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Habitat degradation and loss are severely affecting macroalgal forests worldwide, and their successful mitigation depends on the identification of the drivers of loss and the implementation of effective conservation and restoration actions. We made an extensive literature review 1- to document the historical (1789–1999) and recent (2000−2020) occurrence of the genus Cystoseira, Ericaria and Gongolaria reported in the literature along the 8000 km of the coasts of Italy, 2- to assess their decline and patterns of extinction, 3- to ascertain the drivers responsible for these changes, 4- to highlight the existence of success stories in their conservation and natural recovery. In the last twenty years, overall information on the distribution of Cystoseira s.l. exponentially increased, although research focused almost exclusively on intertidal reefs. Despite the lack of systematic monitoring programs, the local extinction of 371 populations of 19 different species of Cystoseira s.l. was documented across several regions, since 2000. Coastal engineering and poor quality of waters due to urban, agricultural or industrial activities were often documented as leading causes of habitat loss. However, the drivers of extinction were actually unknown for the majority of the populations and cause-effects relationships are scarcely documented. Although the proportion of protected populations increased to 77.8%, Marine Protected Areas are unlikely to guarantee adequate conservation efficacy, possibly also for the widespread lack of management and monitoring plans dealing specifically with Cystoseira s.l. species, and few evidences of natural recovery were observed. Our review shows the dramatic lack of baseline information for macroalgal forests, highlighting the urgent need for the monitoring of less accessible habitats, the collection of long-term data to unveil drivers of loss, and an updated reporting about the conservation status of the species of interest to plan future interventions.

Macroalgal forests are one of the most productive and valuable marine ecosystems, but yet strongly exposed to fragmentation and loss. Detailed large-scale information on their distribution is largely lacking, hindering conservation initiatives. In this study, a systematic effort to combine spatial data on Cystoseira C. Agardh canopies (Fucales, Phaeophyta) was carried out to develop a Habitat Suitability Model (HSM) at Mediterranean scale, providing critical tools to improve site prioritization for their management, restoration and protection. A georeferenced database on the occurrence of 20 Cystoseira species was produced collecting all the available information from published and grey literature, web data portals and co-authors personal data. Data were associated to 55 predictor variable layers in the (ASCII) raster format and were used in order to develop the HSM by means of a Random Forest, a very effective Machine Learning technique. Knowledge about the distribution of Cystoseira canopies was available for about the 14% of the Mediterranean coastline. Absence data were available only for the 2% of the basin. Despite these gaps, our HSM showed high accuracy levels in reproducing Cystoseira distribution so that the first continuous maps of the habitat across the entire basin was produced. Misclassification errors mainly occurred in the eastern and southern part of the basin, where large gaps of knowledge emerged. The most relevant drivers were the geomorphological ones, followed by anthropogenic variables proxies of pollution and urbanization. Our model shows the importance of data sharing to combine a large number of spatial and environmental data, allowing to individuate areas with high probability of Cystoseira occurrence as suitable for its presence. This approach encourages the use of this modelling tool for the prediction of Cystoseira distribution and for supporting and planning conservation and management initiatives. The step forward is to refine the spatial information of presence-absence data about Cystoseira canopies and of environmental predictors in order to address species-specific assessments.