Assessing temporal and spatial variation in communities associated with Gongolaria barbata forests in the Conero Riviera (Ancona) using molecular approaches

Fucalean brown algae are key foundation species in the Mediterranean Sea, where they form macroalgal forests that structure coastal habitats and provide essential ecosystem services. Within this group, Cystoseira sensu lato forests are experiencing a marked decline, often leading to biodiversity loss and shifts towards barren grounds or algal turfs. The algal and bacterial communities associated with these ecosystems represent an important component of habitat complexity, and their composition and dynamics can serve as sensitive indicators of environmental disturbance and ecosystem degradation. Monitoring these associated communities can therefore provide valuable insights into the ecological status of macroalgal forests. However, identifying such organisms is often time consuming and technically challenging. To overcome these limitations, integrating morphological approaches with environmental DNA (eDNA) metabarcoding can provide a more comprehensive assessment of the communities associated with macroalgal forests. Compared to the most used genetic markers (e.g. 18S rDNA, COI), the Klank Domain V of the 23S plastid rRNA gene (23S rDNA) is less used even though has emerged as a particularly promising tool for identifying genetic signatures belonging to photosynthetic taxa (Sherwood & Presting, 2007; Azevedo et al., 2020). To date, the 23S marker has been widely used in freshwater and terrestrial vegetation studies, while its potential for marine algal communities remains largely unexplored, due mainly to the limited data available in genetic databases. This study aimed to characterize the photosynthetic communities associated with Gongolaria barbata forests along the Conero Riviera (Central Adriatic, Ancona by sequencing 23S genetic signatures present in eDNA isolated from seawater samples. Water samples were collected from four sites along the Conero Riviera (Spiaggia dei Sassi Neri, Spiaggia delle Due Sorelle, Spiaggia della Vedova, and Spiaggia della Scalaccia) in early May of 2024 and 2025. Bioinformatic analyses were performed using QIIME2 (Bolyen et al., 2019), implementing a reference database including macroalgal, microalgal and cyanobacterial sequences retrieved from NCBI. Since only a limited number of macroalgal taxa are currently represented in public databases, additional 23S gene sequences were generated and incorporated into the reference library to improve the resolution of eDNA based-identiKication. These sequences were obtained by barcoding macroalgal samples collected along the Conero Riviera. Macroalgal samples were ampliKied using both 23S and additional genetic markers, including tufA, rbcL, psbA, ITS, and COI. The combined barcoding approach was particularly valuable for resolving the identity of several cryptic taxa. DNA was extracted from 147 macroalgal samples collected across different sites and seasons. Of these, 114 samples were successfully ampliKied with species-speciKic primers, yielding 98 high-quality sequences. AmpliKication with 23S primers was successful for 98 samples, producing 59 high-quality sequences. Metabarcoding analyses identiKied a total of 64 taxa across both sampling years, including 35 macroalgae, 22 microalgae, and 7 cyanobacteria. In 2024, 57 taxa were detected (30 macroalgae, 19 microalgae, 7 cyanobacteria), while in 2025, 29 taxa were identiKied (10 macroalgae, 12 microalgae, 6 cyanobacteria). Metabarcoding results suggested strong temporal variability, with signiKicant differences in species richness and composition between 2024 and 2025, and a moderate spatial variability reKlecting the geographical distribution of sampling sites. Overall, this study highlighted the potential of the 23S marker, and environmental DNA approaches more broadly, for investigating marine photosynthetic communities. The enhanced taxonomic resolution obtained through the complementary barcoding efforts emphasizes the critical importance of expanding algal reference databases. Enriching these databases with high quality sequences will be essential to unlock the full potential of eDNA metabarcoding for accurately characterizing macroalgal-associated biodiversity.