Population genetics of the giant barrel sponge, Xestospongia muta, reveal distinct, hybridizing lineages across the Florida Reef Tract

Population genetics of the giant barrel sponge, Xestospongia muta, reveal distinct, hybridizing lineages across the Florida Reef Tract | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Population genetics of the giant barrel sponge, Xestospongia muta, reveal distinct, hybridizing lineages across the Florida Reef Tract Ryan J. Eckert, Alexis B. Sturm, Ashley M. Carreiro, Joshua D. Voss This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5875893/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract With recent anthropogenically driven coral reef declines, reef biodiversity and resilience have become a top priorities for natural resource management. Population genetic analyses can not only provide useful data for understanding genetic diversity and connectivity but also help guide the restoration and conservation of critical species and habitats. The Giant Barrel Sponge, Xestospongia muta , is among the most conspicuous and abundant sponges on the Florida Reef Tract and provides important ecosystem services including nutrient cycling and three-dimensional habitat for fishes and invertebrates. To better understand X. muta population structure and connectivity throughout Florida Keys National Marine Sanctuary and Kristin Jacobs Coral Aquatic Preserve we genotyped individuals using 2bRAD-Seq across seven reef locations. Our analyses revealed strong evidence of connectivity among X. muta populations across the Florida Reef Tract, except for a relatively distinct population located in Fort Lauderdale. Two highly divergent lineages comprise Florida’s X. muta populations, with clear evidence of hybridization indicating they are likely not separate species. While the lineage from Ft. Lauderdale exhibits greater genetic diversity than the other more common lineage, the genetic diversity of X. muta observed across the Florida reef were relatively consistent with several coral species sampled in this region. These data contribute to our growing understanding of the genetic diversity and connectivity of important benthic invertebrate populations across the Florida Reef Tract. Population connectivity molecular ecology Demospongia single nucleotide polymorphism ecological genomics mesophotic coral ecosystems Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Understanding genetic diversity and connectivity among populations remains an important part of resource management and conservation efforts (Palumbi 2003 ). Marine populations have often been assumed to be relatively open, given the perceived lack of barriers to connectivity (Knowlton and Jackson 1993 ). However, factors such as ocean currents or depth may act as barriers to gene flow (Eckert et al. 2019 , 2024 ; Sturm et al. 2023 ; Grupstra et al. 2024 ). Previous work on MCEs has illustrated that depth can be a segregating factor for depth-generalist conspecifics. However, this split does not occur universally but rather varies by location and species—indicating that multiple biotic and abiotic barriers are likely involved in driving patterns of genetic structuring in benthic marine invertebrates (Serrano et al. 2014 ; Bongaerts et al. 2017 ; Sturm et al. 2023 ). Mesophotic coral ecosystems (MCEs; 30–150 m) are a large component of important marine habitat throughout the Tropical Western Atlantic, yet they remain relatively understudied in comparison to shallow reefs (Turner et al. 2017 ; Radice et al. 2024 ). The degree of exploration and characterization of MCEs varies across regions; in Florida, relatively few studies have focused on these deeper coral ecosystems which often occur just seaward of their shallow reef (< 30 m) counterparts (Pyle and Copus 2019 ). Several MCE-focused studies in Florida center on the Dry Tortugas and Pulley Ridge, approximately 100–160 km west of Key West, potentially leaving many MCEs along the Florida Reef Tract relatively underexplored and not yet fully characterized (Serrano et al. 2014 ; Reed et al. 2015 ; Bernard et al. 2018 ; Studivan and Voss 2018 ; Drury et al. 2020 ). Genetic similarity and connectivity among and between MCEs and shallow coral reef ecosystems can be quantified through population genetic assessments using conserved yet variable genetic markers (Bongaerts et al. 2017 ; Studivan and Voss 2018 ; Eckert et al. 2019 ). The giant barrel sponge, Xestospongia muta is one of the most abundant sponges throughout the Caribbean and Western Atlantic in terms of reef benthic percent cover and is recognized as a keystone species (Loh and Pawlik 2014 ). Marine sponges like X. muta play a critical role in creating habitat complexity, promoting coral reef biodiversity, and facilitating nutrient cycling within these habitats (Diaz and Rützler 2001 ; Henkel and Pawlik 2005 ; Fiore et al. 2010 ). Xestospongia muta is a major component of sponge communities on the Florida Reef Tract and has increased in density over time, with documented shifts in population structure, likely due to available benthic space left after coral loss (Mcmurray et al. 2010 ; McMurray et al. 2015 ). With ongoing declines in coral cover across the Florida Reef Tract, the ecosystem services provided by X. muta will remain important in the Anthropocene and may become disproportionately more important (McMurray et al. 2015 ). Xestospongia muta also has a large depth range and is one of the numerically dominant species found deeper than 10 m (Mcmurray et al. 2010 ). Xestospongia muta and other sponges are typically common components of mesophotic coral ecosystem benthos and increase abundance with depth on some Caribbean reefs (Lesser and Slattery 2018 ). Little is known about X. muta reproduction, such as larval development timing or pelagic larval duration, with few published studies documenting spawning in the literature (Ritson-Williams et al. 2005 ; Neely and Butler 2020 ). However, there is evidence that X. muta is a gonochoric broadcast spawner with males and females releasing sperm and eggs, respectively, with subsequent external fertilization (Ritson-Williams et al. 2005 ; Neely and Butler 2020 ). This reproductive mode is thought to lend itself to greater dispersal through ocean currents to distant reefs depending upon the pelagic larval duration (Miller and Mundy 2003 ). However, X . muta have negatively buoyant eggs and neutral to negatively buoyant sperm with fertilization likely occurring near females, which is hypothesized to result in higher local retention of larvae (Ritson-Williams et al. 2005 ; Neely and Butler 2020 ). Although the pelagic larval duration of X. muta is unknown, it is assumed to be similar to congeners whose larvae typically settle within 3 d (Fromont and Bergquist 1994 ; Richards et al. 2016 ). In Florida, there are two hypothesized sympatric cryptic species of X. muta which may be maintained by reproductive isolation through spawning timing (Deignan et al. 2018 ; Neely and Butler 2020 ; Evans et al. 2021 ). Despite the cosmopolitan distribution of X. muta and multiple population genetics studies, only one study of X. muta includes any mesophotic samples and few contain any samples from the northern Florida Reef Tract (López-Legentil and Pawlik 2009 ; De Bakker et al. 2016 ; Bernard et al. 2018 ). This study aimed to address these gaps by investigating the population genetics of X. muta with a single nucleotide polymorphism (SNP) genotyping approach across both shallow and mesophotic habitats in the Florida Keys National Marine Sanctuary and within shallow habitats in Kristin Jacobs Coral Aquatic Preserve (KJCAP). This study is part of a larger body of work focused on understanding the genetic diversity as well as the vertical and horizontal connectivity of important habitat-forming benthic invertebrate species in important marine protected areas in southeast Florida (Studivan and Voss 2018 ; Dodge et al. 2020 ; Sturm et al. 2021 , 2023 ; Shilling et al. 2023 ; Eckert et al. 2024 ). With documented changes in X . muta population densities and demography between 2000 and 2012, and the susceptibility of the species to both disease outbreaks and bleaching events, characterizing the genetic diversity and connectivity of X. muta through Florida can provide useful information to guide management strategies and potential restoration activities for X. muta (Carilli et al. 2010 ; Mcmurray et al. 2010 ; McMurray et al. 2011 , 2015 ; García-Hernández et al. 2020 ; Cheung et al. 2021 ). Data such as those presented here are beneficial when determining potential areas for focused protection or designing restoration efforts to maintain or enhance genetic diversity. Finally, this study establishes a baseline for X. muta genetic diversity and population structure across shallow and mesophotic locations in Florida to benchmark future genetic diversity data in a rapidly changing ecosystem. METHODS Sample collection We collected Xestospongia muta samples throughout FKNMS and the KJCAP targeting a sample size of 30 individuals per depth zone and site ( n = 352; Table 1 , Fig. 1 ). FKNMS sites were selected based upon presence of targeted species during ROV surveys prior to technical diving. Xestospongia muta samples collected within the KJCAP were selected from long-term coral reef monitoring sites in this marine protected area. Within FKNMS X. muta were collected from both shallow and mesophotic depths. Eight samples were collected via Mohawk ROV and the remainder of the samples via open circuit SCUBA. Mesophotic depth ranges off of KJCAP were not included in this study as they mostly consists of pavement and unconsolidated reef lacking sufficient abundance of the targeted species (Banks et al. 2008). Tissue (~ 4 cm 2 ) from each sponge was excised and placed into an individual, uniquely labeled zip top bag (or separate collection bin on the ROV), with scaled reference photographs of each sponge pre- and post-sampling. Upon returning to the surface, as much saltwater as possible was squeezed from the sample to reduce preservative dilution and the sample was then placed into absolute ethanol. Samples were held at -20 ºC while at sea and transported to FAU Harbor Branch on dry ice. At the lab, the ethanol was replaced with new molecular-grade absolute ethanol and stored samples at -80 ºC until extraction. Table 1 Sample collection metadata MPA Site Depth zone n Depth range (m) Mean depth (m) Collection date KJCAP Jupiter Shallow 30 19.5–21.9 20.7 September 2020 West Palm Shallow 30 12.8–16.8 14.8 September 2020 Boynton Shallow 30 14.3–20.1 17.3 January 2021 Ft. Lauderdale Shallow 30 4.6–8.2 6.2 December 2020 FKNMS Upper Keys Shallow 30 12.8–26.8 22.8 August 2019 Mesophotic 30 33.8–51.2 41.7 August 2019 Lower Keys Shallow 30 16.8–24.7 17.8 August 2019 Mesophotic 30 31.4–35.4 33 August 2019 Tortugas Bank Shallow 30 19.5–29.9 22.1 August 2019 Mesophotic 22 30.2–34.7 30.9 August 2019 Riley’s Hump Shallow 30 25.0–28.3 25.7 August 2019 Mesophotic 30 31.7–46.9 38.5 August 2019 Genomic DNA extraction, library preparation, and sequencing Total genomic DNA was extracted from small tissue subsamples (~ 1 cm 2 ) following a modified dispersion buffer protocol (Sturm et al. 2020 ). Samples were genotyped following modified 2bRAD protocols with the endonuclease BcgI (New England Biolabs, Inc.) and 100 ng of genomic DNA per sample (Wang et al. 2012 ; Matz 2020 ; Eckert et al. 2024 ). The resulting pooled libraries were checked for quality and quantity using NanoDrop and Qubit and then sequenced at UTGSAF on Illumina NovaSeq (SR100 S1) with 20% phiX spike-in. Detailed lab procedures are available in the GitHub repository accompanying this manuscript (Eckert 2025 ). SNP genotyping Complete data analysis scripts and data visualization code are available on GitHub and archived through Zenodo (Eckert, 2024). Returned sequencing reads were demultiplexed, deduplicated, filtered and trimmed with custom perl scripts and cutadapt v3.4 (Martin 2011 ). Sample reads were aligned to the X. muta reference genome with Bowtie2 v1.2.2 (Langmead and Salzberg 2012 ). We used the program angsd v0.930 to generate genotype likelihoods for all samples using the following filters: a minimum mapping quality of 20, minimum base quality score of 30, minimum allele frequency of 0.05, minimum p -value of 10 − 5 for deviation from Hardy-Weinberg equilibrium, minimum p -value of 10 − 5 for strand bias, p -value of 10 − 5 for heterozygosity bias, remove triallelic SNPs, p -value that a locus is variable of 10 − 5 , and had to be present in ≥ 75% of samples (Korneliussen et al. 2014 ). angsd was initially run on all samples to detect naturally occurring clones using a threshold determined by six technical triplicate samples. Only one of each clone/replicate was retained (sample with greatest proportion of reads > 5X coverage) and angsd was run on the clone-free sample set with the above filters. Population genetic structure Principal component analysis (PCA) was conducted with the program pcangsd and PCA biplots were generated from the resulting covariance matrix (Meisner and Albrechtsen 2018 ). ngsAdmix was used to calculate admixture proportions for retained samples for values of K = 1–15, using 20 replicate simulations for each value of K (Skotte et al. 2013 ). The most likely value of K was evaluated using the Evanno and Puechmaille methods with clumpak and StructureSelector , respectively (Evanno et al. 2005 ; Kopelman et al. 2015 ; Puechmaille 2016 ; Li and Liu 2018 ). Values of K were visualized and assessed with ggtree using the IBS matrix and ngsAdmix outputs for K = 1–5 (Yu et al. 2017 ). Lineage diversity Based on resultant dendrogram and K assessments, we continued to analyze values of K = 2 and K = 4. For heterozygosity and site frequency spectra (SFS) calculations, angsd was rerun using only filters which did not affect allelic frequencies, retaining all loci, variant and invariant (Rippe et al. 2021 ; Eckert et al. 2024 ). Only samples with membership assignment ≥ 75% to a single lineage were retained to avoid confounding these analyses (Fifer et al. 2022 ; Eckert et al. 2024 ). SNP loci were calculated separately within each lineage (present within 75% of samples per lineage) and thinned to include only sites present across all lineages (Eckert et al. 2024 ). Heterozygosities were calculated for each sample using custom R scripts (Matz 2020 ; Eckert 2025 ). Due to heterogeneity of variance within the data and uneven sample sizes, 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 non-parametric Games-Howell tests, which are effective as a post-hoc test when variance is heterogeneous and sample sizes are uneven (Games and Howell 1976 ; Midway et al. 2020 ). To calculate weighted fixation index ( F ST ) between lineages and nucleotide diversity we used site frequency spectra calculated with realSFS in angsd . Nucleotide diversity was averaged across scaffolds from the X. muta reference. Effective population size ( N e ) was calculated using the formula \(\:{N}_{e}=\:\frac{}{4}\) , where the mutation rate ( µ ) was estimated to be 2 × 10 −8 per base, per generation (Matz et al. 2018 ; Rippe et al. 2021 ) Lineage hybridization To evaluate the potential of admixed individuals being hybrids of the main divergent lineages when K = 2, we used a reduced sample set. The reduced sample set only included members assigned 100% to either of the putative lineages (Xm1 or Xm2), as well as putative hybrid samples with admixture between the two lineages. angsd was rerun with the resulting subset of 71 samples (putative lineages and putative hybrids) and the resulting .bcf file was split to calculate major/minor allele frequencies per lineage. The results were filtered to retain divergent SNPs which were alternatively fixed (> 0.85, allowing for error and uncertainty in genotype likelihoods) between the main lineages and major allele frequencies were visualized by lineage assignment. Heterozygosities were also calculated for the subset of samples across the divergent SNPs, and differences in heterozygosity by lineage were assessed with Welch’s ANOVA and pairwise Games-Howell tests. Population genetic connectivity Population genetic connectivity was approximated through recent migration rates (immigrant individuals from previous 2–3 generations) with the BA3SNP function of BayesAss v3.0.4.2. BayesAss was only run on the most abundant lineage (dark magenta node, Fig. 2 A; n = 300 samples). Ten independent runs were conducted with random start seeds for 10 million MCMC model simulations using a 2 million burn-in and 1000 run sampling frequency. Mixing parameters (migration rate [ m ] = 0.15, allele frequencies [ a ] = 0.7, inbreeding coefficient [ f ] = 0.03) 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. RESULTS Sample processing 2bRAD sequencing resulted in 2 billion reads, or an average of 5.47 million reads per X. muta sample, with 3.67 million reads on average remaining per sample after post processing. After removing technical replicates, 351 samples remained and were used to generate 8,034 unique SNPs. Generating loci (variant and invariant) common to all lineages resulted in 266,442 and 198,110 sites for K = 2 and K = 4 scenarios, respectively. Xestospongia muta population genetic structure Principal component analysis revealed two main genetic clusters along the first axis with samples mainly from Fort Lauderdale in the smaller cluster (Fig. 2 B). Using the Evanno and Puechmaille methods suggested both K = 2 and K = 4, respectively, as the most likely values of K . The dendrogram revealed little overall structuring of samples by site or depth, but alongside the K = 2 structure plot we see evidence of two main lineages (Xm1 and Xm2) with potential hybridization between them (Fig. 2 A). PCA also demonstrated a lack of structuring across site and depth (Fig. 2 B), other than Fort Lauderdale being more differentiated from other samples, driven by the high presence of the Xm2 lineage. This lack of structuring was also apparent when Xm2 and putative hybrids were removed ( Supplemental Fig. S1 ). When plotting values K = 1–5, the smaller cluster of Ft. Lauderdale samples remained distinct with nearly 100% assignment to the lineage Xm2 (Fig. 2 A). Combined with PCA results, this suggests there are likely 2 main X. muta lineages with potential substructure in Xm1 (Fig. 2 C, D). We assessed photographs of sampled X . muta lineages and there was no clear morphology associated with the unique lineage Xm2, though all of the Xm2 lineage were noticeably small in size (< 15 cm in diameter). Xestospongia muta lineage diversity Across all loci (variant and invariant) the Xm2 lineage exhibited greater heterozygosity in both K = 2 and K = 4 scenarios (Welch’s ANOVA; K = 2: F (2, 35) = 75.5, p < 0.001; K = 4: F (4, 40.9) = 27.2, p < 0.001; Fig. 3 A, B). The Xm2 lineage also had greater nucleotide diversity ( π ), and therefore greater effective population sizes ( N e ) than other lineages (Fig. 3 C). For K = 2, F ST between Xm1 and Xm2 was 0.153. Among the four lineages, Xm2 was highly differentiated from all Xm1 lineages (Xm1, Xm1.2, Xm1.3; F ST : 0.113–0.136) while all Xm1 lineages were more similar to one another ( F ST : 0.02–0.037; Fig. 3 D). Hybridization between lineages Stricter filtering criteria of X. muta samples resulted in a subset of 71 samples to query for alternatively fixed SNPs between the two lineages (Fig. 4 ), and twelve such SNPs were identified (Fig. 4 D). Within these SNPS, many appeared to be heterozygous (major allele frequency closer to 0.5) within the admixed samples, lending credence to the idea that these samples are putative hybrids between the two main lineages (Fig. 4 D). Admixed samples also had greater heterozygosity across the 12 alternatively fixed SNPs, while there was no difference between the Xm1 and Xm2 lineages (Welch’s ANOVA: F (2, 34.5) = 14.2, p < 0.001; Fig. 4 C). Genetic connectivity across Southeast Florida BayesAss analysis revealed patterns of low migration (mean ± SEM = 1.59 ± 0.15%; Table 2 , Fig. 5 ). Shallow sites were slightly greater sources (mean ± SEM = 1.60 ± 0.16% vs 1.58 ± 0.32%) and mesophotic sites were greater sinks (mean ± SEM = 1.77 ± 0.27% vs 1.50 ± 0.18%). Shallow to mesophotic subsidy was slightly greater than mesophotic to shallow (mean ± SEM = 1.88 ± 0.34% vs 1.63 ± 0.43%). Many of the sampling sites provided little substantial migration (i.e. HPD range inclusive of 0; Fig. 5 ). Sites within the Florida Keys, especially shallow Upper Keys and shallow and mesophotic Lower Keys, were greater sources than those within the northern portion of the Florida Reef Tract (Fig. 5 ). It should be noted that assignment analyses may suffer from the lack of sampling all populations due to individuals from unsampled populations being misassigned (Christie et al. 2017 ). BayesAss includes several generations of geneflow and assigns migration rates using Bayesian methodology across our relatively small (100s of km) sampling area, making these estimates useful as a baseline to understanding the relative connectivity among these critical habitats, which could be bolstered through future sampling and modelling efforts (Wilson and Rannala 2003 ; Meirmans 2014 ; Christie et al. 2017 ). Table 2 Mean migration rates from BayesAss by depth zone. Dataset m SD SE Global 1.59% 1.73 0.15 Mesophotic Source 1.58% 2.15 0.32 Shallow Source 1.60% 1.49 0.16 Mesophotic Sink 1.77% 1.76 0.27 Shallow Sink 1.50% 1.72 0.18 Mesophotic → Shallow 1.63% 2.43 0.43 Mesophotic → Mesophotic 1.47% 1.15 0.33 Shallow → Mesophotic 1.88% 1.95 0.34 Shallow → Shallow 1.43% 1.14 0.20 Mean migration ( m ); standard deviation (SD); Standard error (SE) DISCUSSION Across south Florida in KJCAP and FKNMS X. muta is comprised of two distinct cryptic lineages. The less abundant, genetically distinct population found in Ft. Lauderdale exhibits strong differentiation from the other main X. muta lineage found across all sites and depths. Two cryptic linages of X. muta have been previously documented (Deignan et al. 2018 ; Evans et al. 2021 ), and it has been hypothesized that these two cryptic lineages may have arisen from reproductive isolation via asynchrony in spawning (Neely and Butler 2020 ; Evans et al. 2021 ). With high potential for local retention in some sponge species, corresponding drift of F ST through generations, and limited migration between populations, this genetic divergence is plausible given what is known about X. muta reproductive timing and the differentiation observed between the two main lineages here (Tills 1977; Neely and Butler 2020 ). In Florida, X. muta have increased in numerical density and abundance (Mcmurray et al. 2010 ; McMurray et al. 2015 ). Meanwhile, population structure of X. muta in the Florida Keys has been documented to shift towards a second genetic lineage of X . muta , which was previously mainly observed in smaller size class sponges and rarely in larger size classes (Deignan et al. 2018 ). Similarly, our 2bRAD data indicates the dominant lineage Xm1 consists of both small and large sponges, while all of the sponges assigned to the Xm2 lineage were exclusively smaller in size. Many of the X. muta in Ft. Lauderdale may have recruited from an unsampled population in the Florida Keys, or a similar genetically distinct source population of this second lineage, and have not yet expanded further to the north. Xestospongia muta is known to exhibit different morphologies, some of which are potentially novel species (López-Legentil and Pawlik 2009 ; Pawlik et al. 2021 ; Díaz et al. 2023 ). Other than size, we did not find any unique morphology associated with either of these X. muta lineages. Previous population genetic studies of X. muta have demonstrated varying levels of connectivity among sampled populations. Earlier work using mtDNA demonstrated population structure as well as genetic connectivity across islands in the Caribbean Sea (López-Legentil and Pawlik 2009 ; De Bakker et al. 2016 ). In the Florida Keys, X. muta populations were found to be indistinguishable via microsatellite analysis (Richards et al. 2016 ). Another study spanning from Palm Beach to the Dry Tortugas also found strong evidence for two or four genetic clusters for X. muta with X. muta from Palm Beach reported as genetically similar to those from Key Largo, similar to our observations in this study (Bernard et al. 2018 ). However, unlike what we report here, Bernhard et al. (2018) documented X. muta in the Dry Tortugas were distinct from those in Palm Beach and Key Largo. On a larger scale, these microsatellite studies also found that samples from Flower Garden Banks and the Caribbean were highly differentiated from Florida samples, which is congruent with the hypothesis that shorter pelagic larval duration and negatively buoyant eggs would limit X. muta dispersal distance, and thereby connectivity among distant populations (Richards et al. 2016 ; Bernard et al. 2018 ). While previous work in the Florida Keys has demonstrated two lineages of X. muta , the increased resolution provided by 2bRAD detected putative hybrids with admixture between the two lineages, not found in earlier work (Deignan et al. 2018 ; Evans et al. 2021 ). These admixed samples in our study displayed higher heterozygosity and major allele frequencies closer to what would be expected for hybrid individuals across the 12 divergent SNP loci we identified. Cryptic lineages have also been documented in several coral species in the Florida Keys. Montastraea cavernosa , Stephanocoenia intersepta , and Siderastrea siderea are all comprised of up to four cryptic lineages, but S. intersepta and S. siderea demonstrate virtually no admixture among lineages while M. cavernosa exhibit substantial admixture among lineages, similar to X. muta (Rippe et al. 2021 ; Sturm et al. 2021 ; Eckert et al. 2024 ). In addition to the two main cryptic lineages, there is strong evidence for sub-structure of the predominant cluster Xm1, with the Northern sites (Boynton, West Palm, and Jupiter) exhibiting differing structure from the Lower Keys and the Dry Tortugas. With increasing values of K the main lineage (Xm1) was further divided, leaving few individuals with majority assignment to a single lineage and many more admixed individuals; the Xm2 lineage and hybrids remained essentially unchanged in their Xm2 assignment proportions. The lack of predominant assignment to these new sub-lineages (Xm1.2, Xm1.3) and the much lower F ST values among all Xm1 sub-lineages suggest sub-structuring among the predominant lineage found throughout the Florida Reef Tract. These patterns may continue to strengthen if there is some level of reproductive isolation among these lineages and they continue to experience genetic drift and/or selection, potentially leading to much greater differentiation as seen between the Xm1 and Xm2 lineages. Despite only accounting for a small fraction of the total collected samples, the Xm2 lineage found in Ft. Lauderdale exhibited more genetic diversity than the Xm1 lineage, or any of the Xm1 sub-lineages observed throughout the rest of the Florida Reef Tract. Though not every site along the Florida Reef Tract was sampled, our study spans from the Dry Tortugas to Jupiter Reef in Palm Beach County. North of Jupiter, X. muta become relatively infrequent. While there may be similar X. muta populations to Ft. Lauderdale in Southeast Florida that were not sampled in this study, these results and previous work on other benthic invertebrates point toward Ft. Lauderdale being relatively unique and isolated in terms of benthic species connectivity (Dodge et al. 2020 ; Shilling et al. 2023 ; Sturm et al. 2023 ). Patterns of genetic structuring in scleractinian corals across Florida's Reef Tract can mirror the patterns we identified in X. muta . Notably, M. cavernosa and Porites astreoides collected from Ft. Lauderdale belong to unique lineages not found frequently across other sites, suggesting that there may be ecological or biophysical factors driving inter-species patterns of genetic uniqueness in this area (Dodge et al. 2020 ; Shilling et al. 2023 ). Orbicella faveolata off of Ft. Lauderdale also include a genetically unique lineage clustered together, indicative of a potential episodic recruitment event (Klein et al. 2024 ). This indicates that many Ft. Lauderdale benthic populations may require further considerations when it comes to protection and management. The greater genetic diversity of this unique Xm2 lineage and the putative hybridization with the dominant Xm1 lineage indicates the Xm2 lineage may be of great importance to the larger Florida Reef Tract metapopulation in terms of maintaining higher genetic diversity. As with other sessile benthic invertebrates, X. muta rely on oceanographic patterns to disperse larvae (Cowen and Sponaugle 2009 ; Neely and Butler 2020 ). The Florida Current is highly influential in the dispersal of larvae, and can potentially transport individual larvae across the entirety of the Florida Reef Tract (Sponaugle et al. 2005 ; King et al. 2023 ). Mesoscale, counter-current eddies spin off near shore in Florida and entrain pelagic larvae, which may be driving the unique patterns of relative genetic isolation observed in Ft. Lauderdale, the shallowest site (Yeung et al. 2001 ; Frys et al. 2020 ; Limer et al. 2020 ). Changes in the Florida Current near the Bahamas Fault Zone are hypothesized to drive differences observed in population genetic structure between northern and southern populations in Southeast Florida (Dodge et al. 2020 ). It is possible that the Xm2 lineage may be more prevalent at shallower depths across other areas of Florida’s coral reef that were not sampled in this study, perhaps driven by larvae’s habitat selection preferences or phenotype-environment mismatch preventing successful recruitment of larvae settled at deeper depths (Grether 2005 ; Lecchini et al. 2017 ). However, in FKNMS previous studies have documented two distinct lineages at deeper depths (~ 20 m) than we sampled in Ft. Lauderdale (Deignan et al. 2018 ). Depth is known to be a segregating factor in some cryptic lineages of zooxanthellate coral species (Eckert et al. 2024 ; Grupstra et al. 2024 ). For heterotrophic sponges like X. muta , relative independence from light might explain the overall lack of genetic structuring across depth in our study. Xestospongia muta throughout the Florida Reef Tract exhibit varying levels of genetic connectivity. X. muta from shallow sites were greater sources than mesophotic sites in FKNMS, albeit only slightly. In this case, it is more likely that following any local, episodic disturbance most X. muta populations in Florida (excluding Ft. Lauderdale) could be repopulated from any Xestospongia muta gamete characteristics likely have a large influence on the levels of recent migration in South Florida. Xestospongia muta notably produce negatively buoyant eggs (Ritson-Williams et al. 2005 ; Neely and Butler 2020 ). After females release eggs, they are often found filling the atrium of the sponge and blanketing the substrate nearby (Ritson-Williams et al. 2005 ; Neely and Butler 2020 ). With the relatively low migration rates in X. muta we might expect to see highly structured populations by sampling site or depth, and yet we demonstrate a notable lack of geographic structuring across the Florida Reef Tract with relatively open populations. Given the similarity across sites, the large distribution of the Xm1 lineage, and the long lifespan of X. muta , it is likely that the strong Florida current is dispersing X. muta great distances, despite the likely short pelagic larval duration (Fromont and Bergquist 1994 ; Richards et al. 2016 ). However, given that little is known about X. muta larval behavior and dispersal, future work in these areas is necessary to aide in developing more accurate larval dispersal models that could be cross compared to genetic data such as those we present here. Xestospongia muta is a keystone species on the Florida Reef Tract, providing many important ecosystem services including water filtration, nutrient cycling, and habitat provisioning, all of which are likely to become more important as coral cover continues to decrease from global climate change and other anthropogenic stressors (Diaz and Rützler 2001 ; Henkel and Pawlik 2005 ; McMurray et al. 2008 ; Fiore et al. 2010 ; Loh and Pawlik 2014 ). However, Xestospongia muta , like other sponges on the Florida Reef Tract are susceptible to disease outbreaks and have suffered significant losses in the recent past (Cowart et al. 2006 ; Webster 2007 ; García-Hernández et al. 2021 ). With the present and continuing pressures along the Florida Reef Tract threatening coral reef biodiversity, understanding the connectivity patterns and genetic diversity of critical benthic community members becomes increasingly valuable. We documented relatively well-mixed populations of the giant barrel sponge, X. muta with a notably unique and genetically diverse population in Ft. Lauderdale which may warrant individualized management and protections. This unique population may represent an expansion of the genetic lineage identified in smaller size class sponges in the Florida Keys (Deignan et al. 2018 ). Future monitoring of X. muta recruitment along the northern Florida Reef Tract paired with continued population genetic sampling would help gain further insight into the potential shifts of population structure under increasing anthropogenic stressors. Declarations 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. Additional funding for including KJCAP was provided by Florida Department of Environmental Protection to J. Voss (awards C01954, C3D275). 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 . We appreciate productive conversations with P. Bongaerts that helped improve this study. Samples were collected under permits from Florida Keys National Marine Sanctuary (FKNMS-2019-088) and Florida Fish and Wildlife Conservation Commission (SAL-2-2022-SRP). Sequencing was performed by the Genomic Sequencing and Analysis Facility at UT Austin, Center for Biomedical Research Support (RRID# SCR_021713). Computation capacity was provided by Research Computing Services at Florida Atlantic University. The Xestospongia muta genomic reference used was from The Aquatic Symbiosis Genomics Project (McKenna et al. 2024 ; ENA Assembly: GCA_963693275.1). This is contribution #XXXX from Harbor Branch Oceanographic Institute. Author Contribution RJE and JDV conceived the study and sampling design. RJE, ABS, and JDV collected the samples. RJE, ABS, and AMC performed DNA extractions. RJE prepared sequencing libraries performed bioinformatic analyses, analyzed the data, and prepared figures. All authors contributed to the final edited manuscript prepared by RJE. This study comprised a portion of RJE’s dissertation research supervised by JDV. Acknowledgement 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. Additional funding for sampling and analyses for KJCRAP was provided by Florida Department of Environmental Protection to J. Voss (awards C01954, C3D275). 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. Thank you to P. Bongaerts for productive conversations about these data which helped improve the manuscript. Samples were collected under permits from Florida Keys National Marine Sanctuary (FKNMS-2019-088) and Florida Fish and Wildlife Conservation Commission (SAL-2-2022-SRP). Sequencing was performed by the Genomic Sequencing and Analysis Facility at UT Austin, Center for Biomedical Research Support (RRID# SCR_021713). Computation capacity was provided by Research Computing Services at Florida Atlantic University. The Xestospongia muta genomic reference used was from The Aquatic Symbiosis Genomics Project (McKenna et al. 2024; ENA Assembly: GCA_963693275.1). This is contribution #XXXX from Harbor Branch Oceanographic Institute. Data Availability Sequence data from this manuscript can be accessed at the NCBI BioProject Accession PRJNA1186221. Detailed laboratory protocols and complete data analysis code and scripts are housed on GitHub and archived with Zenodo (Eckert 2025; Github.com/RyanEckert/Xestospongia_FL_PopGen). References 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 Sci 95:161–175 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. 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Mar Freshw Res 52:1217 Yu G, Smith DK, Zhu H, Guan Y, Lam TT (2017) ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol 8:28–36 Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYMATERIALS.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5875893","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":431758348,"identity":"8c37a8b2-5769-47ac-a0d4-449ac85fc07b","order_by":0,"name":"Ryan J. 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Sturm","email":"","orcid":"","institution":"Harbor Branch Oceanographic Institute, Florida Atlantic University","correspondingAuthor":false,"prefix":"","firstName":"Alexis","middleName":"B.","lastName":"Sturm","suffix":""},{"id":431758352,"identity":"a012b735-867c-4eb4-b420-874faf92c25a","order_by":2,"name":"Ashley M. Carreiro","email":"","orcid":"","institution":"Harbor Branch Oceanographic Institute, Florida Atlantic University","correspondingAuthor":false,"prefix":"","firstName":"Ashley","middleName":"M.","lastName":"Carreiro","suffix":""},{"id":431758353,"identity":"4cb567ef-23d0-4878-a707-d6e7596a8ed9","order_by":3,"name":"Joshua D. Voss","email":"","orcid":"","institution":"Harbor Branch Oceanographic Institute, Florida Atlantic University","correspondingAuthor":false,"prefix":"","firstName":"Joshua","middleName":"D.","lastName":"Voss","suffix":""}],"badges":[],"createdAt":"2025-01-21 20:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5875893/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5875893/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79114059,"identity":"8d8c7502-f0e6-4332-b5cf-ec01ada927d7","added_by":"auto","created_at":"2025-03-24 14:47:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":239999,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap of sampling locations throughout Southeast Florida.\u003c/strong\u003e Color indicates sampling site and shape indicates depth zone for samples. Boundaries for Florida Keys National Marine Sanctuary (FKNMS) and Kristin Jacobs Coral Aquatic Preserve (KJCAP) are also shown.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5875893/v1/3cdf114c1b1a240c24d62304.png"},{"id":79115116,"identity":"943f30ff-80b9-4e25-b584-3a659ba9d018","added_by":"auto","created_at":"2025-03-24 14:55:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":518718,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eXestospongia muta\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e population structure. A\u003c/strong\u003e Dendrogram of samples based on IBS matrix from ANGSD with matching site, depth zone, and depth (m) data and structure plots for K =2–5 for each \u003cem\u003eX. muta \u003c/em\u003esample. Colored points on dendrogram nodes represent Lineages Xm1 and Xm2 as well as putative hybrids (admixed samples). \u003cstrong\u003eB \u003c/strong\u003ePrincipal component analysis from pcangsd, where color represents sampling site and shape represents sampling depth zone. Larger, opaque shapes are sample population centroids and smaller, transparent shapes individual samples. \u003cstrong\u003eC\u003c/strong\u003eSame plot as \u003cstrong\u003eB \u003c/strong\u003ewith samples colored by lineage assignment from \u003cem\u003eK\u003c/em\u003e = 2 data set. \u003cstrong\u003eD \u003c/strong\u003eSame plot as \u003cstrong\u003eC \u003c/strong\u003ebut for \u003cem\u003eK\u003c/em\u003e = 4.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5875893/v1/27ffef65d673c57462ec8ecb.png"},{"id":79114063,"identity":"bf5ac9d3-ed19-4142-af9b-9305ce8a539a","added_by":"auto","created_at":"2025-03-24 14:47:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":170798,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eXestospongia muta\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e lineage diversity. A \u003c/strong\u003eLineage heterozygosity across variant and invariant loci for \u003cem\u003eK\u003c/em\u003e = 2. \u003cstrong\u003eB \u003c/strong\u003eLineage heterozygosity across variant and invariant loci for \u003cem\u003eK\u003c/em\u003e = 4. Letters indicate significant differences among lineages in \u003cstrong\u003eA \u0026amp; B\u003c/strong\u003e. \u003cstrong\u003eC\u003c/strong\u003e Lineage nucleotide diversity (\u003cem\u003eπ\u003c/em\u003e) for \u003cem\u003eK\u003c/em\u003e = 2 and \u003cem\u003eK\u003c/em\u003e = 4. Effective population size (\u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e) is listed within each bar. \u003cstrong\u003eD \u003c/strong\u003ePairwise fixation index (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e) heatmap among lineages. Darker coloration indicates higher \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e. Colors in\u003cstrong\u003e A–D \u003c/strong\u003edepict lineage assignments.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5875893/v1/c9d35a50440e42c97156acff.png"},{"id":79114061,"identity":"e9a913c4-0a1d-4f81-bcc5-620a47e7c70a","added_by":"auto","created_at":"2025-03-24 14:47:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":165669,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlternatively fixed SNPs. A \u003c/strong\u003eDendrogram and structure plot (\u003cem\u003eK\u003c/em\u003e =2) of subset of 71 samples representing Xm1 and Xm2 lineages as well as potential hybrids. \u003cstrong\u003eB \u003c/strong\u003ePCA plot of same 71 samples in \u003cstrong\u003eA \u003c/strong\u003ecolored by lineage assignment using strict criteria (100% assignment to Xm1 or Xm2). \u003cstrong\u003eC \u003c/strong\u003eLineage heterozygosity in subset of 71 samples from \u003cstrong\u003eA\u003c/strong\u003e across 12 alternatively fixed SNPs. Letters indicate significant differences among lineages. \u003cstrong\u003eD \u003c/strong\u003eHeatmap of 12 alternatively fixed SNPs by lineage assignment. Loci with more red coloration have a greater frequency of the major allele, those with more green a greater frequency of the minor allele, and more yellow color indicates frequency closer to 0.5 (i.e. heterozygous at that locus).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5875893/v1/50a3e93953f01e6ce0acf6a7.png"},{"id":79115117,"identity":"1d42ac78-b840-4443-b53c-240859a28e36","added_by":"auto","created_at":"2025-03-24 14:55:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":674398,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRecent genetic migration inference from BayesAss. \u003c/strong\u003eHeatmap showing mean posterior distribution of recent migration rates (\u003cem\u003em\u003c/em\u003e), the proportion of recent migrants with uncertainty (95% high posterior density) shown below. Bolded values indicate migration with HPD range exclusive of 0. Within site retention (i.e., self-recruitment) is listed along diagonal with gray fill.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5875893/v1/2f5b2d6a03b7fa9c749cbcd5.png"},{"id":93693846,"identity":"287711eb-d75d-4b44-9114-1a7de2bba137","added_by":"auto","created_at":"2025-10-16 14:24:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2710783,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5875893/v1/7a9f291c-118f-481d-a3df-df7557fda2d2.pdf"},{"id":79114058,"identity":"880f743c-d766-4f16-8301-951369c446ec","added_by":"auto","created_at":"2025-03-24 14:47:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":634135,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYMATERIALS.docx","url":"https://assets-eu.researchsquare.com/files/rs-5875893/v1/b6101f60599ba5265cff21c5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Population genetics of the giant barrel sponge, Xestospongia muta, reveal distinct, hybridizing lineages across the Florida Reef Tract","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eUnderstanding genetic diversity and connectivity among populations remains an important part of resource management and conservation efforts (Palumbi \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Marine populations have often been assumed to be relatively open, given the perceived lack of barriers to connectivity (Knowlton and Jackson \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). However, factors such as ocean currents or depth may act as barriers to gene flow (Eckert et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sturm et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Grupstra et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Previous work on MCEs has illustrated that depth can be a segregating factor for depth-generalist conspecifics. However, this split does not occur universally but rather varies by location and species\u0026mdash;indicating that multiple biotic and abiotic barriers are likely involved in driving patterns of genetic structuring in benthic marine invertebrates (Serrano et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bongaerts et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sturm et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMesophotic coral ecosystems (MCEs; 30\u0026ndash;150 m) are a large component of important marine habitat throughout the Tropical Western Atlantic, yet they remain relatively understudied in comparison to shallow reefs (Turner et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Radice et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The degree of exploration and characterization of MCEs varies across regions; in Florida, relatively few studies have focused on these deeper coral ecosystems which often occur just seaward of their shallow reef (\u0026lt;\u0026thinsp;30 m) counterparts (Pyle and Copus \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Several MCE-focused studies in Florida center on the Dry Tortugas and Pulley Ridge, approximately 100\u0026ndash;160 km west of Key West, potentially leaving many MCEs along the Florida Reef Tract relatively underexplored and not yet fully characterized (Serrano et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Reed et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bernard et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Studivan and Voss \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Drury et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Genetic similarity and connectivity among and between MCEs and shallow coral reef ecosystems can be quantified through population genetic assessments using conserved yet variable genetic markers (Bongaerts et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Studivan and Voss \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Eckert et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe giant barrel sponge, \u003cem\u003eXestospongia muta\u003c/em\u003e is one of the most abundant sponges throughout the Caribbean and Western Atlantic in terms of reef benthic percent cover and is recognized as a keystone species (Loh and Pawlik \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Marine sponges like \u003cem\u003eX. muta\u003c/em\u003e play a critical role in creating habitat complexity, promoting coral reef biodiversity, and facilitating nutrient cycling within these habitats (Diaz and R\u0026uuml;tzler \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Henkel and Pawlik \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Fiore et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). \u003cem\u003eXestospongia muta\u003c/em\u003e is a major component of sponge communities on the Florida Reef Tract and has increased in density over time, with documented shifts in population structure, likely due to available benthic space left after coral loss (Mcmurray et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; McMurray et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). With ongoing declines in coral cover across the Florida Reef Tract, the ecosystem services provided by \u003cem\u003eX. muta\u003c/em\u003e will remain important in the Anthropocene and may become disproportionately more important (McMurray et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). \u003cem\u003eXestospongia muta\u003c/em\u003e also has a large depth range and is one of the numerically dominant species found deeper than 10 m (Mcmurray et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). \u003cem\u003eXestospongia muta\u003c/em\u003e and other sponges are typically common components of mesophotic coral ecosystem benthos and increase abundance with depth on some Caribbean reefs (Lesser and Slattery \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLittle is known about \u003cem\u003eX. muta\u003c/em\u003e reproduction, such as larval development timing or pelagic larval duration, with few published studies documenting spawning in the literature (Ritson-Williams et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Neely and Butler \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, there is evidence that \u003cem\u003eX. muta\u003c/em\u003e is a gonochoric broadcast spawner with males and females releasing sperm and eggs, respectively, with subsequent external fertilization (Ritson-Williams et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Neely and Butler \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This reproductive mode is thought to lend itself to greater dispersal through ocean currents to distant reefs depending upon the pelagic larval duration (Miller and Mundy \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, \u003cem\u003eX\u003c/em\u003e. \u003cem\u003emuta\u003c/em\u003e have negatively buoyant eggs and neutral to negatively buoyant sperm with fertilization likely occurring near females, which is hypothesized to result in higher local retention of larvae (Ritson-Williams et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Neely and Butler \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although the pelagic larval duration of \u003cem\u003eX. muta\u003c/em\u003e is unknown, it is assumed to be similar to congeners whose larvae typically settle within 3 d (Fromont and Bergquist \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Richards et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In Florida, there are two hypothesized sympatric cryptic species of \u003cem\u003eX. muta\u003c/em\u003e which may be maintained by reproductive isolation through spawning timing (Deignan et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Neely and Butler \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Evans et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the cosmopolitan distribution of \u003cem\u003eX. muta\u003c/em\u003e and multiple population genetics studies, only one study of \u003cem\u003eX. muta\u003c/em\u003e includes any mesophotic samples and few contain any samples from the northern Florida Reef Tract (L\u0026oacute;pez-Legentil and Pawlik \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; De Bakker et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bernard et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This study aimed to address these gaps by investigating the population genetics of \u003cem\u003eX. muta\u003c/em\u003e with a single nucleotide polymorphism (SNP) genotyping approach across both shallow and mesophotic habitats in the Florida Keys National Marine Sanctuary and within shallow habitats in Kristin Jacobs Coral Aquatic Preserve (KJCAP). This study is part of a larger body of work focused on understanding the genetic diversity as well as the vertical and horizontal connectivity of important habitat-forming benthic invertebrate species in important marine protected areas in southeast Florida (Studivan and Voss \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dodge et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sturm et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shilling et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Eckert et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). With documented changes in \u003cem\u003eX\u003c/em\u003e. \u003cem\u003emuta\u003c/em\u003e population densities and demography between 2000 and 2012, and the susceptibility of the species to both disease outbreaks and bleaching events, characterizing the genetic diversity and connectivity of \u003cem\u003eX. muta\u003c/em\u003e through Florida can provide useful information to guide management strategies and potential restoration activities for \u003cem\u003eX. muta\u003c/em\u003e (Carilli et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mcmurray et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; McMurray et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Garc\u0026iacute;a-Hern\u0026aacute;ndez et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cheung et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Data such as those presented here are beneficial when determining potential areas for focused protection or designing restoration efforts to maintain or enhance genetic diversity. Finally, this study establishes a baseline for \u003cem\u003eX. muta\u003c/em\u003e genetic diversity and population structure across shallow and mesophotic locations in Florida to benchmark future genetic diversity data in a rapidly changing ecosystem.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003eWe collected \u003cem\u003eXestospongia muta\u003c/em\u003e samples throughout FKNMS and the KJCAP targeting a sample size of 30 individuals per depth zone and site (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;352; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). FKNMS sites were selected based upon presence of targeted species during ROV surveys prior to technical diving. \u003cem\u003eXestospongia muta\u003c/em\u003e samples collected within the KJCAP were selected from long-term coral reef monitoring sites in this marine protected area. Within FKNMS \u003cem\u003eX. muta\u003c/em\u003e were collected from both shallow and mesophotic depths. Eight samples were collected via Mohawk ROV and the remainder of the samples via open circuit SCUBA. Mesophotic depth ranges off of KJCAP were not included in this study as they mostly consists of pavement and unconsolidated reef lacking sufficient abundance of the targeted species (Banks et al. 2008). Tissue (~\u0026thinsp;4 cm\u003csup\u003e2\u003c/sup\u003e) from each sponge was excised and placed into an individual, uniquely labeled zip top bag (or separate collection bin on the ROV), with scaled reference photographs of each sponge pre- and post-sampling. Upon returning to the surface, as much saltwater as possible was squeezed from the sample to reduce preservative dilution and the sample was then placed into absolute ethanol. Samples were held at -20 \u0026ordm;C while at sea and transported to FAU Harbor Branch on dry ice. At the lab, the ethanol was replaced with new molecular-grade absolute ethanol and stored samples at -80 \u0026ordm;C until extraction.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample collection metadata\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDepth zone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDepth range (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean depth (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCollection date\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKJCAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJupiter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShallow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.5\u0026ndash;21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSeptember 2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWest Palm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShallow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.8\u0026ndash;16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSeptember 2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoynton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShallow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.3\u0026ndash;20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJanuary 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFt. Lauderdale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShallow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.6\u0026ndash;8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDecember 2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFKNMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpper Keys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShallow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.8\u0026ndash;26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAugust 2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMesophotic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.8\u0026ndash;51.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAugust 2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower Keys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShallow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.8\u0026ndash;24.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAugust 2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMesophotic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.4\u0026ndash;35.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAugust 2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTortugas Bank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShallow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.5\u0026ndash;29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAugust 2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMesophotic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.2\u0026ndash;34.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAugust 2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRiley\u0026rsquo;s Hump\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShallow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.0\u0026ndash;28.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAugust 2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMesophotic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.7\u0026ndash;46.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAugust 2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenomic DNA extraction, library preparation, and sequencing\u003c/h3\u003e\n\u003cp\u003eTotal genomic DNA was extracted from small tissue subsamples (~\u0026thinsp;1 cm\u003csup\u003e2\u003c/sup\u003e) following a modified dispersion buffer protocol (Sturm et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Samples were genotyped following modified 2bRAD protocols with the endonuclease \u003cem\u003eBcgI\u003c/em\u003e (New England Biolabs, Inc.) and 100 ng of genomic DNA per sample (Wang et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Matz \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Eckert et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The resulting pooled libraries were checked for quality and quantity using NanoDrop and Qubit and then sequenced at UTGSAF on Illumina NovaSeq (SR100 S1) with 20% \u003cem\u003ephiX\u003c/em\u003e spike-in. Detailed lab procedures are available in the GitHub repository accompanying this manuscript (Eckert \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSNP genotyping\u003c/h3\u003e\n\u003cp\u003eComplete data analysis scripts and data visualization code are available on GitHub and archived through Zenodo (Eckert, 2024). Returned sequencing reads were demultiplexed, deduplicated, filtered and trimmed with custom \u003cem\u003eperl\u003c/em\u003e scripts and \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ecutadapt\u003c/span\u003e v3.4 (Martin \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Sample reads were aligned to the \u003cem\u003eX. muta\u003c/em\u003e reference genome with \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eBowtie2\u003c/span\u003e v1.2.2 (Langmead and Salzberg \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe used the program \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eangsd\u003c/span\u003e v0.930 to generate genotype likelihoods for all samples using the following filters: a minimum mapping quality of 20, minimum base quality score of 30, minimum allele frequency of 0.05, minimum \u003cem\u003ep\u003c/em\u003e-value of 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e for deviation from Hardy-Weinberg equilibrium, minimum \u003cem\u003ep\u003c/em\u003e-value of 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e for strand bias, \u003cem\u003ep\u003c/em\u003e-value of 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e for heterozygosity bias, remove triallelic SNPs, \u003cem\u003ep\u003c/em\u003e-value that a locus is variable of 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, and had to be present in \u0026ge;\u0026thinsp;75% of samples (Korneliussen et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eangsd\u003c/span\u003e was initially run on all samples to detect naturally occurring clones using a threshold determined by six technical triplicate samples. Only one of each clone/replicate was retained (sample with greatest proportion of reads\u0026thinsp;\u0026gt;\u0026thinsp;5X coverage) and \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eangsd\u003c/span\u003e was run on the clone-free sample set with the above filters.\u003c/p\u003e\n\u003ch3\u003ePopulation genetic structure\u003c/h3\u003e\n\u003cp\u003ePrincipal component analysis (PCA) was conducted with the program \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003epcangsd\u003c/span\u003e and PCA biplots were generated from the resulting covariance matrix (Meisner and Albrechtsen \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003engsAdmix\u003c/span\u003e was used to calculate admixture proportions for retained samples for values of \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u0026ndash;15, using 20 replicate simulations for each value of \u003cem\u003eK\u003c/em\u003e (Skotte et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The most likely value of \u003cem\u003eK\u003c/em\u003e was evaluated using the Evanno and Puechmaille methods with \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eclumpak\u003c/span\u003e and \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eStructureSelector\u003c/span\u003e, respectively (Evanno et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Kopelman et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Puechmaille \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Li and Liu \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Values of K were visualized and assessed with \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eggtree\u003c/span\u003e using the IBS matrix and \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003engsAdmix\u003c/span\u003e outputs for \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u0026ndash;5 (Yu et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eLineage diversity\u003c/h3\u003e\n\u003cp\u003eBased on resultant dendrogram and \u003cem\u003eK\u003c/em\u003e assessments, we continued to analyze values of \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2 and \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4. For heterozygosity and site frequency spectra (SFS) calculations, \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eangsd\u003c/span\u003e was rerun using only filters which did not affect allelic frequencies, retaining all loci, variant and invariant (Rippe et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Eckert et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Only samples with membership assignment\u0026thinsp;\u0026ge;\u0026thinsp;75% to a single lineage were retained to avoid confounding these analyses (Fifer et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Eckert et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). SNP loci were calculated separately within each lineage (present within 75% of samples per lineage) and thinned to include only sites present across all lineages (Eckert et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHeterozygosities were calculated for each sample using custom \u003cem\u003eR\u003c/em\u003e scripts (Matz \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Eckert \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Due to heterogeneity of variance within the data and uneven sample sizes, Welch\u0026rsquo;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 non-parametric Games-Howell tests, which are effective as a post-hoc test when variance is heterogeneous and sample sizes are uneven (Games and Howell \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1976\u003c/span\u003e; Midway et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To calculate weighted fixation index (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e) between lineages and nucleotide diversity we used site frequency spectra calculated with \u003cem\u003erealSFS\u003c/em\u003e in \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eangsd\u003c/span\u003e. Nucleotide diversity was averaged across scaffolds from the \u003cem\u003eX. muta\u003c/em\u003e reference. Effective population size (\u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e) was calculated using the formula \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{e}=\\:\\frac{}{4}\\)\u003c/span\u003e\u003c/span\u003e, where the mutation rate (\u003cem\u003e\u0026micro;\u003c/em\u003e) was estimated to be 2 \u0026times; 10\u003csup\u003e\u0026minus;8\u003c/sup\u003e per base, per generation (Matz et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rippe et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLineage hybridization\u003c/h2\u003e \u003cp\u003eTo evaluate the potential of admixed individuals being hybrids of the main divergent lineages when \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2, we used a reduced sample set. The reduced sample set only included members assigned 100% to either of the putative lineages (Xm1 or Xm2), as well as putative hybrid samples with admixture between the two lineages. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eangsd\u003c/span\u003e was rerun with the resulting subset of 71 samples (putative lineages and putative hybrids) and the resulting .bcf file was split to calculate major/minor allele frequencies per lineage. The results were filtered to retain divergent SNPs which were alternatively fixed (\u0026gt;\u0026thinsp;0.85, allowing for error and uncertainty in genotype likelihoods) between the main lineages and major allele frequencies were visualized by lineage assignment. Heterozygosities were also calculated for the subset of samples across the divergent SNPs, and differences in heterozygosity by lineage were assessed with Welch\u0026rsquo;s ANOVA and pairwise Games-Howell tests.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePopulation genetic connectivity\u003c/h3\u003e\n\u003cp\u003ePopulation genetic connectivity was approximated through recent migration rates (immigrant individuals from previous 2\u0026ndash;3 generations) with the \u003cem\u003eBA3SNP\u003c/em\u003e function of \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eBayesAss\u003c/span\u003e v3.0.4.2. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eBayesAss\u003c/span\u003e was only run on the most abundant lineage (dark magenta node, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;300 samples). Ten independent runs were conducted with random start seeds for 10\u0026nbsp;million MCMC model simulations using a 2\u0026nbsp;million burn-in and 1000 run sampling frequency. Mixing parameters (migration rate [\u003cem\u003em\u003c/em\u003e]\u0026thinsp;=\u0026thinsp;0.15, allele frequencies [\u003cem\u003ea\u003c/em\u003e]\u0026thinsp;=\u0026thinsp;0.7, inbreeding coefficient [\u003cem\u003ef\u003c/em\u003e]\u0026thinsp;=\u0026thinsp;0.03) were adjusted to allow adequate mixing within model runs (acceptance rates\u0026thinsp;=\u0026thinsp;0.2\u0026ndash;0.6; Wilson and Rannala \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Trace files from independent runs were visualized with \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003etracer\u003c/span\u003e 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 (\u003cem\u003em\u003c/em\u003e) were calculated as the mean of the posterior distribution and their uncertainty as 95% high posterior density (HPD) intervals.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSample processing\u003c/h2\u003e \u003cp\u003e2bRAD sequencing resulted in 2\u0026nbsp;billion reads, or an average of 5.47\u0026nbsp;million reads per \u003cem\u003eX. muta\u003c/em\u003e sample, with 3.67\u0026nbsp;million reads on average remaining per sample after post processing. After removing technical replicates, 351 samples remained and were used to generate 8,034 unique SNPs. Generating loci (variant and invariant) common to all lineages resulted in 266,442 and 198,110 sites for \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2 and \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4 scenarios, respectively.\u003c/p\u003e \u003cp\u003e \u003cb\u003eXestospongia muta\u003c/b\u003e \u003cb\u003epopulation genetic structure\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePrincipal component analysis revealed two main genetic clusters along the first axis with samples mainly from Fort Lauderdale in the smaller cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Using the Evanno and Puechmaille methods suggested both \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2 and \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4, respectively, as the most likely values of \u003cem\u003eK\u003c/em\u003e. The dendrogram revealed little overall structuring of samples by site or depth, but alongside the \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2 structure plot we see evidence of two main lineages (Xm1 and Xm2) with potential hybridization between them (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). PCA also demonstrated a lack of structuring across site and depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), other than Fort Lauderdale being more differentiated from other samples, driven by the high presence of the Xm2 lineage. This lack of structuring was also apparent when Xm2 and putative hybrids were removed (\u003cb\u003eSupplemental Fig. S1\u003c/b\u003e). When plotting values \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u0026ndash;5, the smaller cluster of Ft. Lauderdale samples remained distinct with nearly 100% assignment to the lineage Xm2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Combined with PCA results, this suggests there are likely 2 main \u003cem\u003eX. muta\u003c/em\u003e lineages with potential substructure in Xm1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D). We assessed photographs of sampled \u003cem\u003eX\u003c/em\u003e. \u003cem\u003emuta\u003c/em\u003e lineages and there was no clear morphology associated with the unique lineage Xm2, though all of the Xm2 lineage were noticeably small in size (\u0026lt;\u0026thinsp;15 cm in diameter).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eXestospongia muta\u003c/b\u003e \u003cb\u003elineage diversity\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAcross all loci (variant and invariant) the Xm2 lineage exhibited greater heterozygosity in both \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2 and \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4 scenarios (Welch\u0026rsquo;s ANOVA; \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2: \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 35)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;75.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4: \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(4, 40.9)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;27.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). The Xm2 lineage also had greater nucleotide diversity (\u003cem\u003eπ\u003c/em\u003e), and therefore greater effective population sizes (\u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e) than other lineages (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). For \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2, \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e between Xm1 and Xm2 was 0.153. Among the four lineages, Xm2 was highly differentiated from all Xm1 lineages (Xm1, Xm1.2, Xm1.3; \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e: 0.113\u0026ndash;0.136) while all Xm1 lineages were more similar to one another (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e: 0.02\u0026ndash;0.037; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHybridization between lineages\u003c/h2\u003e \u003cp\u003eStricter filtering criteria of \u003cem\u003eX. muta\u003c/em\u003e samples resulted in a subset of 71 samples to query for alternatively fixed SNPs between the two lineages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), and twelve such SNPs were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Within these SNPS, many appeared to be heterozygous (major allele frequency closer to 0.5) within the admixed samples, lending credence to the idea that these samples are putative hybrids between the two main lineages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Admixed samples also had greater heterozygosity across the 12 alternatively fixed SNPs, while there was no difference between the Xm1 and Xm2 lineages (Welch\u0026rsquo;s ANOVA: \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 34.5)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;14.2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGenetic connectivity across Southeast Florida\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eBayesAss\u003c/span\u003e analysis revealed patterns of low migration (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM\u0026thinsp;=\u0026thinsp;1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15%; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Shallow sites were slightly greater sources (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM\u0026thinsp;=\u0026thinsp;1.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16% vs 1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32%) and mesophotic sites were greater sinks (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM\u0026thinsp;=\u0026thinsp;1.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27% vs 1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18%). Shallow to mesophotic subsidy was slightly greater than mesophotic to shallow (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM\u0026thinsp;=\u0026thinsp;1.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34% vs 1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43%). Many of the sampling sites provided little substantial migration (i.e. HPD range inclusive of 0; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Sites within the Florida Keys, especially shallow Upper Keys and shallow and mesophotic Lower Keys, were greater sources than those within the northern portion of the Florida Reef Tract (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). It should be noted that assignment analyses may suffer from the lack of sampling all populations due to individuals from unsampled populations being misassigned (Christie et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eBayesAss\u003c/span\u003e includes several generations of geneflow and assigns migration rates using Bayesian methodology across our relatively small (100s of km) sampling area, making these estimates useful as a baseline to understanding the relative connectivity among these critical habitats, which could be bolstered through future sampling and modelling efforts (Wilson and Rannala \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Meirmans \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Christie et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean migration rates from \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eBayesAss\u003c/span\u003e by depth zone.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003em\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMesophotic Source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShallow Source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMesophotic Sink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShallow Sink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMesophotic \u0026rarr; Shallow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMesophotic \u0026rarr; Mesophotic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShallow \u0026rarr; Mesophotic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShallow \u0026rarr; Shallow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMean migration (\u003cem\u003em\u003c/em\u003e); standard deviation (SD); Standard error (SE)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eAcross south Florida in KJCAP and FKNMS \u003cem\u003eX. muta\u003c/em\u003e is comprised of two distinct cryptic lineages. The less abundant, genetically distinct population found in Ft. Lauderdale exhibits strong differentiation from the other main \u003cem\u003eX. muta\u003c/em\u003e lineage found across all sites and depths. Two cryptic linages of \u003cem\u003eX. muta\u003c/em\u003e have been previously documented (Deignan et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Evans et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and it has been hypothesized that these two cryptic lineages may have arisen from reproductive isolation via asynchrony in spawning (Neely and Butler \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Evans et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). With high potential for local retention in some sponge species, corresponding drift of \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e through generations, and limited migration between populations, this genetic divergence is plausible given what is known about \u003cem\u003eX. muta\u003c/em\u003e reproductive timing and the differentiation observed between the two main lineages here (Tills 1977; Neely and Butler \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Florida, \u003cem\u003eX. muta\u003c/em\u003e have increased in numerical density and abundance (Mcmurray et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; McMurray et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Meanwhile, population structure of \u003cem\u003eX. muta\u003c/em\u003e in the Florida Keys has been documented to shift towards a second genetic lineage of \u003cem\u003eX\u003c/em\u003e. \u003cem\u003emuta\u003c/em\u003e, which was previously mainly observed in smaller size class sponges and rarely in larger size classes (Deignan et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, our 2bRAD data indicates the dominant lineage Xm1 consists of both small and large sponges, while all of the sponges assigned to the Xm2 lineage were exclusively smaller in size. Many of the \u003cem\u003eX. muta\u003c/em\u003e in Ft. Lauderdale may have recruited from an unsampled population in the Florida Keys, or a similar genetically distinct source population of this second lineage, and have not yet expanded further to the north. \u003cem\u003eXestospongia muta\u003c/em\u003e is known to exhibit different morphologies, some of which are potentially novel species (L\u0026oacute;pez-Legentil and Pawlik \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Pawlik et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; D\u0026iacute;az et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Other than size, we did not find any unique morphology associated with either of these \u003cem\u003eX. muta\u003c/em\u003e lineages.\u003c/p\u003e \u003cp\u003ePrevious population genetic studies of \u003cem\u003eX. muta\u003c/em\u003e have demonstrated varying levels of connectivity among sampled populations. Earlier work using mtDNA demonstrated population structure as well as genetic connectivity across islands in the Caribbean Sea (L\u0026oacute;pez-Legentil and Pawlik \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; De Bakker et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In the Florida Keys, \u003cem\u003eX. muta\u003c/em\u003e populations were found to be indistinguishable via microsatellite analysis (Richards et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Another study spanning from Palm Beach to the Dry Tortugas also found strong evidence for two or four genetic clusters for \u003cem\u003eX. muta\u003c/em\u003e with \u003cem\u003eX. muta\u003c/em\u003e from Palm Beach reported as genetically similar to those from Key Largo, similar to our observations in this study (Bernard et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, unlike what we report here, Bernhard et al. (2018) documented \u003cem\u003eX. muta\u003c/em\u003e in the Dry Tortugas were distinct from those in Palm Beach and Key Largo. On a larger scale, these microsatellite studies also found that samples from Flower Garden Banks and the Caribbean were highly differentiated from Florida samples, which is congruent with the hypothesis that shorter pelagic larval duration and negatively buoyant eggs would limit \u003cem\u003eX. muta\u003c/em\u003e dispersal distance, and thereby connectivity among distant populations (Richards et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bernard et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile previous work in the Florida Keys has demonstrated two lineages of \u003cem\u003eX. muta\u003c/em\u003e, the increased resolution provided by 2bRAD detected putative hybrids with admixture between the two lineages, not found in earlier work (Deignan et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Evans et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These admixed samples in our study displayed higher heterozygosity and major allele frequencies closer to what would be expected for hybrid individuals across the 12 divergent SNP loci we identified. Cryptic lineages have also been documented in several coral species in the Florida Keys. \u003cem\u003eMontastraea cavernosa\u003c/em\u003e, \u003cem\u003eStephanocoenia intersepta\u003c/em\u003e, and \u003cem\u003eSiderastrea siderea\u003c/em\u003e are all comprised of up to four cryptic lineages, but \u003cem\u003eS. intersepta\u003c/em\u003e and \u003cem\u003eS. siderea\u003c/em\u003e demonstrate virtually no admixture among lineages while \u003cem\u003eM. cavernosa\u003c/em\u003e exhibit substantial admixture among lineages, similar to \u003cem\u003eX. muta\u003c/em\u003e (Rippe et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sturm et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Eckert et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to the two main cryptic lineages, there is strong evidence for sub-structure of the predominant cluster Xm1, with the Northern sites (Boynton, West Palm, and Jupiter) exhibiting differing structure from the Lower Keys and the Dry Tortugas. With increasing values of \u003cem\u003eK\u003c/em\u003e the main lineage (Xm1) was further divided, leaving few individuals with majority assignment to a single lineage and many more admixed individuals; the Xm2 lineage and hybrids remained essentially unchanged in their Xm2 assignment proportions. The lack of predominant assignment to these new sub-lineages (Xm1.2, Xm1.3) and the much lower \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e values among all Xm1 sub-lineages suggest sub-structuring among the predominant lineage found throughout the Florida Reef Tract. These patterns may continue to strengthen if there is some level of reproductive isolation among these lineages and they continue to experience genetic drift and/or selection, potentially leading to much greater differentiation as seen between the Xm1 and Xm2 lineages.\u003c/p\u003e \u003cp\u003eDespite only accounting for a small fraction of the total collected samples, the Xm2 lineage found in Ft. Lauderdale exhibited more genetic diversity than the Xm1 lineage, or any of the Xm1 sub-lineages observed throughout the rest of the Florida Reef Tract. Though not every site along the Florida Reef Tract was sampled, our study spans from the Dry Tortugas to Jupiter Reef in Palm Beach County. North of Jupiter, \u003cem\u003eX. muta\u003c/em\u003e become relatively infrequent. While there may be similar \u003cem\u003eX. muta\u003c/em\u003e populations to Ft. Lauderdale in Southeast Florida that were not sampled in this study, these results and previous work on other benthic invertebrates point toward Ft. Lauderdale being relatively unique and isolated in terms of benthic species connectivity (Dodge et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shilling et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sturm et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Patterns of genetic structuring in scleractinian corals across Florida's Reef Tract can mirror the patterns we identified in \u003cem\u003eX. muta\u003c/em\u003e. Notably, \u003cem\u003eM. cavernosa\u003c/em\u003e and \u003cem\u003ePorites astreoides\u003c/em\u003e collected from Ft. Lauderdale belong to unique lineages not found frequently across other sites, suggesting that there may be ecological or biophysical factors driving inter-species patterns of genetic uniqueness in this area (Dodge et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shilling et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003eOrbicella faveolata\u003c/em\u003e off of Ft. Lauderdale also include a genetically unique lineage clustered together, indicative of a potential episodic recruitment event (Klein et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This indicates that many Ft. Lauderdale benthic populations may require further considerations when it comes to protection and management. The greater genetic diversity of this unique Xm2 lineage and the putative hybridization with the dominant Xm1 lineage indicates the Xm2 lineage may be of great importance to the larger Florida Reef Tract metapopulation in terms of maintaining higher genetic diversity.\u003c/p\u003e \u003cp\u003eAs with other sessile benthic invertebrates, \u003cem\u003eX. muta\u003c/em\u003e rely on oceanographic patterns to disperse larvae (Cowen and Sponaugle \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Neely and Butler \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Florida Current is highly influential in the dispersal of larvae, and can potentially transport individual larvae across the entirety of the Florida Reef Tract (Sponaugle et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; King et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Mesoscale, counter-current eddies spin off near shore in Florida and entrain pelagic larvae, which may be driving the unique patterns of relative genetic isolation observed in Ft. Lauderdale, the shallowest site (Yeung et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Frys et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Limer et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Changes in the Florida Current near the Bahamas Fault Zone are hypothesized to drive differences observed in population genetic structure between northern and southern populations in Southeast Florida (Dodge et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is possible that the Xm2 lineage may be more prevalent at shallower depths across other areas of Florida\u0026rsquo;s coral reef that were not sampled in this study, perhaps driven by larvae\u0026rsquo;s habitat selection preferences or phenotype-environment mismatch preventing successful recruitment of larvae settled at deeper depths (Grether \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Lecchini et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, in FKNMS previous studies have documented two distinct lineages at deeper depths (~\u0026thinsp;20 m) than we sampled in Ft. Lauderdale (Deignan et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Depth is known to be a segregating factor in some cryptic lineages of zooxanthellate coral species (Eckert et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Grupstra et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For heterotrophic sponges like \u003cem\u003eX. muta\u003c/em\u003e, relative independence from light might explain the overall lack of genetic structuring across depth in our study.\u003c/p\u003e \u003cp\u003e \u003cem\u003eXestospongia muta\u003c/em\u003e throughout the Florida Reef Tract exhibit varying levels of genetic connectivity. \u003cem\u003eX. muta\u003c/em\u003e from shallow sites were greater sources than mesophotic sites in FKNMS, albeit only slightly. In this case, it is more likely that following any local, episodic disturbance most \u003cem\u003eX. muta\u003c/em\u003e populations in Florida (excluding Ft. Lauderdale) could be repopulated from any \u003cem\u003eXestospongia muta\u003c/em\u003e gamete characteristics likely have a large influence on the levels of recent migration in South Florida. \u003cem\u003eXestospongia muta\u003c/em\u003e notably produce negatively buoyant eggs (Ritson-Williams et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Neely and Butler \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). After females release eggs, they are often found filling the atrium of the sponge and blanketing the substrate nearby (Ritson-Williams et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Neely and Butler \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). With the relatively low migration rates in \u003cem\u003eX. muta\u003c/em\u003e we might expect to see highly structured populations by sampling site or depth, and yet we demonstrate a notable lack of geographic structuring across the Florida Reef Tract with relatively open populations. Given the similarity across sites, the large distribution of the Xm1 lineage, and the long lifespan of \u003cem\u003eX. muta\u003c/em\u003e, it is likely that the strong Florida current is dispersing \u003cem\u003eX. muta\u003c/em\u003e great distances, despite the likely short pelagic larval duration (Fromont and Bergquist \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Richards et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, given that little is known about \u003cem\u003eX. muta\u003c/em\u003e larval behavior and dispersal, future work in these areas is necessary to aide in developing more accurate larval dispersal models that could be cross compared to genetic data such as those we present here.\u003c/p\u003e \u003cp\u003e \u003cem\u003eXestospongia muta\u003c/em\u003e is a keystone species on the Florida Reef Tract, providing many important ecosystem services including water filtration, nutrient cycling, and habitat provisioning, all of which are likely to become more important as coral cover continues to decrease from global climate change and other anthropogenic stressors (Diaz and R\u0026uuml;tzler \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Henkel and Pawlik \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; McMurray et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Fiore et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Loh and Pawlik \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, \u003cem\u003eXestospongia muta\u003c/em\u003e, like other sponges on the Florida Reef Tract are susceptible to disease outbreaks and have suffered significant losses in the recent past (Cowart et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Webster \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Garc\u0026iacute;a-Hern\u0026aacute;ndez et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). With the present and continuing pressures along the Florida Reef Tract threatening coral reef biodiversity, understanding the connectivity patterns and genetic diversity of critical benthic community members becomes increasingly valuable. We documented relatively well-mixed populations of the giant barrel sponge, \u003cem\u003eX. muta\u003c/em\u003e with a notably unique and genetically diverse population in Ft. Lauderdale which may warrant individualized management and protections. This unique population may represent an expansion of the genetic lineage identified in smaller size class sponges in the Florida Keys (Deignan et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Future monitoring of \u003cem\u003eX. muta\u003c/em\u003e recruitment along the northern Florida Reef Tract paired with continued population genetic sampling would help gain further insight into the potential shifts of population structure under increasing anthropogenic stressors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003efor 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. Additional funding for including KJCAP was provided by Florida Department of Environmental Protection to J. Voss (awards C01954, C3D275). 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 \u003cem\u003eR/V F.G. WALTON SMITH\u003c/em\u003e. We appreciate productive conversations with P. Bongaerts that helped improve this study. Samples were collected under permits from Florida Keys National Marine Sanctuary (FKNMS-2019-088) and Florida Fish and Wildlife Conservation Commission (SAL-2-2022-SRP). Sequencing was performed by the Genomic Sequencing and Analysis Facility at UT Austin, Center for Biomedical Research Support (RRID# SCR_021713). Computation capacity was provided by Research Computing Services at Florida Atlantic University. The \u003cem\u003eXestospongia muta\u003c/em\u003e genomic reference used was from The Aquatic Symbiosis Genomics Project (McKenna et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; ENA Assembly: GCA_963693275.1). This is contribution #XXXX from Harbor Branch Oceanographic Institute.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRJE and JDV conceived the study and sampling design. RJE, ABS, and JDV collected the samples. RJE, ABS, and AMC performed DNA extractions. RJE prepared sequencing libraries performed bioinformatic analyses, analyzed the data, and prepared figures. All authors contributed to the final edited manuscript prepared by RJE. This study comprised a portion of RJE\u0026rsquo;s dissertation research supervised by JDV.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eFunding 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. Additional funding for sampling and analyses for KJCRAP was provided by Florida Department of Environmental Protection to J. Voss (awards C01954, C3D275). 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. Thank you to P. Bongaerts for productive conversations about these data which helped improve the manuscript. Samples were collected under permits from Florida Keys National Marine Sanctuary (FKNMS-2019-088) and Florida Fish and Wildlife Conservation Commission (SAL-2-2022-SRP). Sequencing was performed by the Genomic Sequencing and Analysis Facility at UT Austin, Center for Biomedical Research Support (RRID# SCR_021713). Computation capacity was provided by Research Computing Services at Florida Atlantic University. The Xestospongia muta genomic reference used was from The Aquatic Symbiosis Genomics Project (McKenna et al. 2024; ENA Assembly: GCA_963693275.1). This is contribution #XXXX from Harbor Branch Oceanographic Institute.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eSequence data from this manuscript can be accessed at the NCBI BioProject Accession PRJNA1186221. Detailed laboratory protocols and complete data analysis code and scripts are housed on GitHub and archived with Zenodo (Eckert 2025; Github.com/RyanEckert/Xestospongia_FL_PopGen).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBernard 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 Sci 95:161\u0026ndash;175\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBongaerts 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 Adv 3:e1602373\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarilli JE, Norris RD, Black B, Walsh SM, Mcfield M (2010) Century-scale records of coral growth rates indicate that local stressors reduce coral thermal tolerance threshold. 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Methods Ecol Evol 8:28\u0026ndash;36\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Population connectivity, molecular ecology, Demospongia, single nucleotide polymorphism, ecological genomics, mesophotic coral ecosystems","lastPublishedDoi":"10.21203/rs.3.rs-5875893/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5875893/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith recent anthropogenically driven coral reef declines, reef biodiversity and resilience have become a top priorities for natural resource management. Population genetic analyses can not only provide useful data for understanding genetic diversity and connectivity but also help guide the restoration and conservation of critical species and habitats. The Giant Barrel Sponge, \u003cem\u003eXestospongia muta\u003c/em\u003e, is among the most conspicuous and abundant sponges on the Florida Reef Tract and provides important ecosystem services including nutrient cycling and three-dimensional habitat for fishes and invertebrates. To better understand \u003cem\u003eX. muta\u003c/em\u003e population structure and connectivity throughout Florida Keys National Marine Sanctuary and Kristin Jacobs Coral Aquatic Preserve we genotyped individuals using 2bRAD-Seq across seven reef locations. Our analyses revealed strong evidence of connectivity among \u003cem\u003eX. muta\u003c/em\u003e populations across the Florida Reef Tract, except for a relatively distinct population located in Fort Lauderdale. Two highly divergent lineages comprise Florida\u0026rsquo;s \u003cem\u003eX. muta\u003c/em\u003e populations, with clear evidence of hybridization indicating they are likely not separate species. While the lineage from Ft. Lauderdale exhibits greater genetic diversity than the other more common lineage, the genetic diversity of \u003cem\u003eX. muta\u003c/em\u003e observed across the Florida reef were relatively consistent with several coral species sampled in this region. These data contribute to our growing understanding of the genetic diversity and connectivity of important benthic invertebrate populations across the Florida Reef Tract.\u003c/p\u003e","manuscriptTitle":"Population genetics of the giant barrel sponge, Xestospongia muta, reveal distinct, hybridizing lineages across the Florida Reef Tract","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-24 14:47:34","doi":"10.21203/rs.3.rs-5875893/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6e440da5-7dd8-4b63-9387-6edb71af6b09","owner":[],"postedDate":"March 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-22T21:53:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-24 14:47:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5875893","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5875893","identity":"rs-5875893","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}