Edited by: Sandie M. Degnan, University of Queensland, Australia
Reviewed by: Emre Keskin, Ankara University, Turkey; Luke Thompson, Southwest Fisheries Science Center (NOAA), United States; Shane Lavery, University of Auckland, New Zealand
*Correspondence: Xabier Irigoien
Laura Casas
This article was submitted to Marine Molecular Biology and Ecology, a section of the journal Frontiers in Marine Science
†Present Address: Xabier Irigoien, AZTI-Marine Research, Herrera Kaia, Portualdea z/g–20110 Pasaia (Gipuzkoa), Spain; IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
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Very little is known about the composition and the annual cycle of zooplankton assemblages in the Red Sea, a confined water body characterized by a high biodiversity and endemism but at the same time one of the most understudied areas in the world in terms of marine biodiversity. This high diversity together with the lack of references for several of the groups poses a problem in obtaining basic information on zooplankton seasonal patterns. In the present work, we used high throughput sequencing to examine the temporal and spatial distribution of the zooplankton communities inhabiting the epipelagic zone in the central Red Sea. The analysis of zooplankton assemblages collected at two sites—coastal and offshore—twice a month at several depth strata by using MANTA, Bongo and WP2 nets provides baseline information of the seasonal patterns of the zooplankton community over 1 year. We show that the seasonal fluctuation of zooplankton communities living in the upper 100 m of the water column is driven mainly by the annual changes in seawater temperature. The 18S rRNA gene was used for metabarcoding of zooplankton assemblages revealing 630 metazoan OTUs (97% similarity) in five phyla, highlighting the richness of the Red Sea community. During colder months, communities were characterized by lower richness and higher biomass than communities found during the hot season. Throughout the year the zooplankton communities were dominated by the class Maxillopoda, mainly represented by copepods and class Hydrozoa. The rise in the water temperature favors the appearance of classes Malacostraca, Cephalopoda, Gastropoda, and Saggitoidea. The present study provides essential baseline information for future monitoring and improves our knowledge of the marine ecosystem in the Red Sea while reporting the main environmental variable structuring zooplankton assemblages in this region.
The zooplankton community acts as a link for the transfer of energy and material from protists to the higher trophic levels and has a pivotal role in the recycling and export of nutrients (Valiela,
While zooplankton communities inhabiting temperate regions have been widely studied (e.g., Beaugrand,
The few studies that have addressed the analysis of zooplankton communities in the Red Sea were mostly conducted in the Northern region, particularly in the Gulf of Aqaba (e.g., Echelman and Fishelson,
However, studies of zooplankton assemblages in the Red Sea are especially relevant now, since this harsh tropical environment mimics conditions of future climate change scenarios which are likely to occur in the euphotic zone of the world's oceans (Behrenfeld et al.,
In addition, species living in warm tropical climates, traditionally considered aseasonal environments, might be particularly vulnerable to increases in temperature due to their limited acclimation capacity (Hoegh-Guldberg et al.,
For all these reasons, it is important to obtain baseline information of the relatively unexplored Red Sea ecosystem (Cantin et al.,
Molecular techniques offer the possibility to simplify the analysis of marine zooplankton communities by allowing simultaneous assessment of the whole community without the need for specific experts in morphological taxonomy of each taxon. Additionally, the molecular identification is more objective and reliable for the identification of larval and juvenile stages, which are difficult to differentiate morphologically, even by experts. Although metagenetic approaches have been developed primarily for microbial community analysis (Margulies et al.,
In the present work, we examined the temporal and spatial distribution of the zooplankton community inhabiting the epipelagic zone in the central Red Sea for 1 year. The species composition and biomass of zooplankton assemblages was investigated in relation to hydrological conditions at two study areas off the coast of Thuwal (Saudi Arabia), an inshore (50 m depth) and an offshore station (~500 m depth). The nuclear-small subunit (18S) rRNA was characterized using amplicon pyrosequencing of 154 samples collected throughout the year 2013. The aim of this study was to shed light on the seasonal cycle of zooplankton communities inhabiting one of the most understudied areas in the world in terms of marine biodiversity. The Red Sea is a harsh tropical environment that mimics conditions of future climate change scenarios, thus obtaining baseline information on zooplankton community structures is essential not only to monitor the reshaping of the communities as the temperatures increase but also provides a unique opportunity to predict how zooplankton communities might change in the following decades in other marine environments around the globe.
Zooplankton sampling was carried out from January 2013 to December 2013 on a twice per month schedule, over two stations situated on the continental shelf off the Saudi Arabian coast in the central Red Sea (offshore station: N22°31′21.08″, E38°99′48.54″; inshore station: N22°18′46.08″, E38°55′41.70″; Figure
Bathymetry Red Sea map showing the positions of the offshore and inshore sampling stations (highlighted using a red star) and their corresponding depths.
Samples collected using the MANTA and bongo nets were filtered through Whatman Folded filter paper (grade 113v, diam. 24 cm) to eliminate the ethanol. The weight was recorded using a digital weighing scale and the total biomass, defined as the animal density of total zooplankton, was calculated by subtracting the wet filter weight. Biomass of the samples collected using the vertical WP2 net was not estimated.
Aliquots of 5–10% of the total volume were used for the genomic analysis. Samples were digested in ATL lysis buffer (Qiagen, Valencia, CA) with 0.5 mg/ml proteinase K at 56°C overnight. Genomic DNA was isolated using the standard phenol-chloroform method (Sambrook and Russell,
Amplification was performed using the general eukaryotic primer set targeting the hypervariable region of the 18S rRNA gene designed by Amaral-Zettler et al. (
Library construction was performed using the Roche XLR70 kit and each pooled sample was sequenced on a ¼ run either on a Roche GS FLX system at the KAUST core facility or a Roche GS FLX+ system at IMGM Laboratories (Martinsried, Germany). The sff files generated during sequencing were deposited in the National Center for Biotechnological Information (NCBI) Short Read Archive (SRA) under the study accession number
Sequences were firstly demultiplexed based on the barcode and raw reads were filtered based on quality (
The resulting chimera cleaned reference sequences were taxonomically assigned using the naïve Bayesian classifier rdp (Wang et al.,
Taxonomic compositions were constructed using the R package
Canonical Correspondence Analysis (CCA) was used to elucidate the role played by different environmental variables in modulating the structure and seasonal succession of zooplankton assemblages.
The data sets used consisted of bimonthly species abundance represented by number of reads and nine environmental parameters: Sea Surface Temperature and Salinity (SST and SSS), Mean Temperature and Salinity of the water column for the whole CTD cast (T.mean and S.mean), Mean Temperature and Salinity of the water column from 0 to 50 m (T.50 and S.50) (for inshore stations is the same as T.mean and S.mean), Temperature and Salinity at 20 m (T.20 and S20) and UV index (UVI). The significance of these variables was assessed using Monte Carlo permutation tests (with 999 unrestricted permutations). The species data were log (x+1) transformed and rarely occurring taxa (less than 0.5% of the total abundance) were down-weighted in order to prevent them from greatly influencing the analyses (Ter Braak and Šmilauer,
The mean volume of water filtered by the MANTA, Bongo horizontal tow, Bongo oblique tow and vertical WP2 nets was 70.4 m3, 236/210 m3 (offshore/inshore), 252/200 m3 (offshore/inshore) and 28/15 m3 (offshore/inshore), respectively (Table
The temperatures ranged between 22.83–31.78°C at the offshore station and 25.31–31.36°C inshore. Salinities oscillated between 39.4–40.5 and 39.3–39.8 psu offshore and inshore respectively (Figure
Contour and density plots of changes in salinity (psu) and temperature (°C) with depth (m) over the annual cycle at the offshore
A total of 3,407,473 sequenced reads were obtained from all 154 samples. Of these, 2,033,939 reads passed quality checks, including the removal of chimeras (for further details, see Table
Similar numbers of OTUs were observed in the offshore station (558 OTUs) (when the MANTA net samples were removed) compared with the inshore station (572 OTUs). There was a significant level of overlap in OTU composition between both sites with a total of 507 OTUs observed at both stations. Comparison of sampling methods showed that the MANTA net revealed the lowest number of OTUs (288 OTUs) in the offshore station followed by the Bongo horizontal net (371 OTUs) which accounted for the lowest number of OTUs (389 OTUs) in the inshore station (where the MANTA net was not used). Inshore, 41% of the OTUs (239) were shared between all sampling methods whilst only 28% (162) were shared between the 4 sampling methods in the offshore station. Removing the MANTA net samples revealed that 45% of the OTUs (251) were shared between the three sampling methods utilized in the inshore station. In the inshore station the vertical net had the most unique OTUs (77 OTUs) while the oblique net revealed the most unique OTUs in the offshore station (45 OTUs).
The surface zooplankton community diversity was assessed using a MANTA net, which was trawled only at the offshore station. The taxonomic analysis revealed the lowest number of OTUs (288 OTUs) of all the samples surveyed in this study, with the majority of the biodiversity represented by only 4–5 classes in any given sampling date (Figure
Metazoan composition of the samples collected using the MANTA net, trawled only at the offshore station. Proportion of reads produced by the MANTA samples assigned to metazoan taxa at the class level showing the monthly trends for 1 year.
During the colder months, December to May, hereafter referred to as cold season, when the average sea surface temperature ranged between 25.41 and 27.10°C (Figure
The two main peaks of zooplankton biomass were recorded at the beginning and the end of the cold season (December and April), while the lowest biomass densities were detected in May and June, when the temperatures started rising (Figures
Monthly trends of bulk zooplankton biomass (μg/l) collected using the MANTA and bongo nets offshore
The analysis of the samples collected with the bongos towed horizontally at 20 m depth showed remarkably different zooplankton assemblages between the two sampling stations. During the cold season, the average temperature at 20 m depth ranged between 25.73–28.56°C offshore and 25.79–27.28°C inshore (Figure
Metazoan composition of the samples collected using the WP2 tows (Vertical:
The analysis of the samples collected with the bongo nets obliquely towed from 50 m depth to the surface, revealed consistent and similar biodiversity patterns to the horizontal tows (Figure
As expected, the samples collected using the WP2 vertical net showed the highest OTU richness of all samples, since the strata sampled was the widest in this study. However, the filtered volume and the biomass collected were the lowest (see Table
In order to assess statistical differences in the zooplankton communities under study, an Adonis analysis was performed to investigate the effects of the net and the location. Significant interactions were observed between the factors (Net and Location), indicating that there were variations in the trends for both the weighted (
NMDS plot based on
To investigate the effects of environmental variables (temperature, salinity, UV index) on zooplankton seasonal composition in the central Red Sea, we measured changes in community assemblages for each of the sampling methods separately (MANTA, Bongo horizontal, Bongo oblique, and WP2 vertical) using Canonical Correspondence Analysis (CCA) in R. The CCA analysis revealed clear differences between the cold and hot season communities, reinforcing the results described above. Temperature was found to be the main factor shaping the seasonal structure of the zooplankton communities.
The first CCA axis clearly separated the cold and hot season communities for all sampling methods, showing the highest correlation with temperature followed by salinity (Figure
Canonical Correspondence Analysis (CCA) ordination diagrams of samples in relation to environmental variables [Sea Surface Temperature (SST), Mean Temperature of the water column (T.mean), Mean Temperature of the water column from 0 to 50 m (T.50), Mean Temperature at 20 m (T.20), Sea Surface Salinity (SSS), Mean Salinity of the water column (S. mean), Mean Salinity of the water column from 0 to 50 m (S.50), Mean Temperature at 20 m (S.20), and UV index (UVI] for zooplankton taxonomic composition data sampled over the central Red Sea.
For the MANTA samples (Figure
For the samples collected with the bongos towed horizontally (Figure
The Monte Carlo permutation test of the samples collected with the bongo nets obliquely towed from 50 m depth to the surface (Figure
Finally, for the samples collected using the WP2 vertical net (Figure
To summarize, our study reveals two marked seasonal phases in terms of species composition of the zooplankton communities living in the upper water column (top 100 m) in the central Rea Sea, driven mainly by temperature. The cold season was characterized by a lower biodiversity being mainly dominated by classes Maxillopoda and Hydrozoa. Biodiversity increased during the hot season and was defined by the appearance of classes Malacostraca, Cephalopoda and Gastropoda, among the most abundant in terms of number of reads. Table
Compilation of the top 20 OTUs in terms of abundance for the epipelagic zooplankton community inhabiting the top 100 m of the water column identified in the inshore and offshore stations (n, where n indicates percentage of reads).
13 | Arthropoda | – | 16 | ||||
7 | Arthropoda | Insecta | 11 | – | |||
25 | Arthropoda | Malacostraca | Decapoda | – | 11 | ||
17 | Arthropoda | Malacostraca | Decapoda | – | 4 | ||
8 | Arthropoda | Malacostraca | Decapoda | 7 | 17 | ||
44 | Arthropoda | Malacostraca | Euphausiacea | Euphausiidae | Euphausia | 16 | – |
289 | Arthropoda | Maxillopoda | Calanoida | – | 19 | ||
196 | Arthropoda | Maxillopoda | Calanoida | 6 | – | ||
2 | Arthropoda | Maxillopoda | Calanoida | 9 | – | ||
22 | Arthropoda | Maxillopoda | Calanoida | Acartiidae | Acartia | – | 9 |
398 | Arthropoda | Maxillopoda | Calanoida | Calanidae | Undinula | – | 3 |
4 | Arthropoda | Maxillopoda | Calanoida | Calanidae | Undinula | 2 | 1 |
498 | Arthropoda | Maxillopoda | Calanoida | Candaciidae | Candacia | 14 | – |
6 | Arthropoda | Maxillopoda | Calanoida | Candaciidae | Candacia | 4 | 18 |
282 | Arthropoda | Maxillopoda | Calanoida | Paracalanidae | Paracalanus | 19 | – |
37 | Arthropoda | Maxillopoda | Calanoida | Paracalanidae | Paracalanus | 15 | 13 |
1 | Arthropoda | Maxillopoda | Calanoida | Paracalanidae | Paracalanus | 8 | 6 |
35 | Arthropoda | Maxillopoda | Poecilostomatoida | Sapphirinidae | Copilia | 17 | – |
16 | Chaetognatha | Sagittoidea | Aphragmophora | Sagittidae | Aidanosagitta | 18 | – |
43 | Cnidaria | 10 | 8 | ||||
54 | Cnidaria | Hydrozoa | Leptothecata | Campanulariidae | Clytia | – | 10 |
401 | Cnidaria | Hydrozoa | Siphonophorae | 12 | – | ||
286 | Cnidaria | Hydrozoa | Siphonophorae | 3 | 12 | ||
272 | Cnidaria | Hydrozoa | Siphonophorae | 13 | – | ||
20 | Cnidaria | Hydrozoa | Siphonophorae | – | 14 | ||
0 | Cnidaria | Hydrozoa | Siphonophorae | 1 | 2 | ||
117 | Cnidaria | Hydrozoa | Trachymedusae | Geryoniidae | 20 | ||
34 | Cnidaria | Hydrozoa | Trachymedusae | Geryoniidae | Liriope | – | 5 |
5 | Mollusca | Cephalopoda | 5 | 15 | |||
15 | Mollusca | Cephalopoda | Oegopsida | Enoploteuthidae | Abralia | – | 20 |
3 | Mollusca | Gastropoda | Thecosomata | Cavoliniidae | Creseis | – | 7 |
Average number of reads detected during the hot (highlighted in red) and cold season (highlighted in blue) for the overall dominant taxonomic group, subclass Copepods, at the species level, at the inshore and offshore stations for each of the nets (MANTA (only offshore); horizontal and oblique bongos; vertical WP2).
Acartiidae | 0.10 | 0.79 | 0.86 | 1.18 | 765.67 | 0.64 | 2.15 | 855.45 | |||||||
0.43 | 277.46 | 0.84 | 22.82 | 14.78 | 194.65 | 1.14 | 55.90 | 0.46 | 9.89 | 0.34 | 12.99 | 0.80 | 287.95 | ||
0.11 | 0.43 | 0.62 | 1.39 | 0.68 | 0.13 | 15.52 | |||||||||
Calanidae | 0.73 | 12.67 | 5.65 | 1.19 | 231.80 | 889.85 | 25.76 | 0.19 | 2.45 | 4.15 | 2.93 | 4.89 | 44.60 | 358.27 | |
0.03 | 0.29 | 0.19 | 0.75 | 0.11 | 3.60 | 1.25 | 7.00 | 0.67 | 3.46 | 0.55 | |||||
1, 547.85 | 431.33 | 215.48 | 558.56 | 92.76 | 152.25 | 14.35 | 15.27 | 123.13 | 441.79 | 22.82 | 52.83 | 32.98 | 1351.86 | ||
Candaciidae | 0.02 | 1.87 | 0.49 | 0.88 | 27.58 | 0.55 | 4.62 | 0.94 | 8.66 | ||||||
0.13 | 1.33 | 0.44 | 0.22 | 2.80 | 0.16 | 2.57 | 1.46 | 4.22 | 1.75 | ||||||
33.98 | 4.71 | 79.62 | 54.14 | 24.56 | 1, 533.48 | 61.56 | 0.23 | 292.98 | 121.85 | 53.94 | 189.42 | 127.35 | 954.96 | ||
Centropagidae | 0.31 | 1.42 | 1.75 | 0.75 | 24.58 | 0.50 | 0.56 | 3.45 | |||||||
Clausocalanidae | 0.28 | 36.48 | 54.93 | 1.53 | 0.33 | 0.17 | 148.87 | 255.55 | |||||||
4.40 | 0.45 | 0.32 | 0.15 | 3.80 | |||||||||||
Eucalanidae | 0.92 | 5.34 | 0.19 | 0.94 | 3.88 | 8.98 | 0.44 | 0.81 | 26.43 | 14.73 | 71.58 | 11.28 | 41.27 | 7.27 | |
0.08 | 2.17 | 0.77 | 0.23 | 0.32 | 1.62 | 1.29 | 0.66 | 4.99 | 0.43 | 0.40 | |||||
0.22 | 4.23 | 0.79 | 8.96 | 3.29 | 14.25 | 0.54 | 28.76 | 2.55 | 18.27 | 5.76 | |||||
Lucicutiidae | 0.46 | 11.76 | 48.20 | 2.58 | 0.85 | 0.32 | 78.66 | 132.96 | |||||||
Paracalanidae | 0.12 | 0.05 | 0.59 | 0.47 | 1.64 | 28.30 | 29.23 | 47.12 | 0.75 | 0.47 | 7.45 | 54.19 | |||
2.13 | 0.18 | 76.87 | 5.85 | 0.81 | 0.17 | 0.17 | 0.22 | 55.60 | 5.83 | ||||||
13.88 | 0.03 | 0.11 | 171.45 | 1, 865.49 | 11.12 | 84.12 | 0.33 | 0.38 | 82.98 | 1635.92 | |||||
27.50 | 12.75 | 564.65 | 162.68 | 281.82 | 0.29 | 0.65 | 0.81 | 267.17 | 4773.76 | ||||||
0.17 | 0.52 | 1.22 | 0.28 | 0.70 | 0.43 | 0.33 | 0.19 | 2.12 | 6.64 | ||||||
Pontellidae | 0.94 | ||||||||||||||
0.16 | 0.44 | 0.56 | 3.26 | 3.98 | 0.18 | 4.48 | 0.53 | 6.42 | 1.17 | 33.27 | |||||
Temoridae | 1.16 | 1.53 | 0.10 | 0.16 | 0.53 | 1.29 | 0.25 | ||||||||
Tortanidae | 0.12 | 0.37 | 2.53 | 0.62 | 0.77 | 0.93 | 2.68 | 0.25 | 0.24 | 0.35 | 0.83 | ||||
Miraciidae | 0.01 | 0.21 | 4.35 | 0.68 | 11.16 | 0.29 | 6.16 | ||||||||
Corycaeidae | 0.01 | 0.72 | 0.94 | 83.57 | 2.79 | 4.18 | 0.36 | 0.24 | 0.69 | 0.18 | 2.17 | 13.55 | |||
Oncaeidae | 0.04 | 0.83 | 1.64 | 0.49 | 0.13 | 36.54 | |||||||||
0.02 | 3.53 | 0.19 | 0.14 | 16.74 | |||||||||||
0.40 | 2.82 | 0.59 | 22.66 | ||||||||||||
Sapphirinidae | 0.20 | 2.35 | 0.21 | 0.33 | 7.95 | 0.24 | 1.67 | 65.75 | 2.14 | 128.36 | 0.59 | ||||
Sapphirina opalina | 1.73 | 0.27 | 0.22 | 2.90 | 5.37 | 2.85 |
A significant body of research has been undertaken for reef fishes and other common sessile organisms like corals or sponges in the Red Sea (e.g., Berumen et al.,
This study takes advantage of high throughput sequencing technology, which allows a rapid characterization of whole communities, to assess the seasonal profiles of zooplankton assemblages in two locations of the central Red Sea through an annual cycle. Our results constitute the first baseline data on the richness and seasonality of the central Red Sea zooplankton. However, it is important to note that the results presented here are based on the molecular data available in public reference databases and are therefore limited by them (Carugati et al.,
Nonetheless it is important to highlight that high throughput amplicon sequencing analysis applied to diversity studies is still in its infancy and presents several limitations in its current stage. So far, it has not shown good agreement with species abundance data from morphological taxonomic analysis (Lindeque et al.,
Our study reports changes in total biomass and in species composition during the annual cycle of the zooplankton communities inhabiting the upper 100 m of the water column in the central Rea Sea. The main abiotic factor driving the biodiversity of the zooplankton communities was found to be temperature, based on the CCA analysis of the seasonal structure of the zooplankton communities, which define two distinct seasonal phases (Figures
The two seasonal phases correspond well with the two seasonal periods of stratification and vertical mixing described in the Red Sea (Calbet et al.,
Similar annual trends of biomass densities were described for the zooplankton communities living in coastal areas in the Gulf of Aqaba (Khalil and El-Rakman,
Differences in species composition of the zooplankton assemblages between the communities inhabiting the offshore and the inshore station were detected by Adonis analysis being significant for both the weighted and unweighted UniFrac distance matrices. This observation may reflect fine-scale geographic structuring in terms of biodiversity for the samples collected with the bongo nets; however due to the limited geographic scope of the present study, more stations are needed to confirm this pattern.
The neuston community analyzed in this study revealed the lowest number of OTUs of all the sampling methods. The MANTA net samples the plankton inhabiting the upper 0.15 m of the sea, which is a restricted ecological niche occupied by a unique community (reviewed in Marshall and Burchardt,
Throughout the year the dominant phyla found in our study were Arthropoda and Cnidaria, at all depth strata studied. The majority of reads consisted of classes Maxillopoda and Hydrozoa, the latter being especially abundant during the cold season, accounting for on average 48.1 and 25.6% of the reads respectively. In the surface (MANTA samples, offshore) these two classes together contributed on average 85.8% to the total number of metazoan reads, while their contribution was on average 56.0, 66.4, 81.6% offshore and 67.2, 73.3, 85.9% inshore for the samples collected using the horizontal (20 m), oblique (50 to surface) and WP2 (100/50 to surface) nets respectively. The rise in the water temperature was accompanied by an increase in biodiversity with the appearance of classes Malacostraca, Cephalopoda, Gastropoda and Saggitoidea, among the most abundant, accounting for on average 17.1, 9.3, 1.6 and 0.7% offshore and 20.1, 2.5, 6.2 and 1.1% inshore during the hot season. Similar groups were found to be the most abundant in previous studies in the Red Sea using both metagenetics (Pearman et al.,
As expected, copepods were the dominant component of the Arthropoda inhabiting the upper 100 m of the epipelagic zone. This is consistent with previous studies across the northern and central Red Sea that have reported similar findings in terms of copepod abundance (Khalil and El-Rakman,
In summary, this study has shown that the seasonal fluctuation of the zooplankton communities living in the upper water column (top 100 m) in the central Rea Sea is driven mainly by temperature and the annual change between vertical mixing and stratification. During the cold season, communities are characterized by a lower biodiversity in terms of number of species and a higher biomass than communities found during the hot season. All throughout the year the zooplankton communities are dominated by class Maxillopoda, mainly represented by copepods and class Hydrozoa. The rise in the water temperature favors the appearance of classes Malacostraca, Cephalopoda, Gastropoda, and Saggitoidea, among the most abundant.
We found evidence for fine-scale geographic differences in terms of biodiversity for the two close proximity areas under study, providing an essential foundation for future comparative studies.
Conceived and designed the study: LC and XI. Performed the field, laboratory work and biomass analysis, interpreted the data and wrote the paper: LC. Performed the analysis of the sequence data and bioinformatics tasks: JP. Critically reviewed and edited the manuscript: JP and XI. All authors approved the final version of the manuscript.
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.
The authors would like to thank the Coastal and Marine Resources Core Lab, particularly Ioannis Georgakakis, for their invaluable support during fieldwork. We would like to express our gratitude to Craig T. Michell and Sylvain P. Guillot for technical assistance and the three reviewers for their constructive comments on the manuscript.
The Supplementary Material for this article can be found online at: