

Figure 10.

Monthly variations of zooplankton biomass in 2013-2015, in the AG (Khasab region) and the SO (Muscat region).

Figure 10.

Monthly variations of zooplankton biomass in 2013-2015, in the AG (Khasab region) and the SO (Muscat region).

The net primary production to zooplankton biomass ratio

Net primary production (NPP) is one of the important factors in the carbon production process in plankton communities. This parameter characterizes the formation of organic material from inorganic compounds, minus the respiratory losses of the photosynthesizing organisms (Finkel, 2014). The ratio of NPP to the zooplankton biomass reflects the turnover rate of the net primary production through the trophic level of nearest consumers (Piontkovski et al., 2003; Vinogradov and Shushkina, 1987). Moreover, in terms of trophic interactions, this ratio indicates the intensity of organic matter flow per zooplankton biomass unit. For this review paper, the NPP was computed for the region by the model utilizing remotely sensed sea surface temperature and chlorophyll-a as basic parameters (Saba et al., 2010). Data were retrieved from the NOAA NMFS SWFSC ERD database (http://coastwatch.pfeg.noaa.gov/erddap). Our pilot assessments carried out for 2015 showed NPP in the upper 25m layer in the SO to be twice that of the AG by annually averaged value and three times by the seasonal coefficient of variation. The index of spatial heterogeneity of NPP in the SO (which is the variance to mean ratio) was 15 times higher than that of the AG.

The NPP to the zooplankton biomass ratio was computed by using monthly images of the spatial distribution of net primary production over regions (NPP) available from the NOAA/NMFS database and monthly data on zooplankton biomass (Bzoo) sampled in 2015 in both regions (Figure 11). Plots showed the persistence of seasonal patterns of the NPP/Bzoo ratio, which demonstrated different seasonal magnitudes, as well as the timing of seasonal peaks over regions. In the SO, the peak was observed in February, while in the AG, the seasonal peak was smaller and developed in March. The presence of the second (summer) peak in the SO reflects the impact of the bimodal seasonal pattern of Arabian Sea productivity, which is driven by the south-west and north-east monsoons.

Figure 11.

Monthly variations of the net primary production to the total zooplankton biomass ratio in the AG (Khasab region) and the SO (Muscat region).

Figure 11.

Monthly variations of the net primary production to the total zooplankton biomass ratio in the AG (Khasab region) and the SO (Muscat region).

Physical-biological coupling on high trophic levels

Spatial-temporal variability of fish larvae

In the AG (Khasab) coastal waters, a total of 31 families, 34 genera and 15 fish species were identified in the samples of fish larvae collected in 2013-2015. The top five families (which contributed 76% to the total larval catch) were Engraulidae, Mullidae, Haemulidae, Apogonidae and Pomacentridae. The other 21 families occurred in relatively low abundances contributing less than 1%. Engraulidae (49%), the larvae of which have been observed throughout the year represented the dominant family. In the SO (Sohar region), 27 families, 35 genera and 19 species were identified. The top five families were Mullidae, Engraulidae, Gerreidae, Carangidae and Pomacentridae, which have accounted for 65% of the total catch. Thirteen families were found to be in low abundances (less than 1%). In the Muscat region, five families (Clupeidae, Pomacentridae, Mullidae, Scombridae and Sparidae), represented the highest larval abundances and accounted for 54% of the total catch. Clupeidae was the dominant larvae contributing 22% to the total catch. This group was represented by four genera -Sardinella spp, Herklotsichthys sp, Hilsa kelee and Etrumeus sp. Pomacentridae was the second most abundant family, representing 12% of the total catch.

Fish larvae exhibit high mortality rates, which could reach 99% from the time of hatching to adulthood. The four main stages of development refer to the yolk sac stage, pre-flexion, flexion and post-flexion stages (Leis and Carson-Ewartm, 2000). The abundance of the pre-flexion stage (which we treated conditionally as the “environmentally independent stage”) was analyzed versus the abundance of the next two stages –flexion and post-flexion (which was treated conditionally as “environmentally dependent stages”). Seasonal patterns of these two categories appeared to be different (Figure 12). Both exhibited multiple peaks of abundance corresponding to the monsoon and inter-monsoon periods. However, in the abundance of “environmentally dependent” larvae, the fall inter-monsoon season (in particularly the month of October) was the most pronounced. Presumably, the difference between two categories reflects various adaptation strategies of larval fish assemblages in the AG and SO regions.

Figure 12.

Monthly variations of fish larvae abundance in the AG and SO regions (2013-2015): pre-flexion stages (a), and the total abundance of flexion and post-flexion stages (b).

Figure 12.

Monthly variations of fish larvae abundance in the AG and SO regions (2013-2015): pre-flexion stages (a), and the total abundance of flexion and post-flexion stages (b).

The contribution of large and small pelagic species to the total landings is gradually different in the AG versus the SO. For instance, the records for 2003-2016 point out that landings of large and small pelagic fishes in the SO by Oman, to be -1.4 and two times higher, respectively, than those of the AG (Table 1). In the AG, the Long tail tuna dominated in large pelagic fish landings, whereas in the SO the Yellowfin tuna contributed markedly to the total landings of this group, and was 24 times higher than that of the AG. In the group of small pelagic fishes, sardines dominated in landings in the SO (with a 9-fold difference), whereas anchovies were dominant in the AG and four times higher than landings in the SO (Fisheries Statistics Book, 2016). It should be noted that the total number of Oman fishing boats in the SO is slightly (1.3 times) than that in the AG.

Table 1.

Landings (in metric tons) of small and large pelagic species by Oman in 2003-2016 (Fisheries Statistics Book, 2016).

Year20032004200520062007200820092010201120122013201420152016
Arabian Gulf (Musandam):Large pelagics 2123 2129 2164 2314 2192 3122 3880 4773 7157 10008 7700 8998 8585 9923 
Longtail tuna 857 414 657 653 571 793 2194 2210 4666 5162 3390 3087 2733 4403 
Yellowfin tuna 10 86 32 39 30 405 59 43 28 122 221 181 
Small pelagics 1694 2716 2150 2222 2613 2385 1959 5357 3174 3912 5652 6187 7051 7182 
Anchovy 828 235 360 474 490 218 4191 1370 1395 2642 1302 852 1206 
Ind. mackerel 615 712 688 682 949 627 499 275 594 693 1342 1967 1959 2345 
Sardines 674 440 725 623 593 578 411 460 383 751 640 630 1312 1027 
Sea of Oman (Muscat):Large pelagics 5970 8881 6798 7065 7566 5147 5538 6085 5291 4702 6521 7292 9398 18211 
Longtail tuna 1744 1626 1282 1573 1490 1457 1527 1791 739 582 1540 1930 2514 4756 
Yellowfin tuna 2273 5551 3717 3687 4313 1684 971 356 252 687 479 874 1883 5114 
Small pelagics 12998 15454 11089 15619 14291 11108 8013 4552 3041 3279 5530 4386 7037 3506 
Anchovy 21 19 71 37 42 308 117 103 121 137 313 485 1725 545 
Ind. mackerel 839 674 811 761 1025 650 667 673 593 866 1650 1618 1201 941 
Sardines 11323 13697 9110 13706 12135 9085 6325 2705 1132 695 1055 874 2361 506 
Year20032004200520062007200820092010201120122013201420152016
Arabian Gulf (Musandam):Large pelagics 2123 2129 2164 2314 2192 3122 3880 4773 7157 10008 7700 8998 8585 9923 
Longtail tuna 857 414 657 653 571 793 2194 2210 4666 5162 3390 3087 2733 4403 
Yellowfin tuna 10 86 32 39 30 405 59 43 28 122 221 181 
Small pelagics 1694 2716 2150 2222 2613 2385 1959 5357 3174 3912 5652 6187 7051 7182 
Anchovy 828 235 360 474 490 218 4191 1370 1395 2642 1302 852 1206 
Ind. mackerel 615 712 688 682 949 627 499 275 594 693 1342 1967 1959 2345 
Sardines 674 440 725 623 593 578 411 460 383 751 640 630 1312 1027 
Sea of Oman (Muscat):Large pelagics 5970 8881 6798 7065 7566 5147 5538 6085 5291 4702 6521 7292 9398 18211 
Longtail tuna 1744 1626 1282 1573 1490 1457 1527 1791 739 582 1540 1930 2514 4756 
Yellowfin tuna 2273 5551 3717 3687 4313 1684 971 356 252 687 479 874 1883 5114 
Small pelagics 12998 15454 11089 15619 14291 11108 8013 4552 3041 3279 5530 4386 7037 3506 
Anchovy 21 19 71 37 42 308 117 103 121 137 313 485 1725 545 
Ind. mackerel 839 674 811 761 1025 650 667 673 593 866 1650 1618 1201 941 
Sardines 11323 13697 9110 13706 12135 9085 6325 2705 1132 695 1055 874 2361 506 

Spatial-temporal variability of small pelagic fishes

In analyzing small pelagic fishes, we placed our preference on sardines (the leading group in fisheries). Among “Sardines” (standardized this way in historical records- namely the “Fisheries Statistics Book”), three species (Sardinella longiceps, S.gibbosa and S. sindensis) were common in the catches from the SO and AG. Along the coast of Oman, approximately 80% of sardine landings from the artisanal fishery are of the Indian oil-sardine Sardinella longiceps (Al-Abdessalam, 1995). In the SO, sardines are below the critical level of exploitation rate (Al-Anbouri et al., 2011). This means that fishing is not the limiting factor of population sustainability. Since sardines are planktivorous fishes, available data on chlorophyll-a concentration might be used, as one of the indicators of phytoplankton biomass. Previous studies have shown seasonal variations of sardine catches in the Muscat region to be statistically associated with the zonal component of wind speed and chlorophyll-a concentration (Piontkovski et al., 2014a).

The correlation analysis for sardine catches and remotely sensed chlorophyll-a concentration available for the period from 2002 to 2013 has been compared between the AG and SO. Monthly sardine catches exhibited no correlation with chlorophyll-a in the AG, and exhibited correlation in the SO (Piontkovski et al., 2014a). Interestingly, seasonal patterns of sardine landings are qualitatively similar in both regions showing the winter peak, although annual means and seasonal magnitudes are different (Figure 13). Landings in the AG are an order of magnitude smaller than in SO and seasonal fluctuations are much more pronounced. It should be emphasized that the number of Omani fish boats in the Musandam region (in the AG) exceeded that of the Muscat region (SO).

Figure 13.

Monthly variations of sardine landings by Oman in the AG (a) and SO (b), in 2003-2016. Black dots stand for monthly medians. Boxes stand for 25-75% quartiles. The trend curve was approximated by the Distance Weighted Least Squares method.

Figure 13.

Monthly variations of sardine landings by Oman in the AG (a) and SO (b), in 2003-2016. Black dots stand for monthly medians. Boxes stand for 25-75% quartiles. The trend curve was approximated by the Distance Weighted Least Squares method.

Presumably, one of the main factors affecting catches in the SO is trophic interactions. In Omani waters, phytoplankton and zooplankton are major constituents of sardine diet (Haleem et al., 2011). The SO turnover rate of the net primary production through zooplankton is higher compared to that of the AG waters (Figure 11). This might affect sardine population recruitment, which is reflected by the level of fish catches. The abundance of the fish larvae over regions also supports this assumption. The fish larvae in the last stage of development are much more abundant in the SO waters throughout the year (Figure 12). In general, small pelagic fish biomass in UAE waters using acoustic techniques ranged from 43000 t. in July to 220 000 t. in March (Al-Abdessalaam et al., 2007). However, the migratory nature of this resource and small school sizes make commercial exploitation problematic (Shallard and Associates, 2003).

Pelagic populations of fishes along Arabian coasts experience periodic losses. Harmful algal blooms (HABs), oxygen depletion, and fish kill incidents are causative factors in the AG and SO regions (Al-Ansi et al., 2002; Piontkovski et al., 2012). In Qatar waters, for instance, fish kills are periodic events associated with the development of summer picnocline, which prevents mixing of the oxygen-saturated water of the upper layer (formed in the SO) with deep waters of the AG. This situation results in hypoxic conditions characterised by the oxygen concentrations of less than 0.9 ml L−1, which are the lowest values recorded for the AG (Al-Ansari et al., 2015). One of the most devastating HAB affecting both regions was in the fall of 2008 (Richlen et al., 2010). The bloom caused by the dinoflagellate Cochlodinium polykrikoides, originated in the SO, expanded into the AG (with the Iranian Current) and caused massive fish kills. In the AG, incidents were observed over more than 1000 km of coastline (Zhao and Ghedira, 2014; Todorova, 2009). In the SO, the bloom caused fish kills as well (Al-Gheilani et al., 2012), along with the complete loss of the branching corals and substantial reduction of the biomass and richness of coral reef fish species (Bauman et al., 2010).

In the SO offshore waters, the oxycline is pronounced below 50 m, where the oxygen concentration exhibits the two to three fold drop and reaches a concentration of less than 1.5 ml O2 L−1 (El Sarma and El Gindy, 1990). Monthly time series of the dissolved oxygen at a 65 m depth obtained in the Shinas region (which is 300 km to the north-west of Muscat), enabled the seasonality to be monitored over years (in 2007, 2008 and 2009). These data showed periodic declines of oxygen throughout the year. In fall, for instance, concentrations decrease down to 2 mg L−1. Data from Shinas and Muscat regions exhibited similar seasonal changes. The decline of oxygen was driven by monsoonal winds resulting in winter coastal upwelling raising already low oxygen water higher in the water column (DiMarco et al., 2010). With regard to seasonal fluctuations of the oxygen concentration, it should be noted that, concentrations of less than 3.5 ml L−1 induce symptoms of stress for many tropical pelagic fishes. Therefore, this concentration is interpreted as the hypoxic threshold (Prince and Goodyear, 2006; Stramma et al., 2012). A periodic development of hypoxia, with the summer concentrations of the dissolved oxygen less than 2 ml L−1 at a 40m depth, were reported recently for Qatari coastal waters (Al-Ansari et al., 2015).

Conclusions

The present review focused on ecologically important ecological parameters, with a special reference to their seasonality and trophic structure of epipelagic communities in the Arabian Gulf (AG) and Sea of Oman (SO). Key findings include:

  • Due to bathymetry differences, the carrying capacity of pelagic habitats is different, with the SO gradually exceeding that of the AG.

  • In the SO, high seasonal peaks of chlorophyll-a are associated with relatively high concentrations of nitrates and phosphates. The seasonality of these concentrations is controlled by high kinetic energy of surface currents, which exceeded those of the AG.

  • Only a few species form large algal blooms in the AG and SO. The dinoflagellate Noctiluca scintillans is one of those, which dominate (by biomass) blooms in the SO, whereas diatom species are more common in AG blooms. In general, the phytoplankton and zooplankton species diversity observed during the winter exceeded that of the summer period. Variations of zooplankton abundance and biomass, and fish larvae abundance (especially at a later developmental stage) exhibit multiple peaks throughout a seasonal cycle.

  • Catches of small pelagic fish (in particular sardines), in the SO exceed those in the AG. This might be associated, in part, with differences in trophic interactions. In the SO, the turnover rate of the net primary production through zooplankton is higher compared to that in the AG waters. The other driver of regional differences in seasonal peaks of artisanal catches is hypoxia. In the SO, the pelagic habitat compression, driven by hypoxia, is well pronounced, which makes fish populations more exposed to this stressor. Recent publications reported the development of seasonal hypoxia phenomena in the south-eastern AG as well.

Acknowledgements

This research was funded by the SQU-UAEU Collaborative Research Grant CL/SQU-UAEU//18/04 and 31S321). We appreciate the Environmental Agency of Abu Dhabi (EAD) for supplying water quality averages data of Abu Dhabi. We would like to thank L. Galkovskaya for the historical data mining dealing with CTD casts and S.Al-Husaibi for sampling.

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