Genetically divergent shallow and mesophotic Stephanocoenia intersepta coral populations across Florida Keys National Marine Sanctuary

doi:10.21203/rs.3.rs-3314946/v1

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Genetically divergent shallow and mesophotic Stephanocoenia intersepta coral populations across Florida Keys National Marine Sanctuary

https://doi.org/10.21203/rs.3.rs-3314946/v1

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published 27 Jun, 2024

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Population genetic analyses can provide useful data on species’ regional connectivity and diversity which can inform conservation and restoration efforts. In this study, we quantified the genetic connectivity and diversity of Stephanocoenia intersepta corals from shallow (<30 m) to mesophotic (30–45 m) depths across Florida Keys National Marine Sanctuary. We generated single nucleotide polymorphism (SNP) markers to identify genetic structuring of shallow and mesophotic S. intersepta populations. We uncovered four distinct genetic lineages with varying levels of depth-specificity. Shallow lineages exhibited lower heterozygosity and higher inbreeding relative to depth-generalist lineages found predominantly on mesophotic reefs. Estimation of recent genetic migration rates demonstrated that mesophotic populations are more prolific sources than shallow populations, particularly in the Lower Keys and Tortugas Bank. Additionally, we compared endosymbiotic Symbiodiniaceae communities among S. intersepta populations using the ITS2 region and SymPortal analysis framework, identifying symbionts from the genera Symbiodinium, Breviolum, and Cladocopium. Symbiodiniaceae communities varied significantly across depth and location and exhibited significant, but weak correlation with host population structure and genotype. Together, these data demonstrate that despite population genetic structuring across depth, mesophotic populations may provide refuge for shallow populations moving forward and remain important contributors to the overall genetic diversity of this species throughout the region. This study highlights the importance of including mesophotic as well as shallow coral populations in population genetic assessments and informs future science-based management, conservation, and restoration efforts within Florida Keys National Marine Sanctuary.

Biological sciences/Ecology/Ecological genetics

Biological sciences/Genetics/Population genetics

Inbreeding

migration

coral reef

ecological genomics

mesophotic coral ecosystems

symbiosis

Mesophotic coral ecosystems (MCEs) are relatively light-limited coral reefs that typically occur between 30–150 m, characterized by the presence of light-dependent biota including many scleractinian coral species (Lesser et al., 2009). Mesophotic coral ecosystems, like shallow coral reefs, are abundant throughout the tropics and subtropics (Pyle and Copus, 2019). Community composition among MCEs is distinct from shallow coral reefs; yet many species, including some corals, are depth-generalists with broad depth distribution spanning across both shallow and upper mesophotic depth zones (30–60 m; Bongaerts et al., 2010). In the Caribbean Sea, there is as much as 25–40% scleractinian coral species overlap between shallow reefs and upper mesophotic reefs (Bongaerts et al., 2010).

Coral reefs around the world are facing devastating declines in coral cover from a profusion of stressors including, but not limited to, overfishing, terrestrial runoff and pollution, coral disease outbreaks, and global climate change (Hughes, 1994; Gardner et al., 2003; Mumby et al., 2006; De’ath et al., 2012). Mesophotic coral ecosystems have the potential to be buffered from anthropogenic stressors due to their depth and/or relative isolation from shore in many regions (Glynn, 1996; Bongaerts et al., 2010). The deep reef refuges hypothesis (DRRH) posits that MCEs may function as refuges for depth-generalist coral species, providing viable larvae to degraded shallow reefs after isolated episodic disturbance events, thereby aiding in the recovery of shallow reefs (Glynn, 1996; Bongaerts et al., 2010).

Understanding the degree of connectivity and similarity of coral populations and their endosymbiotic dinoflagellate microalgae (Family Symbiodiniaceae) across shallow and mesophotic depth zones is useful for developing effective ecosystem management or restoration practices (Palumbi, 2003; Bongaerts et al., 2010). Population genetic approaches and internal transcribed spacer 2 (ITS2) metagenomics have often been used to estimate these levels of similarity and connectedness between coral populations and their in hospite Symbiodiniaceae on shallow reefs and MCEs (Bongaerts et al., 2017; Sturm et al., 2022). The potential reseeding ability of MCEs varies across both region and coral species (Bongaerts et al., 2017; Studivan and Voss, 2018). In this aspect, the DRRH, which is already limited to depth-generalist corals species, is further limited in its universal applicability. Although MCEs are not completely isolated from disturbance events common to shallow reefs and the degree of connectivity between mesophotic and shallow populations varies widely, the DRRH remains an important framework for understanding where MCEs have the potential to function as refuges within local and regional coral metapopulations (Smith et al., 2016; Rocha et al., 2018).

Since the 1970s, coral reefs in Florida Keys National Marine Sanctuary (FKNMS) have experienced catastrophic declines in coral cover, with as much as a 50% reduction between 1998 and 2011 alone (Ruzicka et al., 2013; Toth et al., 2014). Coral reefs within FKNMS have been heavily studied, but research in the mesophotic zone is historically limited in this region, consistent with the relative proportion of MCE to shallow coral reef research worldwide (Jaap et al., 2008; Turner et al., 2017). Previous studies on MCEs within Florida have been largely focused on Pulley Ridge, a deep ridge to the southwest of FKNMS that supports the deepest known scleractinian coral reef in the United States (Reed et al., 2019). Studies not specific to Pulley Ridge have often focused on the genetic connectivity of MCE benthic invertebrate populations (Bernard et al., 2018; Studivan and Voss, 2018). Notably, the majority of vertical connectivity studies within MCEs at FKNMS have focused on a single coral species, Montastraea cavernosa, and have demonstrated that levels of vertical connectivity are highly variable across the Florida Keys region (Serrano et al., 2014; Sturm et al., 2021).

Stephanocoenia intersepta, like M. cavernosa, is a depth-generalist scleractinian coral species found throughout the Caribbean Sea and the Gulf of Mexico (Lang, 2003). Stephanocoenia intersepta is typically not as numerically dominant on shallow coral reefs as M. cavernosa, but as depth increases S. intersepta increases in abundance and cover on many tropical western Atlantic MCEs (Buckel et al., 2014; Goodbody-Gringley et al., 2019). Stephanocoenia intersepta implements a gonochoric broadcast spawning reproductive strategy, whereby male and female colonies release gametes, and eggs are fertilized externally allowing the potential for dispersal through ocean currents. However, unlike many other broadcasting species, S. intersepta females have been observed to hold multiple eggs in their tentacles rather than their oral disc, often only releasing eggs after nearby (<2 m) males have released sperm (Hagman, Gittings, and Vize, 1998; Vermeij et al., 2010). Given this reproductive mode, and S. intersepta’s relative abundance, particularly at mesophotic depths, S. intersepta is a useful candidate species to investigate connectivity between shallow reefs and MCEs.

To further our understanding of population connectivity and the potential for mesophotic to shallow subsidy in the Florida Keys we sampled populations of S. intersepta from paired shallow/mesophotic reefs throughout FKNMS. We implemented a seascape genomics approach to investigate patterns of host genetic diversity, differentiation, and connectivity as well as in hospite communities of Symbiodiniaceae through single nucleotide polymorphism (SNP) genotyping and ITS2 sequencing analyses. Using similar approaches and sampling designs across the same sites as previous population genetics work in FKNMS (Sturm et al., 2021) we expand upon the dynamics of metapopulation function and MCE refuge potential to aid in future management decisions of this National Marine Sanctuary.

Sample collection and DNA extraction

Stephanocoenia intersepta colonies were sampled from shallow (<30 m) and upper mesophotic (30–45 m) depths by SCUBA and technical divers, respectively, from four sites across FKNMS (Supplementary Table S1; Fig. 1). Small fragments (~5 cm²) of skeleton and tissue were removed from colonies with a hammer and masonry chisel. Sampling depths were recorded and scaled photographs were taken for each sample. At the surface, samples were placed into absolute molecular-grade ethanol and frozen at -20 °C while at sea. Samples were transported on dry ice to Harbor Branch where the ethanol was replaced with fresh ethanol and samples were stored at -80 °C prior to genomic DNA extraction.

Small tissue subsamples (~1 cm²) were transferred into 500 µL of TRI reagent (Invitrogen) and allowed to soak overnight at 4 °C. Genomic DNA was extracted using a chaotropic buffer extraction optimized for increased DNA yield and quality from marine benthic invertebrates (Sturm et al., 2021). DNA extracts were purified with the Zymo DCC-5 kit and DNA quality/quantity were measured with NanoDrop (Thermo Fisher) and Qubit 4.0 fluorometer (Thermo Fisher). DNA extracts were normalized to 25 ng µL^-1 and used for both coral host genotyping and algal symbiont molecular characterization.

Stephanocoenia intersepta SNP genotyping

SNP genotyping libraries were prepared with the endonuclease BcgI (New England BioLabs, Inc.) and 100 ng of genomic DNA per sample, using an updated protocol for the 2bRAD-seq method (Wang, Meyer, et al., 2012). Wet lab protocols were adapted from the current 2bRAD GitHub repository (Matz, 2020; Sturm et al., 2021) and detailed lab protocols are found in this manuscript’s accompanying GitHub repository (Eckert, 2022). Pooled 2bRAD libraries were size selected and sequenced at The University of Texas Genome Sequencing and Analysis Facility on Illumina NovaSeq (SR100 S1) with 20% PhiX spike-in.

Returned sequencing reads were demultiplexed, deduplicated, filtered and trimmed with custom perl scripts and cutadapt v3.4 (Martin, 2011). Reads from each sample were first aligned to a concatenated Symbiodiniaceae reference consisting of representative genomes for four genera (Symbiodinium, Breviolum, Cladocopium, and Durusdinium) using Bowtie2 v1.2.2 (Langmead and Salzberg, 2012; Shoguchi et al., 2013, 2021; Aranda et al., 2016; Liu et al., 2018). After excluding the Symbiodiniaceae aligned reads, resulting high-quality reads were used to create a de novo RAD tag reference assembly for S. intersepta consisting of clustered (91% similarity) unique sequences found in at least 10% of all samples using custom perl scripts and CD-HIT v4.8.1 (Fu et al., 2012). Potential contamination sequences were removed from the clustered sequences with Kraken2 and remaining sequences were assembled into a reference of 30 equally-sized pseudo-chromosomes for mapping (Wood et al., 2019; Rippe et al., 2021). Sample reads were aligned to the de novo reference with Bowtie2 v1.2.2 (Langmead and Salzberg, 2012). The program angsd v0.933 was used to determine SNP loci from sequencing reads, generate genotype likelihoods, and create an identity-by-state (IBS) matrix (Korneliussen et al., 2014). SNP loci underwent stringent filtering, requiring a minimum mapping quality of 20, minimum base quality score of 30, minimum allele frequency of 0.05, minimum p-value of 10^-5for deviation from Hardy-Weinberg equilibrium, minimum p-value of 10^-5for strand bias, p-value of 10^-5for heterozygosity bias, remove triallelic SNPs, p-value that a locus is variable of 10^-6, and had to be present in ≥ 85% of samples. No clonal multi-locus genotypes were identified through hierarchical clustering using an IBS threshold determined by the technical triplicate samples (Supplementary Fig. S1; Manzello et al., 2019). Two of each of the three technical replicates were removed and angsd was run on the clone-free sample set with the previously described filters.

Stephanocoenia intersepta population genetic structure

Principal component analysis based on IBS distances was conducted using pcangsd (Meisner and Albrechtsen). ngsadmix was used to calculate population structure models for cluster values from K = 1–11 (sample populations +3; Skotte et al., 2013). In addition to calculating clusters with pcangsd, the most likely value of K was evaluated using Clumpak (Kopelman et al., 2015). Analysis of molecular variance (AMOVA) and pairwise fixation indices (F_ST) were calculated using poppr and StAMPP to quantify any genetic differentiation between sample populations (Pembleton et al., 2013; Kamvar et al., 2014).

Stephanocoenia intersepta lineage demographics

To avoid ascertainment bias from the presence of four putative genetic lineages within the sample set, angsd was run within each lineage using the above filters. Samples were considered to belong to a lineage if they had ≥ 75% assignment to that lineage, otherwise they were considered admixed and removed from the following lineage-specific analyses (Fifer et al., 2022). angsd was used to calculate SNPs within each lineage using the above filters with the following modifications: minimum base quality score of 25, p-value that a locus is variable of 10^-5, and present in ≥ 80% of samples. SNPs were refined to only sites found across all lineages with the -sites flag in angsd (4,604 SNPs retained). Inbreeding coefficients were calculated for all samples with the program ngsrelate (Hanghøj et al., 2019).

For heterozygosity and site frequency spectra (SFS) calculations, the above filters were used in angsd, removing filters affecting allelic frequencies to retain all loci, variant and invariant (Rippe et al., 2021). Heterozygosities were calculated for each sample using custom R scripts (Matz, 2020). Welch’s ANOVA was used to test for differences between heterozygosity, mean inbreeding coefficients, and depth distribution within each lineage. Pairwise comparisons for significant ANOVA results were implemented with Games-Howell tests. Site frequency spectra (SFS) were calculated for each lineage with realSFS and were used to calculate nucleotide diversity (π) and pairwise F_ST among lineages, which was averaged across the 30 pseudochromosomes used as a de novo reference. We tested the relationship between mean nucleotide diversity and mean depth of each lineage with linear regression.

Effective population sizes of each lineage throughout time were reconstructed using stairwayplot v2 on folded SFS (Liu and Fu, 2020). Prior to running stairwayplot, F_ST outliers were detected with bayescan v2.1 (Foll and Gaggiotti, 2008). To ensure only neutral loci were used for analysis we used conservative filtering, removing all loci with q-value <0.5 prior to SFS calculations (101 loci removed; Rippe et al., 2021). stairwayplot runs were run using suggested default parameter estimates and an estimated mutation rate (2×10⁻⁸; Matz et al., 2018; Rippe et al., 2021) with an assumed generation time of 15 years, based on previous estimates and growth rates of massive hermatypic corals (Szmant, 1991; Soong, 1993).

Population genetic connectivity

Genetic connectivity between S. intersepta populations was measured through recent migration rate estimates (immigrant individuals from the previous 2–3 generations) with the BA3SNP function of the program bayesass v3.0.4.2 (Wilson and Rannala, 2003). Ten independent runs were conducted with random start seeds for 6.5 million Markov-Chain Monte Carlo (MCMC) model simulations with a 3 million burn-in and 100 run sampling frequency. Mixing parameters (migration rate [m] = 0.2, allele frequencies [a] = 0.6, inbreeding coefficient [f] = 0.04) were adjusted to allow adequate mixing within model runs (acceptance rates = 0.2–0.6; Wilson and Rannala, 2003). Trace files from independent runs were visualized with tracer v1.7 to ensure model convergence and consistency between independent model runs (Rambaut et al., 2018). Bayesian deviance was calculated for each independent run and the run with the lowest deviance was used for further analysis (Faubet et al., 2007). Migration rate estimates (m) were calculated as the mean of the posterior distribution and their uncertainty as 95% high posterior density (HPD) intervals.

Isolation by distance and distance-based redundancy analyses

To test for isolation by distance we used a Procrustes analysis using the IBS matrix for genetic distance and site coordinates with Albers equal-area conic projection for geographic locations (Wang, Zöllner, et al., 2012). Procrustes analysis was conducted with the protest function in the R package vegan with the first two principal components calculated from the IBS matrix generated from the reduced suite of SNPs common to all lineages and 9,999 permutations (Oksanen et al., 2020).

We implemented a distance-based redundancy analysis (dbRDA) to determine the relative contribution of environmental factors to genetic diversity in S. intersepta populations. We used S. intersepta IBS distances for genetic data and mean values of environmental variables were extracted for sample site locations from the BioOracle v2.0 database with the sdmpredictors package for each sampling location in addition to sampling depth recorded in situ (Assis et al., 2018; Bosch and Fernandez, 2021). Variables were assessed for collinearity through correlation analysis and for pairs of variables where only the more biologically relevant variable was retained for analysis. Variables were also checked for multicollinearity by assessing variance inflation factors (VIF), ensuring that no variables had VIF > 10. The remaining variables (current velocity [sea surface; m s^-1], light [mean benthic; E m^-2 yr^-1], temperature [mean benthic; ºC], primary productivity [mean benthic; g m^-3 d^-1], and sample depth [m]) were tested for explanatory importance using forward model selection and ANOVA with the ordiR2step function in vegan to yield the most parsimonious model for inclusion in the dbRDA using the dbrda function in vegan (Oksanen et al., 2020).

Symbiodiniaceae ITS2 sequencing

Extracted and purified genomic DNA was used to generate ITS2 amplicon libraries to identify in hospite putative Symbiodiniaceae taxa and to determine if algal symbiont communities differed with respect to location and/or depth. Symbiodiniaceae ITS2 sequences were amplified using Symbiodiniaceae-specific primers with Illumina MiSeq adapter sequences at the 5′ ends (SYM_VAR_5.8SII/SYM_VAR_REV; Hume et al., 2018). PCR products were cleaned using the Zymo DCC-5 kit and normalized to 10 ng µL^-1. Unique double identifying indices were incorporated into each sample with an additional PCR using 50 ng PCR product and 7 cycles of PCR. Detailed lab protocols are found in this manuscript’s accompanying GitHub repository (Eckert, 2022). Libraries were pooled equimolarly and sequenced on Illumina MiSeq V2 (500 cycle) with 20% PhiX spike-in.

Symbiodiniaceae SymPortal ITS2 analyses

Demultiplexed Symbiodiniaceae ITS2 .fastq files generated from the sequencing facility were run remotely through the symportal analytical framework (Hume et al., 2019). Sequences occurring in a sufficient number of samples within both the S. intersepta dataset and the entire database of samples run through SymPortal were identified as “defining intragenomic variants” (DIVs) which were used to characterize ITS2 type profiles, representative of putative Symbiodiniaceae taxa (Hume et al., 2019).

Multivariate ordination of ITS2 type profiles was conducted using principal coordinates analysis on Bray-Curtis distances in the package vegan (Oksanen et al., 2020). The betadisper function in the package vegan was used to calculate multivariate homogeneity of dispersion (PERMDISP) using Bray-Curtis distances. Pairwise comparisons for significant factors were calculated with permutation tests using the permutest function in vegan (9,999 permutations). Permutational multivariate analysis of variance (PERMANOVA) was implemented with the adonis2 function in vegan to test for differences in Symbiodiniaceae ITS2 type profiles with depth and sampling site used as fixed factors from Bray-Curtis distances (9,999 permutations). Pairwise PERMANOVA tests were conducted on significant factors with the package pairwiseAdonis using false discovery rate corrected p-values (Martinez Arbizu, 2017). Procrustes analysis was used to test for correlation between algal symbiont communities (ITS2 type profile) and host genotype (IBS) as well as host genetic lineage assignment (admixture analysis).

A dbRDA was run to test the effects of environmental variables on in hospite Symbiodiniaceae communities. As detailed above, analyses were conducted using symportal ITS2 types and the same BioOracle environmental database and analysis parameters as for the S. intersepta host dbRDA.

Additionally, we validated the accuracy of using 2bRAD Symbiodiniaceae sequence alignments as a proxy for in hospite Symbiodiniaceae genera, as proposed in Manzello et al. (2019). 2bRAD sequences which mapped to each of the four Symbiodiniaceae genomes were counted for the genus they mapped to for each sample and symportal ITS2 type profile counts were based on the genera they represented. Both 2bRAD and ITS2 counts were normalized and Bray-Curtis distances were calculated in the vegan R package. The protest function in vegan was used to conduct a symmetric Procrustes analysis (9,999 permutations) to test for similarities between principal coordinates analyses of each dataset.

Stephanocoenia intersepta SNP data processing

2bRAD sequencing returned a total of ~796.1 million reads, an average of ~3.52 million reads per sample. After deduplicating, filtering, and trimming a total of ~443.8 million reads remained, with an average of ~2.01 million reads per sample (excluding technical replicates). On average, 2.16% of 2bRAD reads mapped to the Symbiodiniaceae metagenome, with a total of 9.79 million reads removed, or an average of 44,510 reads per sample. Samples had an average of 90.54% mapping efficiency to the de novo S. intersepta reference, resulting in a suite of 24,670 high-quality SNPs after filtering and processing with angsd. Filtering SNPs to those common among independent angsd runs within lineage resulted in a total of 4,604 sites.

Stephanocoenia intersepta population genetic structure

Populations demonstrated structuring across depth zones (Fig. 2). AMOVA attributed a significant amount of observed genetic variation between individuals (11.2%, SS = 35,867.77, p < 0.001) and between sample populations (1.2%, SS = 680,293.73, p < 0.001). Pairwise F_STcomparisons demonstrated generally lower F_ST between shallow-shallow (F_ST= 0 to 0.014) and mesophotic-mesophotic (F_ST= 0 to 0.008) comparisons and higher F_ST in most shallow-mesophotic (F_ST = 0 to 0.03) comparisons (Fig. 2E). All non-significant comparisons were between sites within the same depth zone, except for Riley’s Hump mesophotic-shallow comparison (F_ST = 0; Fig. 2E).

clumpak and pcangsd both determined K = 4 to be the most likely number of genetic clusters. Most samples had very little admixture, with a large majority of assignment to a single genetic lineage (Fig. 2B,C,D). All four lineages were present in shallow populations, although the Yellow lineage was not found in the shallow Upper Keys population, except within admixed individuals (Fig. 2E). The Blue and Teal lineages appear to be depth-generalists and were the dominant lineages within mesophotic populations comprising 81 and 15% of all mesophotic samples, respectively. The Green and Yellow lineages were not observed in mesophotic populations, except for two individuals at Riley’s Hump and within some admixed individuals in Tortugas Bank and Lower Keys populations and accounted for 24.17 and 12.5% of all shallow individuals, respectively (Fig. 2D).

Stephanocoenia intersepta lineage diversity and demography

There was a significant depth distribution of S. intersepta lineages, with the Blue lineage being most abundant and found predominantly in mesophotic populations (Welch’s ANOVA; F_{(3, 67.5)} = 49.01, p < 0.001; Fig. 3A). Shallow specific S. intersepta lineages (i.e., Green and Yellow) exhibited lower heterozygosity than deeper, depth generalist lineages across all loci (variant and invariant; Welch’s ANOVA; F_{(3, 65.3)} = 64.79, p < 0.001; Fig. 3B). Shallow lineages also had higher inbreeding coefficients (Welch’s ANOVA; F_{(3, 54.9)} = 886.05, p < 0.001) in comparison to mesophotic lineages (Fig. 3C). Nucleotide diversity (π) ranged from 4.741 x 10^-3 to 5.674 x 10^-3, and there was a positive correlation between nucleotide diversity and depth (Fig. 3D). Reconstruction of effective population size throughout time revealed stark differences between lineages. The Blue lineage underwent a drastic expansion ~100 kyr before present and was by far the largest population in the region throughout the recent past. Most lineages also experienced slight expansion followed by contraction in the last ~50–25 kyr (Fig. 3E). F_ST among lineage ranged from 0.048 to 0.212. Most lineages were highly differentiated from one another, with the exception of the depth-generalist lineages (Blue, Teal; F_ST = 0.048) which included 96% of all mesophotic samples (Fig. 3F).

Isolation-by-distance and distance-based redundancy analysis

Procrustes analysis indicated a lack of isolation-by-distance, with no significant similarity between genetic and geographic distances (Procrustes: t₀ = 0.1156, p = 0.074).

Forward model selection revealed that sample depth and mean benthic primary productivity significantly correlated with the observed genetic differentiation among samples; all non-significant variables were excluded (Supplementary Table S2). The global genetic variation explained by the model was 1.67%. Depth explained 1.5% and primary productivity 0.12%. The effect of depth is apparent along the first axis, where samples are separated by depth zone to some extent, similar to PCA (79.5% of fitted variation; 7.6% of total variation; Fig. 4A). Clustering observed with dbRDA along the first axis also aligned almost perfectly with lineage assignment (Fig. 4A). The effect of primary productivity was more variable across sample sites than sampling depth and was closely aligned with the second axis (20.5% of fitted variation; 3.6% of total variation; Fig. 4A). Ordination from dbRDA was also similar to F_ST comparisons, with the Blue and Teal lineages grouping closely together and all other lineages demonstrating much greater separation between clusters (Figs. 3F & 4A).

Genetic connectivity across FKNMS

Analysis of recent migration with bayesass was low overall (mean ± SEM = 3.74 ± 0.63%) but indicated that mesophotic populations were greater sources than shallow populations (mean ± SEM = 5.52 ± 1.11%; 1.96 ± 0.39%, respectively; Supplementary Table S3). Additionally, mesophotic to shallow subsidy was greater than shallow to mesophotic (mean ± SEM = 4.89 ± 0.98%; 1.35 ± 0.22%, respectively). Many shallow sites provided little substantial migration (i.e., HPD range ≯ 0) to other sites (e.g., Shallow Riley’s Hump and Upper Keys; Fig. 5). The largest sources were mesophotic sites in Tortugas Bank (1.8–8.7% of recent migrants into sink populations) and mesophotic sites in Lower Keys (8.8–23.1% of recent migrants into sink populations; Fig. 5A). In comparison, the shallow Lower Keys population was the most productive shallow source site (0.9–9.6%), illustrating the potential importance of both the shallow and mesophotic Lower Keys populations to metapopulation persistence (Fig. 5A).

In hospite Symbiodiniaceae communities

ITS2 sequencing was successful for all but one sample, returning a total of ~7.38 million reads, or an average of 33,723 reads per sample. Following symportal quality filtering, a total of ~3.28 million sequences were retained and used in ITS2 type profile calculation. symportal analysis calculated 19 unique ITS2 types. 78.45% of type profile sequences were assigned to the genus Cladocopium, 21.33% to Symbiodinium, 0.22% to Breviolum, and none to Durusdinium (Fig. 5A). In hospite Symbiodiniaceae beta diversity was greater in shallow populations (PERMDISP; F_1,217= 10.815, p = 0.0013) but did not vary across sampling site (PERMDISP; F_3,215= 0.772, p = 0.516). Symbiodiniaceae communities were significantly different across depth and site with a significant interaction (PERMANOVA; depth: F_1,211 = 16.0030, p < 0.0001; site: F_3,211 = 8.5195, p < 0.0001; interaction: F_3,211 = 7.6290, p < 0.0001). All pairwise comparisons of site were significant (pairwise PERMANOVA; all p < 0.05) and only three site by depth zone comparisons were not significantly different (Riley's Hump Shallow:Riley's Hump Mesophotic, Riley's Hump Mesophotic:Upper Keys Mesophotic, Tortugas Bank Mesophotic:Lower Keys Mesophotic; Supplementary Table S4; Fig. 6). The majority of Symbiodinium type profiles were found within shallow S. intersepta populations, namely Tortugas Bank and Lower Keys (Fig. 6A). There was a strong correlation between Symbiodiniaceae genera calculated using 2bRAD reads and symportal results grouped to genera (t₀= 0.9175, p = 0.0001; Figs. 6B & C). Procrustes analyses indicated a correlation between S. intersepta host genotype and in hospite Symbiodiniaceae (t₀= 0.2283, p = 0.0001) as well as between S. intersepta genetic lineage and in hospite Symbiodiniaceae (t₀= 0.2531, p = 0.0001), though neither relationship was very strong.

Forward model selection for ITS2 dbRDA retained depth, sea surface current velocity, and benthic maximum light as explanatory variables. Globally, 13.3% of ITS2 type profile variation was explained by the environmental variables (Supplementary Table S2). As with S. intersepta genetic variation, depth explained the largest percentage of variation in ITS2 type profiles (7.56%), followed by sea surface current velocity, and light (3.23 and 1.47%, respectively; Supplementary Table S2). The effect of depth is illustrated along the first axis, where samples are generally separated by sampling depth zone (Fig. 4B).

Low diversity in shallow lineages

Here we uncovered four putative genetic lineages of S. intersepta throughout the Florida Keys. This, along with the depth specificity of certain lineages is very similar to M. cavernosa and Siderastrea siderea populations in the Florida Keys (Rippe et al., 2021). We found that shallow-specific lineages (<30 m) exhibit lower genetic diversity than depth-generalist lineages, which were abundant on mesophotic reefs (Fig. 3). The abundance and diversity of the depth-generalist Blue lineage, which dominates the mesophotic sites has persisted throughout the recent past, with a large population expansion circa 100 kyr before present and contraction shortly thereafter. This pattern is also similar to M. cavernosa and S. siderea in the region, where multiple lineages undergo similar population flux. While in S. intersepta, M. cavernosa, and S. siderea population changes appear to occur during glacial cycles, the estimation of mutation rate and generation time make the absolute values of N_e and time uncertain (Rippe et al., 2021).

Samples for this study were collected in August–September 2019, when SCTLD was present in the Upper Keys and Lower Keys, but before it emerged in the Dry Tortugas (Dobbelaere et al., 2022). The patterns of lower diversity within shallow lineages were not driven by the SCTLD outbreak as they are consistent across all sampling sites, including those within the Dry Tortugas that had not yet been affected by SCTLD at the time of sampling. If a genetic bottleneck event is responsible for these observed patterns, it would be more likely that the decades of coral loss seen throughout shallow reefs in FKNMS contributed to the lack of genetic diversity. Particularly, events like dark spots syndrome outbreaks where S. intersepta experienced mortality of 3.8% yr^-1 from 2002–2004 and episodic ocean warming which significantly decreased shallow forereef scleractinian cover between 1999 and 2009 (Porter et al., 2011; Ruzicka et al., 2013).

The observed lack of diversity within shallow lineages is expectedly correlated with a higher prevalence of more highly inbred individuals. While clonality of coral populations is typically assessed in population genetic analyses, many studies have not explicitly examined inbreeding within populations. Inbreeding may have fitness and evolutionary consequences and has often been considered rare in marine environments, due to the perceived lack of barriers and a large potential for larval dispersal (Knowlton and Jackson, 1993; Olsen et al., 2020). However, inbreeding may be more common in benthic marine invertebrates than previously thought, with many species showing inbreeding frequencies as high as that of terrestrial plants (Olsen et al., 2020). The high levels of inbreeding detected in shallow lineages may be driven by individuals from discrete founder events (i.e., “sweepstakes reproductive success”; Hedgecock, 1994). If cohorts of larvae from a cross between closely related parents successfully co-recruit to the same reef, we would expect patterns of closely related and inbred groups of individuals within a population, as we see here with shallow S. intersepta populations. Although collective dispersal of larvae has been more commonly applied to fish species, there is recent evidence in benthic marine species, including corals (Barfield et al., 2020). Collective dispersal, and thereby co-recruitment patterns are influenced by larval swimming behavior, which may be similar among inbred and related larvae (Burgess et al., 2022). Together these phenotypic similarities and settlement preferences may contribute to the patterns observed in shallow S. intersepta populations. Stephanocoenia intersepta exhibits high rates of fertilization (>95%) among collected eggs, thought to be a consequence of colonies holding multiple eggs within polyp tentacles that are not released until after fertilization (Hagman, Gittings, and Vize, 1998; Vermeij et al., 2010). This reproductive strategy may contribute to high rates of inbreeding if nearest neighbors are also close relatives or clones, as has been found in some coral species (Lord et al., 2023; Shilling et al., (in press)) Phenotypic settlement behavior, co-recruitment patters, and elevated rates of local larval retention and recruitment may all have an effect on the lower diversity observed in shallow S. intersepta lineages in FKNMS.

This highlights an important need to evaluate multiple populations of critical coral species for genetic diversity, especially in the context of conservation biology (Beger et al., 2014). Mesophotic populations in FKNMS have so far been less impacted by SCTLD and have the potential to provide genetic diversity to shallow reefs throughout the region. Even if mesophotic S. intersepta populations are unable to successfully provide viable larvae to degraded shallow reefs in the future, they may be useful moving forward for “seed bank” collection or colony “rescue” and rearing/breeding within land-based nurseries to aid in ongoing and future restoration efforts (Boström-Einarsson et al., 2020). Though collection of colonies and/or gametes from mesophotic depths presents unique challenges in and of itself.

Population structuring across depth

Population genetic structure analyses indicate S. intersepta populations are structured by depth throughout FKNMS. Populations were significantly differentiated across depth zones throughout most of FKNMS, however, this was not demonstrated at Riley’s Hump (F_ST = 0) where populations exhibited similar population structure across depth zones (Fig. 2). As dbRDA identified depth as the largest contributor to the observed genetic differentiation across S. intersepta populations, it is not surprising that of all sample sites Riley’s Hump lacked vertical differentiation and structuring, as these shallow and mesophotic sample populations had the lowest average difference in depth (<10 m; Supplementary Table S1; Fig. 1). Even in regions with significant genetic structuring across depth, genetic structure and differentiation may not align with depth specific definitions of shallow (<30 m) and mesophotic (30–150 m). This is illustrated by M. cavernosa populations in Belize, where 25 and 35 m populations were genetically similar to each other, but distinct from shallower 10 and 15 m populations (Eckert et al., 2019).

Overall, differentiation was greatest across depth zone as compared to within depth zone pairwise contrasts among sites (Fig. 2E), despite small overwater distances between depth zones within sampling site (Fig. 1). Similar patterns have been observed in other coral population genetic studies throughout the tropical western Atlantic. Montastraea cavernosa populations are depth stratified on the Belize Barrier Reef, despite extremely small horizontal distances between depth zones (i.e., < 10 m; Eckert et al., 2019). In Mexico, M. cavernosa populations are also strongly structured by depth, with some lineages almost exclusively found in mesophotic populations (Sturm et al., 2022). However, in this study we identified two depth-generalized lineages (Blue and Teal; Figs. 2D, 3A) and two lineages mostly endemic to shallow populations (Green and Yellow; Figs. 2D, 3A). In contrast, S. intersepta populations in Bermuda exhibit a total lack of genetic structuring across depth zones (Bongaerts et al., 2017).

The variable patterns of differentiation observed across depth in FKNMS S. intersepta populations are not unique within FKNMS. In the species M. cavernosa, populations across the same sampling sites also exhibit variable patterns of vertical differentiation (Sturm et al., 2021). Montastraea cavernosa populations in Lower Keys and Tortugas Bank are less differentiated across depth than in Riley’s Hump and Upper Keys (Sturm et al., 2021). This variation holds across regional scales where deep and shallow M. cavernosa populations in the northwestern Gulf of Mexico function as a single, panmictic population, and populations in Florida are structured across depth (Studivan and Voss, 2018).

The timing of spawning, the dominant current regimes post-fertilization, and abiotic factors influencing settlement selection and recruitment success all may contribute to variable patterns of horizontal or vertical population structuring. Stephanocoenia intersepta typically spawn annually after the full moon in August/September, in a tight temporal window (Hagman, Gittings, and Deslarzes, 1998). In some locations, shallow and mesophotic conspecifics, including S. intersepta, spawn within the same hour (Vize, 2006). Slight asynchrony in spawning across depth may even be beneficial to mixing of gametes, with earlier spawning at depth allowing positively buoyant gametes or gamete bundles more time to reach the surface and mix with gametes from shallow colonies (Levitan et al., 2004).

Mesophotic S. intersepta populations not only harbor greater genetic diversity than shallow populations in FKNMS, but they also act as larger source populations throughout the sanctuary. Generation time is relatively long in many scleractinian coral species, thus the two to three generations estimated through bayesass migration rates likely encompass decades. Patterns of migration are fairly congruent with previous biophysical models for the region, where dispersal of larvae is strongly eastward across the Florida Keys, but mesoscale coastal counter-currents may emerge from mesoscale eddies and result in episodic westward movements (Lee et al., 1994; Yeung et al., 2001; Frys et al., 2020). With this in mind, mesophotic S. intersepta populations have the potential to function as genetic refuges, providing future gene flow and potential genetic rescue if oceanographic and environmental regimes remain conducive to successful migration and post settlement success (Harley et al., 2006; Matz et al., 2020).

Variable Symbiodiniaceae communities

Structuring was also identified in endosymbiotic Symbiodiniaceae communities. Cladocopium was the most abundant Symbiodiniaceae genus found among S. intersepta across FKNMS, particularly in mesophotic S. intersepta populations. Similar to M. cavernosa Symbiodiniaceae assemblages in Belize, we found deeper populations generally harbored fewer Symbiodiniaceae type profiles and shallow populations were more likely to harbor unique profiles (Eckert et al., 2020). This pattern of depth-generalist and shallow-specialist ITS2 type profiles differs from studies of M. cavernosa in Mexico and the Bahamas, where depth-specific Symbiodiniaceae were found (Lesser et al., 2010; Sturm et al., 2022). The dominance of Cladocopium spp. within S. intersepta colonies is consistent with results from Curaçao; however, S. intersepta in Curaçao only harbor Cladocopium spp. and also have depth-specific symbionts at 50 and 60 m (Bongaerts et al., 2015). The patterns and dominance we observe among in hospite Cladocopium across FKNMS S. intersepta are rather expected, as Cladocopium is a genetically and ecologically diverse genus (LaJeunesse et al., 2018).

We found significantly different Symbiodiniaceae communities across sampling sites and depths in FKNMS (Fig. 6), consistent with M. cavernosa Symbiodiniaceae in Mexico, where communities differed between Alacranes and Bajos del Norte reefs (Sturm et al., 2022). Some ITS2 type profiles are only found in shallow S. intersepta colonies, possibly due to adaptation to shallow reef conditions (e.g., light, nutrients). Since S. intersepta is a broadcast spawning species with a large depth range and potential for long-distance dispersal, acquiring symbionts from the local environment where larvae settle is advantageous, especially if there is prevalent endosymbiont zonation across depth and/or site, such as we identified here (Bongaerts et al., 2015). It is likely that different site and depth zone combinations in FKNMS harbor specific Symbiodiniaceae within the environment, as seen in American Samoa (Cunning et al., 2015). In the case of S. intersepta in FKNMS, there is some correlation between host genotype and Symbiodiniaceae association, these differences across depth and site may potentially avoid any issues with maladapted Symbiodiniaceae associations which could occur through symbiont transmission from parent to offspring (Bongaerts et al., 2010). Environmental factors other than depth (light and current velocity) were also found to significantly contribute to Symbiodiniaceae community structure across FKNMS (Fig. 4B; Supplementary Table S2). These factors may be influencing the most abundant taxa within a site, influencing host-symbiont associations, or inhibiting the dispersal of taxa across site (e.g., current velocity), thereby shaping the differences observed here (Cooper et al., 2011; Tonk et al., 2013).

RADseq alignments to Symbiodiniaceae spp. genomes were highly concordant with ITS2 sequences at the genus level (Figs. 6B & C). While using markers such as ITS2 or psbA^ncr allows a much finer level of differentiation (i.e., species or genotype), these methods require additional lab work and sequencing costs. Despite the reference genomes coming from congener algal taxa, and despite the high level of diversity and homology across Symbiodiniaceae, using RADseq SNPs to identify algal symbionts, as first proposed by Manzello et al. (2019) was highly effective and significantly correlated to the ITS2 assignment at the genus level. Identifying algal endosymbionts via RADseq-based approaches can provide critical time and cost savings if genus-level resolution is sufficient to answer the research questions at hand.

Both shallow and mesophotic populations of S. intersepta exhibit variable population structure and are widely differentiated across depth throughout FKNMS. The lower diversity, increased relatedness, and higher levels of inbred individuals across shallow reefs are quite worrisome and may portend potential risks moving forward, especially for conservation and restoration efforts. Successful migration from sources such as mesophotic S. intersepta populations, with greater genetic diversity, may provide genetic rescue over the course of several generations (Ingvarsson, 2001). Mesophotic reefs have been relatively understudied to date, yet they are critical to assessments of ecosystem biodiversity and genetic diversity (Turner et al., 2017). Throughout FKNMS, mesophotic reefs harbor greater genetic diversity of S. intersepta and mesophotic genets may offer an opportunity to increase lost shallow population diversity through restoration-based fragmentation and/or reproductive propagation, thereby increasing the likelihood of long-term population persistence in the region (Reynolds et al., 2012).

ACKNOWLEDGEMENTS

Funding for this research was awarded to J. Voss by NOAA Ocean Exploration and Research under award NA14OAR4320260 through the Cooperative Institute for Ocean Exploration, Research, and Technology and by the NOAA National Center for Coastal Ocean Science under award NA18NOS4780166 To J. Voss and S. Herrera through the Connectivity of Coral Ecosystems in the Northwest Gulf of Mexico project. All corals were collected under permit FKNMS-2019-088 from Florida Keys National Marine Sanctuary. We thank the participants of the 2019 FAU Harbor Branch CIOERT Florida Keys Expedition including M. Studivan, J. Emmert, M. McCallister, E. Shilling, I. Combs, J. Beal, C. Haymaker, S. Farrington, and the crew of R/V F.G. WALTON SMITH. The University of Texas at Austin’s Genome Sequencing and Analysis Facility provided sequencing support and computation capacity was provided by Research Computing Services at Florida Atlantic University. This is contribution [XXX] from Harbor Branch Oceanographic Institute.

AUTHOR CONTRIBUTIONS

JDV and RJE conceived and designed the study. JDV, RJE, and ABS collected the samples. RJE, ABS, AMC, and AMK performed lab work. RJE performed bioinformatic analyses, analyzed the data and prepared figures. All authors contributed to the final version of the manuscript prepared by RJE.

COMPETING INTERESTS

The authors declare no competing interests.

DATA AVAILABILITY

Sequence data from this manuscript can be accessed at the NCBI BioProject Accession PRJNA884416. All wet lab, bioinformatic, and data analysis procedures used in this study are available in the GitHub repository associated with this manuscript (Eckert, 2022).

Aranda M, Li Y, Liew YJ, Baumgarten S, Simakov O, Wilson MC, et al. (2016). Genomes of coral dinoflagellate symbionts highlight evolutionary adaptations conducive to a symbiotic lifestyle. Sci Rep6: 1–15.
Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrão EA, De Clerck O, et al. (2018). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Glob Ecol Biogeogr27: 277–284.
Barfield S, Davies SW, Matz MV (2020). Co-recruitment of relatives in a broadcast-spawning coral ( Acropora hyacinthus ) facilitates emergence of an inbred, genetically distinct group within a panmictic population. Evolutionary Biology.
Beger M, Selkoe KA, Treml E, Barber PH, von der Heyden S, Crandall ED, et al. (2014). Evolving coral reef conservation with genetic information. Bull Mar Sci90: 159–185.
Bernard AM, Finnegan KA, Shivji MS (2018). Genetic connectivity dynamics of the giant barrel sponge, Xestospongia muta , across the Florida reef tract and Gulf of Mexico. Bull Mar Sci95: 161–175.
Bongaerts P, Carmichael M, Hay KB, Tonk L, Frade PR, Hoegh-Guldberg O (2015). Prevalent endosymbiont zonation shapes the depth distributions of scleractinian coral species. R Soc Open Sci2: 140297.
Bongaerts P, Ridgway T, Sampayo EM, Hoegh-Guldberg O (2010). Assessing the ‘deep reef refugia’ hypothesis: focus on Caribbean reefs. Coral Reefs29: 309–327.
Bongaerts P, Riginos C, Brunner R, Englebert N, Smith SR, Hoegh-Guldberg O (2017). Deep reefs are not universal refuges: reseeding potential varies among coral species. Sci Adv3: e1602373.
Bosch S, Fernandez S (2021). sdmpredictors: Species Distribution Modelling Predictor Datasets.
Boström-Einarsson L, Babcock RC, Bayraktarov E, Ceccarelli D, Cook N, Ferse SCA, et al. (2020). Coral restoration – A systematic review of current methods, successes, failures and future directions (CN Bianchi, Ed.). PLOS ONE15: e0226631.
Buckel CA, Viehman TS, Edwards K, Tester PA, Piniak GA, Muñoz RC, et al. (2014). Benthic Communities of the Coral Reef. In: Clark R, Taylor JC, Buckel CA, Kracker LM (eds) Fish and benthic communities of the Flower Garden Banks National Marine Sanctuary: science to support sanctuary management, NOAA Technical Memorandum NOS NCCOS 179: Silver Spring, MD, pp 21–70.
Burgess SC, Bode M, Leis JM, Mason LB (2022). Individual variation in marine larval-fish swimming speed and the emergence of dispersal kernels. Oikos2022.
Cooper TF, Ulstrup KE, Dandan SS, Heyward AJ, Kühl M, Muirhead A, et al. (2011). Niche specialization of reef-building corals in the mesophotic zone: metabolic trade-offs between divergent Symbiodinium types. Proc R Soc B Biol Sci278: 1840–1850.
Cunning R, Yost DM, Guarinello ML, Putnam HM, Gates RD (2015). Variability of Symbiodinium communities in waters, sediments, and corals of thermally distinct reef pools in American Samoa (CA Chen, Ed.). PLoS ONE10: e0145099.
De’ath G, Fabricius KE, Sweatman H, Puotinen M (2012). The 27-year decline of coral cover on the Great Barrier Reef and its causes. Proc Natl Acad Sci U S A109: 17995–17999.
Dobbelaere T, Holstein DM, Muller EM, Gramer LJ, McEachron L, Williams SD, et al. (2022). Connecting the Dots: Transmission of Stony Coral Tissue Loss Disease From the Marquesas to the Dry Tortugas. Front Mar Sci9: 778938.
Eckert RJ (2022). Stephanocoenia_FKNMS_PopGen. Zenodo.
Eckert RJ, Reaume AM, Sturm AB, Studivan MS, Voss JD (2020). Depth Influences Symbiodiniaceae Associations Among Montastraea cavernosa Corals on the Belize Barrier Reef. Front Microbiol11: 518.
Eckert RJ, Studivan MS, Voss JD (2019). Populations of the coral species Montastraea cavernosa on the Belize Barrier Reef lack vertical connectivity. Sci Rep9: 7200.
Faubet P, Waples RS, Gaggiotti OE (2007). Evaluating the performance of a multilocus Bayesian method for the estimation of migration rates. Mol Ecol16: 1149–1166.
Fifer JE, Yasuda N, Yamakita T, Bove CB, Davies SW (2022). Genetic divergence and range expansion in a western North Pacific coral. Sci Total Environ813: 152423.
Foll M, Gaggiotti O (2008). A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics180: 977–993.
Frys C, Saint-Amand A, Le Hénaff M, Figueiredo J, Kuba A, Walker B, et al. (2020). Fine-scale coral connectivity pathways in the Florida reef tract: implications for conservation and restoration. Front Mar Sci7: 1–42.
Fu L, Niu B, Zhu Z, Wu S, Li W (2012). CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics28: 3150–3152.
Gardner TA, Côté IM, Gill JA, Grant A, Watkinson AR (2003). Long-term region-wide declines in Caribbean corals. Science301: 958–960.
Glynn PW (1996). Coral reef bleaching: facts, hypotheses and implications. Glob Change Biol2: 495–509.
Goodbody-Gringley G, Noyes T, Smith SR (2019). Bermuda. In: Loya Y, Puglise KA, Bridge TCL (eds) Mesophotic Coral Ecosystems, Coral Reefs of the World. Springer International Publishing: Cham Vol 12, pp 31–45.
Hagman DK, Gittings SR, Deslarzes KJP (1998). Timing, Species Participation, and Environmental Factors Influencing Annual Mass Spawning at the Flower Garden Banks (Northwest Gulf of Mexico). Gulf Mex Sci16.
Hagman DK, Gittings SR, Vize PD (1998). Fertilization in Broadcast-Spawning Corals of the Flower Garden Banks National Marine Sanctuary. Gulf Mex Sci16.
Hanghøj K, Moltke I, Andersen PA, Manica A, Korneliussen TS (2019). Fast and accurate relatedness estimation from high-throughput sequencing data in the presence of inbreeding. GigaScience8.
Harley CDG, Randall Hughes A, Hultgren KM, Miner BG, Sorte CJB, Thornber CS, et al. (2006). The impacts of climate change in coastal marine systems: Climate change in coastal marine systems. Ecol Lett9: 228–241.
Hedgecock D (1994). Does variance in reproductive success limit effective population size of marine organisms? In: Beaumont A (ed) Genetics and Evolution of Aquatic Organisms,, pp 122–134.
Hughes TP (1994). Catastrophes, phase shifts, and large-scale degradation of a Caribbean coral reef. Science265: 1547–1551.
Hume BCC, Smith EG, Ziegler M, Warrington HJM, Burt JA, LaJeunesse TC, et al. (2019). SymPortal: a novel analytical framework and platform for coral algal symbiont next-generation sequencing ITS2 profiling. Mol Ecol Resour19: 1063–1080.
Hume BCC, Ziegler M, Poulain J, Pochon X, Romac S, Boissin E, et al. (2018). An improved primer set and amplification protocol with increased specificity and sensitivity targeting the Symbiodinium ITS2 region. PeerJ2018: 1–22.
Ingvarsson PK (2001). Restoration of genetic variation lost – the genetic rescue hypothesis. Trends Ecol Evol16: 62–63.
Jaap WC, Szmant A, Jaap K, Dupont J, Clarke R, Somerfield P, et al. (2008). A perspective on the biology of Florida Keys coral reefs. Coral Reefs USA1: 75–125.
Kamvar ZN, Tabima JF, Gr̈unwald NJ (2014). Poppr: An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ2013: 1–14.
Knowlton N, Jackson JBC (1993). Inbreeding and outbreeding in marine invertebrates. In: Thornhill NW (ed) The Natural history of inbreeding and outbreeding: theoretical and empirical perspectives, University of Chicago Press: Chicago, pp 200–249.
Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I (2015). Clumpak : a program for identifying clustering modes and packaging population structure inferences across K. Mol Ecol Resour15: 1179–1191.
Korneliussen TS, Albrechtsen A, Nielsen R (2014). ANGSD: Analysis of Next Generation Sequencing Data. BMC Bioinformatics15: 356.
LaJeunesse TC, Parkinson JE, Gabrielson PW, Jeong HJ, Reimer JD, Voolstra CR, et al. (2018). Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr Biol28: 2570-2580.e6.
Lang JC (2003). Status of coral reefs in the western Atlantic: results of initial surveys, Atlantic and Gulf Rapid Reef Assessment (AGRRA) program. Atoll Res Bull Tech Rep: 496.
Langmead B, Salzberg SL (2012). Fast gapped-read alignment with Bowtie 2. Nat Methods9: 357–359.
Lee TN, Clarke M, Williams E, Szmant AF, Berger T (1994). Evolution of the Tortugas Gyre and its influence on recruitment in the Florida Keys. Bull Mar Sci54: 621–646.
Lesser MP, Slattery M, Leichter JJ (2009). Ecology of mesophotic coral reefs. J Exp Mar Biol Ecol375: 1–8.
Lesser MP, Slattery M, Stat M, Ojimi M, Gates RD, Grottoli A (2010). Photoacclimatization by the coral Montastraea cavernosa in the mesophotic zone: light, food, and genetics. Ecology91: 990–1003.
Levitan DR, Fukami H, Jara J, Kline D, McGovern TM, McGhee KE, et al. (2004). Mechanisms of reproductive isolation among sympatric broadcast-spawning corals of the Montastraea annularis species complex. Evolution58: 308–323.
Liu X, Fu Y-X (2020). Stairway Plot 2: demographic history inference with folded SNP frequency spectra. Genome Biol21: 280.
Liu H, Stephens TG, González-Pech RA, Beltran VH, Lapeyre B, Bongaerts P, et al. (2018). Symbiodinium genomes reveal adaptive evolution of functions related to coral-dinoflagellate symbiosis. Commun Biol1: 95.
Lord KS, Lesneski KC, Buston PM, Davies SW, D’Aloia CC, Finnerty JR (2023). Rampant asexual reproduction and limited dispersal in a mangrove population of the coral Porites divaricata. Proc R Soc B Biol Sci290: 20231070.
Manzello DP, Matz MV, Enochs IC, Valentino L, Carlton RD, Kolodziej G, et al. (2019). Role of host genetics and heat-tolerant algal symbionts in sustaining populations of the endangered coral Orbicella faveolata in the Florida Keys with ocean warming. Glob Change Biol: 1016–1031.
Martin M (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal17: 10–12.
Martinez Arbizu P (2017). pairwiseAdonis: Pairwise multilevel comparison using adonis.
Matz MV (2020). Whole genome de novo genotyping with 2bRAD.
Matz MV, Treml EA, Aglyamova GV, Bay LK (2018). Potential and limits for rapid genetic adaptation to warming in a Great Barrier Reef coral (HE Hoekstra, Ed.). PLOS Genet14: e1007220.
Matz MV, Treml EA, Haller BC (2020). Estimating the potential for coral adaptation to global warming across the Indo-West Pacific. Glob Change Biol26: 3473–3481.
Meisner J, Albrechtsen A Inferring Population Structure and Admixture Proportions in Low-Depth NGS Data.
Mumby PJ, Dahlgren CP, Harborne AR, Kappel CV, Micheli F, Brumbaugh DR, et al. (2006). Fishing, trophic cascades, and the process of grazing on coral reefs. Science311: 98–101.
Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. (2020). vegan: Community Ecology Package.
Olsen KC, Ryan WH, Winn AA, Kosman ET, Moscoso JA, Krueger-Hadfield SA, et al. (2020). Inbreeding shapes the evolution of marine invertebrates. Evolution74: 871–882.
Palumbi SR (2003). Population genetics, demographic connectivity, and the design of marine reserves. Ecol Appl13: 146–158.
Pembleton LW, Cogan NOI, Forster JW (2013). StAMPP: an R package for calculation of genetic differentiation and structure of mixed-ploidy level populations. Mol Ecol Resour13: 946–952.
Porter JW, Torres C, Sutherland KP, Meyers MK, Callahan MK, Ruzicka R, et al. (2011). Prevalence, severity, lethality, and recovery of dark spots syndrome among three Floridian reef-building corals. J Exp Mar Biol Ecol408: 79–87.
Pyle RL, Copus JM (2019). Mesophotic Coral Ecosystems: Introduction and Overview. In: Loya Y, Puglise KA, Bridge TCL (eds) Mesophotic Coral Ecosystems of the World, Springer New York, pp 3–27.
Rambaut A, Drummond AJ, Xie D, Baele G, Suchard MA (2018). Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7. Syst Biol67: 901–904.
Reed JK, Farrington S, David A, Harter S, Pomponi SA, Cristina Diaz M, et al. (2019). Pulley Ridge, Gulf of Mexico, USA. : 57–69.
Reynolds LK, McGlathery KJ, Waycott M (2012). Genetic Diversity Enhances Restoration Success by Augmenting Ecosystem Services (RKF Unsworth, Ed.). PLoS ONE7: e38397.
Rippe JP, Dixon G, Fuller ZL, Liao Y, Matz M (2021). Environmental specialization and cryptic genetic divergence in two massive coral species from the Florida Keys Reef Tract. Mol Ecol30: 3468–3484.
Rocha LA, Pinheiro HT, Shepherd B, Papastamatiou YP, Luiz OJ, Pyle RL, et al. (2018). Mesophotic coral ecosystems are threatened and ecologically distinct from shallow water reefs. Science361: 281–284.
Ruzicka RR, Colella MA, Porter JW, Morrison JM, Kidney JA, Brinkhuis V, et al. (2013). Temporal changes in benthic assemblages on Florida Keys reefs 11 years after the 1997/1998 El Niño. Mar Ecol Prog Ser489: 125–141.
Serrano XM, Baums IB, O’Reilly K, Smith TB, Jones RJ, Shearer TL, et al. (2014). Geographic differences in vertical connectivity in the Caribbean coral Montastraea cavernosa despite high levels of horizontal connectivity at shallow depths. Mol Ecol23: 4226–4240.
Shilling EN, Eckert RJ, Sturm AB, Voss JD (in press). Porites astreoides populations demonstrate high clonality and connectivity in southeast Florida.
Shoguchi E, Beedessee G, Hisata K, Tada I, Narisoko H, Satoh N, et al. (2021). A New Dinoflagellate Genome Illuminates a Conserved Gene Cluster Involved in Sunscreen Biosynthesis (S Phadke, Ed.). Genome Biol Evol13: evaa235.
Shoguchi E, Shinzato C, Kawashima T, Gyoja F, Mungpakdee S, Koyanagi R, et al. (2013). Draft Assembly of the Symbiodinium minutum Nuclear Genome Reveals Dinoflagellate Gene Structure. Curr Biol23: 1399–1408.
Skotte L, Korneliussen TS, Albrechtsen A (2013). Estimating Individual Admixture Proportions from Next Generation Sequencing Data. Genetics195: 693–702.
Smith TB, Gyory J, Brandt ME, Miller WJ, Jossart J, Nemeth RS (2016). Caribbean mesophotic coral ecosystems are unlikely climate change refugia. Glob Change Biol22: 2756–2765.
Soong K (1993). Colony size as a species character in massive reef corals. Coral Reefs12: 77–83.
Studivan MS, Voss JD (2018). Population connectivity among shallow and mesophotic Montastraea cavernosa corals in the Gulf of Mexico identifies potential for refugia. Coral Reefs37: 1183–1196.
Sturm AB, Eckert RJ, Carreiro AM, Simões N, Voss JD (2022). Depth-Dependent Genetic Structuring of a Depth-Generalist Coral and Its Symbiodiniaceae Algal Communities at Campeche Bank, Mexico. Front Mar Sci9: 835789.
Sturm AB, Eckert RJ, Carreiro AM, Voss JD (2021). Population genetic structure of the broadcast spawning coral, Montastraea cavernosa, demonstrates refugia potential of upper mesophotic populations in the Florida Keys. Coral Reefs.
Szmant AM (1991). Sexual reproduction by the Caribbean reef corals Montastrea annularis and M. cavernosa. Mar Ecol Prog Ser74: 13–25.
Tonk L, Sampayo EM, Weeks S, Magno-Canto M, Hoegh-Guldberg O (2013). Host-Specific Interactions with Environmental Factors Shape the Distribution of Symbiodinium across the Great Barrier Reef (CR Voolstra, Ed.). PLoS ONE8: e68533.
Toth LT, van Woesik R, Murdoch TJT, Smith SR, Ogden JC, Precht WF, et al. (2014). Do no-take reserves benefit Florida’s corals? 14 years of change and stasis in the Florida Keys National Marine Sanctuary. Coral Reefs33: 565–577.
Turner JA, Babcock RC, Hovey R, Kendrick GA (2017). Deep thinking: A systematic review of mesophotic coral ecosystems. ICES J Mar Sci74: 2309–2320.
Vermeij MJA, Barott KL, Johnson AE, Marhaver KL (2010). Release of eggs from tentacles in a Caribbean coral. Coral Reefs29: 411–411.
Vize PD (2006). Deepwater broadcast spawning by Montastraea cavernosa, Montastraea franksi, and Diploria strigosa at the Flower Garden Banks, Gulf of Mexico. Coral Reefs25: 169–171.
Wang S, Meyer E, McKay JK, Matz MV (2012). 2b-RAD: a simple and flexible method for genome-wide genotyping. Nat Methods9: 808–810.
Wang C, Zöllner S, Rosenberg NA (2012). A Quantitative Comparison of the Similarity between Genes and Geography in Worldwide Human Populations (SM Williams, Ed.). PLoS Genet8: e1002886.
Wilson GA, Rannala B (2003). Bayesian Inference of Recent Migration Rates Using Multilocus Genotypes. Genetics163: 1177–1191.
Wood DE, Lu J, Langmead B (2019). Improved metagenomic analysis with Kraken 2. Genome Biol20: 257.
Yeung C, Jones DL, Criales MM, Jackson TL, Richards WilliamJ (2001). Influence of coastal eddies and counter-currents on the influx of spiny lobster, Panulirus argus, postlarvae into Florida Bay. Mar Freshw Res52: 1217.

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published 27 Jun, 2024

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You are reading this latest preprint version

Genetically divergent shallow and mesophotic Stephanocoenia intersepta coral populations across Florida Keys National Marine Sanctuary

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Journal Publication

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Abstract

Figures

Introduction

Materials And Methods

Sample collection and DNA extraction

Stephanocoenia intersepta SNP genotyping

Stephanocoenia intersepta population genetic structure

Stephanocoenia intersepta lineage demographics

Population genetic connectivity

Isolation by distance and distance-based redundancy analyses

Symbiodiniaceae ITS2 sequencing

Symbiodiniaceae SymPortal ITS2 analyses

Results

Stephanocoenia intersepta SNP data processing

Stephanocoenia intersepta population genetic structure

Stephanocoenia intersepta lineage diversity and demography

Isolation-by-distance and distance-based redundancy analysis

Genetic connectivity across FKNMS

In hospite Symbiodiniaceae communities

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1