The projected distributions of mangrove and saltmarsh in this dataset were developed using the method described in Hughes et al (2022) which can be downloaded from https://www.frontiersin.org/articles/10.3389/fenvs.2022.749950/full

The method involved the use of machine learning techniques to develop a statistical classification model for predicting the position of mangrove and saltmarsh in the present-day landscape in terms of topographic, hydrological and sedimentological predictor variables. That model was then used to identify and map positions in the future landscape displaying similar attributes. Three future landscapes were modelled based on the three SLR scenarios +0.5 m, +1.0 m and +1.5m. To bracket the potential outcomes three sets of projections were prepared. One where there was no restriction on future wetland locations in the landscape other than exclusion from urban areas, and two where future wetlands were restricted to specific natural and semi-natural land covers.

While the topographic, hydrological and sedimentological factors associated with the present-day distribution of mangrove and saltmarsh can provide a guide to where they might occur in future landscapes associated with SLR, numerous other factors not considered here will also have an influence (e.g. changes in catchment inflows, estuary entrance conditions and sediment supply; climate impacts on species present; existing tidal exclusion infrastructure; present and future management actions etc.).