| Summary: | Blue Carbon ecosystems such as mangroves, saltmarshes and seagrasses have been shown to sequester large amounts of carbon, and subsequently are receiving renewed interest from policy experts in light of climate change. Globally, seagrasses remain the most understudied of these ecosystems, with their total geographic extent largely unknown due to challenges in mapping dynamic coastal environments. As such, species distribution models (SDMs) have been used to identify areas of high suitability, in order to inform our understanding of where unmapped meadows may be located or to identify suitable sites for restoration and/or enhancement efforts. However, many SDMs parameterized to project seagrass distributions focus on physical and not anthropogenic variables (i.e., dredging, aquaculture), which can have negative impacts on seagrass meadows. Here we used verified datasets to identify the potential distribution of <i>Zostera marina</i> and <i>Zostera noltei</i> at a national level for the Republic of Ireland, using 19 environmental variables including both physical and anthropogenic. Using the Maximum Entropy method for developing the SDM, we estimated approximately 95 km<sup>2</sup> of suitable habitat for <i>Z. marina</i> and 70 km<sup>2</sup> for <i>Z. noltei</i> nationally with high accuracy metrics, including Area Under the Curve (AUC) values of 0.939 and 0.931, respectively for the two species. We found that bathymetry, maximum sea-surface temperature (SST) and minimum salinity were the most important environmental variables that explained the distribution of <i>Z. marina</i> and that high standard deviation of SST, mean SST and maximum salinity were the most important variables in explaining the distribution of <i>Z. noltei.</i> At a national level, we noted that it was primarily physical variables that determined the geographic distribution of seagrass, not anthropogenic variables. We unexpectedly modelled areas of high suitability in locations of anthropogenic disturbance (i.e., dredging, high pollution risk), although this may be due to the binary nature of SDMs capturing presence-absence and not the size and condition of the meadows, suggesting a need for future research to explore the finer scale impacts of anthropogenic activity. Subsequently, this research should foster discussion for researchers and practitioners working on sustainability projects related to Blue Carbon.
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