Algal habitat-forming forests composed of fucalean brown seaweeds (Cystoseira, Ericaria, and Gongolaria) have severely declined along the Mediterranean coasts, endangering the maintenance of essential ecosystem services. Numerous factors determine the loss of these assemblages and operate at different spatial scales, which must be identified to plan conservation and restoration actions. To explore the critical stressors (natural and anthropogenic) that may cause habitat degradation, we investigated (a) the patterns of variability of fucalean forests in percentage cover (abundance) at three spatial scales (location, forest, transect) by visual estimates and or photographic sampling to identify relevant spatial scales of variation, (b) the correlation between semi-quantitative anthropogenic stressors, individually or cumulatively (MA-LUSI index), including natural stressors (confinement, sea urchin grazing), and percentage cover of functional groups (perennial, semi-perennial) at forest spatial scale. The results showed that impacts from mariculture and urbanization seem to be the main stressors affecting habitat-forming species. In particular, while mariculture, urbanization, and cumulative anthropogenic stress negatively correlated with the percentage cover of perennial fucalean species, the same stressors were positively correlated with the percentage cover of the semi-perennial Cystoseira compressa and C. compressa subsp. pustulata. Our results indicate that human impacts can determine spatial patterns in these fragmented and heterogeneous marine habitats, thus stressing the need of carefully considering scale-dependent ecological processes to support conservation and restoration.

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