Phytoplankton–Macrophyte Interaction in the Lagoon of Venice (Northern Adriatic Sea, Italy)

: The coexistence of phytoplankton and macrophytes in the Lagoon of Venice (Northern Adriatic Sea, Italy) was investigated using in situ data collected monthly as part of International Long Term Ecosystem Research (LTER), together with satellite imagery for the period 1998–2017. The concentrations of chlorophyll a and hydrochemical parameters were measured in three areas of the lagoon, where the expansion of well-developed stands of submerged vegetation was observed by remote sensing. Our results suggest interaction between phytoplankton and macrophytes (macroalgae and seagrasses) in the last few years of the time series, evidenced by decreasing chlorophyll a concentrations in the vicinity of the macrophyte stands. The integration of LTER and remotely sensed data made it possible to evaluate the interaction of macrophytes and phytoplankton at the ecosystem scale for the ﬁrst time in the Lagoon of Venice.


Introduction
Coastal lagoons are characterized by strong spatial heterogeneity, widely fluctuating hydrological variables and seasonal patterns [1][2][3]. In these ecosystems, the pronounced heterogeneity of the habitat and the shallow waters allow the development of a range of trophic conditions. This, in turn, gives rise to a very diverse community of primary producers on multiple functional levels, such as macroalgae, marine phanerogams, benthic microalgae and phytoplankton.
Macrophytes (macroalgae and seagrasses) respond univocally to environmental stressors in aquatic environments and thus represent sensitive indicators of water quality and the ecological status of transitional waters [4][5][6]. They play a key role in primary production, regulating the nutrient cycles and contributing to water oxygenation [7]. Water clarity tends to be enhanced by submerged vegetation, because it prevents sediment resuspension and erosive processes [8], and, by attenuating current and wave energy, it also traps suspended particles. On the other hand, the distribution of submerged vegetation is controlled by light availability [9,10]. In very turbid waters, light cannot penetrate to the bottom, leading to a decline in macrophyte growth and distribution, while phytoplankton become the only primary producers [6]. In transitional waters, light attenuation can be due to both natural and human-driven processes such as (i) the physical characteristics of the area (water depth, sea-floor properties, tidal currents, freshwater run-off and associated sediment delivery from the drainage basin and wind-and wave-driven sediment resuspension), (ii) human activities (increased nutrient and sediment loading from runoff and sewage disposal leading to eutrophication and algal blooms, ship and boat wakes, fishing and clam harvesting, dredging and coastal engineering works), and (iii) regional weather patterns (e.g., extreme storms and altered rainfall patterns) [10][11][12]. These factors play a crucial (iii) to ascertain how lagoon trophic trends influence phytoplankton and macrophyte coexistence and interaction.

Area of Studies
The LoV (Northern Adriatic Sea, Mediterranean Sea; Figure 1) is the largest wetland (550 km 2 ) in the Mediterranean Sea [39]. It is surrounded by densely inhabited and industrial areas and affected by a high touristic pressure, as well as intensive fishing and aquaculture. It has an average depth of 1 m and is morphologically characterized by the presence of large shallow areas (tidal and subtidal flats) and a network of deeper (5-10 m) channels. The LoV is connected to the sea through three inlets, Lido, Malamocco and Chioggia, which divide the lagoon itself into three morphological basins: north, central and south [22], delimited by the Malamocco-Marghera industrial channel in the South and by the Burano-Torcello salt-marshes in the North (Figure 1). The area considered in this study encompasses the central and northern basins. The central basin is subject to the highest environmental impact due to strong nutrient enrichment from the city of Venice and the major islands, treated urban waste water from the urban centre of Mestre, the west of Venice on the lagoon margin and its hinterland, and the discharge of wastewaters and cooling waters from the nearby industrial areas [20,22]. It is in this basin that the highest concentration of nutrients is observed: specifically, ammonium concentrations are double those found in the northern and southern basins, while nitrate levels are four to five times higher [22]. The northern basin is characterized by the presence of tributaries draining the waters from an intensively cultivated hinterland. Both north and central basins have higher turbidity values than the southern lagoon, especially near the industrial area of Porto Marghera and on the northern side of Venice close to the mouths of the Marzenego, Dese and Siloncello rivers, where turbidity is very high [22]. In this study, 245 sampling campaigns were conducted monthly from 1998 to 2017 at three stations (San Giuliano, Fusina and Palude della Rosa: St

Area of Studies
The LoV (Northern Adriatic Sea, Mediterranean Sea; Figure 1) is the largest wetland (550 km 2 ) in the Mediterranean Sea [39]. It is surrounded by densely inhabited and industrial areas and affected by a high touristic pressure, as well as intensive fishing and aquaculture. It has an average depth of 1 m and is morphologically characterized by the presence of large shallow areas (tidal and subtidal flats) and a network of deeper (5-10 m) channels. The LoV is connected to the sea through three inlets, Lido, Malamocco and Chioggia, which divide the lagoon itself into three morphological basins: north, central and south [22], delimited by the Malamocco-Marghera industrial channel in the South and by the Burano-Torcello salt-marshes in the North (Figure 1). The area considered in this study encompasses the central and northern basins. The central basin is subject to the highest environmental impact due to strong nutrient enrichment from the city of Venice and the major islands, treated urban waste water from the urban centre of Mestre, the west of Venice on the lagoon margin and its hinterland, and the discharge of wastewaters and cooling waters from the nearby industrial areas [20,22]. It is in this basin that the highest concentration of nutrients is observed: specifically, ammonium concentrations are double those found in the northern and southern basins, while nitrate levels are four to five times higher [22]. The northern basin is characterized by the presence of tributaries draining the waters from an intensively cultivated hinterland. Both north and central basins have higher turbidity values than the southern lagoon, especially near the industrial area of Porto Marghera and on the northern side of Venice close to the mouths of the Marzenego, Dese and Siloncello rivers, where turbidity is very high [22]. In this study, 245 sampling campaigns were conducted monthly from 1998 to 2017 at three stations (San Giuliano, Fusina and Palude della Rosa: St.1, St.3 and St.5, respectively).   The stations, located in the central and northern basins, are influenced to varying degrees by marine and freshwater inputs: St.1, in the central basin, is located in an area of intense maritime traffic [40] that receives urban wastewaters from the town of Mestre, while St.3 is located in the central basin near the industrial area of Marghera in the vicinity of the thermoelectric power plant of Fusina and is also influenced by commercial ship traffic [27,41]. St.5 is located in the northern basin, a typical inland marshy lagoon area [40]. St.1 showed higher nutrient concentrations, greater phytoplankton biomass and abundance and lower salinity than St.3 and St.5. By contrast, the phytoplankton species compositions at the three stations did not vary significantly, the most abundant blooming species being largely shared by the three stations, whilst specific taxa associated with individual stations were rare [29]. In the first year of our time series, in the areas identified for sampling, the main primary producer was phytoplankton. Furthermore, in the central and northern basins, the macroalgal biomass appeared to be retreating, while seagrasses were the main primary producers in the southern basin and close to the seaward inlets [42,43].

Hydrochemical Parameters
Samples were collected monthly from January 1998 to December 2017 at a depth of 0.5 m at neap tide in order to minimize the effect of tidal hydrodynamics. The following parameters were measured: transparency, temperature, salinity, dissolved oxygen, chlorophyll a and dissolved nutrients, including nitrogen as ammonium (N-NH 3 ), nitrites (N-NO 2 ) and nitrates (N-NO 3 ) (summed and reported as dissolved inorganic nitrogen, DIN) and phosphorus as orthophosphates (P-PO 4 ). Analytical quality was assessed via participation in the Quality Assurance of Information for Marine Environmental Monitoring in Europe (QUASIMEME [44]) international laboratory proficiency-testing programme. The laboratory analysis methods are described in detail in [31].

Statistical Analyses
An ANOVA (ANalysis Of VAriance) test followed by Tukey's test for post-hoc analysis was applied to compare the differences in the twenty-year hydrochemical datasets for the three sampling stations.
Interannual trends were analysed by applying the seasonal Mann-Kendall test, which takes account of the seasonality in the time series, to the whole dataset. This analysis determines the direction of trends (+ or −), goodness of fit (Kendall's tau) and statistical significance of the fit [45]. If a linear trend is present in a time series, then the true slope (change per unit time) can be estimated by using Sen's slope estimator [46,47]. To assess the evolution of macrophyte coverage, estimated every year in late spring-summer, which is representative of the maximum biomass extent [20,48,49], a simple Mann-Kendall test was used. Principal component analysis (PCA, R-mode) was used to identify relationships between environmental variables, displaying groups of observations (years) in a 2-dimensional space. For each year, the average macrophyte coverage, measured as a percentage in the three stations, and the respective yearly average values of the hydrochemical parameters were used. Thus, 19 values were obtained, one for each sampling year except for 2004, when the quality of the satellite image was not good enough to retrieve macrophyte coverage data. Any missing values were replaced by the means of the variables. The normality of the data for the PCA R-mode was verified using the Kolmogorov-Smirnov test. Differences between Cluster A and Cluster B means evidenced by principal component analysis were established using Student's t-test. ANOVA, Student's t-test, the Mann-Kendall test, PCA and the Kolmogorov-Smirnov test were performed using XLSTAT 2018.1. (Adinsoft, Paris, France).

Satellite Data and Processing
The retrospective macrophyte coverage (seagrasses and macroalgae) was assessed using Landsat satellite imagery acquired in late spring-summer (i.e., in May and June), which is representative of the maximum biomass extent [20,48]. NASA's Landsat Earth global observation program started in the 1970s, providing a continuous record of the Earth's surface and making it possible to observe land use and land use change over time. The Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) sensors, mounted on the Landsat satellites, have a spatial resolution of 30 m, acquiring images every 16 days. They operate a suite of spectral bands covering the visible-to-near-infrared range, which is the region of interest for this study. Cloud-free and clear-water images from TM, ETM+ and OLI were used for mapping macrophyte coverage in the central and northern lagoon. Maps of species composition were not considered, because the Landsat spatial and radiometric resolution, and spectral band location and width are not suitable for distinguishing species types in very heterogeneous environments [50]. A common image-processing chain was used to obtain macrophyte distribution maps from the Landsat data, in accordance with [51][52][53]. In detail, the L1b level products, downloaded from USGS EarthExplorer (https://earthexplorer.usgs.gov), were converted into Top-of-Atmosphere radiances by applying radiometric calibration gains and atmospherically corrected using the vector version (6SV) of the Second Simulation of the Satellite Signal in the Solar Spectrum model [54]. The code was parameterized with reference to the Aerosol Optical Thickness at the time of image acquisition, obtained from AERONET (AErosol RObotic NETwork) [55], at the Acqua Alta Oceanographic platform, located 8 miles off Venice. The 6SV products were corrected for specular effects and converted to remote sensing reflectance (R RS ) in accordance with [56]. The R RS products were then used as the input for a non-linear optimization algorithm called BOMBER (Bio-Optical Model Based tool for Estimating water quality and bottom properties from Remote sensing images) [57] in order to estimate substrate coverage in the lagoon. BOMBER was run in shallow water mode for the whole extent of the LoV, after the masking channel network. The algorithm was parameterized with reference to the extensive in situ dataset, which included spectral signatures of bottom cover, biogeophysical parameters, and apparent and inherent optical properties, collected in the last fifteen years in the LoV and along the coasts of the northern Adriatic Sea [58,59]. The outputs of BOMBER were the distribution maps of three different types of substrate: bare sediments, greenish macrophytes and darker macrophytes. The two macrophyte classes were added up and aggregated in order to obtain a macrophyte coverage map, in which each pixel of the maps quantified the proportion of the surface covered by submerged vegetation.
The BOMBER outputs were validated with thematic maps of seagrass distribution for the years 2009 [60] and 2017 [61], based on field surveys. A total of 47 randomly located control points were used to compare in situ and satellite-derived vegetation coverage, in a subset area colonized only by seagrasses. Satellite-derived product accuracy was quantified using a confusion matrix, built with three coverage classes.
The resulting submerged vegetation coverage maps for each year of the study period were imported into the QGIS (Quantum GIS Development Team, Version 3.6.0, Noosa) application as raster files. Each map was then converted into a vector file, in order to group the pixels into classes. Four categories of bottom coverage were selected in order to clusterize the data: 0 to 5% in the case of bare sediment, i.e., the absence of vegetation; 5 to 50% when the bottom was partially covered, i.e., with sparse vegetation; 50 to 75%, indicating the widespread presence of vegetation in the form of moderately dense macrophyte meadows; and 75 to 100%, corresponding to dense macrophyte meadows. The statistical data regarding bottom coverage for the three sampling sites were then extracted for each year of the study period using the statistical tool in QGIS for the spatial analysis.

Hydrochemical Data: Comparison of Stations
The stations are located in lagoon areas classified as polyhaline [62]. St

Hydrochemical Data: Interannual Variability
To obtain an overview of interannual variability, the data were averaged (yearly mean for each station), and the results are shown in Figures 2-4. Yearly averages of hydrochemical parameters in the three sampling stations for the whole study period are shown in Table S1_1

Macrophyte Coverage
The list of selected Landsat images used for the analysis and the derived maps of macrophyte coverage retrieved by BOMBER are shown in Table S2_1 and Figure_S2_1 Supplementary Materials, respectively. Thematic maps of submerged vegetation coverage were used to assess the accuracy of the outputs of the BOMBER bio-optical modelling and its ability to discriminate between bottom cover classes. Three different classes were selected to build a confusion matrix: bare sediment = BS (0-5%), Sparse Macrophyte = SM (5-50%) and Dense Macrophyte = DM (50-100%). The results of the confusion matrix showed that all the classes were recognized with an overall accuracy of 80.4% ( Table 2). The two classes of BS and DM were mapped with high accuracy (81.5% and 97.5%, respectively). A slight misidentification was found for SM (56.7%), because the relatively large pixel size of the Landsat data was unable to delineate small patterns of vegetation patches. Our results are consistent with findings obtained by [50]. Representative subsets of maps of the submerged vegetation around each sampling station were selected with reference to hydrodynamic and morphological criteria. For St.5, located at the entrance of a semi-enclosed basin, we took the whole confined basin into account. For St.1 and St.3, the subsets were selected considering the water circulation, the renewal capacity and the role of the main channels in confining the various sub-basins [64]. A graphical representation of the submerged vegetation coverage maps obtained for 2016 is presented in Figure 7. Representative subsets of maps of the submerged vegetation around each sampling station were selected with reference to hydrodynamic and morphological criteria. For St.5, located at the entrance of a semi-enclosed basin, we took the whole confined basin into account. For St.1 and St.3, the subsets were selected considering the water circulation, the renewal capacity and the role of the main channels in confining the various sub-basins [64]. A graphical representation of the submerged vegetation coverage maps obtained for 2016 is presented in Figure 7.

Statistical Analysis of Trends
At all three sampling sites, the transparency increased significantly. The temperature increased significantly at St.5, and salinity, at St.3 (Table 3). In each of the three sampling stations, there was a significant increase in relative oxygenation and a significant fall in ammonia concentrations. As for the oxidized forms of nitrogen, nitrite did not show any trend at St

Statistical Analysis of Trends
At all three sampling sites, the transparency increased significantly. The temperature increased significantly at St.5, and salinity, at St.3 (Table 3). In each of the three sampling stations, there was a significant increase in relative oxygenation and a significant fall in ammonia concentrations. As for the oxidized forms of nitrogen, nitrite did not show any trend at St

Principal Component Analysis of Hydrochemical Parameters and Macrophyte Coverage
The bi-plot of the first two PCA components highlighted the relations among environmental parameters and the similarities/dissimilarities among years or groups of years, explaining about 60% of the total variance ( Figure 9). The bi-plot showed the following: (i) the oxygen, macrophyte coverage, temperature and transparency have positive loadings on Component 1 and are positively correlated with each other and negatively correlated with ammonia and chlorophyll a; (ii) ammonia has a negative influence on Component 1 and is positively correlated with chlorophyll a and negatively so with macrophyte coverage; (iii) salinity has a negative influence on Component 2, unlike the oxidized forms of nitrogen and orthophosphates, which have a positive influence on the same component; (iv) the early years (1998-2006) form a cluster (A; Quadrants III and IV); (v) the years from 2013 to 2017, characterized by dense macrophyte coverage and high relative oxygen, temperature and transparency values, form a cluster (B; Quadrants I and II). Table 4 shows the significant differences between the averages of the hydrochemical parameters of Clusters A and B. The differences are significant for transparency (A = 0.9 m and B = 1.  Table 4 shows the significant differences between the averages of the hydrochemical parameters of Clusters A and B. The differences are significant for transparency (A = 0.9 m and B = 1.

Discussion
This study involved a twenty-year hydrochemical and biological survey in three areas of the LoV, each with distinct environmental conditions due to urban, industrial and agricultural pollution and the influence of marine and fresh waters. Despite these differences, the three stations showed similar trends: rising transparency, dissolved oxygen and macrophyte coverage, as well as decreasing nitrogen nutrients (due to the application of more stringent environmental regulations) [31] and phytoplankton biomass (Table 3). This enables us to ascertain that an overall improvement in the ecological conditions of the LoV can be stated for the last years. Statistical analyses of the long-term hydrological and biological data, together with macrophyte maps derived from satellite images, made it possible to emphasize the succession of primary producers in the lagoon waters over the twenty years considered. However, it is difficult to identify the multiple driving factors, which overlap and counteract each other in determining interannual variations in macrophyte and phytoplankton distribution. Thus, in addition to the analysed data, other information derived from local authorities and scientific publications can contribute to explaining the different trends of macrophyte growth and development.
At the beginning of our long-term series (1998), in the sites identified for sampling, the main primary producer was phytoplankton [29,42]: the highest phytoplankton biomass was in the central basin [29,43]. Since 2004, only rather low levels of chlorophyll a have been recorded. A peak in chlorophyll a concentration was measured at the three stations in 2001. This was also observed, during the summer, in other areas of the LoV, mainly influenced by the tributaries and closer to the industrial area [65]. In these areas, the dissolved nutrient concentrations were, on average, the highest. Moreover, an enormously dense bloom of picocyanobacteria occurred in July-August of 2001 both in the LoV and along the coast of the northern Adriatic Sea [66]. These phenomena could be driven by regional factors such as the significant loads of organic suspension, nutrients and pollutants from the extreme flood of the Po River in the last months of 2000 [67,68].
From 1998 until 2003, the three investigated areas were almost uncolonized by macrophytes. It was extensively reported that in this period, the disappearance of submerged vegetation was at least partly due to the significant illegal clam fishing [69]. This activity strongly interacted the benthic compartment and contributed to the process of sediment resuspension and erosion, causing a decrease in water transparency and light availability. In 2005, to overcome social, economic and ecological concerns related to unregulated clam fishing, local administrators allocated some areas of the lagoon to clam harvesting. As a likely consequence of clam fishing regulation, macrophytes started to recolonize the areas. In the years 2007 and 2008, all three sampling sites were characterized by an increase of 1-31% in macrophyte coverage with a density of 50-100%. Water transparency gradually increased, confirming its positive feedback on the development of aquatic submerged vegetation. Additionally, favourable climatic conditions (i.e., mild air temperatures) could have influenced macrophyte growth ( Figure 5). The adverse meteorological conditions in 2010-2012 probably played a very important role, with several consecutive, longer-than-usual cold seasons, which reduced the over-wintering of the macrophytes, as occurred in the 1990s [20]. Meteo-climatic analysis performed by the Veneto Regional Environmental Protection Agency (ARPAV) points out how the first months of 2010 were particularly cold and rainy compared to the average of the last 25 years, resulting in conditions less favourable for the growth and proliferation of submerged vegetation [70]. During the late winter of 2012, the northern Adriatic Sea was heavily impacted by a severe cold spell, which caused a large decrease in the surface temperature and the onset of severe north-easterly Bora winds [71]. Shallow water areas of the LoV froze, likely hampering onward macrophyte growth in April and May, as also reported by [20] in the early 1990s. Moreover, the increased coverage in 2013 was due to the exceptional rains characterizing the spring of 2013, which enriched the lagoon with dissolved nutrients from the drainage basin, through the leaching of agricultural lands subjected in spring to the usual fertilization practices, as confirmed by higher chlorophyll values in March and April. During summer, the nutrient enrichment favoured the proliferation of algae (both micro-and macro-algae) in the lagoon. The strong production of oxygen during the daytime followed by the consumption of oxygen in the night triggered anaerobic degradation processes and the production of hydrogen sulfide. The relative stagnation of the waters at the neap tide and the highest temperatures then produced hypoxia and prolonged anoxia in some areas of the lagoon. Fortunately, this was a transitory phenomenon that lasted only for a few days [72].
The results of the present study show that in recent years (2013-2017), in the three sampling sites considered, there has been an increase in dissolved oxygen together with transparency and macrophyte coverage and a concomitant decrease in nutrients and phytoplankton biomass (Table 4 and Figure 8). Our hypothesis is that in the last few years, the decrease in nutrients loads enhanced macrophyte growth at the expense of phytoplankton. Moreover, the increased area covered by macrophytes could explain why relative oxygenation, always measured during the daytime, increased despite the decrease in phytoplankton biomass. To confirm this, we found a positive correlation between relative oxygenation and macrophyte coverage ( Figure 9). Finally, in recent years, although hypoxia/anoxia events have occurred in the sampling areas, they have been short and spatially limited, often lasting less than 24 h [73], while the serious and recurrent episodes of hypoxia/anoxia caused by Ulva in the 1980s have never occurred.
Since 2013, it is assumed that the decreased phytoplankton biomass and increased water transparency are enhancing macrophyte growth, and that macrophyte growth is, in turn, reducing water turbidity and phytoplankton blooms, as reported for the LoV in the past [74,75]. The sharp decrease in ammonia concentrations (Figure 2, Figure 3 andFigure 4) may also have contributed to the drop in phytoplankton biomass: according to [75], ammonia represents the main N source (accounting for > 70% of total N uptake rates) for phytoplankton nutrition. In addition, dense assemblages of macrophytes influence the nutrient cycle, because they are likely to intercept nutrients from both the water column and the sediments [76], making them less available to phytoplankton. Lastly, it is difficult to establish whether the significant decrease in the N/P ratio has favoured the proliferation of macrophytes. Indeed, there does not seem to be any direct relationship between the growth of macroalgae and the N/P ratio in the waters of the LoV [77].
Another factor that could cause macrophytes to inhibit the growth of phytoplankton is allelopathy. The allelopathic effects of macrophytes on phytoplankton are no longer questioned [78], and although it is difficult to demonstrate in situ [79] and we have no data on it, it can be assumed that the release of allelochemicals by macrophytes inhibits the growth of phytoplankton. In this regard, it has been reported that extracts from the leaves of Zostera noltii could inhibit microalgae [80]. In addition, several species of the green macroalgal genus Ulva display the ability to cause the rapid lysis of certain phytoplankton species (Prorocentrum micans, Prorocentrum donghaiense and Heterosigma akashiwo) and to reduce the growth of Alexandrium tamarense and Chaetoceros gracile [81]. Furthermore, regarding the alternating dominance of macro-or micro-producers in the LoV, [19] reported that the predominance of phytoplankton resulted from low water transparency, high grazing pressure on macroalgae and high concentrations of particulate matter on the fronds or in the water column. In addition, the interactions that determine the composition, abundance and distribution of macroalgal groups and seagrasses and their relations with their physico-chemical environment are complex and depend on sediment composition, climate, patterns of gamete dispersion, tidal currents, predation and other factors [77].
To summarize, the results of this study show that in the years 2013-2017, the overall quality of the central and northern basins of the LoV improved and these areas were recolonized by macrophytes at the expense of phytoplankton, as reported by other authors for macrophyte-dominated shallow-water systems with similar nutrient loadings [82].
To conclude, in transitional environments such as lagoons, managing macrophyte and microalgal biomass and its propagation is a key issue in order to avoid the occurrence of hypoxia/anoxia and hence the onset of critical conditions in ecosystem functioning. Mapping macrophyte coverage with a high temporal and spatial resolution, using remote sensing techniques, appears to be a useful and cost-effective method for assessing macrophyte coverage and/or for monitoring its spatial and temporal evolution. Moreover, although Landsat TM and ETM+ were designed for terrestrial applications and their applications in aquatic environments are limited, they are recognized as usable in aquatic habitat mapping [83,84]. The long data series from Landsat satellites provides a unique opportunity to study long-term changes in shallow-water environments. The launch of Landsat-8 in 2013 has ensured the continuation of the data series, and the improved radiometric resolution (12-bit instead of 8-bit) is beneficial for all aquatic science applications [85][86][87]. However, the effectiveness of this approach requires that remote sensing by satellite is accompanied by adequate field measurements.
Future work on macrophyte seasonal patterns and coverage will provide useful information about intra-annual fluctuations of their phenology, improving the assessment of ecological status in transitional waters. Further efforts will be dedicated to distinguishing macrophyte functional groups, i.e., macroalgae and seagrasses, by exploiting the capability of the new hyperspectral mission, PRISMA (Italian Space Agency). The first tests are encouraging for inland and coastal applications [88]. In order to adequately understand the growth and distribution of various primary producers, the implementation of long-term series of both satellite and in situ hydro-bio-chemical data is essential, as is the contribution of taxonomists. This knowledge base could provide effective information to local authorities, reducing the costs of monitoring activities in connection with the European Water Framework Directive, and would be a useful tool for evaluating and managing future changes to the LoV ecosystem.