Population genomics, polyploidy, climate resilience and the microbiota of two habitat-building coralline algae

Population genomics, polyploidy, climate resilience and the microbiota of two habitat-building coralline algae | 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 genomics, polyploidy, climate resilience and the microbiota of two habitat-building coralline algae Tom L. Jenkins, Magnus Axelsson, Angela Gall, Frances Ratcliffe, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7035383/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 Maerl bed habitats support an array of marine biodiversity and are recognised as important blue carbon ecosystems. However, little is currently known about the population structure, spatial distribution of genetic diversity, or microbiota of the coralline algae that build these habitats, or how resilient they are to climate change. Here, we investigate these knowledge gaps in two key maerl-forming species, Phymatolithon calcareum and Lithothamnion corallioides . In southern England, we identified genetically distinct populations of maerl of the two species in Dorset, while there was evidence of admixture between maerl beds in Cornwall, the extent of which differed among sites and species. A genetically distinct clonal lineage, however, was revealed in the Helford River for L. corallioides . Additionally, we distinguished the genetically distinct coarse growth form of P. calcareum in Falmouth as a unique, apparently triploid, phenotype of P. calcareum . In south-west Wales, only L. corallioides was found in Milford Haven, a population that is genetically diverse and isolated from populations in England. Analysis of climate change resilience indicated that P. calcareum maerl in Cornwall may have slightly higher risks of being maladapted to temperatures and salinities predicted in 2050 compared to other P. calcareum maerl sampled. Lastly, metagenomics analysis revealed differences in microbiota between dense healthy maerl beds compared to scattered dead maerl. As a habitat of conservation priority, our findings will be central to ensuring maerl beds receive appropriate levels of protection to prevent further loss or degradation by human activity and climate change. Population Genetics Conservation Biology Marine and Freshwater Ecology Epigenetics & Genomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Conservation genomics has revolutionised our understanding of the levels, distribution and functional significance of genetic variation in natural populations (Allendorf et al. 2010 ). One area where genomics has progressed our understanding is in the detection of local adaptation and how species or populations are likely to respond to rapid climate change (Aguirre-Liguori et al. 2021 ; Bernatchez et al. 2024 ). It has also provided new insights into genetic diversity and population structure through the increased resolution and precision offered by the analysis of many thousands of genetic markers from across the genome (Davey et al. 2011 ). This is particularly important for conservation biology as it aids the accurate identification of biologically relevant management units and connectivity scales (Funk et al. 2012 ). Progress in conservation genomics has laid the groundwork for the development of Essential Biodiversity Variables (EBVs) for genetic composition (Hoban et al. 2022 ). The EBVs concept categorises biodiversity into several classes, one of which is genetic composition, and proposes standardised and scalable metrics that are designed to identify and monitor the status and trends of biodiversity at all levels (genetic, species, and ecosystems) over space and time (Pereira et al. 2013 ; Kissling et al. 2018 ). Hoban et al. ( 2022 ) proposed four genetic EBVs: (i) genetic diversity (e.g. heterozygosity), (ii) genetic differentiation (e.g. F ST ), (iii) inbreeding (e.g. F IS ), and (iv) effective population size ( N e ), which the authors advocate for integration into conservation management strategies. Although genetic diversity has been generally overlooked in global biodiversity policy (Laikre 2010 ; Laikre et al. 2020 ), there are encouraging signs given the recent commitments and improved targets set by the Convention on Biological Diversity to monitor and conserve genetic diversity (Hoban et al. 2023 ). This would be a positive shift since genetic diversity is crucial for providing resilience to rapid environmental change (e.g. climate, land use change, pollution, etc.) and for resisting virulent or emerging diseases. However, despite the information gained from genetic / genomic studies, management recommendations by researchers and scientists do not always impact regional conservation practices due to the ‘conservation genetics gap’ (Sandström et al. 2019 ), whereby the challenges of translating genetic data to non-specialists inhibit their effective integration into policy (Shafer et al. 2015 ; Jenkins & Stevens 2018 ). To overcome this gap, it is therefore important to engage and design research projects in conjunction with policy and conservation advisors. Here, we have adopted such a partnership with two UK non-departmental public bodies, Natural England and Natural Resources Wales, both of whom advise on the natural environment and have a central role in shaping policies to protect species and habitats of conservation priority. Coralline algae are calcareous red seaweeds that are found in almost every coastal ecosystem globally and are highly diverse in appearance and life history, with many species recognised as important ecosystem engineers (Schubert et al. 2020 ). In terms of genomics research, red macroalgae as a group have been underrepresented compared to other eukaryotic groups (Borg et al. 2023 ), although recent initiatives have been established to improve genomic resources, such as Rhodoexplorer (Lipinska et al. 2023 ) and macro-ALG-ALL-CODE (Nelson et al. 2024 ). For coralline algae, there have also been developments in transcriptomic resources (Page et al. 2019 ; Xue et al. 2022 ). Given the overall decreases in sequencing costs and development of more sophisticated bioinformatics tools, the generation and availability of reference genomes and transcriptomes will likely increase over the next decade, enabling more conservation genomics research in red macroalgae. Free-living, unattached sporophytes of coralline algae on the seafloor are called maerl (or rhodoliths). Maerl-forming species can build complex and extensive three-dimensional ‘carpets’ of maerl on the seabed, known as maerl beds or rhodolith beds, which provide a vital biogenic reef-like habitat in shallow coastal waters for animals and other seaweeds (Kamenos et al. 2004 ; Peña et al. 2014 ; Tuya et al. 2023 ), and are important blue carbon ecosystems (Mao et al. 2020 ; James et al. 2024 ). However, maerl is extremely slow-growing, with a growth rate of 0.5–1.5 mm per thallus tip per year (Blake & Maggs 2003 ), resulting in very slow recovery potential. Moreover, given the global distribution and ecosystem services provided by maerl beds, they are greatly understudied compared to other coastal habitats (Tuya et al. 2023 ), despite being threatened by climate change (Qui-Minet et al. 2019 ) and by disturbance or pollution from human activities (Hall-Spencer 2000 ; Bernard et al. 2019 ; Legrand et al. 2024 ). Therefore, more research is needed to understand the adaptability and resilience of maerl bed habitats and how they may be impacted by climate change. Until very recently, genetic research on maerl has focused primarily on species delimitation using DNA barcoding (via mitochondrial and chloroplast gene fragments), and on exploring the geographical distribution of maerl-forming species. This has likely been driven by the difficulty in identifying maerl to species level based on external morphology (Pardo et al. 2014 ). Subsequently, this research has generated new information about the species diversity and the distribution of maerl-forming species, notably in the Atlantic and the Mediterranean (Pardo et al. 2014 , 2017 ; Carro et al. 2014 ; Melbourne et al. 2017 ). For example, these data have revealed that Phymatolithon calcareum and Lithothamnion corallioides are the main two species that build maerl beds in the north-east Atlantic (Pardo et al. 2014 ). More recently, microsatellite markers were used to investigate genetic diversity, clonality and population structure in P. calcareum from sites across the European Atlantic (Pardo et al. 2019 ), which was followed up with a whole genome sequencing (WGS) study using single nucleotide polymorphisms (SNPs) on a subset of sites from the original study (Jenkins et al. 2021 ). These studies provided the first insights of clonal diversity and genetic differentiation in P. calcareum . However, sampling of maerl within Britain was particularly limited. Furthermore, an analysis of genetic diversity and population structure in L. corallioides has yet to be undertaken. Interpreting genetic patterns in maerl-forming species is challenging due to their complex life cycle, which is perhaps best understood in P. calcareum (Fig. 1 in Pardo et al. 2019 ). This species is partially clonal, meaning it has the capacity to alternate between sexual and clonal reproduction, the latter of which is defined here as an individual which produces new individuals that are genetically identical to the ancestor at all positions in the genome except for sites which have experienced somatic mutations (Stoeckel et al. 2021 ). In addition, the life cycle is haplodiplontic, and is characterised by a haploid phase (attached gametophyte) and a diploid phase (attached or unattached sporophyte) (Pardo et al. 2019 ). One of the advantages of isolating genome-wide SNP markers using WGS is the enhanced ability to detect unattached sporophyte (maerl) clones and to more accurately assess genetic diversity within populations. In England and Wales, maerl bed habitats and two maerl-forming species, P. calcareum and L. corallioides , are protected by the NERC Act 2006 and the Conservation of Habitats and Species Regulations 2017. As a result, there are several Marine Protected Areas (MPAs), such as Special Areas of Conservation (SAC), that include maerl beds as a feature in their designation order. In Britain, recent maerl bed surveys by Natural England have provided new information about the locations of live (and dead) maerl beds in southern and south-west England (Envision Marine LTD 2024 ), for which genomic profiling would circumvent the sampling limitations apparent in previous genetic studies of maerl across this region. These new surveys also revealed variation in the number and cover of dense live maerl beds versus mixed or dead maerl beds, as well as sites where only sparsely scattered live or dead maerl is found. This provides an opportunity to explore how genetic diversity and the microbiota of maerl compare across sites with different levels of live and dead maerl, which, currently, is not well understood. The microbiota of crustose coralline algae are known to play important roles in the recruitment and settlement of marine invertebrates (Wilson et al. 2020 ), and there is conjecture that maerl microbiota may play a similar role in the settlement of commercially important bivalve species (Scales 2022 ). Therefore, the study of maerl microbiota could provide valuable insights for maerl beds characterised by varying levels of condition. The main aims of this study were to assemble draft reference genomes for P. calcareum and L. corallioides and to perform a population genomics analysis with existing WGS data from a previous study (Jenkins et al. 2021 ) and new data from novel sites sampled in England and Wales. Our research questions were formulated to investigate genetic EBVs and climate adaptation in these maerl-forming species and to explore the microbiota associated with live or degraded/dead maerl beds: (i) What are the levels of genetic diversity and population structure across sites and species? (ii) Are any loci putatively associated with climate adaptation and if so what is the risk of maladaptation to future environmental conditions? (iii) Does the microbiota community of maerl differ across sites and/or in relation to maerl bed condition? MATERIALS AND METHODS Sampling locations and DNA extraction Samples of maerl were collected via SCUBA diving at 4-23m depth from eleven locations along the south-west coast of England, and from two locations in south-west Wales and north Wales; of these, only two had been previously assessed for maerl species identification or genetic analysis (Table 1, Fig. 1). Collection of samples in both non-protected and protected marine sites were coordinated in partnership with Natural England and Natural Resources Wales to ensure all sampling complied with priority species and habitat regulations. Where possible, transects were deployed and samples were collected at least one metre apart to avoid potential over-sampling of locally dominant clonal lineages, with the aim of maximising the species and genetic variation captured from each maerl bed. The number of samples collected per site varied depending on maerl bed density. All maerl samples were fully immersed in 95–100% ethanol as soon as possible and ethanol was replaced after 24 hours. Samples were stored at 4 o C until DNA extraction. Genomic DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen), with some modifications to the protocol designed to increase DNA yield from maerl samples (see Figure S1 for detailed protocol). This was required, particularly for Nanopore sequencing, because the outer layer of live algae is very thin and typically yields extremely low amounts of DNA using the standard protocol. The quantity and concentration of DNA was assessed by fluorometry using a Qubit 1X dsDNA High Sensitivity Assay Kit (Invitrogen). The quality of each aliquot was checked using gel electrophoresis on a 1% agarose gel and only DNA samples showing a ~ 20Kb band or higher with low or no evidence of degradation were selected for whole genome sequencing. Whole genome sequencing Illumina short-read libraries were prepared for all samples using NEBNext Ultra II FS following the protocol for inputs below 50ng (half reactions were performed). Adaptors were diluted 1:10 for all samples as per the protocol. No size selection was implemented and 0.8X bead purification was conducted twice prior to seven cycles of PCR enrichment. Libraries were checked by TapeStation D1000 and pooled equimolar. All samples were sequenced on a NovaSeq 6000 using a paired-end strategy with a read length of 150 bases. Additionally, whole genome sequencing data for maerl samples from a previous study (Jenkins et al. 2021 ) were included in our study (Table 1, Fig. 1); these DNA samples were prepared and sequenced using the same methods described above. Illumina raw reads were filtered and trimmed using fastp v0.23.4 (Chen 2023 ) with the following parameters: (i) polyG tail trimming enabled, (ii) a Phred quality score > = 30 in at least 60% of bases in a read, and (iii) a minimum length of 100bp. Nanopore long-read libraries were prepared for two samples, one representing P. calcareum and one representing L. corallioides , both of which were confirmed to be the correct species by DNA barcoding (see below) and originated from St Mawes, Falmouth (Table 1). For both samples, the same DNA aliquot used to prepare the Illumina libraries was used to prepare Nanopore libraries so that the Illumina data could be used to polish the genome assembled with long reads. Standard Oxford Nanopore Technology libraries were prepared and sequenced on a PromethION 24 flow cell with R9.4.1 chemistry. Guppy v6.3.9 with super-accurate basecalling was used for basecalling and only reads flagged as passed (minimum quality score of 10) were retained in FASTQ format. Porechop v0.2.4 was used to detect and remove adapters from reads passing the basecalling filters. Species identification via DNA barcoding A DNA barcoding approach was used to confirm species identity of each sample; this is necessary because of the difficulty in distinguishing maerl-forming species based on external morphology (Pardo et al. 2014 ). First, published mitochondrial and chloroplast genomes of P. calcareum (MW357900.2, OQ417768.1) and L. corallioides (MW357901.2, OQ417769.1) were downloaded from GenBank in FASTA format to function as seed sequences for each organelle. The GenBank reference files for each of these accessions were also downloaded for use in the organelle gene annotation step. Second, the Illumina trimmed reads for each sample were aligned to the mitogenome and plastome seed sequences separately using Bowtie2 v2.5.3 (Langmead & Salzberg 2012 ). Mapped reads were extracted from each bam file to a FASTQ file using SAMtools v1.19 (Danecek et al. 2021 ). These target reads were then assembled using Unicycler v0.5.0 (Wick et al. 2017 ), with the aim of obtaining a complete mitochondrial and chloroplast genome for each individual sample. Third, for organelle genomes successfully assembled, both mitochondrial and chloroplast genomes were annotated using a custom python script (see data archiving statement) using the GenBank reference files. Lastly, for each sample, nucleotide coding sequences (CDS) of the COI mitochondrial gene and the psbA chloroplast gene were extracted to generate two FASTA files containing CDS for COI and psbA , respectively. These genes were chosen because they are the most common genes used in coralline algae DNA barcoding studies (Pardo et al. 2014 , 2017 ; Peña et al. 2020 ), and have the highest intra- and inter-species representation on GenBank. These two FASTA files were submitted to the NCBI blastn online server to compare our CDS (query) to the nucleotide database (subject). For a given maerl sample, a species identification was assigned if the percentage identity was > = 99% (Carro et al. 2014 ). Genome assembly, QC and contig filtering A reference genome assembly was built for both P. calcareum and L. corallioides using Nanopore long reads. Prior to assembly, long reads were taxonomically classified using BugSeq (Fan et al. 2021 ), which classifies reads against the BugSeq default microbial database. Because of the potential for non-target DNA to be isolated from the sample during extraction, i.e. microbes living on or inside the calcium carbonate skeletal structure of maerl, the rationale here was to identify and remove as many reads of microbial origin as possible prior to the assembly pipeline. From the classification results, only reads classified as 0 (unclassified) or 1 (other) were retained, thus removing all reads classified as Bacteria, Virus or other microbes in the database. Flye v2.9.3 (Kolmogorov et al. 2019 ) was used to assemble the reads into contigs. The input read length was adjusted to explore the optimum minimum read length for input into Flye; for both samples > = 3000bp was deemed the optimum. For Flye, default parameters were used, alternative contigs were not kept (--no-alt-contigs), and only contigs longer than 3000bp were retained. The assemblies were polished with Illumina short read data using Polypolish v0.6.0 (Wick & Holt 2022 ). BlobToolKit v4.2.1 (Challis et al. 2020 ) was used to assess contamination and quality of the assemblies. Both blastn v2.14.1+ (Camacho et al. 2009 ) and diamond blastx v2.1.8 (Buchfink et al. 2021 ) were run using the assemblies as queries to the NCBI nucleotide and protein databases, respectively. In addition, minimap2 v2.26 (Li 2018 ) was used to map Nanopore reads to each assembly. The taxonomic hits and sorted bam alignments, along with the assembly FASTA file, were used as inputs to the blobtools create command to create a BlobDir. Contigs were removed from the assembly if the following conditions were met: (i) contig was of organelle genome origin, (ii) contig was not supported by mapped reads (at least one read supporting each base across the contig), and (iii) contig matched to Bacteria, Archaea, Virus, or any Eukaryote other than Rhodophyta or Streptophyta. Assembly statistics, including total length, number of contigs, N50 and GC content, were computed using the stats command from seqkit v2.5.1 (Shen et al. 2016 ). BUSCO phylogeny BUSCO v5.6.1 (Manni et al. 2021 ) was executed using the eukaryota_odb10 dataset to assess the completeness of near-universal single-copy orthologs in both genome assemblies. Subsequently, a phylogeny of coralline algae (and several other red algal taxa) was built using BUSCO protein sequences, the rationale of which was two-fold: (i) to examine the tree position of P. calcareum and L. corallioides to further confirm validity of the genome assemblies, and (ii) to compute and visualise a phylogeny of coralline algae based on BUSCO genes. Genome and/or transcriptome assemblies were downloaded for all available coralline algae and several red algal species from the Florideophyceae (Table S1). A BUSCO analysis was run for each assembly and the outputs were supplied as inputs to a modified version of the BUSCO phylogenomics pipeline (see data archiving statement). This pipeline identifies complete single-copy BUSCOs present in at least ten assemblies, then aligns sequences, trims alignments, and constructs gene trees using IQ-TREE (Nguyen et al. 2015 ). Finally, Astral v5.7.8 (Mirarab & Warnow 2015 ) was used to build a coalescent-based species tree, with Porphyra umbilicalis (Bangiophyceae) used as the outgroup for visualisation. Variant calling and SNP filtering After each DNA sample was identified to species level, the trimmed Illumina reads were mapped to the P. calcareum or L. corallioides genome assemblies using Bowtie2 v2.5.3 (Langmead & Salzberg 2012 ). The alignments were sorted by coordinate, then read groups were added and duplicates were marked using GATK v4.5 (McKenna et al. 2010 ). Variant calling was conducted with Freebayes v1.3.8 (Garrison & Marth 2012 ) in parallel on 100,000bp chunks using freebayes-parallel and the fasta_generate_regions.py script. The following parameters were used: minimum mapping quality > = 20, minimum base quality > = 30, compute genotype qualities, and the ploidy was set to two. BCFtools v1.21 (Danecek et al. 2021 ) and VCFtools v0.1.16 (Danecek et al. 2011 ) were used to filter variants. First, poor quality variants were removed by enforcing a maximum missing threshold of 0.70, a minor allele count of 3 and a minimum quality score of 30. Second, all samples from Bembridge were removed due to a very high proportion (50–95%) of missing genotypes, likely because of insufficient DNA extracted from the target species (Bembridge samples comprised mostly dead maerl). Third, the following parameters were enforced using BCFtools: (i) no missing data, (ii) genotype quality > = 20, (iii) minimum depth > = 3, (iv) allele depth > = 10, minor allele frequency > = 5%, and allele balance > = 0.10. Lastly, the VCF was read into R v4.3.2 and the following filters were applied to create a high quality biallelic SNP dataset: (i) distance thinning such that SNPs within 1000bp on a given contig were removed (to avoid potential linkage), (ii) SNPs with a minimum depth of 15 and a maximum depth of 100 over all samples were removed, and (iii) monomorphic loci were removed. Ploidy, population structure, clonality and genetic diversity Ploidy of each sample was assessed using the ‘determining ploidy 1’ method outlined in the vcfR R package documentation (Knaus & Grünwald 2017 ). This method examines allele balance information, i.e. the number of times an allele was sequenced, at each heterozygous SNP. In diploids, an allele frequency ratio of 50:50 is expected (both alleles are sequenced equally), although there is inevitably variation around this ratio introduced by sequencing bias and variant calling software. When the allele frequencies of all heterozygous SNPs are visualised, we would expect a peak at 1/2. In contrast, in triploids, we would expect two peaks at 1/3 and 2/3. Preliminary analysis of allele balance indicated that read depth and minimum allele depth were important for interpreting the histograms, so, prior to analysis, SNPs were removed where the depth of at least one allele was = 30 were considered for ploidy determination. Genetic structure was assessed using non-model based principal component analysis (PCA) and model-based admixture inference using PopCluster v1.4.0 (Wang 2022 ). For admixture analyses, the admixture model with unequal allele frequencies and medium scaling was run independently ten times for each ancestral population ( K ). No sample grouping or location of origin information (priors) were supplied to the algorithm. The optimal run and K statistics (determined by D LK 2 and F STIS statistics) (Wang 2022 ) were extracted from the results. Individual admixture proportions were visualised as barplots, and the mean average proportion per cluster per location were visualised as admixture maps using mapmixture v1.1.4 (Jenkins 2024 ). Locations that had fewer than three individuals were not visualised in the admixture maps. Genetic differentiation among sites was assessed by computing pairwise F ST values (Weir & Cockerham 1984 ). Clonality and genetic diversity were investigated for each genetic unit (i.e. each putative population identified based on the genetic structure analyses). Clonal lineages and the number of clones were assessed using the R package poppr v2.9.6 (Kamvar et al. 2014 ). First, pairwise Prevosti genetic distances were calculated between all individuals. Second, cutoff_predictor() was run to define a clone threshold based on the genetic distance matrix. Defining a clone threshold is necessary because somatic mutations can introduce small genetic differences between clones. Lastly, mlg.filter() was run using the distance matrix, clone cut-off threshold and the nearest neighbour algorithm to identify multi-locus genotypes (MLGs); each MLG represents a putative clonal lineage. Finally, Pareto β, observed heterozygosity, expected heterozygosity, and F IS (inbreeding coefficient) were computed for each genetic unit. Pareto β characterises clonal diversity and skewness in the distribution of clones within a population; the presence of a few large clonal lineages and many small ones will result in lower β values, while more balanced lineages of similar sizes will result in higher β values (Arnaud-Haond et al. 2007 ). Genotype-environment association and genomic offset analysis Environmental data were downloaded from Bio-Oracle v3.0 (Assis et al. 2024 ) at a resolution of 0.05 degrees (~ 5.5 km at the equator). NetCDF files were obtained for environmental variables which have been shown or inferred to be important for maerl growth and survival (Martin & Hall-Spencer 2017 ): ocean temperature ( o C), salinity (PSU), pH and oxygen concentration (mmol m − 3 ). For each environmental variable, the following parameters were selected: baseline (2000–2010), benthic (mean average depth per cell) and variable mean (mean average across time period). Future layers (2050–2060) for these variables based on the Shared Socioeconomic Pathway (SSP) SSP245 (‘middle of the road’) scenario from CMIP6 were also obtained. For each individual sample, latitude and longitude coordinates were used to extract values for each variable to use as a set of predictor variables in the analysis. There were only very small differences in pH among locations (8.06–8.08), while multicollinearity checks among the variables revealed a very high correlation between oxygen concentration and ocean temperature ( r 2 =-0.96). Therefore, only temperature (11.3-15.0 o C) and salinity (33.99–35.06 PSU) were retained for genotype-environment association (GEA) analysis. To mitigate spatial autocorrelation, detrending of environmental variables was conducted prior to modelling. First, least-cost geographic distances along coasts were calculated between locations and the output was used to compute dbMEMs (distance-based Moran’s eigenvector maps). Second, a linear model for each environmental variable was modelled as a function of the dbMEMs and the residuals were extracted. The residuals, representing the detrended environmental variables, were used as predictors for the GEA models. Two GEA methods were used to investigate associations between the environmental data and the SNP genotypes. First, a latent factor mixed model (LFMM) was run using the lfmm2() function from LEA v3.12.2 (Gain & François 2021 ) with K = 9 and the default lambda value (1e-05). Using the lfmm.test() function, p -values were adjusted for latent factors and recalibrated using the genomic inflation factor. Multiple comparisons were controlled for using the Benjamini-Hochberg method and only SNPs with an adjusted p -value < 0.05 were considered as candidate loci. Second, a redundancy analysis (RDA) was run using the rda() function from vegan v2.6.4. To control for population structure, the first two principal components of the PCA were used as conditions in the model. The significance of each constrained axis was checked by running the anova.cca() function and SNP loadings were extracted only for significant RDA axes ( p < 0.01). SNPs were considered candidate loci if they were present in the tails of the SNP loading histograms using a standard deviation threshold of 3.0. Lastly, only candidate loci found in both methods were considered as GEA loci potentially under selection (climate-associated SNPs). Putative climate-associated SNP genotypes were used as input for the genetic.offset() function from LEA. A matrix of climate data representing both present-day baseline environments (2000–2010) and future environments (2050–5060) were used as covariates. This function calculates a geometric genomic offset value for each sample location using a genetic gap algorithm (Gain et al. 2023 ). In a population genetic context, the geometric genomic offset can be interpreted as the average value of Nei’s D ST (divided by two) for the set of loci assumed to be involved in local adaptation (Gain et al. 2023 ). Offset estimates predict the relative allele frequency change needed for a population to lower their risk of being potentially maladapted to future environments. Microbiota assignment using metagenomics For each maerl sample, Illumina reads that did not align to the reference genome assemblies were extracted to FASTQ files using the fastq command from SAMtools. Kraken2 v2.1.3 (Wood et al. 2019 ) was used to taxonomically classify unmapped reads against a custom marine prokaryotic database, which was built by extracting all sequences from the NCBI nucleotide database that matched taxonomic IDs in the MarDB v1.6 marine metagenomics database (Klemetsen et al. 2018 ). The following parameters were used in Kraken2: (i) a minimum hit groups of 3, (ii) a minimum base quality of 20, and (iii) a confidence threshold of 0.10. The report output files for all samples were amalgamated to a single tabular file for visualisation using a custom python script (see data archiving statement). After initial inspection of the results, we focused on certain microbial groups that were common across samples or have been reported in previous studies of coralline algae microbiota (Brodie et al. 2016 ; Valdespino-Castillo et al. 2021 ), or groups that appeared to differ across sites. To statistically assess differences among these groups among sites, proportions of each group were modelled as a function of sites using an Analysis of Variance (ANOVA), and the Tukey honestly significant difference (HSD) test was used to investigate differences between sites using a 95% confidence interval. To minimise the risk of taxonomic misassignments, we only considered assignments to the level of class. This analysis was conducted with six sites, all of which were selected to represent different quality/density categories of maerl bed habitats based on a recent Natural England report (Axelsson 2023 ). This report details a standardised classification system for maerl bed habitats in England. The first three categories cover the range of sites sampled in our study: “Dense Maerl” (Category A), “Maerl Sediment” (Category B), and “Sparse/Scattered Maerl” (Category C), which also have sub-categories (from 1 up to 3) describing physical size, structure, % cover, live/dead proportion and substratum. Thus, we grouped the following sites using this classification system: St Mawes (dense maerl – mostly A1), Helford River (dense maerl – mostly A3), Milford Haven (maerl sediment – mostly B1), The Manacles (maerl sediment – mostly B3), Swanage (maerl sediment – mostly B3) and Bembridge (scattered maerl – mostly C2). This broadly represents a gradient of maerl bed status from a dense, healthy maerl bed (St Mawes) to mostly scattered dead maerl (Bembridge). RESULTS Species identification Of the 207 samples with whole genome sequencing data, DNA barcoding analysis using COX1 and psbA identified 136 as Phymatolithon calcareum and 66 as Lithothamnion corallioides (Table 1). Five samples from Bembridge could not be identified to species. Elsewhere in Britain, there were some sites where all samples were identified to be a single species, while at other sites samples of both species were observed (Fig. 1A). Sites that only contained samples identified as P. calcareum were: The Manacles, Bizzies Reef, Gerrans Bay, Nare Head (all southern Cornwall), and Bembridge (Isle of Wight). In contrast, the following sites only contained samples identified as L. corallioides : Helford River (southern Cornwall), Milford Haven (south-west Wales), and St Tudwal’s Island (north-west Wales). Sites with both species were St Mawes, St Austell Bay, Little Gribbin (all southern Cornwall), and Weymouth and Swanage (both Dorset). The remaining samples from Northern Ireland, France and Spain were all identified as P. calcareum , as expected from previous analysis (Jenkins et al. 2021 ). Draft reference genome and BUSCO phylogeny A draft reference genome was assembled for each species using Nanopore long reads. The assembly statistics were comparable to or improved upon the two coralline red algal species, Amphiroa fragilissima and Porolithon onkodes , for which draft reference genomes are currently available (Table S2). For P. calcareum , the reference genome was 150 Mbp in length, 6,375 contigs, with an N50 of 35 Kbp and an overall GC content of 43%. For L. corallioides , the reference genome was 145 Mbp in length, 9,939 contigs, with an N50 of 19 Kbp and an overall GC content of 40%. Using the Eukaryota orthoDB v10 database ( N = 255), the BUSCO results for P. calcareum were C:62.7% [S:59.6%, D:3.1%], F:12.5%, M:24.8%; and for L. corallioides were C:40.8% [S:39.2%, D:1.6%], F:11.8%, M:47.4%. The species tree of coralline algae and other Florideophyceae red algal species was built using 157 concatenated BUSCO genes that were complete and single-copy in at least ten species (Fig. 1C). This tree showed that all coralline algae were positioned on a branch separate from all other Florideophyceae species. Within this branch, the evolutionary relationships amongst species were as expected based on previous research on coralline algae phylogenetics (Peña et al. 2020 ). For the maerl-forming species in our study, the tree (Fig. 1C) showed that both Lithothamnion species, L. corallioides and L. proliferum , were monophyletic, sharing a recent common ancestor, and that both of these species shared their most recent common ancestor with P. calcareum . SNP dataset summary For P. calcareum , filtering the raw variant calls from Freebayes resulted in 124,751 SNPs. Five individuals were removed at this stage because of very high missing data (> 95%); these individuals were all from Bembridge, which is probably because these samples had very little live algal material. Further filtering of these variants by distance thinning (linkage disequilibrium), mean read depth and keeping only biallelic SNPs resulted in a final dataset of 131 individuals genotyped at 15,330 SNPs for P. calcareum . Note this is less than the 136 individuals identified as P. calcareum because of the removal of five individuals from Bembridge (see above). For L. corallioides , filtering the raw variant calls from Freebayes resulted in 192,041 SNPs. Further filtering of these variants resulted in a final dataset of 66 individuals genotyped at 10,215 SNPs for L. corallioides . Triploidy in P. calcareum coarse growth form At the St Mawes maerl bed, we found two growth forms both identified as P. calcareum by DNA barcoding, one of which was typically around 2cm in diameter with characteristic delicate thalli, while the other was typified by a much larger rhodolith (4-5cm in diameter) with far bulkier branching structures (Fig. 1, Figure S2). Hereafter, we refer to the latter growth form in St Mawes as the ‘coarse’ form (MawC). Allele balance analysis of all coarse maerl samples showed a clear pattern of triploidy (Fig. 2), with two peaks at 1/3 and 2/3 as per expectations of triploids. These samples were compared with all other P. calcareum samples which demonstrated that all other samples were diploid, as evidenced by a single peak for allele balance at 1/2 (Fig. 2). All L. corallioides samples with sufficient median read depth showed a peak at 1/2 and were deemed to be diploids. Genetic structure For P. calcareum , PopCluster was first run using sites only from Britain and Northern Ireland, because we were primarily interested in genetic structure of newly sampled maerl across this region. PopCluster statistics ( D LK 2 and F STIS ) showed highest support for five K ancestral populations (genetic clusters) (Figure S3, S4), so K = 5 was used for visualising admixture results in the structure plot and admixture map (Fig. 3). The results indicate that most maerl samples (individuals) from the following sites were primarily derived from four separate ancestral source populations: Zara Shoal (blue cluster), Weymouth (green cluster), Nare Head (grey cluster), and the coarse triploid maerl from St Mawes (orange cluster). All coarse maerl (regardless of whether they were collected in 2011 or 2022) were virtually genetically distinct from all other sites, while two individuals in Weymouth and a single individual in Nare Head had shared ancestry with other sites. The remaining sites across Cornwall: The Manacles, St Mawes (non-coarse maerl), the Bizzies, Gerrans Bay and St Austell, all showed evidence of admixture with one another (mostly blue and pink clusters, Fig. 3). These patterns of genetic structure were supported by PCA when the first three principal components were visualised (Figure S5). To check whether any newly identified site-specific genetic clusters found in Britain (e.g. Weymouth and Nare Head) were also found in other European sites, PopCluster was run a second time including sites from France and Spain; this showed that all four sites from France and Spain were genetically differentiated from Weymouth and Cornwall (Figure S6), but with some site-specific admixture, which was expected based on previous research (Jenkins et al. 2021 ). For L. corallioides , PopCluster analyses showed a similar range of structuring at different values of K , with K = 4 and K = 5 showing the highest statistical support overall (Figure S7, S8). Because there was support for five genetic clusters in the PCA (Figure S9), K = 5 was used for visualising admixture results in the structure plot and admixture map (Fig. 4). The results indicate that most individuals from Weymouth and Swanage are primarily derived from a single ancestral population (green cluster) that is mostly found in this area (although there is evidence of some admixture with other sites), and in St Tudwal’s Island. At K = 8, there was evidence that St Tudwal’s Island might also be genetically differentiated (Figure S8), although only two samples were available for this site. Individuals from St Austell Bay were mostly derived from a single ancestral source population (pink cluster), which appeared to be admixed with St Mawes and with some individuals from Helford River. The remaining seven individuals from Helford River were mostly derived from a different ancestral population (blue cluster) that is primarily found in this area. In Milford Haven, individuals from both sites were mostly derived from a single ancestral population (red cluster), but with some admixture with other sites, and an additional distinct ancestral population was observed (yellow cluster) which was dominant in four individuals. Pairwise genetic differentiation measures of F ST ranged from 0.02 (Man-Biz) to 0.42 (Zar-MawC) in P. calcareum and from 0.001 (Mil1-Mil2) to 0.11 (Hel-Swa) in L. corallioides (Figure S10). The most differentiated site in P. calcareum was the coarse triploidy maerl from St Mawes ( F ST range: 0.33–0.42) followed by Weymouth ( F ST range: 0.19–0.33), while in L. corallioides the most differentiated sites were Helford River ( F ST range: 0.07–0.11) and Swanage ( F ST range: 0.02–0.11). Overall, patterns of pairwise F ST emulated the patterns of genetic structure. Clonal lineages and genetic diversity Clonality and genetic diversity is only reported for sites in Britain as data for P. calcareum from sites in France and Spain have been analysed in a previous study (Jenkins et al. 2021 ). Based on Prevosti's genetic distances, the clone cut-off threshold (i.e. the threshold at which multi-locus lineages should be defined), was estimated as 0.068 in P. calcareum and 0.127 in L. corallioides (Figure S11, S12). For P. calcareum , this translated to 78 clonal lineages (out of 131 individual samples, which includes the samples from France and Spain), while for L. corallioides , this translated to 52 clonal lineages (out of 66 individual samples). The number of clonal linages and the abundance of clones in each lineage was visualised for both species (Fig. 5). Note in L. corallioides we present clonality and genetic diversity results for Weymouth and Swanage as a single genetic unit (putative maerl population), as informed by the genetic structure analyses. This is also the case for both St Austell Bay sites in both species, and for both Milford Haven sites in L. corallioides . Additionally, these analyses were not carried out for Swanage ( P. calcareum ) or St Tudwal’s Island ( L. corallioides ) due to their low sample sizes (less than three individuals). For P. calcareum , one notable result is a single clonal lineage for all coarse triploidy maerl in St Mawes (MawC), which also showed much higher observed heterozygosity and lower negative F IS (Table 2). In contrast, clonal diversity was variable across the other putative maerl populations, evidenced by the range of Pareto β values (Table 2). For instance, a single clonal lineage was dominant at Nare Head, which also has the lowest Pareto β value (excluding the coarse maerl). In comparison, there are lineages of more even distribution in other sites, such as at The Manacles, St Mawes (Maw), the Bizzies, and St Austell Bay. Two lineages were found across two different sites: MLG19 was found in both St Austell Bay and in St Mawes (Maw) and MLG22 was found in both St Austell Bay and the Bizzies. Observed heterozygosity in P calcareum ranged from 0.26–0.65, while expected heterozygosity ranged from 0.19–34. For L. corallioides , we identified one large clonal lineage and five smaller ones in Helford River, which is evidenced by its lower Pareto β value (Table 2). In contrast, the other five sites showed either more balanced lineages of similar sizes (Milford Haven and St Mawes), supported by their slightly higher Pareto β values, or no clones were detected (St Austell Bay and Weymouth/Swanage). Observed heterozygosity in L. corallioides ranged from 0.32–0.39, while expected heterozygosity ranged from 0.29–0.30 (Table 2). For both species, and in sites where clones were detected, Pareto β values did not exceed 2.10. In addition, expected heterozygosity was lower than observed heterozygosity in all putative maerl populations and all F IS values were negative. Genotype-environment associations and genomic offset For P. calcareum , using benthic ocean temperature and salinity as climate predictors, 2,476 and 267 GEA outlier loci were identified by LFMM and RDA, respectively. Of these, 138 were common across both methods and were considered as putative climate-associated SNPs. RDA was modelled for only the 138 climate-associated SNPs, which explained 19.2% (adjusted r 2 ) of the variation in the allele frequencies, and the results were visualised as an RDA biplot (Fig. 6A). This showed that most individuals from Zara Shoal, Weymouth and Trévignon were associated with lower salinities and generally lower temperatures. The Bizzies Reef was generally associated with lower temperatures and mostly higher salinities. In contrast, individuals from Morlaix were associated with higher temperatures. The remaining sites were generally positioned in the centre of the RDA biplot, with some minor exceptions within sites (such as a group of individuals from Nare Head that were linked to higher salinities). For L. corallioides , salinity was highly correlated with temperature ( r 2 = 0.94); therefore, genotypes were only modelled as a function of temperature. Very few GEA outlier loci were detected: 11 by LFMM and 34 by RDA, with only a single locus common across both methods. As a result, no further GEA analyses were conducted with L. corallioides . All values for future climate variables are predicted to increase (temperature) or decrease (salinity) by 2050 for the SSP245 scenario. The degree of these changes, however, varied by site. The predicted changes for each variable were as follows: (i) ocean temperature increases ranging from 0.41–0.44 o C (Galicia) to 0.68 o C (Weymouth), and (ii) salinity decreases ranging from − 0.26 PSU (Trévignon) to -0.42–0.44 PSU (all sites from Cornwall and Zara Shoal). Genomic offsets were averaged for each site and visualised on a map (Fig. 6B). The lowest genomic offsets for 2050 SSP245 were observed in Trévignon, while the highest genomic offsets were observed in all sites from Cornwall, Zara Shoal and Illa de Ons. Microbiota community of maerl across sites Microbiota site averages showed bacteria were dominant (78–96%) relative to archaea (4–22%), the latter of which were found in higher proportions in Bembridge and Swanage, likely due to the presence of nitrifying Archaea (see below). Overall, one of the most common group of bacteria detected were the Pseudomonadota, ranging from 26–51%, which were mainly composed of Alphaproteobacteria (7–23%) and Gammaproteobacteria (5–26%) (Fig. 7). Actinobacteria and Cyanobacteria were detected but in much lower proportions. The mean proportion of Flavobacteria appears to follow a downward trend from St Mawes (dense live maerl – mostly category A1) to Bembridge (scattered mostly dead maerl – mostly category C2). Flavobacteria were on average 18% (13–23%, p < 0.001) and 12% (7–18%, p < 0.001) higher in St Mawes and Helford River, respectively, compared to Bembridge. Additionally, Flavobacteria were on average 5.4% (0.55-10%, p = 0.024) higher in Milford Haven compared to Bembridge. No differences in the proportion of Flavobacteria were observed amongst The Manacles, Swanage and Bembridge sites. In contrast, the opposite trend was apparent in Nitrospirota (Bacteria) and Nitrososphaerota (Archaea), whereby there was an upward trend in proportion from St Mawes to Bembridge. Nitrospirota were on average 2.9–3.8% higher in Bembridge compared to each of St Mawes, Helford River, Milford Haven and The Manacles (all comparisons p < 0.001). Similarly, Nitrososphaerota were on average 8.8–17% higher in Bembridge compared to each of St Mawes, Helford River and Milford Haven (all comparisons p < 0.001), and The Manacles ( p = 0.016). Swanage showed on average a slightly lower proportion of Nitrospirota compared to Bembridge (1.0%, p = 0.008), but no difference was observed in Nitrososphaerota ( p = 0.997). The microbiota proportions for all six sites and for each individual sample are available in the Supporting Information (Figure S13). DISCUSSION Combining whole genome sequencing and de novo genome assembly, we investigated population genomics, climate adaptation and microbiota communities in two species of coralline algae that form maerl bed habitats in the north-east Atlantic. Our study presents the first draft reference genome assemblies for two maerl-forming species: Phymatolithon calcareum (GCA_040759855) and Lithothamnion corallioides (GCA_040759835). We built a species tree using BUSCO protein sequences extracted from all currently available coralline algae genomes and transcriptomes; this showed very similar evolutionary relationships to a previous comprehensive phylogenetic study of coralline algae that used seven organelle and nuclear genes (Peña et al. 2020 ). Whole genome sequence data were mapped to the draft genomes of P. calcareum and L. corallioides to generate SNP datasets for investigating population genomics and potential genetic adaptations to climate, while unmapped reads were used to explore the microbiota community living on or within maerl thalli. These results, as well as the identification of genetic management units and conservation implications, are discussed below. Species identification of maerl sampled in England and Wales We sampled novel sites in England and Wales containing maerl that have yet to be assigned species identifications in the scientific literature and in UK maerl bed conservation. Our results confirm the coralline algal species forming maerl beds (or scattered live maerl) at these locations: Bembridge ( P. calcareum ), the Bizzies Reef ( P. calcareum ), Gerrans Bay ( P. calcareum ), Nare Head ( P. calcareum ), St Austell Bay ( P. calcareum and L. corallioides ), Weymouth ( P. calcareum and L. corallioides ), Swanage ( P. calcareum and L. corallioides ), Helford River ( L. corallioides ), Milford Haven ( L. corallioides ), and St Tudwal’s Island ( L. corallioides ). It should be noted, however, that sampling may not have covered the full spatial distribution of maerl at each location and, therefore, other maerl-forming species may be present but were undetected in the present study. Marine Conservation Zones (MCZs), a type of national MPA in England, have been designated to protect a range of nationally important, rare or threatened habitats and species. However, while several MCZs list maerl beds as a feature in their designation (e.g. The Manacles, Purbeck Coast, Bembridge), the particular maerl-forming species is not identified as a feature in the listing. This is primarily because Natural England, and other national public bodies in the UK, emphasise the importance of the maerl habitat rather than the specific species (Axelsson 2023 ). In addition, because maerl is extremely challenging to identify to species level based on external morphology, and especially in the field, the maerl-forming species at these locations are typically unknown. Consequently, this has also made it challenging to design and carry out effective sampling strategies for specific maerl-forming species, which in part explains why some sites in our study have low sample sizes for P. calcareum or L. corallioides (the other reason for this is that live maerl is very sparse at some sites, e.g. Bembridge). Nevertheless, given that both P. calcareum and L. corallioides are identified as priority species under the UK Biodiversity Action Plan (now replaced by the UK Biodiversity Framework 2024), our results enable adding species information to the designation orders and/or to the statutory conservation advice for the MCZs outlined above. Knowledge of the species forming live maerl at each MCZ and each location sampled will be informative for future research on these maerl-forming species and for any restoration projects, where it is important to know the diversity of maerl-forming species, particularly the dominant species, present at a given maerl habitat (Axelsson 2023 ). Coarse triploidy maerl in St Mawes In a previous study, P. calcareum from St Mawes was found to be genetically unique compared to all other maerl beds sampled in western Europe (Jenkins et al. 2021 ). In the current study, additional sampling of maerl beds in and around Falmouth, and higher sequencing depth, has enabled further insight into this finding. First, we found that only the newly recognised coarse growth form of P. calcareum in St Mawes is genetically unique. Second, there is strong evidence that this is due to triploidy, whereby one or more chromosomes have three copies rather than the two expected in maerl (Pardo et al. 2019 ). Triploidy in this P. calcareum form from the Fal Estuary was also postulated by Pardo and colleagues using microsatellite genotypes (Pardo et al. 2019 ), which further supports our hypothesis that triploidy is driving the genetic differentiation observed in the coarse maerl. These findings raise two follow-up questions. First, is triploidy the result of hybridisation (allopolyploidy) or whole genome duplication (autopolyploidy)? And, second, has triploidy contributed to the development of the markedly larger and bulkier growth morphology of this form of P. calcareum , and if so, is it a neutral or adaptive change? Given that no genomic data exist for other species in the Phymatolithon genus, and that no information is available on the karyology or physiology of the coarse growth form, further research will be needed to resolve these questions. Population structure and clonal lineages Analysis of genetic structure and clonality revealed both similarities and differences in locations where both maerl-forming species co-exist in sympatry. For instance, maerl from both species in Weymouth (and Swanage in L. corallioides ) are genetically differentiated from their respective maerl in Cornwall. This is likely a result of geographical isolation, possibly driven by limited dispersal capacity (Pardo et al. 2019 ), and indicates that maerl in Weymouth/Swanage should be considered as separate populations for both P. calcareum and L. corallioides . When comparing clonality in Weymouth/Swanage, no clones were detected in L. corallioides , whereas one clonal lineage had three (out of five) individual members in P. calcareum , all three of which were also assigned to a single genetically distinct ancestral cluster with no evidence of admixture with Cornwall. These differences could represent evolutionary histories between P. calcareum and L. corallioides , possibly driven by different rates of clonal versus sexual reproduction. However, given sample sizes of five ( P. calcareum ) and seven ( L. corallioides ) in these areas, we may not have sampled all potential clonal lineages. Therefore, additional sampling of maerl would enhance inferences of clonality and population structure at these locations. In Cornwall, discounting the genetically distinct coarse triploidy maerl (discussed above), there are also interesting similarities between species, but some subtle and potentially important differences. For example, in both species the St Mawes maerl bed is admixed with other maerl beds in Cornwall, with similar levels of clonality, characterised by one or two relatively dominant lineages and several singleton lineages. For P. calcareum , this contrasts with previous studies where only the coarse triploid maerl was sampled (Pardo et al. 2019 ; Jenkins et al. 2021 ), because our results indicate that there is shared ancestry between St Mawes (the non-coarse maerl form) and maerl sampled elsewhere in Cornwall. Indeed, there is evidence of admixture among all sites from Cornwall in P. calcareum (with the potential exception of Nare Head). Given that a single maerl thallus is potentially over 100 years old (Foster 2001 ), and that the dispersal capacity of maerl-forming species is thought to be limited (Pardo et al. 2019 ), this suggests that the genetic connectivity observed among these locations has likely acted over a very long temporal scale. This is supported by evidence of some mixed ancestry with Zara Shoal (Northern Ireland) and other European maerl beds (Jenkins et al. 2021 ), which suggests that P. calcareum has a complex evolutionary history that has likely been influenced by many factors, such as periodic changes in habitat suitability, connectivity and glacial refugia. Nonetheless, we infer that P. calcareum maerl from sites sampled in Cornwall are mostly admixed populations which may represent a contemporary/historic metapopulation. In L. corallioides , we found two ancestral clusters in the Helford River, one of which was only found at this location, and based on our clonal analysis this is a distinct and large clonal lineage. Similarly, we observed a congruent pattern at Nare Head for P. calcareum , whereby a dominant clonal lineage was present. This may suggest that the same set of environmental and evolutionary processes have shaped the population structure of maerl at these locations. In contrast, there are some subtle genetic differences between the two species across Cornwall. For instance, while P. calcareum maerl in St Austell Bay exhibits shared ancestry from two ancestral sources, L. corallioides maerl in St Austell Bay is primarily derived from a single ancestral source. Moreover, no clones were detected in St Austell Bay for L. corallioides , whereas two clonal lineages composed of two or three clones were detected for P. calcareum . This could reflect the differences in evolutionary history between the two species in this area, whether it be geographical isolation, historic (re)colonisation, connectivity, or a mixture of these. In Milford Haven, only L. corallioides maerl was found at this location in our study, so no comparison could be made with P. calcareum . Nevertheless, we found that Milford Haven maerl is genetically differentiated from all other sites sampled in England. In addition, Milford Haven maerl is clonally diverse with high heterozygosity relative to other sites, and the two ancestral sources dominant in this region are mostly found here, with very little or no admixture with maerl from Cornwall or Weymouth/Swanage. This suggests that Milford Haven harbours at least one distinct maerl population that is genetically diverse and geographically isolated from other populations in England. Climate adaptation in maerl-forming species Climate adaptation was only assessed for P. calcareum because only a single GEA outlier locus was detected for L. corallioides , which may be because there is not enough environmental variability across the sites where L. corallioides was sampled. For P. calcareum , after controlling for spatial autocorrelation (the potential influence of geographic distances correlating with environmental gradients among locations), the GEA analysis found 138 putative climate-associated SNPs. An RDA using just these SNPs showed some site-specific clustering with temperature and salinity. For example, maerl from Morlaix were associated with higher temperatures, more so than maerl from Galicia (Bornalle and Illa de Ons), despite present-day temperatures being on average higher in Galicia, though the next highest benthic temperatures after Galicia (15.0 o C) are in Morlaix (13.7 o C). This suggests that maerl in Morlaix may have putative genetic adaptations to benthic ocean temperatures. However, despite potential adaptations to temperature, Morlaix appears to have a lower (genomic offset) risk of being maladapted to temperature and salinity conditions in 2050 compared to many other sites. According to our analysis, the maerl bed with the lowest risk of maladaptation is Trévignon; this is probably because this area has the smallest predicted decreases in salinity (0.26 PSU compared to the next decrease of 0.38 PSU in Weymouth) and the second smallest increase in temperature (0.51 o C after 0.41 o C–0.44 o C in Galicia). Interestingly, Bornalle, a maerl bed located within an inner estuary, had a lower maladaptation risk compared with Illa de Ons, a maerl bed located within an outer estuary. This was an unexpected result given that temperature and salinity is broadly similar across both Galician sites, both in terms of present-day and future change. However, coastal configuration and the location of maerl beds within estuaries has been found to shape genetic structure and clonality in P. calcareum (Pardo et al. 2019 ); this may suggest that subtle changes in temperature or salinity could have higher ramifications for maerl populations situated in the outer zones of estuaries in Galicia. In the UK, maerl from Cornwall and Zara Shoal had higher risks of maladaptation to conditions in 2050 compared to Weymouth and two maerl beds from France. Weymouth has the highest projected temperature increase (0.68 o C) of any site, while all sites from Cornwall and Zara Shoal have the highest projected salinity decrease (0.42–0.44 PSU). However, even though Weymouth also has projected salinity decreases (0.35 PSU), it appears that predicted salinity decreases, as well as temperature increases in Zara Shoal and Cornwall (0.53-0.60 o C), may result in a greater risk of maladaptation to conditions in 2050 for maerl across these areas. This indicates that P. calcareum maerl from Cornwall, Zara Shoal and Illa de Ons will have to have greater shifts in their allele frequencies to mitigate the risk of being maladapted to future environments. It should be noted, however, that the RDA explained 19.2% of the variation in the allele frequencies of the climate-associated SNPs, which suggests that a large portion of variation in these outlier loci is explained by other factors, e.g. other localised environmental parameters or biological processes. Moreover, predictions of maladaptation risk do not consider changes in other conditions that may have additional impacts on the habitat suitability and survival of maerl, such as increased exposure to physical disturbances, pollution or invasive species. Microbiota differences between maerl beds of varying condition Metagenomics analysis revealed that Alphaproteobacteria and Gammoproteobacteria, both classes of the Proteobacteria phylum, were the most common microbe found on or within maerl samples across sites (except at Bembridge and Swanage). These results accord with findings in other coralline algae, such as Corallina officinalis (Brodie et al. 2016 ) and Neogoniolithon trichotomum (Valdespino-Castillo et al. 2021 ), whereby the core microbiota was made up of Proteobacteria, along with several other groups that were also detected in our study, including Bacteroidetes (which includes the Flavobacteria class), Actinobacteria and Cyanobacteria. Flavobacteria comprised a larger proportion of the microbiota community in St Mawes and Helford River (mostly dense maerl beds with live maerl samples) compared to Bembridge (mostly dead maerl bed with decaying or dead maerl samples). This group of aerobic bacteria can be found free-living, attached to detritus in the water column, or colonising the surface of macroalgae (Mann et al. 2013 ; Gavriilidou et al. 2020 ), the latter of which explains why these bacteria were detected on maerl. In contrast, a lower proportion of nitrifying microbes on maerl were found in St Mawes, Helford River and Milford Haven, while a much higher proportion of nitrifying microbes were found on maerl in Bembridge and Swanage. Proportions of microbiota from maerl at The Manacles were roughly in between these two profiles. The apparent replacement of Flavobacteria with nitrifying microbes may suggest a functional role for Flavobacteria on live maerl thalli. Alternatively, the proportional increase in nitrifying microbes could indicate the presence of more nitrogenous compounds in the environment, potentially originating from nearby sewage discharges or fish farms (Legrand et al. 2024 ). Eutrophication is problematic for maerl because excessive growth of other epiphytic algae can increase sedimentation and create hypoxic conditions, both of which inhibit photosynthesis and respiration (Grall & Hall-Spencer 2003 ). If this is true, our findings may suggest that the Bembridge maerl bed, and to some extent the Swanage maerl bed, may be (or may have been) in a state of degradation caused by eutrophication. However, this is speculative without further spatial and temporal sampling, as it is not possible to ascertain whether these differences in microbiota are consistent across space and time, and whether the patterns observed are the cause or a symptom of degradation in Bembridge or Swanage maerl. Genetic units and conservation implications We assigned locations where maerl was sampled to genetic management units based on all the data collected and analysed in this study (Table 2). Because genetic analysis is conducted on single species, the units are specified for each species ( P. calcareum and L. corallioides ). However, as some locations harbour maerl from both species, it seems probable that new or improved conservation actions in one area could benefit more than one maerl-forming species. For each genetic unit, we assessed genetic diversity (alpha diversity) and genetic differentiation (beta diversity) EBVs and used this information to guide our conservation insights (see below). We do not report inbreeding or effective population size ( N e ) EBVs because the complex life history and reproduction of maerl-forming species make these metrics difficult to determine with confidence and, particularly for N e , we do not yet have large enough sample sizes for accurate and meaningful assessments. Four P. calcareum genetic units are currently located within a designated MPA: St Mawes coarse form (Fal and Helford SAC), St Mawes non-coarse form (Fal and Helford SAC), The Manacles (The Manacles MCZ), and Weymouth (Purbeck Coast MCZ). The Fal and Helford SAC also encompasses L. corallioides maerl from the Helford River (a distinct genetic unit in this species), which overlaps with the Helford River MCZ, but this MCZ is designated specifically for native oysters. The coarse, triploid maerl from St Mawes are all clones that descend from a single genetically distinct lineage. Thus, this genetic unit of P. calcareum has no clonal diversity but interestingly has high observed heterozygosity (Table 2), which is likely a result of triploidy introducing additional alleles at many SNP loci. Indeed, it could be considered as an Evolutionary Significant Unit (ESU) because it has likely been segregated for many generations and is defined by a unique set of genetic (triploidy) and phenotypic (coarse growth form) traits (Funk et al. 2012 ). The loss of such an ESU would be equivalent to losing a distinct product of evolutionary history that may have evolved unique and/or adaptive traits (Hoban et al. 2022 ). In addition, we also found a relatively large clonal lineage in the Helford River that was genetically distinct from all other L. corallioides maerl analysed. Accordingly, considering the high maerl bed density and the distinct genetic diversity found in St Mawes and the Helford River, we advocate that the Fal Estuary and Helford River area remains a high priority for marine conservation. For the remaining sites in Cornwall, only The Manacles has a MCZ designated to protect maerl beds, which has a byelaw to prohibit scallop dredging and bottom towed gear under The Manacles Marine Conservation Zone (Fishing Restrictions) Byelaw 2017 (Cornwall IFCA). In Nare Head, we found one dominant clonal lineage of P. calcareum , further analysis of which suggested that this may be genetically differentiated from other Cornish maerl. In addition, P. calcareum maerl from The Manacles, the Bizzies, Gerrans Bay and St Austell Bay are possibly individual populations that have been or are connected by historic or contemporary gene flow. All these sites also have a higher risk of being maladapted to future temperatures and salinities relative to other sites in England and France. We therefore recommend that an additional MPA be considered for Gerrans Bay which would cover both the maerl from Gerrans Bay and Nare Head and safeguard the genetic diversity at these locations. A secondary priority in Cornwall would be to establish another MPA in St Austell Bay to protect both maerl-forming species, the benefit of which could enhance genetic connectivity across the region. In addition, for L. corallioides , this would conserve the high clonal diversity observed at this location, which may be indicative of higher rates of sexual reproduction generating new lineages. The prevalence of sexual reproduction could be explored at this site, or indeed any site, by conducting additional surveys to search for attached gametophytes with uniporate conceptacles (Pardo et al. 2019 ). To our knowledge, the only reported gametophyte of any maerl-forming species has been found in a P. calcareum maerl bed in Brittany, France (Pardo et al. 2017 ). Nevertheless, the detection of these attached gametophytes would have important implications for maerl bed conservation since they are central to introducing novel genetic variants into the population via sexual reproduction. In Weymouth and Swanage, both maerl-forming species have isolated populations at one or both of these locations and these should be considered as separate genetic units to maerl beds in Cornwall. Currently, Purbeck Coast MCZ encompasses maerl beds at these locations. As mentioned previously for genetically diverse / distinct areas, we recommend that conservation of these sites is prioritised, particularly as there is likely very limited connectivity between these maerl beds and those in Cornwall. On the east coast of the Isle of Wight, we also collected maerl samples from Bembridge, which comprised mostly dead maerl with very little cover of living (pink) algae on each thallus. Purbeck Coast MCZ and Bembridge MCZ list maerl beds as a protected feature in their designation (in addition to other features), with a current management goal of “ recover to favourable condition ”. Although we could not determine any genetic EBVs for Bembridge due to insufficient SNP data (likely a result of low DNA yield due to very little algal material on maerl thalli), we were able to identify five samples as P. calcareum . Thus, any future restoration project in Bembridge involving the translocation of live maerl from another maerl bed should take this into consideration. In south-west Wales, all maerl from Milford Haven was classified as a single genetic unit, which has high genetic diversity and is genetically differentiated from other L. corallioides maerl beds in England. Milford Haven had the highest and second highest measures of clonal diversity (Pareto β) and observed heterozygosity, respectively. This suggests that, over many past generations, both sexual and clonal reproduction has occurred to produce new lineages and to proliferate existing lineages in the population. It is therefore important that the Pembrokeshire Marine SAC, which encompasses the Milford Haven area, provides adequate protection for maerl from existential threats, since any loss or regression of this maerl bed could erode the unique genetic diversity found in L. corallioides at this location. In addition, although we were able to obtain some preliminary data on the genetic structure of maerl from St Tudwal’s Island (north-west Wales), more samples will be needed to accurately assess population structure and compute genetic EBVs. Lastly, although population structure and clonality in other European maerl beds (Northern Ireland, France and Spain) were reported in previous studies for P. calcareum (Pardo et al. 2019 ; Jenkins et al. 2021 ), by including these WGS data in this study we were able to assess their risk of maladaptation to future climates in 2050 under the SSP245 scenario. Zara Shoal (Strangford Lough, Northern Ireland), Illa de Ons (Galicia, Spain) and all Cornish maerl beds were at elevated risk compared to all other sites. The Strangford Lough SAC lists maerl beds as a protected feature in its conservation objectives, while the Espacio marino de las Rias Baixas de Galicia MPA appears to encompass Illa de Ons (but not Bornalle). At present, therefore, it appears as though the European maerl beds listed above, that have higher risks of maladaptation, are currently positioned within MPAs. However, although analysis of genetic data can tell us about genetic diversity and potential resilience to change, deciding where to prioritise resources should also consider the density and health of maerl beds, as well as potential mechanisms for effectively protecting these habitats from threats in the short-term (e.g. physical disturbances and pollution) and the long-term (e.g. climate change). Conclusion In this study, genomic data was used to address important knowledge gaps in the population structure, genetic diversity, climate resilience and microbiota of two maerl-forming species, P. calcareum and L. corallioides , found across south-west Britain, Northern Ireland, France and Spain. In both species, the discovery of distinct genetic structure suggests that some maerl populations harbour unique genetic diversity and are genetically and/or geographically isolated. For example, maerl of both species in Weymouth/Swanage (Dorset) were found to be genetically differentiated from all maerl in Cornwall. Additionally, in L. corallioides , the Milford Haven (south-west Wales) maerl population was genetically diverse and isolated from other maerl populations in England. In Cornwall, we identified the genetically and phenotypically distinct coarse growth form of P. calcareum and found that this coarse form co-exists with a non-coarse form in St Mawes, the latter of which, unlike the coarse form, shares ancestry with neighbouring maerl populations (e.g. The Manacles and St Austell Bay). Moreover, in the Helford River, we found a relatively large clonal lineage of L. corallioides that is genetically distinct and only found at this location. These findings further emphasise the high conservation priority of these maerl beds around Falmouth, both in terms of maerl bed density and species/genetic diversity, and underline the important role of designated MPAs, such as the Fal and Helford SAC, to protect the Fal Estuary and Helford River area from dredging and bottom towed gear. In addition, we found that populations of P. calcareum from Cornwall, Zara Shoal (Northern Ireland) and Illa de Ons (Spain) are predicted to have an elevated risk of maladaptation to projected changes in temperature and salinity in 2050 under the SSP245 `middle of the road` climate change scenario. Lastly, we report differences in the microbiota community (i.e. bacteria and archaea) between maerl from dense, healthy maerl beds (e.g. St Mawes) compared to scattered, mostly dead maerl (e.g. Bembridge). Altogether, we use these data to propose genetic management units for maerl populations in south-west Britain, which will inform efforts to prevent further loss or degradation to maerl beds and aid conservation interventions to recover and restore these irreplaceable, high natural capital value habitats. Declarations Data archiving statement Raw DNA sequence data and draft genome assemblies are available from the NCBI (BioProject: PRJNA682082; Phymatolithon calcareum assembly: SAMN41910763; Lithothamnion corallioides assembly: SAMN41910813). The modified BUSCO phylogenomics pipeline is available from GitHub: Tom-Jenkins/BUSCO_phylogenomics. The custom python scripts used for organelle annotation (extract_CDS.py) and for processing Kraken2 reports (process_kraken2_reports.py) are part of the nextflow pipelines repository available from Zenodo: https://doi.org/10.5281/zenodo.14056754. R code and supporting data used in the analyses are also available from Zenodo: https://doi.org/10.5281/zenodo.14592386. References Aguirre-Liguori JA, Ramírez-Barahona S, Gaut BS (2021) The evolutionary genomics of species’ responses to climate change. Nature Ecology & Evolution , 5 , 1350–1360. Allendorf FW, Hohenlohe PA, Luikart G (2010) Genomics and the future of conservation genetics. 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Location Year Latitude Longitude Code N TOTAL N GENO Depth (m) Reference Phymatolithon calcareum England, Cornwall, St Austell Bay 2023 50.327 -4.728 Aus 2 2 18 This study England, Cornwall, St Austell Bay, Little Gribbin 2022 50.319 -4.689 Gri 8 8 5-10 This study England, Cornwall, Falmouth, Bizzies Reef 2022 50.136 -4.987 Biz 10 10 23 This study England, Cornwall, Falmouth, St Mawes 2015, 2022 REF 50.156 -5.031 Maw 6, 3 9 4-6 This study & Jenkins et al. 2021 England, Cornwall, Falmouth, St Mawes COARSE 2011, 2022 REF 50.166 -5.031 MawC 7, 8 15 4-6 This study & Jenkins et al. 2021 England, Cornwall, Gerrans Bay 2023 50.199 -4.938 Ger 5 5 18 This study England, Cornwall, The Manacles 2015 50.039 -5.060 Man 12 12 5-10 This study & Jenkins et al. 2021 England, Cornwall, Nare Head, nr Hera Wreck 2022 50.200 -4.907 Nar 10 10 13-15 This study England, Dorset, Swanage 2022 50.650 -1.910 Swa 1 1 15-19 This study England, Dorset, Weymouth 2022 50.616 -2.320 Wey 5 5 15-20 This study England, Isle of Wight, Bembridge 2022 50.637 -1.141 Bem* 10 – 13-21 This study Northern Ireland, Strangford Lough, Zara Shoal 2011 54.379 -5.564 Zar 11 11 10 Jenkins et al. 2021 France, Brittany, Morlaix 2011 48.711 -3.951 Mor 11 11 10-11 Jenkins et al. 2021 France, Brittany, Trévignon 2011 47.795 -3.887 Tre 9 9 15 Jenkins et al. 2021 Spain, Galicia, Bornalle 2011 42.789 -9.020 Bor 12 12 11 Jenkins et al. 2021 Spain, Galicia, Illa de Ons 2011 42.395 -8.915 Ons 11 11 13 Jenkins et al. 2021 Lithothamnion corallioides England, Cornwall, St Austell Bay 2023 50.327 -4.728 Aus 10 10 18 This study England, Cornwall, St Austell Bay, Little Gribbin 2022 50.319 -4.689 Gri 4 4 5-10 This study England, Cornwall, Falmouth, St Mawes 2015, 2022 50.156 -5.031 Maw 2, 3 5 4-6 This study & Jenkins et al. 2021 England, Cornwall, Helford River 2015 50.098 -5.123 Hel 13 13 5-10 This study England, Dorset, Swanage 2022 50.650 -1.910 Swa 3 3 15-19 This study England, Dorset, Weymouth 2022 50.616 -2.320 Wey 4 4 15-20 This study Wales, Pembrokeshire, Milford Haven west 2023 51.705 -5.082 Mil1 13 13 5 This study Wales, Pembrokeshire, Milford Haven east 2023 51.703 -5.076 Mil2 12 12 5 This study Wales, Llyn Peninsula, St Tudwal’s Island 2023 52.809 -4.456 Tud 2 2 12 This study N TOTAL Total number of maerl samples collected; N GENO Number of maerl samples successfully genotyped. REF A reference genome was assembled for one individual sample using Nanopore long read sequencing. * Only five out of ten samples were successfully identified to species level using DNA barcoding. Table 2 Genetic management units of maerl sampled from England and Wales. Metrics of clonal and genetic diversity for each unit are reported, alongside current information about Marine Protected Area (MPA) designations and regulations. Genetic unit Genetic diversity Marine Protected Area (MPA) Designation year Regulations Pareto β H o H e F IS Phymatolithon calcareum St Mawes 1.63 0.31 0.22 -0.35 Fal and Helford SAC 2005 Prohibition of scallop dredging and bottom towed gear under The Fal & Helford Designated Area (Fishing Restrictions) Order 2008 (Defra). St Mawes coarse triploid 0.00 0.65 0.34 -0.85 Fal and Helford SAC – – St Austell Bay 1.78 0.28 0.23 -0.22 Falmouth Bay to St Austell Bay SPA 2017 No known regulations to protect maerl beds. The Bizzies 2.00 0.26 0.21 -0.22 Falmouth Bay to St Austell Bay SPA – – Gerrans Bay 1.00 0.32 0.21 -0.46 Falmouth Bay to St Austell Bay SPA – – Nare Head 0.53 0.26 0.16 -0.38 Falmouth Bay to St Austell Bay SPA – – Weymouth 1.00 0.28 0.19 -0.39 Purbeck Coast MCZ 2019 Partial prohibition of scallop dredging and bottom tower gear under the Bottom Towed Fishing Dear Byelaw 2016 (Southern IFCA) The Manacles 2.10 0.26 0.21 -0.20 The Manacles MCZ 2013 Prohibition of scallop dredging and bottom towed gear under The Manacles Marine Conservation Zone (Fishing Restrictions) Byelaw 2017 (Cornwall IFCA). Lithothamnion corallioides Helford River 0.88 0.39 0.29 -0.25 Fal and Helford SAC Helford Estuary MCZ – 2019 – No known regulations to protect maerl beds. St Mawes 2.00 0.37 0.30 -0.21 Fal and Helford SAC – – St Austell Bay Inf 0.34 0.29 -0.11 Falmouth Bay to St Austell Bay SPA – – Milford Haven 2.07 0.38 0.30 -0.20 Pembrokeshire Marine SAC 2004 Partial prohibition of scallop fishing using towed gear under The Scallop Fishing (Wales) (No.2) Order 2010 . A ‘no anchoring’ buffer zone drawn around sensitive habitats including maerl and seagrass. Weymouth/Swanage Inf 0.32 0.29 -0.07 Purbeck Coast MCZ – – MCZ, Marine Conservation Zone; SAC, Special Area of Conservation; SPA, Special Protection Area; IFCA, Inshore Fisheries and Conservation Authority. Inf , Infinity denotes that no clones were detected (the number of clonal lineages equals the number the samples collected). Additional Declarations The authors declare no competing interests. Supplementary Files FigureS1DNAextractionprotocol.docx Figure S1. DNA extraction protocol. FigureS2S12.docx Figure S2–S12. FigureS13microbiotaproportions.zip Figure S13. Krona interactive plots showing microbiota proportions for each maerl sample. TableS1BUSCOassemblies.xlsx TableS2Assemblystats.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-7035383","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481813319,"identity":"756d39f8-eca2-449c-a5ea-fd717b88e0c2","order_by":0,"name":"Tom L. Jenkins","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIie2RsUoDQRCGZ1lYm8NrZ7k8xIaDJcHDvEpA8Bo7my2CHAQuzZH6Ol/lZMBqH0DRQhGuvnQRRZOFIFrsJaWQ/crZ/2P/YQACgX8JLwABlABgL7/nyidEwH4U7lJ4mLILCDxImThlZJ7T02TRmlWZ3cRJw7o1UNpfzLZaDKx+rO0lyuWUywpI9yqypEzglXiKDKGyAAkAZf3Kl1Py9vpTfePEAv/YrxSkBU41B9OgikC4X/zFiBUN3lO6LZbKyl7I2rJyXKncu/7JYn73hjMa3tb5a/densdxxelhbc6Ghc/h2w74d+Qu5b3KLtL1vwcCgcCxswHsfEjf0No/FgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-5774-0278","institution":"University of Exeter","correspondingAuthor":true,"prefix":"","firstName":"Tom","middleName":"L.","lastName":"Jenkins","suffix":""},{"id":481813320,"identity":"8bf67c5d-d24b-4ce5-b9cc-f7b2916b194b","order_by":1,"name":"Magnus Axelsson","email":"","orcid":"","institution":"Natural England","correspondingAuthor":false,"prefix":"","firstName":"Magnus","middleName":"","lastName":"Axelsson","suffix":""},{"id":481813321,"identity":"edd9ee87-c094-48df-ab91-8170bd51b375","order_by":2,"name":"Angela Gall","email":"","orcid":"","institution":"Natural England","correspondingAuthor":false,"prefix":"","firstName":"Angela","middleName":"","lastName":"Gall","suffix":""},{"id":481813322,"identity":"40318842-265d-4ca9-a873-c45a1be7356d","order_by":3,"name":"Frances Ratcliffe","email":"","orcid":"","institution":"Natural Resources Wales","correspondingAuthor":false,"prefix":"","firstName":"Frances","middleName":"","lastName":"Ratcliffe","suffix":""},{"id":481813323,"identity":"de7fd612-929d-4e07-8689-175f628f7382","order_by":4,"name":"Charlie D. Ellis","email":"","orcid":"","institution":"University of Exeter","correspondingAuthor":false,"prefix":"","firstName":"Charlie","middleName":"D.","lastName":"Ellis","suffix":""},{"id":481813324,"identity":"47a85111-d828-4c18-82aa-0b039798468e","order_by":5,"name":"Jamie R. Stevens","email":"","orcid":"","institution":"University of Exeter","correspondingAuthor":false,"prefix":"","firstName":"Jamie","middleName":"R.","lastName":"Stevens","suffix":""}],"badges":[],"createdAt":"2025-07-03 07:32:31","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7035383/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7035383/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86780046,"identity":"2a4930cd-1964-4b87-8785-5b2df7826085","added_by":"auto","created_at":"2025-07-15 13:15:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2411092,"visible":true,"origin":"","legend":"\u003cp\u003eMap of sampling locations and BUSCO species tree. (A) Map of sites where maerl was sampled. Coloured labels denote the maerl-forming species that was identified at each site. (B) Photo of St Mawes maerl bed (image credit: Matt Slater, Cornwall Wildlife Trust). The coarse growth form of \u003cem\u003ePhymatolithon calcareum\u003c/em\u003e (MawC) is highlighted by the white arrow; the surrounding maerl is a mix of non-coarse \u003cem\u003eP. calcareum \u003c/em\u003eand \u003cem\u003eLithothamnion corallioides\u003c/em\u003e. (C) Red algae species tree of the Florideophyceae (purple vertical line) and one outgroup (Bangiophyceae) (red vertical line), built using 157 BUSCO complete and single-copy genes that were present in at least ten species. The two species analysed in our study are highlighted in bold. At the end of each species name, the superscript label denotes whether the assembly used for BUSCO analysis was a genome (DNA) or a transcriptome (RNA). The light purple box highlights all calcifying red algal lineages in the Corallinophycidae, which includes the coralline algae orders Corallinales and Hapalidiales.\u003c/p\u003e","description":"","filename":"Figure01.png","url":"https://assets-eu.researchsquare.com/files/rs-7035383/v1/ea33e64af060088eaf45a892.png"},{"id":86780047,"identity":"3079fc02-35b5-4d9c-ae99-c5d8b565cb57","added_by":"auto","created_at":"2025-07-15 13:15:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":599051,"visible":true,"origin":"","legend":"\u003cp\u003eAllele balance and ploidy analysis. The first column shows allele frequency histograms of the expected distribution for a diploid (single peak at 1/2) and a triploid (two peaks at 1/3 and 2/3). The remaining columns show histograms of allele balance proportions from heterozygous SNPs in samples from St Mawes (\u003cem\u003ePhymatolithon calcareum\u003c/em\u003e), Nare Head (\u003cem\u003eP. calcareum\u003c/em\u003e) and Milford Haven (\u003cem\u003eLithothamnion corallioides\u003c/em\u003e); coarse \u003cem\u003eP. calcareum\u003c/em\u003e maerl samples are presented on the bottom panel. Colours denote whether the pattern is indicative of diploidy (blue) or triploidy (green).\u003c/p\u003e","description":"","filename":"Figure02.png","url":"https://assets-eu.researchsquare.com/files/rs-7035383/v1/7a2a25e534e11d805cba6c95.png"},{"id":86780049,"identity":"68fd0b0d-9d7d-4114-9a34-cd2dbf88b7f8","added_by":"auto","created_at":"2025-07-15 13:15:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":767680,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePhymatolithon calcareum\u003c/em\u003e admixture map and structure plot. The admixture map (top panel) shows pie charts on a zoomed in map of Cornwall (England), where each pie represents the average admixture proportion across all individuals for a given cluster at a location. An inset admixture map shows the same data for Zara Shoal (Strangford Lough) and Weymouth (Dorset). The structure plot (bottom panel) shows the same admixture data but per individual, where each bar represents the proportion of their genome derived from each \u003cem\u003eK\u003c/em\u003e source ancestral population.\u003c/p\u003e","description":"","filename":"Figure03.png","url":"https://assets-eu.researchsquare.com/files/rs-7035383/v1/9682f0ed4bc428b514ab3237.png"},{"id":86779032,"identity":"033de64c-5cbd-4974-b0a6-d79a45fc74cf","added_by":"auto","created_at":"2025-07-15 13:07:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":528346,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLithothamnion corallioides\u003c/em\u003e admixture map and structure plot. The admixture map (top panel) shows pie charts on a zoomed in map of south-west England and southern Wales, where each pie represents the average admixture proportion across all individuals for a given cluster at a location. The structure plot (bottom panel) shows the same admixture data but per individual, where each bar represents the proportion of their genome derived from each \u003cem\u003eK\u003c/em\u003e source ancestral population.\u003c/p\u003e","description":"","filename":"Figure04.png","url":"https://assets-eu.researchsquare.com/files/rs-7035383/v1/0f99844039629bc188363d55.png"},{"id":86779035,"identity":"5ab42519-f3fb-4325-bb6a-6d7b095a2e7b","added_by":"auto","created_at":"2025-07-15 13:07:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":483116,"visible":true,"origin":"","legend":"\u003cp\u003eClonality analysis per genetic unit in \u003cem\u003ePhymatolithon calcareum\u003c/em\u003e and \u003cem\u003eLithothamnion corallioides\u003c/em\u003e. The clonal lineage (multi-locus genotype, MLG) is numbered on the x-axis and the number of individuals in each clonal lineage is represented by bars on the y-axis. Each clonal lineage was identified by defining a clone threshold (see main text), which is necessary because somatic mutations can introduce very small genetic differences between clones. Grey bars indicate that a clonal lineage is only present in that location, whereas coloured bars indicate that a lineage was found in multiple locations.\u003c/p\u003e","description":"","filename":"Figure05.png","url":"https://assets-eu.researchsquare.com/files/rs-7035383/v1/ef94363ea07e799a280bf024.png"},{"id":86779030,"identity":"58d9e4e9-ba98-4fec-9a30-e3c9459b3d65","added_by":"auto","created_at":"2025-07-15 13:07:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1277387,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePhymatolithon calcareum\u003c/em\u003e outlier redundancy analysis and genomic offset map. (A) Redundancy analysis using allele frequencies of the outlier loci, and temperature and salinity as predictor variables. Each point represents an individual, and colours denote the location of origin. (B) Genomic offset map using allele frequencies of the outlier loci and future climate predictions for temperature and salinity under the shared socioeconomic pathway (SSP) 245 scenario. The offset is a relative measure, meaning that the risk of being maladapted to future climates is only relevant in this analysis (not comparable across studies).\u003c/p\u003e","description":"","filename":"Figure06.png","url":"https://assets-eu.researchsquare.com/files/rs-7035383/v1/9ae9f162da61eb4d3521a4a0.png"},{"id":86780051,"identity":"d42304c6-05c8-48bc-96d4-e785a4d51f22","added_by":"auto","created_at":"2025-07-15 13:15:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":257661,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobiota community of maerl assessed using metagenomics. The mean average proportion for groups of bacteria or archaea are presented for six locations; these locations broadly represent a gradient of maerl bed status from a dense, healthy maerl bed (St Mawes) to scattered, mostly dead maerl (Bembridge). See main text for classification descriptions in parentheses.\u003c/p\u003e","description":"","filename":"Figure07.png","url":"https://assets-eu.researchsquare.com/files/rs-7035383/v1/aa9aea58d5765b019a5f2236.png"},{"id":86781515,"identity":"b7b20f9b-827c-4148-8522-66c763d9d73b","added_by":"auto","created_at":"2025-07-15 13:31:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7896825,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7035383/v1/92e4d02c-1309-43f4-b112-616e32c02b3a.pdf"},{"id":86779024,"identity":"2d1cb840-e9f6-4957-a0a9-1da11243f0b8","added_by":"auto","created_at":"2025-07-15 13:07:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":601860,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1. DNA extraction protocol.\u003c/p\u003e","description":"","filename":"FigureS1DNAextractionprotocol.docx","url":"https://assets-eu.researchsquare.com/files/rs-7035383/v1/3ba2c856a59bf9b1e87a6134.docx"},{"id":86779027,"identity":"f1813dea-63cd-4a27-a7e4-b8f9fca646bd","added_by":"auto","created_at":"2025-07-15 13:07:07","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3444865,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S2–S12.\u003c/p\u003e","description":"","filename":"FigureS2S12.docx","url":"https://assets-eu.researchsquare.com/files/rs-7035383/v1/5804c6e933399ce82b9b1edf.docx"},{"id":86779034,"identity":"1ccf2274-ba77-47c3-8be1-b2cdf2c134fd","added_by":"auto","created_at":"2025-07-15 13:07:07","extension":"zip","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1194633,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S13. Krona interactive plots showing microbiota proportions for each maerl sample.\u003c/p\u003e","description":"","filename":"FigureS13microbiotaproportions.zip","url":"https://assets-eu.researchsquare.com/files/rs-7035383/v1/14e98ed80cc337a03810e00c.zip"},{"id":86780050,"identity":"52442d99-c6b1-4def-ad44-7db5df64f297","added_by":"auto","created_at":"2025-07-15 13:15:07","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":12568,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1BUSCOassemblies.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7035383/v1/f52cb12dbc9afab7abafca8a.xlsx"},{"id":86780561,"identity":"9b4be416-7b7a-40d0-a3c7-32631894ec94","added_by":"auto","created_at":"2025-07-15 13:23:07","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":97262,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2Assemblystats.docx","url":"https://assets-eu.researchsquare.com/files/rs-7035383/v1/f0b1613dc05489eceb98eb4f.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Population genomics, polyploidy, climate resilience and the microbiota of two habitat-building coralline algae","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eConservation genomics has revolutionised our understanding of the levels, distribution and functional significance of genetic variation in natural populations (Allendorf et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). One area where genomics has progressed our understanding is in the detection of local adaptation and how species or populations are likely to respond to rapid climate change (Aguirre-Liguori et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bernatchez et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It has also provided new insights into genetic diversity and population structure through the increased resolution and precision offered by the analysis of many thousands of genetic markers from across the genome (Davey et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This is particularly important for conservation biology as it aids the accurate identification of biologically relevant management units and connectivity scales (Funk et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProgress in conservation genomics has laid the groundwork for the development of Essential Biodiversity Variables (EBVs) for genetic composition (Hoban et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The EBVs concept categorises biodiversity into several classes, one of which is genetic composition, and proposes standardised and scalable metrics that are designed to identify and monitor the status and trends of biodiversity at all levels (genetic, species, and ecosystems) over space and time (Pereira et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kissling et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Hoban et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) proposed four genetic EBVs: (i) genetic diversity (e.g. heterozygosity), (ii) genetic differentiation (e.g. \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e), (iii) inbreeding (e.g. \u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e), and (iv) effective population size (\u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e), which the authors advocate for integration into conservation management strategies. Although genetic diversity has been generally overlooked in global biodiversity policy (Laikre \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Laikre et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), there are encouraging signs given the recent commitments and improved targets set by the Convention on Biological Diversity to monitor and conserve genetic diversity (Hoban et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This would be a positive shift since genetic diversity is crucial for providing resilience to rapid environmental change (e.g. climate, land use change, pollution, etc.) and for resisting virulent or emerging diseases. However, despite the information gained from genetic / genomic studies, management recommendations by researchers and scientists do not always impact regional conservation practices due to the \u0026lsquo;conservation genetics gap\u0026rsquo; (Sandstr\u0026ouml;m et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), whereby the challenges of translating genetic data to non-specialists inhibit their effective integration into policy (Shafer et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Jenkins \u0026amp; Stevens \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To overcome this gap, it is therefore important to engage and design research projects in conjunction with policy and conservation advisors. Here, we have adopted such a partnership with two UK non-departmental public bodies, Natural England and Natural Resources Wales, both of whom advise on the natural environment and have a central role in shaping policies to protect species and habitats of conservation priority.\u003c/p\u003e \u003cp\u003eCoralline algae are calcareous red seaweeds that are found in almost every coastal ecosystem globally and are highly diverse in appearance and life history, with many species recognised as important ecosystem engineers (Schubert et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In terms of genomics research, red macroalgae as a group have been underrepresented compared to other eukaryotic groups (Borg et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), although recent initiatives have been established to improve genomic resources, such as Rhodoexplorer (Lipinska et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and macro-ALG-ALL-CODE (Nelson et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For coralline algae, there have also been developments in transcriptomic resources (Page et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xue et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Given the overall decreases in sequencing costs and development of more sophisticated bioinformatics tools, the generation and availability of reference genomes and transcriptomes will likely increase over the next decade, enabling more conservation genomics research in red macroalgae.\u003c/p\u003e \u003cp\u003eFree-living, unattached sporophytes of coralline algae on the seafloor are called maerl (or rhodoliths). Maerl-forming species can build complex and extensive three-dimensional \u0026lsquo;carpets\u0026rsquo; of maerl on the seabed, known as maerl beds or rhodolith beds, which provide a vital biogenic reef-like habitat in shallow coastal waters for animals and other seaweeds (Kamenos et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Pe\u0026ntilde;a et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tuya et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and are important blue carbon ecosystems (Mao et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; James et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, maerl is extremely slow-growing, with a growth rate of 0.5\u0026ndash;1.5 mm per thallus tip per year (Blake \u0026amp; Maggs \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), resulting in very slow recovery potential. Moreover, given the global distribution and ecosystem services provided by maerl beds, they are greatly understudied compared to other coastal habitats (Tuya et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), despite being threatened by climate change (Qui-Minet et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and by disturbance or pollution from human activities (Hall-Spencer \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Bernard et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Legrand et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, more research is needed to understand the adaptability and resilience of maerl bed habitats and how they may be impacted by climate change.\u003c/p\u003e \u003cp\u003eUntil very recently, genetic research on maerl has focused primarily on species delimitation using DNA barcoding (via mitochondrial and chloroplast gene fragments), and on exploring the geographical distribution of maerl-forming species. This has likely been driven by the difficulty in identifying maerl to species level based on external morphology (Pardo et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Subsequently, this research has generated new information about the species diversity and the distribution of maerl-forming species, notably in the Atlantic and the Mediterranean (Pardo et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Carro et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Melbourne et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For example, these data have revealed that \u003cem\u003ePhymatolithon calcareum\u003c/em\u003e and \u003cem\u003eLithothamnion corallioides\u003c/em\u003e are the main two species that build maerl beds in the north-east Atlantic (Pardo et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). More recently, microsatellite markers were used to investigate genetic diversity, clonality and population structure in \u003cem\u003eP. calcareum\u003c/em\u003e from sites across the European Atlantic (Pardo et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which was followed up with a whole genome sequencing (WGS) study using single nucleotide polymorphisms (SNPs) on a subset of sites from the original study (Jenkins et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These studies provided the first insights of clonal diversity and genetic differentiation in \u003cem\u003eP. calcareum\u003c/em\u003e. However, sampling of maerl within Britain was particularly limited. Furthermore, an analysis of genetic diversity and population structure in \u003cem\u003eL. corallioides\u003c/em\u003e has yet to be undertaken.\u003c/p\u003e \u003cp\u003eInterpreting genetic patterns in maerl-forming species is challenging due to their complex life cycle, which is perhaps best understood in \u003cem\u003eP. calcareum\u003c/em\u003e (Fig.\u0026nbsp;1 in Pardo et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This species is partially clonal, meaning it has the capacity to alternate between sexual and clonal reproduction, the latter of which is defined here as an individual which produces new individuals that are genetically identical to the ancestor at all positions in the genome except for sites which have experienced somatic mutations (Stoeckel et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, the life cycle is haplodiplontic, and is characterised by a haploid phase (attached gametophyte) and a diploid phase (attached or unattached sporophyte) (Pardo et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). One of the advantages of isolating genome-wide SNP markers using WGS is the enhanced ability to detect unattached sporophyte (maerl) clones and to more accurately assess genetic diversity within populations.\u003c/p\u003e \u003cp\u003eIn England and Wales, maerl bed habitats and two maerl-forming species, \u003cem\u003eP. calcareum\u003c/em\u003e and \u003cem\u003eL. corallioides\u003c/em\u003e, are protected by the NERC Act 2006 and the Conservation of Habitats and Species Regulations 2017. As a result, there are several Marine Protected Areas (MPAs), such as Special Areas of Conservation (SAC), that include maerl beds as a feature in their designation order. In Britain, recent maerl bed surveys by Natural England have provided new information about the locations of live (and dead) maerl beds in southern and south-west England (Envision Marine LTD \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), for which genomic profiling would circumvent the sampling limitations apparent in previous genetic studies of maerl across this region. These new surveys also revealed variation in the number and cover of dense live maerl beds versus mixed or dead maerl beds, as well as sites where only sparsely scattered live or dead maerl is found. This provides an opportunity to explore how genetic diversity and the microbiota of maerl compare across sites with different levels of live and dead maerl, which, currently, is not well understood. The microbiota of crustose coralline algae are known to play important roles in the recruitment and settlement of marine invertebrates (Wilson et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and there is conjecture that maerl microbiota may play a similar role in the settlement of commercially important bivalve species (Scales \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, the study of maerl microbiota could provide valuable insights for maerl beds characterised by varying levels of condition.\u003c/p\u003e \u003cp\u003eThe main aims of this study were to assemble draft reference genomes for \u003cem\u003eP. calcareum\u003c/em\u003e and \u003cem\u003eL. corallioides\u003c/em\u003e and to perform a population genomics analysis with existing WGS data from a previous study (Jenkins et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and new data from novel sites sampled in England and Wales. Our research questions were formulated to investigate genetic EBVs and climate adaptation in these maerl-forming species and to explore the microbiota associated with live or degraded/dead maerl beds: (i) What are the levels of genetic diversity and population structure across sites and species? (ii) Are any loci putatively associated with climate adaptation and if so what is the risk of maladaptation to future environmental conditions? (iii) Does the microbiota community of maerl differ across sites and/or in relation to maerl bed condition?\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e \u003cb\u003eSampling locations and DNA extraction\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSamples of maerl were collected via SCUBA diving at 4-23m depth from eleven locations along the south-west coast of England, and from two locations in south-west Wales and north Wales; of these, only two had been previously assessed for maerl species identification or genetic analysis (Table\u0026nbsp;1, Fig.\u0026nbsp;1). Collection of samples in both non-protected and protected marine sites were coordinated in partnership with Natural England and Natural Resources Wales to ensure all sampling complied with priority species and habitat regulations. Where possible, transects were deployed and samples were collected at least one metre apart to avoid potential over-sampling of locally dominant clonal lineages, with the aim of maximising the species and genetic variation captured from each maerl bed. The number of samples collected per site varied depending on maerl bed density. All maerl samples were fully immersed in 95\u0026ndash;100% ethanol as soon as possible and ethanol was replaced after 24 hours. Samples were stored at 4\u003csup\u003eo\u003c/sup\u003eC until DNA extraction.\u003c/p\u003e \u003cp\u003eGenomic DNA was extracted using the DNeasy Blood \u0026amp; Tissue Kit (Qiagen), with some modifications to the protocol designed to increase DNA yield from maerl samples (see Figure S1 for detailed protocol). This was required, particularly for Nanopore sequencing, because the outer layer of live algae is very thin and typically yields extremely low amounts of DNA using the standard protocol. The quantity and concentration of DNA was assessed by fluorometry using a Qubit 1X dsDNA High Sensitivity Assay Kit (Invitrogen). The quality of each aliquot was checked using gel electrophoresis on a 1% agarose gel and only DNA samples showing a\u0026thinsp;~\u0026thinsp;20Kb band or higher with low or no evidence of degradation were selected for whole genome sequencing.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWhole genome sequencing\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIllumina short-read libraries were prepared for all samples using NEBNext Ultra II FS following the protocol for inputs below 50ng (half reactions were performed). Adaptors were diluted 1:10 for all samples as per the protocol. No size selection was implemented and 0.8X bead purification was conducted twice prior to seven cycles of PCR enrichment. Libraries were checked by TapeStation D1000 and pooled equimolar. All samples were sequenced on a NovaSeq 6000 using a paired-end strategy with a read length of 150 bases. Additionally, whole genome sequencing data for maerl samples from a previous study (Jenkins et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) were included in our study (Table\u0026nbsp;1, Fig.\u0026nbsp;1); these DNA samples were prepared and sequenced using the same methods described above. Illumina raw reads were filtered and trimmed using fastp v0.23.4 (Chen \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) with the following parameters: (i) polyG tail trimming enabled, (ii) a Phred quality score\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;30 in at least 60% of bases in a read, and (iii) a minimum length of 100bp.\u003c/p\u003e \u003cp\u003eNanopore long-read libraries were prepared for two samples, one representing \u003cem\u003eP. calcareum\u003c/em\u003e and one representing \u003cem\u003eL. corallioides\u003c/em\u003e, both of which were confirmed to be the correct species by DNA barcoding (see below) and originated from St Mawes, Falmouth (Table\u0026nbsp;1). For both samples, the same DNA aliquot used to prepare the Illumina libraries was used to prepare Nanopore libraries so that the Illumina data could be used to polish the genome assembled with long reads. Standard Oxford Nanopore Technology libraries were prepared and sequenced on a PromethION 24 flow cell with R9.4.1 chemistry. Guppy v6.3.9 with super-accurate basecalling was used for basecalling and only reads flagged as passed (minimum quality score of 10) were retained in FASTQ format. Porechop v0.2.4 was used to detect and remove adapters from reads passing the basecalling filters.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSpecies identification via DNA barcoding\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA DNA barcoding approach was used to confirm species identity of each sample; this is necessary because of the difficulty in distinguishing maerl-forming species based on external morphology (Pardo et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). First, published mitochondrial and chloroplast genomes of \u003cem\u003eP. calcareum\u003c/em\u003e (MW357900.2, OQ417768.1) and \u003cem\u003eL. corallioides\u003c/em\u003e (MW357901.2, OQ417769.1) were downloaded from GenBank in FASTA format to function as seed sequences for each organelle. The GenBank reference files for each of these accessions were also downloaded for use in the organelle gene annotation step. Second, the Illumina trimmed reads for each sample were aligned to the mitogenome and plastome seed sequences separately using Bowtie2 v2.5.3 (Langmead \u0026amp; Salzberg \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Mapped reads were extracted from each bam file to a FASTQ file using SAMtools v1.19 (Danecek et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These target reads were then assembled using Unicycler v0.5.0 (Wick et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), with the aim of obtaining a complete mitochondrial and chloroplast genome for each individual sample. Third, for organelle genomes successfully assembled, both mitochondrial and chloroplast genomes were annotated using a custom python script (see data archiving statement) using the GenBank reference files. Lastly, for each sample, nucleotide coding sequences (CDS) of the \u003cem\u003eCOI\u003c/em\u003e mitochondrial gene and the \u003cem\u003epsbA\u003c/em\u003e chloroplast gene were extracted to generate two FASTA files containing CDS for \u003cem\u003eCOI\u003c/em\u003e and \u003cem\u003epsbA\u003c/em\u003e, respectively. These genes were chosen because they are the most common genes used in coralline algae DNA barcoding studies (Pardo et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pe\u0026ntilde;a et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and have the highest intra- and inter-species representation on GenBank. These two FASTA files were submitted to the NCBI \u003cem\u003eblastn\u003c/em\u003e online server to compare our CDS (query) to the nucleotide database (subject). For a given maerl sample, a species identification was assigned if the percentage identity was \u0026gt;\u0026thinsp;=\u0026thinsp;99% (Carro et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGenome assembly, QC and contig filtering\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA reference genome assembly was built for both \u003cem\u003eP. calcareum\u003c/em\u003e and \u003cem\u003eL. corallioides\u003c/em\u003e using Nanopore long reads. Prior to assembly, long reads were taxonomically classified using BugSeq (Fan et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which classifies reads against the BugSeq default microbial database. Because of the potential for non-target DNA to be isolated from the sample during extraction, i.e. microbes living on or inside the calcium carbonate skeletal structure of maerl, the rationale here was to identify and remove as many reads of microbial origin as possible prior to the assembly pipeline. From the classification results, only reads classified as 0 (unclassified) or 1 (other) were retained, thus removing all reads classified as Bacteria, Virus or other microbes in the database. Flye v2.9.3 (Kolmogorov et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) was used to assemble the reads into contigs. The input read length was adjusted to explore the optimum minimum read length for input into Flye; for both samples\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;3000bp was deemed the optimum. For Flye, default parameters were used, alternative contigs were not kept (--no-alt-contigs), and only contigs longer than 3000bp were retained. The assemblies were polished with Illumina short read data using Polypolish v0.6.0 (Wick \u0026amp; Holt \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBlobToolKit v4.2.1 (Challis et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) was used to assess contamination and quality of the assemblies. Both \u003cem\u003eblastn\u003c/em\u003e v2.14.1+ (Camacho et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and \u003cem\u003ediamond blastx\u003c/em\u003e v2.1.8 (Buchfink et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) were run using the assemblies as queries to the NCBI nucleotide and protein databases, respectively. In addition, minimap2 v2.26 (Li \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) was used to map Nanopore reads to each assembly. The taxonomic hits and sorted bam alignments, along with the assembly FASTA file, were used as inputs to the \u003cem\u003eblobtools create\u003c/em\u003e command to create a BlobDir. Contigs were removed from the assembly if the following conditions were met: (i) contig was of organelle genome origin, (ii) contig was not supported by mapped reads (at least one read supporting each base across the contig), and (iii) contig matched to Bacteria, Archaea, Virus, or any Eukaryote other than Rhodophyta or Streptophyta. Assembly statistics, including total length, number of contigs, N50 and GC content, were computed using the \u003cem\u003estats\u003c/em\u003e command from \u003cem\u003eseqkit\u003c/em\u003e v2.5.1 (Shen et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eBUSCO phylogeny\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBUSCO v5.6.1 (Manni et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) was executed using the eukaryota_odb10 dataset to assess the completeness of near-universal single-copy orthologs in both genome assemblies. Subsequently, a phylogeny of coralline algae (and several other red algal taxa) was built using BUSCO protein sequences, the rationale of which was two-fold: (i) to examine the tree position of \u003cem\u003eP. calcareum\u003c/em\u003e and \u003cem\u003eL. corallioides\u003c/em\u003e to further confirm validity of the genome assemblies, and (ii) to compute and visualise a phylogeny of coralline algae based on BUSCO genes. Genome and/or transcriptome assemblies were downloaded for all available coralline algae and several red algal species from the Florideophyceae (Table S1). A BUSCO analysis was run for each assembly and the outputs were supplied as inputs to a modified version of the BUSCO phylogenomics pipeline (see data archiving statement). This pipeline identifies complete single-copy BUSCOs present in at least ten assemblies, then aligns sequences, trims alignments, and constructs gene trees using IQ-TREE (Nguyen et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Finally, Astral v5.7.8 (Mirarab \u0026amp; Warnow \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) was used to build a coalescent-based species tree, with \u003cem\u003ePorphyra umbilicalis\u003c/em\u003e (Bangiophyceae) used as the outgroup for visualisation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eVariant calling and SNP filtering\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter each DNA sample was identified to species level, the trimmed Illumina reads were mapped to the \u003cem\u003eP. calcareum\u003c/em\u003e or \u003cem\u003eL. corallioides\u003c/em\u003e genome assemblies using Bowtie2 v2.5.3 (Langmead \u0026amp; Salzberg \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The alignments were sorted by coordinate, then read groups were added and duplicates were marked using GATK v4.5 (McKenna et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Variant calling was conducted with Freebayes v1.3.8 (Garrison \u0026amp; Marth \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) in parallel on 100,000bp chunks using \u003cem\u003efreebayes-parallel\u003c/em\u003e and the \u003cem\u003efasta_generate_regions.py\u003c/em\u003e script. The following parameters were used: minimum mapping quality\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;20, minimum base quality\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;30, compute genotype qualities, and the ploidy was set to two. BCFtools v1.21 (Danecek et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and VCFtools v0.1.16 (Danecek et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) were used to filter variants. First, poor quality variants were removed by enforcing a maximum missing threshold of 0.70, a minor allele count of 3 and a minimum quality score of 30. Second, all samples from Bembridge were removed due to a very high proportion (50\u0026ndash;95%) of missing genotypes, likely because of insufficient DNA extracted from the target species (Bembridge samples comprised mostly dead maerl). Third, the following parameters were enforced using BCFtools: (i) no missing data, (ii) genotype quality\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;20, (iii) minimum depth\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;3, (iv) allele depth\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;10, minor allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;5%, and allele balance\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.10. Lastly, the VCF was read into R v4.3.2 and the following filters were applied to create a high quality biallelic SNP dataset: (i) distance thinning such that SNPs within 1000bp on a given contig were removed (to avoid potential linkage), (ii) SNPs with a minimum depth of 15 and a maximum depth of 100 over all samples were removed, and (iii) monomorphic loci were removed.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePloidy, population structure, clonality and genetic diversity\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePloidy of each sample was assessed using the \u0026lsquo;determining ploidy 1\u0026rsquo; method outlined in the vcfR R package documentation (Knaus \u0026amp; Gr\u0026uuml;nwald \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This method examines allele balance information, i.e. the number of times an allele was sequenced, at each heterozygous SNP. In diploids, an allele frequency ratio of 50:50 is expected (both alleles are sequenced equally), although there is inevitably variation around this ratio introduced by sequencing bias and variant calling software. When the allele frequencies of all heterozygous SNPs are visualised, we would expect a peak at 1/2. In contrast, in triploids, we would expect two peaks at 1/3 and 2/3. Preliminary analysis of allele balance indicated that read depth and minimum allele depth were important for interpreting the histograms, so, prior to analysis, SNPs were removed where the depth of at least one allele was \u0026lt;\u0026thinsp;10, and only samples with a median read depth\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;30 were considered for ploidy determination.\u003c/p\u003e \u003cp\u003eGenetic structure was assessed using non-model based principal component analysis (PCA) and model-based admixture inference using PopCluster v1.4.0 (Wang \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For admixture analyses, the admixture model with unequal allele frequencies and medium scaling was run independently ten times for each ancestral population (\u003cem\u003eK\u003c/em\u003e). No sample grouping or location of origin information (priors) were supplied to the algorithm. The optimal run and \u003cem\u003eK\u003c/em\u003e statistics (determined by \u003cem\u003eD\u003c/em\u003e\u003csub\u003e\u003cem\u003eLK\u003c/em\u003e2\u003c/sub\u003e and \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eSTIS\u003c/em\u003e\u003c/sub\u003e statistics) (Wang \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) were extracted from the results. Individual admixture proportions were visualised as barplots, and the mean average proportion per cluster per location were visualised as admixture maps using \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003emapmixture\u003c/span\u003e v1.1.4 (Jenkins \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Locations that had fewer than three individuals were not visualised in the admixture maps. Genetic differentiation among sites was assessed by computing pairwise \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e values (Weir \u0026amp; Cockerham \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1984\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClonality and genetic diversity were investigated for each genetic unit (i.e. each putative population identified based on the genetic structure analyses). Clonal lineages and the number of clones were assessed using the R package poppr v2.9.6 (Kamvar et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). First, pairwise Prevosti genetic distances were calculated between all individuals. Second, \u003cem\u003ecutoff_predictor()\u003c/em\u003e was run to define a clone threshold based on the genetic distance matrix. Defining a clone threshold is necessary because somatic mutations can introduce small genetic differences between clones. Lastly, \u003cem\u003emlg.filter()\u003c/em\u003e was run using the distance matrix, clone cut-off threshold and the nearest neighbour algorithm to identify multi-locus genotypes (MLGs); each MLG represents a putative clonal lineage. Finally, Pareto β, observed heterozygosity, expected heterozygosity, and \u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e (inbreeding coefficient) were computed for each genetic unit. Pareto β characterises clonal diversity and skewness in the distribution of clones within a population; the presence of a few large clonal lineages and many small ones will result in lower β values, while more balanced lineages of similar sizes will result in higher β values (Arnaud-Haond et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGenotype-environment association and genomic offset analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEnvironmental data were downloaded from Bio-Oracle v3.0 (Assis et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) at a resolution of 0.05 degrees (~\u0026thinsp;5.5 km at the equator). NetCDF files were obtained for environmental variables which have been shown or inferred to be important for maerl growth and survival (Martin \u0026amp; Hall-Spencer \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e): ocean temperature (\u003csup\u003eo\u003c/sup\u003eC), salinity (PSU), pH and oxygen concentration (mmol m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e). For each environmental variable, the following parameters were selected: baseline (2000\u0026ndash;2010), benthic (mean average depth per cell) and variable mean (mean average across time period). Future layers (2050\u0026ndash;2060) for these variables based on the Shared Socioeconomic Pathway (SSP) SSP245 (\u0026lsquo;middle of the road\u0026rsquo;) scenario from CMIP6 were also obtained. For each individual sample, latitude and longitude coordinates were used to extract values for each variable to use as a set of predictor variables in the analysis. There were only very small differences in pH among locations (8.06\u0026ndash;8.08), while multicollinearity checks among the variables revealed a very high correlation between oxygen concentration and ocean temperature (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e=-0.96). Therefore, only temperature (11.3-15.0\u003csup\u003eo\u003c/sup\u003eC) and salinity (33.99\u0026ndash;35.06 PSU) were retained for genotype-environment association (GEA) analysis. To mitigate spatial autocorrelation, detrending of environmental variables was conducted prior to modelling. First, least-cost geographic distances along coasts were calculated between locations and the output was used to compute dbMEMs (distance-based Moran\u0026rsquo;s eigenvector maps). Second, a linear model for each environmental variable was modelled as a function of the dbMEMs and the residuals were extracted. The residuals, representing the detrended environmental variables, were used as predictors for the GEA models.\u003c/p\u003e \u003cp\u003eTwo GEA methods were used to investigate associations between the environmental data and the SNP genotypes. First, a latent factor mixed model (LFMM) was run using the \u003cem\u003elfmm2()\u003c/em\u003e function from LEA v3.12.2 (Gain \u0026amp; Fran\u0026ccedil;ois \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) with \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9 and the default lambda value (1e-05). Using the \u003cem\u003elfmm.test()\u003c/em\u003e function, \u003cem\u003ep\u003c/em\u003e-values were adjusted for latent factors and recalibrated using the genomic inflation factor. Multiple comparisons were controlled for using the Benjamini-Hochberg method and only SNPs with an adjusted \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered as candidate loci. Second, a redundancy analysis (RDA) was run using the \u003cem\u003erda()\u003c/em\u003e function from vegan v2.6.4. To control for population structure, the first two principal components of the PCA were used as conditions in the model. The significance of each constrained axis was checked by running the \u003cem\u003eanova.cca()\u003c/em\u003e function and SNP loadings were extracted only for significant RDA axes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). SNPs were considered candidate loci if they were present in the tails of the SNP loading histograms using a standard deviation threshold of 3.0. Lastly, only candidate loci found in both methods were considered as GEA loci potentially under selection (climate-associated SNPs).\u003c/p\u003e \u003cp\u003ePutative climate-associated SNP genotypes were used as input for the \u003cem\u003egenetic.offset()\u003c/em\u003e function from LEA. A matrix of climate data representing both present-day baseline environments (2000\u0026ndash;2010) and future environments (2050\u0026ndash;5060) were used as covariates. This function calculates a geometric genomic offset value for each sample location using a genetic gap algorithm (Gain et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In a population genetic context, the geometric genomic offset can be interpreted as the average value of Nei\u0026rsquo;s \u003cem\u003eD\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e (divided by two) for the set of loci assumed to be involved in local adaptation (Gain et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Offset estimates predict the relative allele frequency change needed for a population to lower their risk of being potentially maladapted to future environments.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMicrobiota assignment using metagenomics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor each maerl sample, Illumina reads that did not align to the reference genome assemblies were extracted to FASTQ files using the \u003cem\u003efastq\u003c/em\u003e command from SAMtools. Kraken2 v2.1.3 (Wood et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) was used to taxonomically classify unmapped reads against a custom marine prokaryotic database, which was built by extracting all sequences from the NCBI nucleotide database that matched taxonomic IDs in the MarDB v1.6 marine metagenomics database (Klemetsen et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The following parameters were used in Kraken2: (i) a minimum hit groups of 3, (ii) a minimum base quality of 20, and (iii) a confidence threshold of 0.10. The report output files for all samples were amalgamated to a single tabular file for visualisation using a custom python script (see data archiving statement). After initial inspection of the results, we focused on certain microbial groups that were common across samples or have been reported in previous studies of coralline algae microbiota (Brodie et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Valdespino-Castillo et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), or groups that appeared to differ across sites. To statistically assess differences among these groups among sites, proportions of each group were modelled as a function of sites using an Analysis of Variance (ANOVA), and the Tukey honestly significant difference (HSD) test was used to investigate differences between sites using a 95% confidence interval. To minimise the risk of taxonomic misassignments, we only considered assignments to the level of class.\u003c/p\u003e \u003cp\u003eThis analysis was conducted with six sites, all of which were selected to represent different quality/density categories of maerl bed habitats based on a recent Natural England report (Axelsson \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This report details a standardised classification system for maerl bed habitats in England. The first three categories cover the range of sites sampled in our study: \u0026ldquo;Dense Maerl\u0026rdquo; (Category A), \u0026ldquo;Maerl Sediment\u0026rdquo; (Category B), and \u0026ldquo;Sparse/Scattered Maerl\u0026rdquo; (Category C), which also have sub-categories (from 1 up to 3) describing physical size, structure, % cover, live/dead proportion and substratum. Thus, we grouped the following sites using this classification system: St Mawes (dense maerl \u0026ndash; mostly A1), Helford River (dense maerl \u0026ndash; mostly A3), Milford Haven (maerl sediment \u0026ndash; mostly B1), The Manacles (maerl sediment \u0026ndash; mostly B3), Swanage (maerl sediment \u0026ndash; mostly B3) and Bembridge (scattered maerl \u0026ndash; mostly C2). This broadly represents a gradient of maerl bed status from a dense, healthy maerl bed (St Mawes) to mostly scattered dead maerl (Bembridge).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cb\u003eSpecies identification\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOf the 207 samples with whole genome sequencing data, DNA barcoding analysis using \u003cem\u003eCOX1\u003c/em\u003e and \u003cem\u003epsbA\u003c/em\u003e identified 136 as \u003cem\u003ePhymatolithon calcareum\u003c/em\u003e and 66 as \u003cem\u003eLithothamnion corallioides\u003c/em\u003e (Table\u0026nbsp;1). Five samples from Bembridge could not be identified to species. Elsewhere in Britain, there were some sites where all samples were identified to be a single species, while at other sites samples of both species were observed (Fig.\u0026nbsp;1A). Sites that only contained samples identified as \u003cem\u003eP. calcareum\u003c/em\u003e were: The Manacles, Bizzies Reef, Gerrans Bay, Nare Head (all southern Cornwall), and Bembridge (Isle of Wight). In contrast, the following sites only contained samples identified as \u003cem\u003eL. corallioides\u003c/em\u003e: Helford River (southern Cornwall), Milford Haven (south-west Wales), and St Tudwal\u0026rsquo;s Island (north-west Wales). Sites with both species were St Mawes, St Austell Bay, Little Gribbin (all southern Cornwall), and Weymouth and Swanage (both Dorset). The remaining samples from Northern Ireland, France and Spain were all identified as \u003cem\u003eP. calcareum\u003c/em\u003e, as expected from previous analysis (Jenkins et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDraft reference genome and BUSCO phylogeny\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA draft reference genome was assembled for each species using Nanopore long reads. The assembly statistics were comparable to or improved upon the two coralline red algal species, \u003cem\u003eAmphiroa fragilissima\u003c/em\u003e and \u003cem\u003ePorolithon onkodes\u003c/em\u003e, for which draft reference genomes are currently available (Table S2). For \u003cem\u003eP. calcareum\u003c/em\u003e, the reference genome was 150 Mbp in length, 6,375 contigs, with an N50 of 35 Kbp and an overall GC content of 43%. For \u003cem\u003eL. corallioides\u003c/em\u003e, the reference genome was 145 Mbp in length, 9,939 contigs, with an N50 of 19 Kbp and an overall GC content of 40%. Using the Eukaryota orthoDB v10 database (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;255), the BUSCO results for \u003cem\u003eP. calcareum\u003c/em\u003e were C:62.7% [S:59.6%, D:3.1%], F:12.5%, M:24.8%; and for \u003cem\u003eL. corallioides\u003c/em\u003e were C:40.8% [S:39.2%, D:1.6%], F:11.8%, M:47.4%. The species tree of coralline algae and other Florideophyceae red algal species was built using 157 concatenated BUSCO genes that were complete and single-copy in at least ten species (Fig.\u0026nbsp;1C). This tree showed that all coralline algae were positioned on a branch separate from all other Florideophyceae species. Within this branch, the evolutionary relationships amongst species were as expected based on previous research on coralline algae phylogenetics (Pe\u0026ntilde;a et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For the maerl-forming species in our study, the tree (Fig.\u0026nbsp;1C) showed that both \u003cem\u003eLithothamnion\u003c/em\u003e species, \u003cem\u003eL. corallioides\u003c/em\u003e and \u003cem\u003eL. proliferum\u003c/em\u003e, were monophyletic, sharing a recent common ancestor, and that both of these species shared their most recent common ancestor with \u003cem\u003eP. calcareum\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSNP dataset summary\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor \u003cem\u003eP. calcareum\u003c/em\u003e, filtering the raw variant calls from Freebayes resulted in 124,751 SNPs. Five individuals were removed at this stage because of very high missing data (\u0026gt;\u0026thinsp;95%); these individuals were all from Bembridge, which is probably because these samples had very little live algal material. Further filtering of these variants by distance thinning (linkage disequilibrium), mean read depth and keeping only biallelic SNPs resulted in a final dataset of 131 individuals genotyped at 15,330 SNPs for \u003cem\u003eP. calcareum\u003c/em\u003e. Note this is less than the 136 individuals identified as \u003cem\u003eP. calcareum\u003c/em\u003e because of the removal of five individuals from Bembridge (see above). For \u003cem\u003eL. corallioides\u003c/em\u003e, filtering the raw variant calls from Freebayes resulted in 192,041 SNPs. Further filtering of these variants resulted in a final dataset of 66 individuals genotyped at 10,215 SNPs for \u003cem\u003eL. corallioides\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTriploidy in\u003c/b\u003e \u003cb\u003eP. calcareum\u003c/b\u003e \u003cb\u003ecoarse growth form\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAt the St Mawes maerl bed, we found two growth forms both identified as \u003cem\u003eP. calcareum\u003c/em\u003e by DNA barcoding, one of which was typically around 2cm in diameter with characteristic delicate thalli, while the other was typified by a much larger rhodolith (4-5cm in diameter) with far bulkier branching structures (Fig.\u0026nbsp;1, Figure S2). Hereafter, we refer to the latter growth form in St Mawes as the \u0026lsquo;coarse\u0026rsquo; form (MawC). Allele balance analysis of all coarse maerl samples showed a clear pattern of triploidy (Fig.\u0026nbsp;2), with two peaks at 1/3 and 2/3 as per expectations of triploids. These samples were compared with all other \u003cem\u003eP. calcareum\u003c/em\u003e samples which demonstrated that all other samples were diploid, as evidenced by a single peak for allele balance at 1/2 (Fig.\u0026nbsp;2). All \u003cem\u003eL. corallioides\u003c/em\u003e samples with sufficient median read depth showed a peak at 1/2 and were deemed to be diploids.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGenetic structure\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor \u003cem\u003eP. calcareum\u003c/em\u003e, PopCluster was first run using sites only from Britain and Northern Ireland, because we were primarily interested in genetic structure of newly sampled maerl across this region. PopCluster statistics (\u003cem\u003eD\u003c/em\u003e\u003csub\u003e\u003cem\u003eLK\u003c/em\u003e2\u003c/sub\u003e and \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eSTIS\u003c/em\u003e\u003c/sub\u003e) showed highest support for five \u003cem\u003eK\u003c/em\u003e ancestral populations (genetic clusters) (Figure S3, S4), so \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5 was used for visualising admixture results in the structure plot and admixture map (Fig.\u0026nbsp;3). The results indicate that most maerl samples (individuals) from the following sites were primarily derived from four separate ancestral source populations: Zara Shoal (blue cluster), Weymouth (green cluster), Nare Head (grey cluster), and the coarse triploid maerl from St Mawes (orange cluster). All coarse maerl (regardless of whether they were collected in 2011 or 2022) were virtually genetically distinct from all other sites, while two individuals in Weymouth and a single individual in Nare Head had shared ancestry with other sites. The remaining sites across Cornwall: The Manacles, St Mawes (non-coarse maerl), the Bizzies, Gerrans Bay and St Austell, all showed evidence of admixture with one another (mostly blue and pink clusters, Fig.\u0026nbsp;3). These patterns of genetic structure were supported by PCA when the first three principal components were visualised (Figure S5). To check whether any newly identified site-specific genetic clusters found in Britain (e.g. Weymouth and Nare Head) were also found in other European sites, PopCluster was run a second time including sites from France and Spain; this showed that all four sites from France and Spain were genetically differentiated from Weymouth and Cornwall (Figure S6), but with some site-specific admixture, which was expected based on previous research (Jenkins et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eL. corallioides\u003c/em\u003e, PopCluster analyses showed a similar range of structuring at different values of \u003cem\u003eK\u003c/em\u003e, with \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4 and \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5 showing the highest statistical support overall (Figure S7, S8). Because there was support for five genetic clusters in the PCA (Figure S9), \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5 was used for visualising admixture results in the structure plot and admixture map (Fig.\u0026nbsp;4). The results indicate that most individuals from Weymouth and Swanage are primarily derived from a single ancestral population (green cluster) that is mostly found in this area (although there is evidence of some admixture with other sites), and in St Tudwal\u0026rsquo;s Island. At \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8, there was evidence that St Tudwal\u0026rsquo;s Island might also be genetically differentiated (Figure S8), although only two samples were available for this site. Individuals from St Austell Bay were mostly derived from a single ancestral source population (pink cluster), which appeared to be admixed with St Mawes and with some individuals from Helford River. The remaining seven individuals from Helford River were mostly derived from a different ancestral population (blue cluster) that is primarily found in this area. In Milford Haven, individuals from both sites were mostly derived from a single ancestral population (red cluster), but with some admixture with other sites, and an additional distinct ancestral population was observed (yellow cluster) which was dominant in four individuals.\u003c/p\u003e \u003cp\u003ePairwise genetic differentiation measures of \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e ranged from 0.02 (Man-Biz) to 0.42 (Zar-MawC) in \u003cem\u003eP. calcareum\u003c/em\u003e and from 0.001 (Mil1-Mil2) to 0.11 (Hel-Swa) in \u003cem\u003eL. corallioides\u003c/em\u003e (Figure S10). The most differentiated site in \u003cem\u003eP. calcareum\u003c/em\u003e was the coarse triploidy maerl from St Mawes (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e range: 0.33\u0026ndash;0.42) followed by Weymouth (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e range: 0.19\u0026ndash;0.33), while in \u003cem\u003eL. corallioides\u003c/em\u003e the most differentiated sites were Helford River (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e range: 0.07\u0026ndash;0.11) and Swanage (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e range: 0.02\u0026ndash;0.11). Overall, patterns of pairwise \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e emulated the patterns of genetic structure.\u003c/p\u003e \u003cp\u003e \u003cb\u003eClonal lineages and genetic diversity\u003c/b\u003e \u003c/p\u003e \u003cp\u003eClonality and genetic diversity is only reported for sites in Britain as data for \u003cem\u003eP. calcareum\u003c/em\u003e from sites in France and Spain have been analysed in a previous study (Jenkins et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Based on Prevosti's genetic distances, the clone cut-off threshold (i.e. the threshold at which multi-locus lineages should be defined), was estimated as 0.068 in \u003cem\u003eP. calcareum\u003c/em\u003e and 0.127 in \u003cem\u003eL. corallioides\u003c/em\u003e (Figure S11, S12). For \u003cem\u003eP. calcareum\u003c/em\u003e, this translated to 78 clonal lineages (out of 131 individual samples, which includes the samples from France and Spain), while for \u003cem\u003eL. corallioides\u003c/em\u003e, this translated to 52 clonal lineages (out of 66 individual samples). The number of clonal linages and the abundance of clones in each lineage was visualised for both species (Fig.\u0026nbsp;5). Note in \u003cem\u003eL. corallioides\u003c/em\u003e we present clonality and genetic diversity results for Weymouth and Swanage as a single genetic unit (putative maerl population), as informed by the genetic structure analyses. This is also the case for both St Austell Bay sites in both species, and for both Milford Haven sites in \u003cem\u003eL. corallioides\u003c/em\u003e. Additionally, these analyses were not carried out for Swanage (\u003cem\u003eP. calcareum\u003c/em\u003e) or St Tudwal\u0026rsquo;s Island (\u003cem\u003eL. corallioides\u003c/em\u003e) due to their low sample sizes (less than three individuals).\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eP. calcareum\u003c/em\u003e, one notable result is a single clonal lineage for all coarse triploidy maerl in St Mawes (MawC), which also showed much higher observed heterozygosity and lower negative \u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e (Table\u0026nbsp;2). In contrast, clonal diversity was variable across the other putative maerl populations, evidenced by the range of Pareto β values (Table\u0026nbsp;2). For instance, a single clonal lineage was dominant at Nare Head, which also has the lowest Pareto β value (excluding the coarse maerl). In comparison, there are lineages of more even distribution in other sites, such as at The Manacles, St Mawes (Maw), the Bizzies, and St Austell Bay. Two lineages were found across two different sites: MLG19 was found in both St Austell Bay and in St Mawes (Maw) and MLG22 was found in both St Austell Bay and the Bizzies. Observed heterozygosity in \u003cem\u003eP calcareum\u003c/em\u003e ranged from 0.26\u0026ndash;0.65, while expected heterozygosity ranged from 0.19\u0026ndash;34. For \u003cem\u003eL. corallioides\u003c/em\u003e, we identified one large clonal lineage and five smaller ones in Helford River, which is evidenced by its lower Pareto β value (Table\u0026nbsp;2). In contrast, the other five sites showed either more balanced lineages of similar sizes (Milford Haven and St Mawes), supported by their slightly higher Pareto β values, or no clones were detected (St Austell Bay and Weymouth/Swanage). Observed heterozygosity in \u003cem\u003eL. corallioides\u003c/em\u003e ranged from 0.32\u0026ndash;0.39, while expected heterozygosity ranged from 0.29\u0026ndash;0.30 (Table\u0026nbsp;2). For both species, and in sites where clones were detected, Pareto β values did not exceed 2.10. In addition, expected heterozygosity was lower than observed heterozygosity in all putative maerl populations and all \u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e values were negative.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGenotype-environment associations and genomic offset\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor \u003cem\u003eP. calcareum\u003c/em\u003e, using benthic ocean temperature and salinity as climate predictors, 2,476 and 267 GEA outlier loci were identified by LFMM and RDA, respectively. Of these, 138 were common across both methods and were considered as putative climate-associated SNPs. RDA was modelled for only the 138 climate-associated SNPs, which explained 19.2% (adjusted \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) of the variation in the allele frequencies, and the results were visualised as an RDA biplot (Fig.\u0026nbsp;6A). This showed that most individuals from Zara Shoal, Weymouth and Tr\u0026eacute;vignon were associated with lower salinities and generally lower temperatures. The Bizzies Reef was generally associated with lower temperatures and mostly higher salinities. In contrast, individuals from Morlaix were associated with higher temperatures. The remaining sites were generally positioned in the centre of the RDA biplot, with some minor exceptions within sites (such as a group of individuals from Nare Head that were linked to higher salinities).\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eL. corallioides\u003c/em\u003e, salinity was highly correlated with temperature (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.94); therefore, genotypes were only modelled as a function of temperature. Very few GEA outlier loci were detected: 11 by LFMM and 34 by RDA, with only a single locus common across both methods. As a result, no further GEA analyses were conducted with \u003cem\u003eL. corallioides\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eAll values for future climate variables are predicted to increase (temperature) or decrease (salinity) by 2050 for the SSP245 scenario. The degree of these changes, however, varied by site. The predicted changes for each variable were as follows: (i) ocean temperature increases ranging from 0.41\u0026ndash;0.44\u003csup\u003eo\u003c/sup\u003eC (Galicia) to 0.68\u003csup\u003eo\u003c/sup\u003eC (Weymouth), and (ii) salinity decreases ranging from \u0026minus;\u0026thinsp;0.26 PSU (Tr\u0026eacute;vignon) to -0.42\u0026ndash;0.44 PSU (all sites from Cornwall and Zara Shoal). Genomic offsets were averaged for each site and visualised on a map (Fig.\u0026nbsp;6B). The lowest genomic offsets for 2050 SSP245 were observed in Tr\u0026eacute;vignon, while the highest genomic offsets were observed in all sites from Cornwall, Zara Shoal and Illa de Ons.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMicrobiota community of maerl across sites\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMicrobiota site averages showed bacteria were dominant (78\u0026ndash;96%) relative to archaea (4\u0026ndash;22%), the latter of which were found in higher proportions in Bembridge and Swanage, likely due to the presence of nitrifying Archaea (see below). Overall, one of the most common group of bacteria detected were the Pseudomonadota, ranging from 26\u0026ndash;51%, which were mainly composed of Alphaproteobacteria (7\u0026ndash;23%) and Gammaproteobacteria (5\u0026ndash;26%) (Fig.\u0026nbsp;7). Actinobacteria and Cyanobacteria were detected but in much lower proportions. The mean proportion of Flavobacteria appears to follow a downward trend from St Mawes (dense live maerl \u0026ndash; mostly category A1) to Bembridge (scattered mostly dead maerl \u0026ndash; mostly category C2). Flavobacteria were on average 18% (13\u0026ndash;23%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 12% (7\u0026ndash;18%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) higher in St Mawes and Helford River, respectively, compared to Bembridge. Additionally, Flavobacteria were on average 5.4% (0.55-10%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) higher in Milford Haven compared to Bembridge. No differences in the proportion of Flavobacteria were observed amongst The Manacles, Swanage and Bembridge sites.\u003c/p\u003e \u003cp\u003eIn contrast, the opposite trend was apparent in Nitrospirota (Bacteria) and Nitrososphaerota (Archaea), whereby there was an upward trend in proportion from St Mawes to Bembridge. Nitrospirota were on average 2.9\u0026ndash;3.8% higher in Bembridge compared to each of St Mawes, Helford River, Milford Haven and The Manacles (all comparisons \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, Nitrososphaerota were on average 8.8\u0026ndash;17% higher in Bembridge compared to each of St Mawes, Helford River and Milford Haven (all comparisons \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and The Manacles (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016). Swanage showed on average a slightly lower proportion of Nitrospirota compared to Bembridge (1.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), but no difference was observed in Nitrososphaerota (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.997). The microbiota proportions for all six sites and for each individual sample are available in the Supporting Information (Figure S13).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eCombining whole genome sequencing and \u003cem\u003ede novo\u003c/em\u003e genome assembly, we investigated population genomics, climate adaptation and microbiota communities in two species of coralline algae that form maerl bed habitats in the north-east Atlantic. Our study presents the first draft reference genome assemblies for two maerl-forming species: \u003cem\u003ePhymatolithon calcareum\u003c/em\u003e (GCA_040759855) and \u003cem\u003eLithothamnion corallioides\u003c/em\u003e (GCA_040759835). We built a species tree using BUSCO protein sequences extracted from all currently available coralline algae genomes and transcriptomes; this showed very similar evolutionary relationships to a previous comprehensive phylogenetic study of coralline algae that used seven organelle and nuclear genes (Pe\u0026ntilde;a et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Whole genome sequence data were mapped to the draft genomes of \u003cem\u003eP. calcareum\u003c/em\u003e and \u003cem\u003eL. corallioides\u003c/em\u003e to generate SNP datasets for investigating population genomics and potential genetic adaptations to climate, while unmapped reads were used to explore the microbiota community living on or within maerl thalli. These results, as well as the identification of genetic management units and conservation implications, are discussed below.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSpecies identification of maerl sampled in England and Wales\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe sampled novel sites in England and Wales containing maerl that have yet to be assigned species identifications in the scientific literature and in UK maerl bed conservation. Our results confirm the coralline algal species forming maerl beds (or scattered live maerl) at these locations: Bembridge (\u003cem\u003eP. calcareum\u003c/em\u003e), the Bizzies Reef (\u003cem\u003eP. calcareum\u003c/em\u003e), Gerrans Bay (\u003cem\u003eP. calcareum\u003c/em\u003e), Nare Head (\u003cem\u003eP. calcareum\u003c/em\u003e), St Austell Bay (\u003cem\u003eP. calcareum\u003c/em\u003e and \u003cem\u003eL. corallioides\u003c/em\u003e), Weymouth (\u003cem\u003eP. calcareum\u003c/em\u003e and \u003cem\u003eL. corallioides\u003c/em\u003e), Swanage (\u003cem\u003eP. calcareum\u003c/em\u003e and \u003cem\u003eL. corallioides\u003c/em\u003e), Helford River (\u003cem\u003eL. corallioides\u003c/em\u003e), Milford Haven (\u003cem\u003eL. corallioides\u003c/em\u003e), and St Tudwal\u0026rsquo;s Island (\u003cem\u003eL. corallioides\u003c/em\u003e). It should be noted, however, that sampling may not have covered the full spatial distribution of maerl at each location and, therefore, other maerl-forming species may be present but were undetected in the present study.\u003c/p\u003e \u003cp\u003eMarine Conservation Zones (MCZs), a type of national MPA in England, have been designated to protect a range of nationally important, rare or threatened habitats and species. However, while several MCZs list maerl beds as a feature in their designation (e.g. The Manacles, Purbeck Coast, Bembridge), the particular maerl-forming species is not identified as a feature in the listing. This is primarily because Natural England, and other national public bodies in the UK, emphasise the importance of the maerl habitat rather than the specific species (Axelsson \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, because maerl is extremely challenging to identify to species level based on external morphology, and especially in the field, the maerl-forming species at these locations are typically unknown. Consequently, this has also made it challenging to design and carry out effective sampling strategies for specific maerl-forming species, which in part explains why some sites in our study have low sample sizes for \u003cem\u003eP. calcareum\u003c/em\u003e or \u003cem\u003eL. corallioides\u003c/em\u003e (the other reason for this is that live maerl is very sparse at some sites, e.g. Bembridge). Nevertheless, given that both \u003cem\u003eP. calcareum\u003c/em\u003e and \u003cem\u003eL. corallioides\u003c/em\u003e are identified as priority species under the UK Biodiversity Action Plan (now replaced by the UK Biodiversity Framework 2024), our results enable adding species information to the designation orders and/or to the statutory conservation advice for the MCZs outlined above. Knowledge of the species forming live maerl at each MCZ and each location sampled will be informative for future research on these maerl-forming species and for any restoration projects, where it is important to know the diversity of maerl-forming species, particularly the dominant species, present at a given maerl habitat (Axelsson \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCoarse triploidy maerl in St Mawes\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn a previous study, \u003cem\u003eP. calcareum\u003c/em\u003e from St Mawes was found to be genetically unique compared to all other maerl beds sampled in western Europe (Jenkins et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the current study, additional sampling of maerl beds in and around Falmouth, and higher sequencing depth, has enabled further insight into this finding. First, we found that only the newly recognised coarse growth form of \u003cem\u003eP. calcareum\u003c/em\u003e in St Mawes is genetically unique. Second, there is strong evidence that this is due to triploidy, whereby one or more chromosomes have three copies rather than the two expected in maerl (Pardo et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Triploidy in this \u003cem\u003eP. calcareum\u003c/em\u003e form from the Fal Estuary was also postulated by Pardo and colleagues using microsatellite genotypes (Pardo et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which further supports our hypothesis that triploidy is driving the genetic differentiation observed in the coarse maerl. These findings raise two follow-up questions. First, is triploidy the result of hybridisation (allopolyploidy) or whole genome duplication (autopolyploidy)? And, second, has triploidy contributed to the development of the markedly larger and bulkier growth morphology of this form of \u003cem\u003eP. calcareum\u003c/em\u003e, and if so, is it a neutral or adaptive change? Given that no genomic data exist for other species in the \u003cem\u003ePhymatolithon\u003c/em\u003e genus, and that no information is available on the karyology or physiology of the coarse growth form, further research will be needed to resolve these questions.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePopulation structure and clonal lineages\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAnalysis of genetic structure and clonality revealed both similarities and differences in locations where both maerl-forming species co-exist in sympatry. For instance, maerl from both species in Weymouth (and Swanage in \u003cem\u003eL. corallioides\u003c/em\u003e) are genetically differentiated from their respective maerl in Cornwall. This is likely a result of geographical isolation, possibly driven by limited dispersal capacity (Pardo et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and indicates that maerl in Weymouth/Swanage should be considered as separate populations for both \u003cem\u003eP. calcareum\u003c/em\u003e and \u003cem\u003eL. corallioides\u003c/em\u003e. When comparing clonality in Weymouth/Swanage, no clones were detected in \u003cem\u003eL. corallioides\u003c/em\u003e, whereas one clonal lineage had three (out of five) individual members in \u003cem\u003eP. calcareum\u003c/em\u003e, all three of which were also assigned to a single genetically distinct ancestral cluster with no evidence of admixture with Cornwall. These differences could represent evolutionary histories between \u003cem\u003eP. calcareum\u003c/em\u003e and \u003cem\u003eL. corallioides\u003c/em\u003e, possibly driven by different rates of clonal versus sexual reproduction. However, given sample sizes of five (\u003cem\u003eP. calcareum\u003c/em\u003e) and seven (\u003cem\u003eL. corallioides\u003c/em\u003e) in these areas, we may not have sampled all potential clonal lineages. Therefore, additional sampling of maerl would enhance inferences of clonality and population structure at these locations.\u003c/p\u003e \u003cp\u003eIn Cornwall, discounting the genetically distinct coarse triploidy maerl (discussed above), there are also interesting similarities between species, but some subtle and potentially important differences. For example, in both species the St Mawes maerl bed is admixed with other maerl beds in Cornwall, with similar levels of clonality, characterised by one or two relatively dominant lineages and several singleton lineages. For \u003cem\u003eP. calcareum\u003c/em\u003e, this contrasts with previous studies where only the coarse triploid maerl was sampled (Pardo et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jenkins et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), because our results indicate that there is shared ancestry between St Mawes (the non-coarse maerl form) and maerl sampled elsewhere in Cornwall. Indeed, there is evidence of admixture among all sites from Cornwall in \u003cem\u003eP. calcareum\u003c/em\u003e (with the potential exception of Nare Head). Given that a single maerl thallus is potentially over 100 years old (Foster \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), and that the dispersal capacity of maerl-forming species is thought to be limited (Pardo et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), this suggests that the genetic connectivity observed among these locations has likely acted over a very long temporal scale. This is supported by evidence of some mixed ancestry with Zara Shoal (Northern Ireland) and other European maerl beds (Jenkins et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which suggests that \u003cem\u003eP. calcareum\u003c/em\u003e has a complex evolutionary history that has likely been influenced by many factors, such as periodic changes in habitat suitability, connectivity and glacial refugia. Nonetheless, we infer that \u003cem\u003eP. calcareum\u003c/em\u003e maerl from sites sampled in Cornwall are mostly admixed populations which may represent a contemporary/historic metapopulation.\u003c/p\u003e \u003cp\u003eIn \u003cem\u003eL. corallioides\u003c/em\u003e, we found two ancestral clusters in the Helford River, one of which was only found at this location, and based on our clonal analysis this is a distinct and large clonal lineage. Similarly, we observed a congruent pattern at Nare Head for \u003cem\u003eP. calcareum\u003c/em\u003e, whereby a dominant clonal lineage was present. This may suggest that the same set of environmental and evolutionary processes have shaped the population structure of maerl at these locations. In contrast, there are some subtle genetic differences between the two species across Cornwall. For instance, while \u003cem\u003eP. calcareum\u003c/em\u003e maerl in St Austell Bay exhibits shared ancestry from two ancestral sources, \u003cem\u003eL. corallioides\u003c/em\u003e maerl in St Austell Bay is primarily derived from a single ancestral source. Moreover, no clones were detected in St Austell Bay for \u003cem\u003eL. corallioides\u003c/em\u003e, whereas two clonal lineages composed of two or three clones were detected for \u003cem\u003eP. calcareum\u003c/em\u003e. This could reflect the differences in evolutionary history between the two species in this area, whether it be geographical isolation, historic (re)colonisation, connectivity, or a mixture of these.\u003c/p\u003e \u003cp\u003eIn Milford Haven, only \u003cem\u003eL. corallioides\u003c/em\u003e maerl was found at this location in our study, so no comparison could be made with \u003cem\u003eP. calcareum\u003c/em\u003e. Nevertheless, we found that Milford Haven maerl is genetically differentiated from all other sites sampled in England. In addition, Milford Haven maerl is clonally diverse with high heterozygosity relative to other sites, and the two ancestral sources dominant in this region are mostly found here, with very little or no admixture with maerl from Cornwall or Weymouth/Swanage. This suggests that Milford Haven harbours at least one distinct maerl population that is genetically diverse and geographically isolated from other populations in England.\u003c/p\u003e \u003cp\u003e \u003cb\u003eClimate adaptation in maerl-forming species\u003c/b\u003e \u003c/p\u003e \u003cp\u003eClimate adaptation was only assessed for \u003cem\u003eP. calcareum\u003c/em\u003e because only a single GEA outlier locus was detected for \u003cem\u003eL. corallioides\u003c/em\u003e, which may be because there is not enough environmental variability across the sites where \u003cem\u003eL. corallioides\u003c/em\u003e was sampled. For \u003cem\u003eP. calcareum\u003c/em\u003e, after controlling for spatial autocorrelation (the potential influence of geographic distances correlating with environmental gradients among locations), the GEA analysis found 138 putative climate-associated SNPs. An RDA using just these SNPs showed some site-specific clustering with temperature and salinity. For example, maerl from Morlaix were associated with higher temperatures, more so than maerl from Galicia (Bornalle and Illa de Ons), despite present-day temperatures being on average higher in Galicia, though the next highest benthic temperatures after Galicia (15.0\u003csup\u003eo\u003c/sup\u003eC) are in Morlaix (13.7\u003csup\u003eo\u003c/sup\u003eC). This suggests that maerl in Morlaix may have putative genetic adaptations to benthic ocean temperatures. However, despite potential adaptations to temperature, Morlaix appears to have a lower (genomic offset) risk of being maladapted to temperature and salinity conditions in 2050 compared to many other sites. According to our analysis, the maerl bed with the lowest risk of maladaptation is Tr\u0026eacute;vignon; this is probably because this area has the smallest predicted decreases in salinity (0.26 PSU compared to the next decrease of 0.38 PSU in Weymouth) and the second smallest increase in temperature (0.51\u003csup\u003eo\u003c/sup\u003eC after 0.41\u003csup\u003eo\u003c/sup\u003eC\u0026ndash;0.44\u003csup\u003eo\u003c/sup\u003eC in Galicia). Interestingly, Bornalle, a maerl bed located within an inner estuary, had a lower maladaptation risk compared with Illa de Ons, a maerl bed located within an outer estuary. This was an unexpected result given that temperature and salinity is broadly similar across both Galician sites, both in terms of present-day and future change. However, coastal configuration and the location of maerl beds within estuaries has been found to shape genetic structure and clonality in \u003cem\u003eP. calcareum\u003c/em\u003e (Pardo et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); this may suggest that subtle changes in temperature or salinity could have higher ramifications for maerl populations situated in the outer zones of estuaries in Galicia.\u003c/p\u003e \u003cp\u003eIn the UK, maerl from Cornwall and Zara Shoal had higher risks of maladaptation to conditions in 2050 compared to Weymouth and two maerl beds from France. Weymouth has the highest projected temperature increase (0.68\u003csup\u003eo\u003c/sup\u003eC) of any site, while all sites from Cornwall and Zara Shoal have the highest projected salinity decrease (0.42\u0026ndash;0.44 PSU). However, even though Weymouth also has projected salinity decreases (0.35 PSU), it appears that predicted salinity decreases, as well as temperature increases in Zara Shoal and Cornwall (0.53-0.60\u003csup\u003eo\u003c/sup\u003eC), may result in a greater risk of maladaptation to conditions in 2050 for maerl across these areas. This indicates that \u003cem\u003eP. calcareum\u003c/em\u003e maerl from Cornwall, Zara Shoal and Illa de Ons will have to have greater shifts in their allele frequencies to mitigate the risk of being maladapted to future environments. It should be noted, however, that the RDA explained 19.2% of the variation in the allele frequencies of the climate-associated SNPs, which suggests that a large portion of variation in these outlier loci is explained by other factors, e.g. other localised environmental parameters or biological processes. Moreover, predictions of maladaptation risk do not consider changes in other conditions that may have additional impacts on the habitat suitability and survival of maerl, such as increased exposure to physical disturbances, pollution or invasive species.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMicrobiota differences between maerl beds of varying condition\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMetagenomics analysis revealed that Alphaproteobacteria and Gammoproteobacteria, both classes of the Proteobacteria phylum, were the most common microbe found on or within maerl samples across sites (except at Bembridge and Swanage). These results accord with findings in other coralline algae, such as \u003cem\u003eCorallina officinalis\u003c/em\u003e (Brodie et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and \u003cem\u003eNeogoniolithon trichotomum\u003c/em\u003e (Valdespino-Castillo et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), whereby the core microbiota was made up of Proteobacteria, along with several other groups that were also detected in our study, including Bacteroidetes (which includes the Flavobacteria class), Actinobacteria and Cyanobacteria.\u003c/p\u003e \u003cp\u003eFlavobacteria comprised a larger proportion of the microbiota community in St Mawes and Helford River (mostly dense maerl beds with live maerl samples) compared to Bembridge (mostly dead maerl bed with decaying or dead maerl samples). This group of aerobic bacteria can be found free-living, attached to detritus in the water column, or colonising the surface of macroalgae (Mann et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gavriilidou et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the latter of which explains why these bacteria were detected on maerl. In contrast, a lower proportion of nitrifying microbes on maerl were found in St Mawes, Helford River and Milford Haven, while a much higher proportion of nitrifying microbes were found on maerl in Bembridge and Swanage. Proportions of microbiota from maerl at The Manacles were roughly in between these two profiles. The apparent replacement of Flavobacteria with nitrifying microbes may suggest a functional role for Flavobacteria on live maerl thalli. Alternatively, the proportional increase in nitrifying microbes could indicate the presence of more nitrogenous compounds in the environment, potentially originating from nearby sewage discharges or fish farms (Legrand et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Eutrophication is problematic for maerl because excessive growth of other epiphytic algae can increase sedimentation and create hypoxic conditions, both of which inhibit photosynthesis and respiration (Grall \u0026amp; Hall-Spencer \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). If this is true, our findings may suggest that the Bembridge maerl bed, and to some extent the Swanage maerl bed, may be (or may have been) in a state of degradation caused by eutrophication. However, this is speculative without further spatial and temporal sampling, as it is not possible to ascertain whether these differences in microbiota are consistent across space and time, and whether the patterns observed are the cause or a symptom of degradation in Bembridge or Swanage maerl.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGenetic units and conservation implications\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe assigned locations where maerl was sampled to genetic management units based on all the data collected and analysed in this study (Table\u0026nbsp;2). Because genetic analysis is conducted on single species, the units are specified for each species (\u003cem\u003eP. calcareum\u003c/em\u003e and \u003cem\u003eL. corallioides\u003c/em\u003e). However, as some locations harbour maerl from both species, it seems probable that new or improved conservation actions in one area could benefit more than one maerl-forming species. For each genetic unit, we assessed genetic diversity (alpha diversity) and genetic differentiation (beta diversity) EBVs and used this information to guide our conservation insights (see below). We do not report inbreeding or effective population size (\u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e) EBVs because the complex life history and reproduction of maerl-forming species make these metrics difficult to determine with confidence and, particularly for \u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e, we do not yet have large enough sample sizes for accurate and meaningful assessments.\u003c/p\u003e \u003cp\u003eFour \u003cem\u003eP. calcareum\u003c/em\u003e genetic units are currently located within a designated MPA: St Mawes coarse form (Fal and Helford SAC), St Mawes non-coarse form (Fal and Helford SAC), The Manacles (The Manacles MCZ), and Weymouth (Purbeck Coast MCZ). The Fal and Helford SAC also encompasses \u003cem\u003eL. corallioides\u003c/em\u003e maerl from the Helford River (a distinct genetic unit in this species), which overlaps with the Helford River MCZ, but this MCZ is designated specifically for native oysters. The coarse, triploid maerl from St Mawes are all clones that descend from a single genetically distinct lineage. Thus, this genetic unit of \u003cem\u003eP. calcareum\u003c/em\u003e has no clonal diversity but interestingly has high observed heterozygosity (Table\u0026nbsp;2), which is likely a result of triploidy introducing additional alleles at many SNP loci. Indeed, it could be considered as an Evolutionary Significant Unit (ESU) because it has likely been segregated for many generations and is defined by a unique set of genetic (triploidy) and phenotypic (coarse growth form) traits (Funk et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The loss of such an ESU would be equivalent to losing a distinct product of evolutionary history that may have evolved unique and/or adaptive traits (Hoban et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, we also found a relatively large clonal lineage in the Helford River that was genetically distinct from all other \u003cem\u003eL. corallioides\u003c/em\u003e maerl analysed. Accordingly, considering the high maerl bed density and the distinct genetic diversity found in St Mawes and the Helford River, we advocate that the Fal Estuary and Helford River area remains a high priority for marine conservation.\u003c/p\u003e \u003cp\u003eFor the remaining sites in Cornwall, only The Manacles has a MCZ designated to protect maerl beds, which has a byelaw to prohibit scallop dredging and bottom towed gear under \u003cem\u003eThe Manacles Marine Conservation Zone (Fishing Restrictions) Byelaw 2017\u003c/em\u003e (Cornwall IFCA). In Nare Head, we found one dominant clonal lineage of \u003cem\u003eP. calcareum\u003c/em\u003e, further analysis of which suggested that this may be genetically differentiated from other Cornish maerl. In addition, \u003cem\u003eP. calcareum\u003c/em\u003e maerl from The Manacles, the Bizzies, Gerrans Bay and St Austell Bay are possibly individual populations that have been or are connected by historic or contemporary gene flow. All these sites also have a higher risk of being maladapted to future temperatures and salinities relative to other sites in England and France. We therefore recommend that an additional MPA be considered for Gerrans Bay which would cover both the maerl from Gerrans Bay and Nare Head and safeguard the genetic diversity at these locations. A secondary priority in Cornwall would be to establish another MPA in St Austell Bay to protect both maerl-forming species, the benefit of which could enhance genetic connectivity across the region. In addition, for \u003cem\u003eL. corallioides\u003c/em\u003e, this would conserve the high clonal diversity observed at this location, which may be indicative of higher rates of sexual reproduction generating new lineages. The prevalence of sexual reproduction could be explored at this site, or indeed any site, by conducting additional surveys to search for attached gametophytes with uniporate conceptacles (Pardo et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To our knowledge, the only reported gametophyte of any maerl-forming species has been found in a \u003cem\u003eP. calcareum\u003c/em\u003e maerl bed in Brittany, France (Pardo et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Nevertheless, the detection of these attached gametophytes would have important implications for maerl bed conservation since they are central to introducing novel genetic variants into the population via sexual reproduction.\u003c/p\u003e \u003cp\u003eIn Weymouth and Swanage, both maerl-forming species have isolated populations at one or both of these locations and these should be considered as separate genetic units to maerl beds in Cornwall. Currently, Purbeck Coast MCZ encompasses maerl beds at these locations. As mentioned previously for genetically diverse / distinct areas, we recommend that conservation of these sites is prioritised, particularly as there is likely very limited connectivity between these maerl beds and those in Cornwall. On the east coast of the Isle of Wight, we also collected maerl samples from Bembridge, which comprised mostly dead maerl with very little cover of living (pink) algae on each thallus. Purbeck Coast MCZ and Bembridge MCZ list maerl beds as a protected feature in their designation (in addition to other features), with a current management goal of \u0026ldquo;\u003cem\u003erecover to favourable condition\u003c/em\u003e\u0026rdquo;. Although we could not determine any genetic EBVs for Bembridge due to insufficient SNP data (likely a result of low DNA yield due to very little algal material on maerl thalli), we were able to identify five samples as \u003cem\u003eP. calcareum\u003c/em\u003e. Thus, any future restoration project in Bembridge involving the translocation of live maerl from another maerl bed should take this into consideration.\u003c/p\u003e \u003cp\u003eIn south-west Wales, all maerl from Milford Haven was classified as a single genetic unit, which has high genetic diversity and is genetically differentiated from other \u003cem\u003eL. corallioides\u003c/em\u003e maerl beds in England. Milford Haven had the highest and second highest measures of clonal diversity (Pareto β) and observed heterozygosity, respectively. This suggests that, over many past generations, both sexual and clonal reproduction has occurred to produce new lineages and to proliferate existing lineages in the population. It is therefore important that the Pembrokeshire Marine SAC, which encompasses the Milford Haven area, provides adequate protection for maerl from existential threats, since any loss or regression of this maerl bed could erode the unique genetic diversity found in \u003cem\u003eL. corallioides\u003c/em\u003e at this location. In addition, although we were able to obtain some preliminary data on the genetic structure of maerl from St Tudwal\u0026rsquo;s Island (north-west Wales), more samples will be needed to accurately assess population structure and compute genetic EBVs.\u003c/p\u003e \u003cp\u003eLastly, although population structure and clonality in other European maerl beds (Northern Ireland, France and Spain) were reported in previous studies for \u003cem\u003eP. calcareum\u003c/em\u003e (Pardo et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jenkins et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), by including these WGS data in this study we were able to assess their risk of maladaptation to future climates in 2050 under the SSP245 scenario. Zara Shoal (Strangford Lough, Northern Ireland), Illa de Ons (Galicia, Spain) and all Cornish maerl beds were at elevated risk compared to all other sites. The Strangford Lough SAC lists maerl beds as a protected feature in its conservation objectives, while the Espacio marino de las Rias Baixas de Galicia MPA appears to encompass Illa de Ons (but not Bornalle). At present, therefore, it appears as though the European maerl beds listed above, that have higher risks of maladaptation, are currently positioned within MPAs. However, although analysis of genetic data can tell us about genetic diversity and potential resilience to change, deciding where to prioritise resources should also consider the density and health of maerl beds, as well as potential mechanisms for effectively protecting these habitats from threats in the short-term (e.g. physical disturbances and pollution) and the long-term (e.g. climate change).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, genomic data was used to address important knowledge gaps in the population structure, genetic diversity, climate resilience and microbiota of two maerl-forming species, \u003cem\u003eP. calcareum\u003c/em\u003e and \u003cem\u003eL. corallioides\u003c/em\u003e, found across south-west Britain, Northern Ireland, France and Spain. In both species, the discovery of distinct genetic structure suggests that some maerl populations harbour unique genetic diversity and are genetically and/or geographically isolated. For example, maerl of both species in Weymouth/Swanage (Dorset) were found to be genetically differentiated from all maerl in Cornwall. Additionally, in \u003cem\u003eL. corallioides\u003c/em\u003e, the Milford Haven (south-west Wales) maerl population was genetically diverse and isolated from other maerl populations in England. In Cornwall, we identified the genetically and phenotypically distinct coarse growth form of \u003cem\u003eP. calcareum\u003c/em\u003e and found that this coarse form co-exists with a non-coarse form in St Mawes, the latter of which, unlike the coarse form, shares ancestry with neighbouring maerl populations (e.g. The Manacles and St Austell Bay). Moreover, in the Helford River, we found a relatively large clonal lineage of \u003cem\u003eL. corallioides\u003c/em\u003e that is genetically distinct and only found at this location. These findings further emphasise the high conservation priority of these maerl beds around Falmouth, both in terms of maerl bed density and species/genetic diversity, and underline the important role of designated MPAs, such as the Fal and Helford SAC, to protect the Fal Estuary and Helford River area from dredging and bottom towed gear. In addition, we found that populations of \u003cem\u003eP. calcareum\u003c/em\u003e from Cornwall, Zara Shoal (Northern Ireland) and Illa de Ons (Spain) are predicted to have an elevated risk of maladaptation to projected changes in temperature and salinity in 2050 under the SSP245 `middle of the road` climate change scenario. Lastly, we report differences in the microbiota community (i.e. bacteria and archaea) between maerl from dense, healthy maerl beds (e.g. St Mawes) compared to scattered, mostly dead maerl (e.g. Bembridge). Altogether, we use these data to propose genetic management units for maerl populations in south-west Britain, which will inform efforts to prevent further loss or degradation to maerl beds and aid conservation interventions to recover and restore these irreplaceable, high natural capital value habitats.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData archiving statement\u003c/p\u003e\n\u003cp\u003eRaw DNA sequence data and draft genome assemblies are available from the NCBI (BioProject: PRJNA682082; \u003cem\u003ePhymatolithon calcareum\u003c/em\u003e assembly: SAMN41910763;\u0026nbsp;\u003cem\u003eLithothamnion corallioides\u003c/em\u003e assembly: SAMN41910813). The modified BUSCO phylogenomics pipeline is available from\u0026nbsp;GitHub:\u0026nbsp;Tom-Jenkins/BUSCO_phylogenomics. The custom python scripts used for organelle annotation (extract_CDS.py) and for processing Kraken2 reports (process_kraken2_reports.py) are part of the nextflow pipelines repository available from Zenodo: https://doi.org/10.5281/zenodo.14056754. R code and supporting data used in the analyses are also available from Zenodo:\u0026nbsp;https://doi.org/10.5281/zenodo.14592386.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAguirre-Liguori JA, Ram\u0026iacute;rez-Barahona S, Gaut BS (2021) The evolutionary genomics of species\u0026rsquo; responses to climate change. \u003cem\u003eNature Ecology \u0026amp; Evolution\u003c/em\u003e, \u003cstrong\u003e5\u003c/strong\u003e, 1350\u0026ndash;1360.\u003c/li\u003e\n\u003cli\u003eAllendorf FW, Hohenlohe PA, Luikart G (2010) Genomics and the future of conservation genetics. \u003cem\u003eNature Reviews Genetics\u003c/em\u003e, \u003cstrong\u003e11\u003c/strong\u003e, 697\u0026ndash;709.\u003c/li\u003e\n\u003cli\u003eArnaud-Haond S, Duarte CM, Alberto F, Serr\u0026atilde;o EA (2007) Standardizing methods to address clonality in population studies. \u003cem\u003eMolecular Ecology\u003c/em\u003e, \u003cstrong\u003e16\u003c/strong\u003e, 5115\u0026ndash;5139.\u003c/li\u003e\n\u003cli\u003eAssis J, Fern\u0026aacute;ndez Bejarano SJ, Salazar VW \u003cem\u003eet al.\u003c/em\u003e (2024) Bio‐ORACLE v3.0. pushing marine data layers to the CMIP6 Earth System Models of climate change research. \u003cem\u003eGlobal Ecology and Biogeography\u003c/em\u003e, \u003cstrong\u003e33\u003c/strong\u003e, e13813.\u003c/li\u003e\n\u003cli\u003eAxelsson M (2023) A categorisation system for maerl bed habitats in England. \u003cem\u003eNatural England\u003c/em\u003e, \u003cstrong\u003eNERR123\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eBernard G, Romero-Ramirez A, Tauran A \u003cem\u003eet al.\u003c/em\u003e (2019) Declining maerl vitality and habitat complexity across a dredging gradient: insights from in situ sediment profile imagery (SPI). \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cstrong\u003e9\u003c/strong\u003e, 1\u0026ndash;12.\u003c/li\u003e\n\u003cli\u003eBernatchez L, Ferchaud A-L, Berger CS, Venney CJ, Xuereb A (2024) Genomics for monitoring and understanding species responses to global climate change. \u003cem\u003eNature Reviews Genetics\u003c/em\u003e, \u003cstrong\u003e25\u003c/strong\u003e, 165\u0026ndash;183.\u003c/li\u003e\n\u003cli\u003eBlake C, Maggs CA (2003) Comparative growth rates and internal banding periodicity of maerl species (Corallinales, Rhodophyta) from northern Europe. \u003cem\u003ePhycologia\u003c/em\u003e, \u003cstrong\u003e42\u003c/strong\u003e, 606\u0026ndash;612.\u003c/li\u003e\n\u003cli\u003eBorg M, Krueger-Hadfield SA, Destombe C \u003cem\u003eet al.\u003c/em\u003e (2023) Red macroalgae in the genomic era. \u003cem\u003eNew Phytologist\u003c/em\u003e, \u003cstrong\u003e240\u003c/strong\u003e, 471\u0026ndash;488.\u003c/li\u003e\n\u003cli\u003eBrodie J, Williamson C, Barker GL \u003cem\u003eet al.\u003c/em\u003e (2016) Characterising the microbiome of \u003cem\u003eCorallina officinalis\u003c/em\u003e, a dominant calcified intertidal red alga. \u003cem\u003eFEMS Microbiology Ecology\u003c/em\u003e, \u003cstrong\u003e92\u003c/strong\u003e, fiw110.\u003c/li\u003e\n\u003cli\u003eBuchfink B, Reuter K, Drost H-G (2021) Sensitive protein alignments at tree-of-life scale using DIAMOND. \u003cem\u003eNature Methods\u003c/em\u003e, \u003cstrong\u003e18\u003c/strong\u003e, 366\u0026ndash;368.\u003c/li\u003e\n\u003cli\u003eCamacho C, Coulouris G, Avagyan V \u003cem\u003eet al.\u003c/em\u003e (2009) BLAST+: architecture and applications. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e, \u003cstrong\u003e10\u003c/strong\u003e, 421.\u003c/li\u003e\n\u003cli\u003eCarro B, Lopez L, Pe\u0026ntilde;a V, B\u0026aacute;rbara I, Barreiro R (2014) DNA barcoding allows the accurate assessment of European maerl diversity: a proof-of-concept study. \u003cem\u003ePhytotaxa\u003c/em\u003e, \u003cstrong\u003e190\u003c/strong\u003e, 176.\u003c/li\u003e\n\u003cli\u003eChallis R, Richards E, Rajan J, Cochrane G, Blaxter M (2020) BlobToolKit \u0026ndash; 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Summary of maerl sampling locations, the number of samples identified as \u003cem\u003ePhymatolithon calcareum\u003c/em\u003e or \u003cem\u003eLithothamnion corallioides\u003c/em\u003e, and the number of samples genotyped at single nucleotide polymorphisms.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"947\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLatitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLongitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCode\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eN\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003eTOTAL\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eN\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003eGENO\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepth (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003e\u003cem\u003ePhymatolithon calcareum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eEngland, Cornwall, St Austell Bay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e50.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-4.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eAus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eEngland, Cornwall, St Austell Bay, Little Gribbin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e50.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-4.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eGri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e5-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eEngland, Cornwall, Falmouth, Bizzies Reef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e50.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-4.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eBiz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eEngland, Cornwall, Falmouth, St Mawes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2015, 2022\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003eREF\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e50.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-5.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eMaw\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e6, 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e4-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study \u0026amp; Jenkins \u003cem\u003eet al.\u003c/em\u003e 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eEngland, Cornwall, Falmouth, St Mawes\u0026nbsp;\u003csup\u003eCOARSE\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2011, 2022\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003eREF\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e50.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-5.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eMawC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e7, 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e4-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study \u0026amp; Jenkins \u003cem\u003eet al.\u003c/em\u003e 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eEngland, Cornwall, Gerrans Bay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e50.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-4.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eGer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eEngland, Cornwall, The Manacles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e50.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-5.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e5-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study \u0026amp; Jenkins \u003cem\u003eet al.\u003c/em\u003e 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eEngland, Cornwall, Nare Head, nr Hera Wreck\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e50.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-4.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eNar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e13-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eEngland, Dorset, Swanage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e50.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-1.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eSwa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e15-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eEngland, Dorset, Weymouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e50.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-2.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eWey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e15-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eEngland, Isle of Wight, Bembridge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e50.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-1.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eBem*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e13-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eNorthern Ireland, Strangford Lough, Zara Shoal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e54.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-5.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eZar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eJenkins \u003cem\u003eet al.\u003c/em\u003e 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eFrance, Brittany, Morlaix\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e48.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-3.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eMor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e10-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eJenkins \u003cem\u003eet al.\u003c/em\u003e 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eFrance, Brittany, Tr\u0026eacute;vignon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e47.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-3.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eTre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eJenkins \u003cem\u003eet al.\u003c/em\u003e 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eSpain, Galicia, Bornalle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e42.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-9.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eBor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eJenkins \u003cem\u003eet al.\u003c/em\u003e 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eSpain, Galicia, Illa de Ons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e42.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-8.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eOns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eJenkins \u003cem\u003eet al.\u003c/em\u003e 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003e\u003cem\u003eLithothamnion corallioides\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eEngland, Cornwall, St Austell Bay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e50.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-4.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eAus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eEngland, Cornwall, St Austell Bay, Little Gribbin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e50.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-4.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eGri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e5-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eEngland, Cornwall, Falmouth, St Mawes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2015, 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e50.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-5.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eMaw\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e2, 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e4-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study \u0026amp; Jenkins \u003cem\u003eet al.\u003c/em\u003e 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eEngland, Cornwall, Helford River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e50.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-5.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eHel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e5-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eEngland, Dorset, Swanage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e50.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-1.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eSwa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e15-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eEngland, Dorset, Weymouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e50.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-2.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eWey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e15-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eWales, Pembrokeshire, Milford Haven west\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e51.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-5.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eMil1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eWales, Pembrokeshire, Milford Haven east\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e51.703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-5.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eMil2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 275px;\"\u003e\n \u003cp\u003eWales, Llyn Peninsula, St Tudwal\u0026rsquo;s Island\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e52.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-4.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eTud\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003eTOTAL\u003c/sub\u003e Total number of maerl samples collected; \u003cem\u003eN\u003c/em\u003e\u003csub\u003eGENO\u003c/sub\u003e Number of maerl samples successfully genotyped.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eREF\u003c/sup\u003e A reference genome was assembled for one individual sample using Nanopore long read sequencing.\u003c/p\u003e\n\u003cp\u003e*\u003csup\u003e\u0026nbsp;\u003c/sup\u003eOnly five out of ten samples were successfully identified to species level using DNA barcoding.\u003c/p\u003e\n\u003cp\u003eTable 2 Genetic management units of maerl sampled from England and Wales. Metrics of clonal and genetic diversity for each unit are reported, alongside current information about Marine Protected Area (MPA) designations and regulations.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"959\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic unit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 202px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic diversity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarine Protected Area (MPA)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDesignation year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 280px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePareto\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003eo\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003ee\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eF\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003eIS\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e\u003cem\u003ePhymatolithon calcareum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 280px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eSt Mawes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eFal and Helford SAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 280px;\"\u003e\n \u003cp\u003eProhibition of scallop dredging and bottom towed gear under \u003cem\u003eThe Fal \u0026amp; Helford Designated Area (Fishing Restrictions) Order 2008\u003c/em\u003e (Defra).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eSt Mawes coarse triploid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eFal and Helford SAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 280px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eSt Austell Bay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eFalmouth Bay to St Austell Bay SPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 280px;\"\u003e\n \u003cp\u003eNo known regulations to protect maerl beds.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eThe Bizzies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eFalmouth Bay to St Austell Bay SPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 280px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eGerrans Bay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eFalmouth Bay to St Austell Bay SPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 280px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eNare Head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eFalmouth Bay to St Austell Bay SPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 280px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eWeymouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003ePurbeck Coast MCZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 280px;\"\u003e\n \u003cp\u003ePartial prohibition of scallop dredging and bottom tower gear under the \u003cem\u003eBottom Towed Fishing Dear Byelaw 2016\u003c/em\u003e (Southern IFCA)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eThe Manacles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eThe Manacles MCZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 280px;\"\u003e\n \u003cp\u003eProhibition of scallop dredging and bottom towed gear under \u003cem\u003eThe Manacles Marine Conservation Zone (Fishing Restrictions) Byelaw 2017\u003c/em\u003e (Cornwall IFCA).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e\u003cem\u003eLithothamnion corallioides\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 280px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eHelford River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eFal and Helford SAC\u003c/p\u003e\n \u003cp\u003eHelford Estuary MCZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 280px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003cp\u003eNo known regulations to protect maerl beds.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eSt Mawes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eFal and Helford SAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 280px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eSt Austell Bay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003eInf\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eFalmouth Bay to St Austell Bay SPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 280px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eMilford Haven\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003ePembrokeshire Marine SAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 280px;\"\u003e\n \u003cp\u003ePartial prohibition of scallop fishing using towed gear under \u003cem\u003eThe Scallop Fishing (Wales) (No.2) Order 2010\u003c/em\u003e. A \u0026lsquo;no anchoring\u0026rsquo; buffer zone drawn around sensitive habitats including maerl and seagrass.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eWeymouth/Swanage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003eInf\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003ePurbeck Coast MCZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 280px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMCZ, Marine Conservation Zone; SAC, Special Area of Conservation; SPA, Special Protection Area; IFCA, Inshore Fisheries and Conservation Authority.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInf\u003c/em\u003e, Infinity denotes that no clones were detected (the number of clonal lineages equals the number the samples collected).\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"a00cad98-ac79-4198-837f-80f16d45b5e7","identifier":"10.13039/501100001293","name":"Natural England","awardNumber":"n/a","order_by":0},{"identity":"56e82f88-5f7a-40d1-8a66-c7ae9e6eb429","identifier":"10.13039/100009261","name":"Natural Resources Wales","awardNumber":"n/a","order_by":1}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Natural England","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":"","lastPublishedDoi":"10.21203/rs.3.rs-7035383/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7035383/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMaerl bed habitats support an array of marine biodiversity and are recognised as important blue carbon ecosystems. However, little is currently known about the population structure, spatial distribution of genetic diversity, or microbiota of the coralline algae that build these habitats, or how resilient they are to climate change. Here, we investigate these knowledge gaps in two key maerl-forming species, \u003cem\u003ePhymatolithon calcareum\u003c/em\u003e and \u003cem\u003eLithothamnion corallioides\u003c/em\u003e. In southern England, we identified genetically distinct populations of maerl of the two species in Dorset, while there was evidence of admixture between maerl beds in Cornwall, the extent of which differed among sites and species. A genetically distinct clonal lineage, however, was revealed in the Helford River for \u003cem\u003eL. corallioides\u003c/em\u003e. Additionally, we distinguished the genetically distinct coarse growth form of \u003cem\u003eP. calcareum \u003c/em\u003ein Falmouth as a unique, apparently triploid, phenotype of \u003cem\u003eP. calcareum\u003c/em\u003e. In south-west Wales, only \u003cem\u003eL. corallioides\u003c/em\u003e was found in Milford Haven, a population that is genetically diverse and isolated from populations in England. Analysis of climate change resilience indicated that \u003cem\u003eP. calcareum \u003c/em\u003emaerl in Cornwall may have slightly higher risks of being maladapted to temperatures and salinities predicted in 2050 compared to other \u003cem\u003eP. calcareum\u003c/em\u003e maerl sampled. Lastly, metagenomics analysis revealed differences in microbiota between dense healthy maerl beds compared to scattered dead maerl. As a habitat of conservation priority, our findings will be central to ensuring maerl beds receive appropriate levels of protection to prevent further loss or degradation by human activity and climate change.\u003c/p\u003e","manuscriptTitle":"Population genomics, polyploidy, climate resilience and the microbiota of two habitat-building coralline algae","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 13:07:02","doi":"10.21203/rs.3.rs-7035383/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":"c856a5c3-1202-4bf1-823d-8c7fa2535f94","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51156486,"name":"Population Genetics"},{"id":51156487,"name":"Conservation Biology"},{"id":51156488,"name":"Marine and Freshwater Ecology"},{"id":51156489,"name":"Epigenetics \u0026 Genomics"}],"tags":[],"updatedAt":"2025-07-15T13:07:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-15 13:07:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7035383","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7035383","identity":"rs-7035383","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}