A Fuzzy Inference System for Seagrass Distribution Modeling in the Mediterranean Sea: A First Approach

Abstract

A Mamdani-type fuzzy-logic model was developed to link Mediterranean seagrass presence to the prevailing environmental conditions. UNEP-WCMC (seagrass presence), CMEMS, and EMODnet (oceanographic/environmental) datasets, along with human-impact parameters were utilized for this expert system. The model structure and input parameters were tested according to their capacity to accurately predict the presence of seagrass families at specific locations. The optimum Fuzzy Inference System (FIS) comprised four input variables: water depth, sea surface temperature, nitrates, and bottom chlorophyll-a concentration, exhibiting reasonable precision (76%). Results illustrated that Posidoniaceae prefers cooler water (16–18 °C) with low chlorophyll-a levels (<0.2 mg/m3); Zosteraceae favors similarly cooler (16–18 °C) and mesotrophic waters (Chl-a > 0.2 mg/m3), but also slightly warmer (18–19.5 °C) with lower Chl-a levels (<0.2 mg/m3); Cymodoceaceae lives in warm, oligotrophic (19.5–21.0 °C, Chl-a < 0.3 mg/m3) to moderately warm mesotrophic sites (18–21.3 °C, 0.3–0.4 mg/m3 Chl-a). Finally, Hydrocharitaceae thrives in the warm Mediterranean waters (21–23 °C) of low chlorophyll-a content (<0.25 mg/m3). Climate change scenarios show that Posidoniaceae and Zosteraceae tolerate bathymetric changes, and Posidoniaceae and Zosteraceae are mostly affected by sea temperature rise, while Hydrocharitaceae exhibits tolerance at higher sea temperatures. This FIS could aid the protection of vulnerable seagrass ecosystems by national and regional policy-makers and public authorities.