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.
Highlights
Seagrasses are the only submerged marine plants with an underground root and rhizome system forming beds and meadows
The dataset illustrates the global distribution of 73 seagrass species and has the form of a geo-referenced shapefile composed of two subsets of points and polygons, indicating the occurrences measured from 1934 to 2015 on seagrass family, genus, and species
This work has developed a simple but novel self-learning expert-system application, based on Mamdani fuzzy logic to predict the occurrence of Mediterranean seagrass habitats at the family level, according to the environmental conditions prevailing in an area
Summary
Seagrasses are the only submerged marine plants with an underground root and rhizome system forming beds and meadows. They play a key role in the ecosystem services of the global coastal zone in relation to nutrient biogeochemical cycling, carbon sequestration, sediment stabilization, fish refugia, and food-web structure [1]. Most aquatic ecosystem health assessment studies rely on seagrass species richness and distribution, since these serve as valuable bio-indicators reflecting recent environmental changes, especially the release of pollutants and eutrophication events. Halophila minor and Halophila ovalis act as bio-indicators for trace metal pollution and sediment accumulation [3], and Zostera marina is an eutrophication indicator [4], while the genus Cymodocea acts as a heavy-metal bioaccumulator and tolerant bioindicator of pollution [5], rapid coastal development, and human intervention [6].
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