The eastern Indian Ocean is bordered by the Bay of Bengal to the north, Sri Lanka and the Indian Peninsula to the northwest, Sumatra and the Andaman Sea to the east, and the Arabian Sea and South Indian Ocean to the west and south, extending from 65°E–100°E, 10°S–15°N. Except for coastal shelf areas, water depths here exceed 2,000 m; and are up to 5,000 m in the south. This region’s location, climate, and currents contribute to its high biodiversity and rich marine biological resources (Wei et al., 2007). Despite this, there has been limited research undertaken on the biodiversity and biological resources of this area compared with elsewhere in the western Indian Ocean near the eastern coast of Africa (Li et al., 2019). Chinese surveys in the eastern Indian Ocean have investigated this region’s hydrometeorology, biology, chemistry, geology, and optics. Biological research has focused on bacteria, zooplankton, and phytoplankton (Wang et al., 2016, 2020; Li et al., 2019; Wei et al., 2019).

The Ninety East Ridge is a submarine volcanic ridge about 5,500 km long oriented in a north–south direction, and 100–200 km wide in an east–west direction. The average water depth of this ridge is 2.5 km, about 2 km shallower than that of flanking ocean basins. Open sea areas near the equator are less affected by terrestrial input, and upwellings transport many nutrients into surface layers, resulting in high and stable productivity (Wei et al., 2007). Few studies have reported species in and around the Ninety East Ridge, and there has been no study on the ichthyoplankton (fish eggs and larvae) of this region.

The planktonic stages of fish life cycles are typically very short relative to adult longevity. This developmental period is associated with changes in fish morphology, physiology, and ecology (Zhao and Zhang, 1985), and fish are most vulnerable during it this period, with the highest number of individuals experiencing the greatest mortality (Wan and Zhang, 2016). Ichthyoplankton can passively drift with ocean currents and are particularly sensitive to environmental variation, with subtle changes strongly affecting their survival, development, and growth. Ichthyoplankton survival rates directly affect recruitment and the strength of subsequent generations (Ellis and Nash, 1997; Wan and Zhang, 2016). Ichthyoplankton surveys plays an important role in marine survey, which is mainly reflected in: (1) biological and taxonomic research; (2) exploring and evaluating fishery resources; (3) assessing numbers of fish populations; (4) identifying different groups of the same species; and (5) discussing relationships between the quantity of fishes and environmental variables to assist with forecasts of the number of supplementary resources (Zhao and Zhang, 1985).

In Indian Ocean regions, only few ichthyoplankton identification studies have been described, mostly based on morphological features (Rathnasuriya et al., 2021). In this study, ichthyoplankton samples were collected from the surveyed area above the Ninety East Ridge, and identified by integrating DNA barcoding and morphological characteristics. Species composition, quantitative distribution characteristics of ichthyoplankton community and their relationship to environmental factors were analyzed for the first time. Baseline data are presented to facilitate subsequent exploration and use of pelagic fishery resources in the eastern Indian Ocean, and to predict patterns in the distribution of regional fishery resources.

Most ichthyoplankton are small and thus are easily overlooked, and have been regarded as a large subset of zooplankton. However, because ichthyoplankton represent an important stage in the life cycles of fish, they are important in assessments of environmental impact, fishery resources, and responses to climate change. Ichthyoplankton also represent an important food source and link in marine food chains; therefore, their identification is important (Shao et al., 2001). Because of their small size, complex external morphology and anatomy, and ontogenetic variability, the characters used to visually identify ichthyoplankton are few and difficult to observe, hampering identification. We augmented our morphological identifications with DNA barcoding—a technique that has been successfully applied in other ichthyoplankton studies (Zhou et al., 2015; Ahern et al., 2018; Duke and Burton, 2020)—to improve the accuracy of our identifications.

We used DNA barcoding to identify 27 taxa to species, two species to genus, and one species to order. Of the 37 taxa identified in samples, 24 species were not collected during a field survey of pelagic fishery resources (unpublished data). Identified species included oceanic migrators (M. nigricans), nearshore species (D. hystrix), uncommon species (Phtheirichthys lineatus), common Exocoetidae species (O. micropterus), and deep-ocean species (M. niger, D. pauciradiatus, E. barbatum, C. pelagica). Among the 10 taxa not identified to species, Vinciguerria sp. (family Phosichthyidae) was most abundant (25.5% of all identified specimens), suggesting a potential resource exists in or near the surveyed sea area. Tetraodontiformes sp., a suspected cryptic species of Tetraodontiformes, was also detected. Our results confirm the effectiveness of DNA barcoding for ichthyoplankton identification; this technique can increase the known diversity and richness of species in an area and provide a scientific reference for studies on variation in fisheries. Because both the abundance of individuals at each station and the number of species was low (only four species at three stations had abundances that exceeded 10 ind./100 m³), the surveyed area might not be a main spawning area, or autumn might not be a main spawning period for most of these fish species.

The H′ for the surveyed area was lower than that for continental shelf and adjacent east Indian Ocean (Holliday et al., 2012; Beckley et al., 2019; Rathnasuriya et al., 2021), perhaps because that the nearshore sea area has relatively high habitat diversity, rich productivity, and bait resources, and is a spawning and breeding ground for many fishes (Holliday et al., 2012). Meanwhile, the surveyed sea area was located in the open ocean and presented a single habitat and complex ocean currents; most involved species are oceanic migratory fish, or few nearshore species transported by ocean currents.

The temporal and spatial distributions of ichthyoplankton are affected by broodstock reproductive characteristics, and the environment, mainly Chl a, temperature, salinity, depth, and ocean currents (Zheng et al., 2003; Xiao et al., 2017). Our GAM model revealed that ichthyoplankton in the surveyed area were concentrated in areas of low SSH and high SSS, SST, and Chl a (Figure 3), with SSH most strongly correlated with ichthyoplankton abundance. In contrast, the distributions of nearshore ichthyoplankton are mainly affected by other variables such as Chl a (Zhang et al., 2019), SSS (Chen et al., 2015), SST, and water depth (Li et al., 2017; Xiao et al., 2017; Chermahini et al., 2021).

Water temperature strongly affects fish spawning and egg development, with high temperatures possibly stimulating both. Fish larval species richness and total abundance increases with water temperature, with species being replaced over time, assisted by increased prey as a consequence of increased water temperature (Meinert et al., 2020). However, our surveyed area is relatively large, and changes in water temperature are relatively small, with maximum SST differences of 2.3°C between stations, and with 75% of stations having SSTs between 28.3 and 29°C. Thus, not surprisingly, we report a low correlation between SST and ichthyoplankton abundance in our GAM.

In addition to seeking suitable water temperatures for survival, marine fishes must find places with better conditions for feeding when migrating. High Chl a can improve ocean primary productivity, increasing the probability of fish feeding, and inducing their aggregation. We assume that the spawning probability of aggregating fishes in these environments is increased, which is why we included Chl a in our analyses. However, our GAM did not show a high correlation between Chl a concentration and fish abundance. Additionally, ichthyoplankton abundance and total species richness at each station (unpublished data) were not obviously correlated either, indicating that areas with many captured individuals may not have more ichthyoplankton.

Variation in SSH is generally caused by a shift in the direction of ocean currents, with low SSHs more often found near where ocean currents change direction or vortices occur (Bakun, 2006; Tittensor et al., 2010; Prants et al., 2014). The average geostrophic flow vector diagram for the survey period revealed that stations S28 and S29 (with the highest ichthyoplankton abundances) are in areas where large ocean currents meander. Vortices forming low SSHs not only gather and passively transport ichthyoplankton, but also increase their feeding opportunities and survival rate (Meinert et al., 2020). Despite this, SSH alone does not adequately explain the distributions of ichthyoplankton across the surveyed sea area, indicating that their abundance is influenced by other variables, such as ocean currents, illuminance, total nitrogen, total phosphorus, pH, suspended matter, dissolved oxygen, and/or chemical oxygen demand (Chen et al., 2015).

The oceanic marine environment is changeable, and ichthyoplankton in different sea areas are affected by different variables, even giving rise to multivariable synergistic effects. Therefore, to truly understand species composition, quantity distribution, and the relationships between ichthyoplankton and environmental variables around the Ninety East Ridge, additional samples collected at different times of the year and more environmental data are needed. Various models could be used to comprehensively analyze biological and environmental data to provide further empirical support for studies on the variation in and development and utilization of fishery resources in this area.

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

The animal study was reviewed and approved by the Ethics Committee of the Laboratory of Animal Welfare and Ethics of Shanghai Ocean University.

LZ performed the data analysis and wrote the manuscript. SL, RW, and XM assisted with GMA model analysis. JZ and JX assisted with molecular analysis. LL and PS conceived and designed the study, and critically revised the manuscript. All authors listed in this manuscript have made a direct and substantial contribution to this work.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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