Comparison of cryptobenthic reef fish communities among microhabitats in the Red Sea

Study site

Fishes were sampled in May 2017 during daylight hours from the southern portion of Al Fahal reef situated in the middle of the continental shelf (22°13.6558N, 38°58.1853E) (Fig. 1A). Sampling was conducted between 10 and 15 m depth (majority of stations between 12 and 13 m) (Table S1). This single reef and narrow depth range were selected to minimize any potential environmental variables, such as temperature, wave energy, current, and turbidity that could be introduced if sampling in varying parts of the reef (e.g., sides exposed to or sheltered from dominant wave action).

Microhabitats

Quadrats (1 m²) constructed from PVC pipe (see Fig. 1) were placed onto a flat or gently sloping surface (<10°) of the reef in one of the three targeted microhabitats. Three microhabitat types were sampled: live hard coral, dead coral rubble, and sand (Figs. 1B–1D). For each microhabitat type, five replicate quadrats were sampled. Hard coral microhabitats were defined as having at least 70% of the survey quadrat covered with hard coral, with the remaining area composed of other microhabitat components, as seen from an aerial view. Tabular Acropora species were targeted for consistency. Rubble microhabitats contained at least 50% coverage of dead hard coral rubble, rocks, and/or empty shells. Sand microhabitats contained at least 90% coverage of sand with little or no rubble mixed in Table S1. Each quadrat was placed at least 1–2 m away from a neighboring microhabitat type. Percent cover of each microhabitat category was calculated using a grid point system. A 10 ×10 grid (i.e., 100 total intercepting points) was overlaid onto a digital image of each quadrat. Each point in the image was categorized into the above microhabitat type to produce a total percent cover. Rugosity of the sample quadrat was measured as a linear distance using a 1-m chain (sensu Risk, 1972).

Fish communities

An ichthyocide (rotenone) was used to collect fish due to its high success rate in targeting cryptic species (Robertson & Smith-Vaniz, 2008). A rotenone mixture was prepared by mixing 500 g of 4% rotenone powder (Consolidated Chemical Company, Dandenong South, Victoria, Australia) with 100 ml 96% ethanol, 250 ml liquid dishwashing detergent, and 100 ml water (adapted from Ackerman & Bellwood, 2000).

A 4 mm mesh net weighted down with chain was placed around the quadrat, enclosing the whole area. The rotenone mixture was gently squirted into the netted area until the whole area was covered. After waiting about 5 min for the rotenone to take effect, the net was removed and fishes were collected using hand nets and tweezers (following Ackerman & Bellwood, 2000). Because some fish were observed escaping the enclosed quadrat (either through the reef matrix or net) while the rotenone took effect, all deceased fishes directly around the sample area were collected for consistency. Water flow across the quadrats was minimal during ichthyocide deployment. Therefore, the effects were restricted primarily to within the sample area. However, we cannot discount some individuals outside the area succumbing to drifting rotenone. In these cases, the small number would have minimal influence in overall community composition. After collecting the visible fishes, three divers intensively searched within each quadrat by lifting up any loose rubble or debris where hidden fishes could have settled. Quadrats were searched until no new fishes were found for a period of 5 min. Larger predatory fishes that came too close to the quadrat were chased away before they could consume any of the asphyxiated fishes.

Immediately after the dive, collected fishes were placed into an ice slurry to preserve coloration for photography (within ∼2 h). Once photographed, fishes were placed into individual labeled vials containing a solution of 96% ethanol for preservation. A tissue sample (pectoral or caudal fin) was collected from each individual for genetic analysis.

Fish sampling was done in accordance with the guidelines and procedures approved under the auspices of the King Abdullah University of Science and Technology (KAUST) Institutional Animal Care and Use Committee (IACUC) under approval number 17-04-004.

Genetic fish identification

Tissue samples were cleaned with 96% ethanol and gently patted dry before placed into 96-well plates containing 100 μl 50 mM NaOH. DNA was extracted from the samples using the HotSHOT protocol (95 °C for 20 min, 4 °C for 10 min) (Meeker et al., 2007). After extraction, 10 μl of 1 M Tris–Hcl (10%) was added to each well and mixed with pipettes.

A total of 80 μl from each sample was transferred to a new plate for DNA amplification. A polymerase chain reaction (PCR) Qiagen Multiplex Mastermix containing Taq polymerase, dNTPs, MgCl₂, and reaction buffers was added to each well. Primers COI Universal Fish R2 5′ ACT TCA GGG TGA CCG AAG AAT CAG AA 3′ and F2 5′ TCG ACT AAT CAT AAA GAT ATC GGC AC 3′ (Ward et al., 2005) were used to amplify the COI region of DNA. Each PCR well contained 6.25 μl MasterMix, 4.25 μl nuclease free water, 0.5 μl forward primer, 0.5 μl reverse primer, and 1 μl DNA, for a total volume of 12.5 μl. Thermocycling occurred with an initial denaturation period of 15 min at 95 °C, followed by 35 cycles of 30 s at 94 °C, 60 s at the annealing temperature of 45 °C, and 60 s at 72 °C, followed by a final extension period of 10 min at 72 °C. PCR products were visualized using a QIAxcel system. Following PCR amplification, 2.14 μl ExoStar was added to each well to clean the PCR product. The mixture was incubated in the thermocycler for 60 min at 37 °C, then 15 min at 85 °C. Cleaned PCR products were sequenced via Sanger sequencing via ABI 3730xl sequencers in the KAUST Bioscience Core Lab.

In the event that a sample sequence did not yield a long enough strand of base pairs to be entered into a database (∼500 bp), DNA was extracted again using a more precise Qiagen DNeasy Blood and Tissue kit. The PCR process was then repeated on those samples.

After sequencing was completed, sample sequences were checked against several sequence databases for potential matches. The National Center for Biotechnology Information (NCBI) GenBank (https://www.ncbi.nlm.nih.gov/genbank) and Barcode of Life Data System (BOLD) (http://www.boldsystems.org) were used as public databases. A custom (in-house) Red Sea fish sequence database was also checked (see DiBattista et al., 2017; Isari et al., 2017; Coker et al., 2018). A sequence was considered a good match for a species in the database if the matched sequence was 98% similar or higher. However, some species of cryptobenthic fishes can differ by fewer than 2% COI similarity (Greenfield & Tornabene, 2014; Tornabene et al., 2015). Sequence matches were then checked against visual guides and morphological keys to double-check identity. In the event of a non-matching sequence, a sample was identified to species level, or as close as possible, with the use of keys that examined morphological features. When a species was unable to be identified using the aforementioned methods, it was assigned an operational taxonomic unit (OTU) so as to still be included in the data analysis. OTUs were named for previously uploaded sequences in the databases if there was a match. In the event a sequence did not match a previously assigned OTU within one of the databases, a new OTU was assigned. COI sequences for sequenced species, including new OTUs, are deposited to GenBank under accession numbers MG583518–MG583524 and MH160733–MH160761.

Morphological fish identification

With the use of keys, fishes that could not be identified genetically were assigned to a species, genus, or family level. The typical morphological characters that were useful in discriminating taxa included: counts of fin spines and soft rays, pelvic fin structure, cephalic pore counts, lateral line scale counts, as well as general meristics. Morphological characters were assessed using light dissection microscopy and an online image analysis tool for measuring morphometrics (Froese & Pauly, 2017). For a list of keys used, see Appendix 1.

Community analysis

Species richness was defined as the number of species or OTUs identified from each quadrat. Diversity (Shannon’s diversity index, H′) was calculated using the following formula: H′ = −Σ pi ln (pi) (Shannon & Weaver, 1963), and then subsequently averaged within each microhabitat type. Analysis of variance (ANOVA) was used to compare species richness, abundance, and diversity among habitat treatments. Significant pairings were identified using a post-hoc Tukey test. Fish communities present at each microhabitat type were plotted using non-metric multidimensional scaling (nMDS) using a square root transformation and Bray–Curtis resemblance matrix. A hierarchical cluster analysis was used to further illustrate the differences between microhabitat types. Permutational analysis of variance (PERMANOVA) was used to analyze fish community differences among microhabitats. A permutational analysis of multivariate dispersions (PERMDISP) test was performed to test for homogeneity of dispersions between microhabitat clusters. The dispersion within the three microhabitats did not differ significantly in all fish (PERMDISP, F_2,12 = 2.67, p = 0.217), or in gobies (PERMDISP, F_2,10 = 3.94, p = 0.116). However, pairwise comparisons between microhabitat types for all fish differed significantly between coral and rubble (PERMDISP, p = 0.038), and between rubble and sand in all gobies (PERMDISP, p = 0.008). A similarity percentage analysis was conducted to identify which fish species contributed most to the differences between microhabitats. Analyses were conducted with R version 3.4.0 (R Core Team, 2017), (vegan package: Oksanen et al., 2017), and PRIMER-e v6 (Clarke & Gorley, 2006).

Fish communities

A total of 326 individuals representing 73 species and 17 families were collected from three microhabitat types (Table S2). The total number of all fishes collected at each quadrat ranged from 1 to 65. Rubble had the highest average numbers of fish abundance (ANOVA F_2,12 = 11.59, p = 0.002) and species richness (F_2,12 = 6.78, p = 0.011), followed by coral, and then sand (Fig. 2). Diversity did not have a significant effect among microhabitats (F_2,12 = 2.80, p = 0.10).

Quadrats that were associated with the same microhabitat type grouped more closely together than unrelated microhabitat types (Fig. 3). Fish communities in all three microhabitats differed significantly (PERMANOVA F_2,12 = 2.63, p < 0.001) (Fig. 3A) with significant differences detected between coral and rubble (p = 0.006), coral and sand (p = 0.008), and rubble and sand (p = 0.012). The dendrogram of the cluster analysis supports the nMDS by showing the groupings of the three microhabitat types sampled (Fig. 3C). Coral and rubble microhabitats are more similar to each other than to the sand microhabitats (Figs. 3C and 3D).

Goby communities

Out of the 326 fish collected, family Gobiidae comprised the majority, with a total of 232 individuals (71.1%) collected, representing 31 species. On average, rubble communities had the highest levels of goby abundance (ANOVA F_2,12 = 24.61, p < 0.001), species richness (F_2,12 = 14.95, p < 0.001), and diversity (F_2,12 = 5.10, p = 0.025), followed by coral communities, and then sand communities (Fig. 2). Abundance ranged from 0 to 46 gobies per quadrat. The most abundant goby was Trimma avidori, with a total of 44 individuals collected in both coral and rubble microhabitats. The second most abundant goby was Callogobius bifasciatus, with 29 individuals collected in mainly rubble quadrats (Fig. 4). Of 31 goby species collected, 11 species (35.4%) were represented by only a single individual. The majority of species (64.5%) had fewer than five individuals collected.

Overall, goby community composition significantly differed among microhabitats (PERMANOVA F_2,12 = 3.67, p < 0.001) (Fig. 3B), with more similar microhabitats grouping together (Fig. 3D). Pairwise comparisons between microhabitats show significant differences between coral and rubble (p = 0.01), coral and sand (p = 0.03), and rubble and sand (p = 0.007).

Gobies C. bifasciatus, Asterropteryx semipunctata, T. avidori, and Eviota zebrina contributed the most to the differences found between rubble and sand microhabitats (average contributions 12.58%, 12.53%, 8.55%, and 6.42%, respectively) (Table 1). These four species are also the highest contributors to differences found between coral and rubble microhabitats (cumulative contribution 30.54%). Between coral and sand microhabitats, Pleurosicya prognatha and Istigobius decoratus were the highest contributors (10.94% and 7.73%), with T. avidori following close behind (5.77%).