Composition of fish egg assemblages varies with depth on the West Florida Shelf

Introduction

Understanding fish spawning patterns, including the timing, location, and depth distribution of fish eggs throughout the water column, is vital to the conservation and management of important fish populations. Most marine fishes are broadcast spawners, releasing their eggs directly into the water column, where they hatch within hours to days (Pauly & Pullin, 1988), making them reliable predictors of spawning locations compared to larvae, which develop over days to months and exhibit behavioral features such as active swimming and vertical migration (Cowen & Sponaugle, 2009; Hawes et al., 2020; Stenevik, Sundby & Cloete, 2001). Sustainability of fisheries on the West Florida Shelf (WFS) is a management priority, and several studies have identified fish eggs through genetic barcoding of the mitochondrial cytochrome c oxidase I gene to identify critical habitats needed for successful reproduction in this region (Breitbart et al., 2023; Burghart et al., 2014; Burrows et al., 2019; Keel et al., 2025; Martínez-Mercado et al., 2025). DNA barcoding of fish eggs is more accurate than relying on morphological characterization due to the lack of discerning features that enable discrimination between different taxa (Hofmann et al., 2017; Kawakami, Aoyama & Tsukamoto, 2010).

Although fish can spawn at different depths in the water column, their eggs are passive particles that exhibit positive buoyancy, rising to surface waters post-spawning (Paris & Cowen, 2004). This migration results from egg hydration, and lower salinity levels of the ovoplasm compared to the surrounding seawater (Baras et al., 2018). Therefore, the vast majority of studies collect fish eggs from surface waters, operating under the assumption that broadcast spawned eggs eventually make their way to the surface (Checkley Jr et al., 1997; Tan et al., 2023). This study directly tests the hypothesis that the assemblage of fish eggs collected at the surface reflects the diversity of fish species spawning throughout the water column. We used fish egg samples collected using a Multiple Opening/Closing Net and Environmental Sensing System (MOCNESS) to determine if eggs from different fish species are detected at various depths in the water column on the WFS in February. MOCNESS systems have been successfully deployed for the collection of fish eggs in prior studies and are notably efficient compared to conventional sampling methods, such as bongo nets, catching a higher number of eggs (Johnson & Fogarty, 2013). We acquired samples at six different depth bins (0–20 m, 21–40 m, 41–60 m, 61–80 m, 81–100 m, and 101–130 m) from five stations and performed genetic barcoding to identify individual fish eggs throughout the depth profile. This investigation provides valuable information regarding the composition of fish egg assemblages with respect to depth, yielding important implications for future sampling design and interpretation of results.

Materials & Methods

Sample collection

The NOAA Southeast Area Monitoring and Assessment Program (SEAMAP) collected ichthyoplankton samples between February 15-24, 2012 on the WFS as part of a fishery-independent survey aboard the NOAA Ship Oregon II. A 1-m² MOCNESS outfitted with 0.505 mm mesh nets was deployed at stations 58 (28° 59′46.8″N 87°30′55.8″W), 66 (29°12′39.0″N, 85°59′46.8″W), 80 (28°00′18.6″N, 85°00′00.6″W), 86 (27° 30′09.0″N, 84°29′28.8″W), and 96 (26°00′36.0″N, 83°59′48.0″W). The nets sampled six discrete depths ranging approximately from: (1) 0–20 m, (2) 21–40 m, (3) 41–60 m, (4) 61–80 m, (5) 81–100 m, and (6) 101–130 m (Fig. 1). Ichthyoplankton samples were initially preserved in 95% ethanol and transferred to fresh 95% ethanol after 24 h. Samples were processed at the Plankton Sorting and Identification Center (ZSIOP) at the Sea Fisheries Institute (MIR) in Poland where zooplankton, fish larvae, and fish eggs were all separated and placed in separate vials. We picked 48 fish eggs per depth bin at each station under a dissecting microscope with tweezers following the pattern of a gridded dish for DNA barcoding analysis. For depth bins containing less than 48 eggs, we picked all available eggs in the same manner.

Results

We picked and extracted DNA from a total of 1304 individual fish eggs. We successfully amplified PCR products from 869 eggs (66.6% success rate), obtained high-quality sequences from 737 eggs, and taxonomically identified 623 eggs. As a combined consequence of PCR failure, poor sequence quality, and database limitations, only 47.8% of the eggs processed yielded identifications. We identified 44 distinct fish taxa belonging to 23 families (Table S1). All but three of these identifications could be resolved to the species level with the remainder assigned to the genus level.

Fish egg assemblage composition was highly heterogeneous throughout the water column, with the majority of taxa at any given station only detected at a single (n = 28) or two depths (n = 24), eight taxa found at more than half the depths, and a single taxon (Caulolatilus cyanops at station 96) found at all depths (Fig. 2). Notably, on several occasions a large number of eggs from a given taxon were recovered in a single depth bin, with that taxon either not seen or poorly represented in the surrounding depth bins (e.g., 10 Maurolicus weitzmani eggs identified between 37-59 m at Station 96, but none recovered from other depths; Fig. 2). Six taxa were detected at three separate stations (Fig. 3), of these only two, Maurolicus weitzmani and Saurida brasiliensis, were observed at all six depth bins across three stations.

Contrary to our hypothesis that the egg assemblage recovered from the surface layer would be representative of eggs found throughout the water column, between 20% (stations 80 and 96) and 57% (station 58) of the taxa present at a given station were found in the surface 20 m (Fig. 2). Similarly, 19 of the 44 taxa identified across the five stations in this study (43%) were observed in the samples collected from the upper 20 m (Table S1). The five most abundant taxa across all stations (Table S1) were Maurolicus weitzmani (Atlantic pearlside), Ariomma bondi (silver-rag driftfish), Scomber colias (Atlantic chub mackerel), Synodus poeyi (offshore lizardfish), and Saurida brasiliensis (Brazilian lizardfish). The combined total eggs from these species accounted for over half (n = 347) of the identifications made. Station 96 had the largest number of eggs successfully identified (n = 201) and yielded the most distinct identifications with 25 taxa, while station 66 had the second highest number of successfully identified eggs (n = 128) yet yielded the lowest number of taxa (n = 6).

Of the 44 taxa observed in this study, a majority are demersal (n = 16) or reef-associated (n = 9) (Table S1). Eggs were observed from species from other habitat categories including bathypelagic, pelagic-neritic, pelagic-oceanic, and benthopelagic. Station 58 contained many bathypelagic species (Fig. 2), these included Vinciguerria nimbaria (oceanic lightfish), Neolotus tripes (black snake mackerel), and Chauliodus sp. (viperfish). Eggs from these species were detected across varying depths. Notable pelagic-neritic fish included Brama brama (Atlantic pomfret), Etumeus sadina (red-eye round herring), and Scomber colias (Atlantic chub mackerel). Pelagic-oceanic fish included Taractichthys longipinnis (big-scale pomfret) and Decapterus macarellus (mackerel scad).

Discussion

In contrast to existing literature that has only analyzed fish eggs collected from surface samples, the present study specifically identified fish eggs collected from different depths within the water column using a MOCNESS system, providing the first depth-specific fish egg assemblage composition data to date. The fish egg assemblage was highly heterogeneous, with most species only detected at one or two depths at any given station. Only 40% of the taxa observed throughout the water column were also recovered from the upper 20 m, suggesting that surface sampling is not representative of all spawning happening in deeper waters. Although a large number of the eggs were unidentifiable, primarily due to failure in the PCR amplification step due to the long-term storage of samples before processing, these samples were lost due to hurricane impacts on the lab in 2024 and we are unable to attempt further techniques to increase the success rates. Therefore, we have focused the results and discussion on the eggs that could be identified through DNA barcoding.

Fish eggs are positively buoyant, but may rise at different rates due to variations in egg size, aqueous contents, oil globules, and densities at different stages of development and across fish species (Jung et al., 2014; Ospina-Álvarez, Palomera & Parada, 2012). Eggs may have been sampled during their ascent to the surface, may have reached a neutrally buoyant state at deeper isopycnals, or may have been spawned below a critical depth, in which case they may sink out of the water column (Sundby & Kristiansen, 2015). In addition, turbulent mixing and stratification within the water column also have the potential to affect the migration and settling of fish eggs, potentially delaying their immediate rise to the surface (Conway, 1997). Finally, fish like mackerel and sardines are known to prey heavily upon pelagic fish eggs (Veríssimo, Fonseca & Garrido, 2024); therefore, predation at the surface and adjacent depths may result in depth-related heterogeneity in fish egg assemblages.

Fish egg identification studies using surface-collected eggs frequently employ backward trajectory modeling to predict likely spawning locations (Keel et al., 2025; Nguyen et al., 2024). Hindcast modeling is beneficial for the prediction of horizontal movement since models efficiently capture wind-driven surface currents; however, these methods do not typically consider subsurface currents affecting the fish eggs at various points during their ascent, or the effects of more complicated external factors such as stratification and turbulent mixing on egg distributions. To fine-tune hindcast models of spawning locations, future work should attempt to account for the specific densities of eggs from various fish species, their rates of ascent in given oceanographic regimes, and the currents they are subjected to during ascent to the surface.

The fish egg samples in this study were mostly located on the outer shelf between 100–200 m isobaths, with one station at >1,000 m in the De Soto Canyon area (Fig. 4). West Florida Shelf circulation has been studied extensively using moored observations and numerical models during the last two decades. Its seasonal variation is pronounced on the inner shelf (Liu & Weisberg, 2012), but not obvious on the outer shelf due to the influence of the offshore eddies associated with the Loop Current system (e.g., Liu et al., 2016). To examine the ocean circulation pattern during the study time period, we show a map of satellite altimetry-derived surface geostrophic currents (Fig. 4), mainly because this current product was found to be more accurate than the data assimilative numerical models in this area (Liu et al., 2014). Sea surface temperature (OSTIA product; (Worsfold et al., 2024)) is also shown in Fig. 4. During our study time period, the Loop Current system stayed off from the “Pressure Point” of the shelf (Liu et al., 2016); thus, a strong offshore forced upwelling is not expected for the shelf. Due to CTD malfunction, we only have vertical temperature profiles from stations 58 and 66. These profiles show a mixed layer depth of about 90 –100 m, below which is a weakly stratified lower water column (Fig. 5). The local oceanographic conditions are considered normal for the time of year.

Supplemental Information