Macroalgal mat species diversity, composition, and seasonality at four coastal sites across the English Channel/La Manche region.

Abstract

Seaweed mats are a consequence of eutrophication in coastal waters and can have negative ecological impacts. Consequently, many European environmental assessment frameworks (e.g. the Water Framework Directive, Marine Strategy Framework Directive) monitor seaweed mat biomass. Ulva, commonly referred to as sea lettuce, is often assumed to be the dominant taxon, but mats can contain multiple Ulva species, and other taxa, and within-mat diversity is often poorly known. Understanding the composition of seaweed mats has implications for nutrient removal from coastal waters because seaweeds uptake and store nutrients differently between species, time of year and location. The use of traditional molecular methods to identify seaweed species cannot easily be applied to mats. In contrast, with the development of Next Generation Sequencing and Cleaved Amplified Polymorphic Sequences assay, a bulk sampling approach for monitoring the seaweed species within mats can be applied. Here, we applied this bulk sampling approach to monitor seaweed species at four sites: three estuarine sites containing mudflats and one exposed sandy site across the English Channel/La Manche region that historically and currently have occurrences of seaweed mats. We assessed variability between sites with respect to biomass, entrainment, species composition and seasonality. The highest mat abundance was found at Holes Bay, Poole, UK and entrainment or burial of seaweed within the sediment was found at two sites, Holes Bay and the Ledano Estuary, France, correlating with similar sediment types and high seaweed biomass. Foliose Ulva lacinulata dominated at Holes Bay, while Langstone Harbour, Portsmouth, UK had many tubular and filamentous green species and one brown filamentous species. At the two French sites we identified foliose Ulva species. This study demonstrates the high seaweed species diversity and biomass that can be found within macroalgal mats, information that will be important in the refinement and development of model-based nutrient removal estimates as part of nutrient mitigation and management strategies.