Proteogenomic Characterization of the Cement and Adhesive Gland of the Pelagic Gooseneck Barnacle Lepas anatifera

We focus on the stalked goose barnacle L. anatifera adhesive system, an opportunistic less selective species for the substrate, found attached to a variety of floating objects at seas. Adhesion is an adaptative character in barnacles, ensuring adequate positioning in the habitat for feeding and reproduction. The protein composition of the cement multicomplex and adhesive gland was quantitatively studied using shotgun proteomic analysis. Overall, 11,795 peptide sequences were identified in the gland and 2206 in the cement, clustered in 1689 and 217 proteinGroups, respectively. Cement specific adhesive proteins (CPs), proteases, protease inhibitors, cuticular and structural proteins, chemical cues, and many unannotated proteins were found, among others. In the cement, CPs were the most abundant (80.5%), being the bulk proteins CP100k and -52k the most expressed of all, and CP43k-like the most expressed interfacial protein. Unannotated proteins comprised 4.7% of the cement proteome, ranking several of them among the most highly expressed. Eight of these proteins showed similar physicochemical properties and amino acid composition to known CPs and classified through Principal Components Analysis (PCA) as new CPs. The importance of PCA on the identification of unannotated non-conserved adhesive proteins, whose selective pressure is on their relative amino acid abundance, was demonstrated.

Incorporating landscape metrics into invertebrate fisheries management: case study of the gooseneck barnacle in Asturias (N. Spain)

Landscape components can affect all the important biological processes of invertebrate populations, including their harvest quality, yet they are rarely considered in fisheries management frameworks. Here, we explore landscape, economic and ecologic variables to demonstrate that landscape metrics can be a valuable component in the management of sessile invertebrate fisheries. We developed a map-derived model that links landscape variables with the quality of a fishing resource, using five topographical variables—coastal convexity, orientation, complexity, exposure, and distance from the coast—all but the latter were tested at 23 different spatial scales. The model was ground-truthed using the case study of the gooseneck barnacle fishery in Asturias (N. Spain). Distance from the coast, coastal convexity on a scale of 25 km and exposure on a scale of 1 km appear to be driving the quality of the resource. Our model can predict high-quality gooseneck barnacle fishing zones with 72% accuracy. Moreover, we used a 10-year time-series of gooseneck barnacle landings and sales to analyse the impact of quality on the fishery. Fishers have a bias towards harvesting high-quality gooseneck barnacles, which are sold at higher market values. Thus, quality directly affects landings and sales. Our results highlight the interest of incorporating landscape metrics in fisheries management to generate and support spatially explicit conservation and exploitation policies.