Research Article |
Corresponding author: Federica Cerino ( fcerino@inogs.it ) Academic editor: Antonella Lugliè
© 2019 Federica Cerino, Daniela Fornasaro, Martina Kralj, Michele Giani, Marina Cabrini.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Cerino F, Fornasaro D, Kralj M, Giani M, Cabrini M (2019) Phytoplankton temporal dynamics in the coastal waters of the north-eastern Adriatic Sea (Mediterranean Sea) from 2010 to 2017. In: Mazzocchi MG, Capotondi L, Freppaz M, Lugliè A, Campanaro A (Eds) Italian Long-Term Ecological Research for understanding ecosystem diversity and functioning. Case studies from aquatic, terrestrial and transitional domains. Nature Conservation 34: 343-372. https://doi.org/10.3897/natureconservation.34.30720
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Phytoplankton community structure was analysed from 2010 to 2017 at C1-LTER, the coastal Long-Term Ecological Research station located in the Gulf of Trieste, which is the northernmost part of the Mediterranean Sea. Phytoplankton abundance and relevant oceanographic parameters were measured monthly in order to describe the seasonal cycle and interannual variability of the main phytoplankton taxa (diatoms, dinoflagellates, coccolithophores and flagellates) and to analyse their relationship with environmental conditions. Overall, phytoplankton abundances showed a marked seasonal cycle characterised by a bloom in spring, with the peak in May. During the summer, phytoplankton abundances gradually decreased until September, then slightly increased again in October and reached their minima in winter. In general, the phytoplankton community was dominated by flagellates (generally <10 µm) and diatoms co-occurring in the spring bloom. In this period, diatoms were also represented by nano-sized species, gradually replaced by larger species in summer and autumn. Phytoplankton assemblages differed significantly between seasons (Pseudo-F = 9.59; p < 0.01) and temperature and salinity were the best predictor variables explaining the distribution of the multivariate data cloud. At the interannual scale, a strong decrease of the late-winter bloom was observed in recent years with the spring bloom being the main phytoplankton increase of the year.
Phytoplankton diversity, interannual variability, seasonality, Long-Term Ecological Research, Adriatic Sea, nutrients
Marine ecosystems are experiencing many different changes in response to natural processes, human activities and climate change. These changes are rapidly altering nearly every chemical, physical and biological property affecting the growth of marine microorganisms (
The analyses of time series available for the northern Adriatic LTER site have highlighted that the northern Adriatic has experienced significant modifications of environmental conditions and trophic structure (
The aim of this study is to analyse the temporal dynamics of the phytoplankton community at a seasonal and interannual scale during the past eight years (2010–2017) in the coastal waters and infer the possible environmental drivers shaping the variability of phytoplankton assemblages.
The Gulf of Trieste is a semi-enclosed basin located in the north-western part of the Adriatic Sea, characterised by shallow depths (maximum 25 m) and a strong influence of freshwater inputs. Two main rivers, the Isonzo and Timavo Rivers, enter the gulf along the shallower north-west coastline, whereas several submarine freshwater springs flow along the eastern karstic coast. The Isonzo River is the major source of freshwater and nutrients in the gulf and deeply modulates the hydrology, biogeochemistry and productivity of this coastal area (
Data considered in this paper were collected at the C1-LTER station (45°42'2.99"N and 13°42'36.00"E, bottom depth: 17 m) located in the Gulf of Trieste. C1-LTER, 270 m far from the coast, north of the town of Trieste (Fig.
Map of the study area in the northern Adriatic Sea (Mediterranean Sea) showing the location of the sampling station (C1-LTER).
CTD profiles of temperature and salinity were obtained with an Idronaut Ocean Seven (models 401 and 316) or SBE 19plus SEACAT multiparametric probe, calibrated every 6/12 months.
Total precipitations data, provided by ARPA FVG – OSMER e GRN, Trieste (http://www.meteo.fvg.it/), were registered at the station situated at Molo Fratelli Bandiera (45°38'59.99"N, 13°45'8.07"E).
The Isonzo River discharge data, provided by Regione Autonoma Friuli Venezia Giulia, were calculated by a rating curve from the hydrometric level registered at Turriaco station (13 km from the Isonzo River mouth).
Samples for the determination of dissolved inorganic nutrient (nitrite, N-NO2, nitrate, N-NO3, ammonia, N-NH4, phosphate, P-PO4, and silicate, Si-Si(OH)4) concentrations were pre-filtered through precombusted size glass-fibre filters (Whatmann GF/F), stored at -20 °C and then analysed colorimetrically with a Bran + Luebbe Autoanalyzer 3, up to December 2013, and afterwards with a QuAAtro (Seal Analytical), according to
Temperature and salinity profiles and nutrient concentrations were plotted using Ocean Data View ver. 4.7.10 (Schlitzer 2015).
For phytoplankton analysis, samples were fixed with pre-filtered and neutralised formaldehyde (1.6% final concentration) (
The distributions of nutrient concentrations and main phytoplankton group abundances were checked for significant differences among seasons, years, months and depths through analysis of variance (Kruskal-Wallis ANOVA). When significant differences were observed (p < 0.05), post hoc comparisons of mean ranks of all pairs of groups (
A non-parametric Spearman rank order correlation was used to assess the relationship in surface waters among oceanographic parameters (temperature, salinity, DIN, P-PO4 and Si-Si(OH)4), total precipitations on the three days preceding sampling, Isonzo River discharge on the day preceding sampling, considering the dataset grouped per season. Additionally, the influence of environmental variables (temperature, salinity, DIN, P-PO4 and Si-Si(OH)4) on the phytoplankton groups and taxa was considered using the water column integrated values. These analyses were performed using the Statistica 7.0 software package (StatSoft).
A reduced taxa dataset (85 taxa) was used to calculate the Indicator Value Index (IndVal) and to perform multivariate statistical analyses: from the whole dataset comprising 122 taxa, any taxa with a lower than 10% frequency percentage were eliminated and species abundances were integrated on four depths using the trapezoidal method. The trapezoidal rule works by approximating the region under the graph of the function as a trapezoid and calculating its area.
Before performing the multivariate analyses, the species abundance values were log(X+1) transformed to diminish the effect of the most abundant species, and the dissimilarity matrix was computed based on the Bray-Curtis index. The environmental variables (temperature, salinity, total precipitations, Isonzo River discharge and nutrient concentrations) were first tested for multi-collinearity and symmetric distribution using PRIMER’s Draftsman Plot tool and then normalised. The dissimilarity matrix was calculated based on the Euclidean distance.
To assess differences in species composition among seasons, a PERMANOVA test was applied considering the ‘season’ as a fixed factor. Unrestricted permutations of row data and 999 permutations were performed.
The effect of abiotic variables (temperature, salinity, total precipitations, Isonzo River discharge, dissolved inorganic nitrogen, phosphates and silicates) on the phytoplankton community was assessed by distance-based redundancy analysis (dbRDA,
All these analyses were performed using the PRIMER software package (v. 7), including the add-on PERMANOVA+ package.
To assess species characterising seasons (winter: January, February and March; spring: April, May and June; summer: July, August and September; autumn: October, November and December), the IndVal (
The seasonal cycle and interannual variability of oceanographic parameters are showed in Figures
Seasonal cycles of temperature profiles (A), salinity profiles (B), precipitations (C) and Isonzo River discharge (D). In the box plot, the bold line represents the median, the box the 25th and 75th percentiles of the distribution, the whisker the non-outlier range, the circle the outliers and the star the extremes.
Interannual distributions of temperature profiles (A), salinity profiles (B), precipitations (C) and Isonzo River discharge (D).
The salinity also showed a clear seasonality with the minimum recorded in spring and the maximum in winter (Fig.
The precipitation regime was characterised by a rainy period in late summer-autumn (September to November) and two drier periods in March-April and July-August (Figs
The annual cycle of the Isonzo River discharge displayed minima in summer and two maxima, the biggest one in autumn and a lower one in winter (Figs
Dissolved inorganic nitrogen (DIN) concentration ranged from undetectable values to 71.27 µmol L-1, measured in January 2014 at the surface (Fig.
Seasonal and interannual distribution of dissolved inorganic nitrogen (DIN) (A–B), phosphate (P-PO4) (C–D) and silicate (Si-Si(OH)4) (E–F) concentrations in the two layers 0.5–5 m (A, C, E) and 10–15 m (B, D, F).
Phosphate concentrations ranged from undetectable values to 0.28 µmol L-1 in June 2015 at the surface, and higher values were observed from late summer throughout autumn and winter, mainly in bottom waters (Fig.
Silicate concentrations ranged from 0.07 µmol L-1 in November 2010, at 5 m depth, to 40.73 µmol L-1 in January 2014 at the surface, with a mean value (± SD) of 3.88 ± 3.67 µmol L-1. Silicate generally showed an increase in summer in the deeper waters (Fig.
The correlations among oceanographic variables and freshwater inputs, considering the seasons separately, are reported in Table
Spearman rank correlations among oceanographic variables and freshwater inputs at surface in different seasons (WIN: winter; SPR: spring; SUM: summer; AUT: autumn). Significant values (< 5%) are marked in bold. *p < 0.05; **p < 0.01; ***p < 0.001.
WIN | Temperature | Salinity | Precipitation | Isonzo discharge | DIN | P-PO4 | Si-Si(OH)4 |
Temperature | 1 | ||||||
Salinity | -0.241 | 1 | |||||
Precipitation | -0.036 | -0.289 | 1 | ||||
Isonzo discharge | 0.497* | -0.439* | 0.336 | 1 | |||
DIN | 0.456* | -0.690*** | 0.199 | 0.528* | 1 | ||
P-PO4 | 0.062 | -0.529** | -0.017 | 0.220 | 0.549** | 1 | |
Si-Si(OH)4 | 0.378 | -0.332 | 0.204 | 0.547** | 0.619** | 0.510* | 1 |
SPR | Temperature | Salinity | Precipitation | Isonzo discharge | DIN | P-PO4 | Si-Si(OH)4 |
Temperature | 1 | ||||||
Salinity | -0.648*** | 1 | |||||
Precipitation | -0.299 | 0.265 | 1 | ||||
Isonzo discharge | 0.253 | -0.533** | 0.191 | 1 | |||
DIN | 0.339 | -0.805*** | -0.183 | 0.493* | 1 | ||
P-PO4 | -0.334 | 0.073 | -0.071 | -0.006 | -0.030 | 1 | |
Si-Si(OH)4 | 0.471* | -0.734*** | -0.203 | 0.580** | 0.773*** | 0.112 | 1 |
SUM | Temperature | Salinity | Precipitation | Isonzo discharge | DIN | P-PO4 | Si-Si(OH)4 |
Temperature | 1 | ||||||
Salinity | -0.464* | 1 | |||||
Precipitation | -0.456* | 0.113 | 1 | ||||
Isonzo discharge | -0.289 | -0.108 | 0.448* | 1 | |||
DIN | -0.403 | -0.176 | 0.392 | 0.176 | 1 | ||
P-PO4 | -0.358 | 0.221 | 0.182 | -0.026 | 0.259 | 1 | |
Si-Si(OH)4 | -0.421* | 0.234 | 0.476* | 0.187 | 0.497* | 0.430* | 1 |
AUT | Temperature | Salinity | Precipitation | Isonzo discharge | DIN | P-PO4 | Si-Si(OH)4 |
Temperature | 1 | ||||||
Salinity | -0.240 | 1 | |||||
Precipitation | 0.275 | -0.683*** | 1 | ||||
Isonzo discharge | 0.344 | -0.609** | 0.630** | 1 | |||
DIN | -0.117 | -0.660*** | 0.504* | 0.329 | 1 | ||
P-PO4 | -0.051 | -0.422* | 0.331 | 0.233 | 0.605** | 1 | |
Si-Si(OH)4 | -0.003 | -0.238 | 0.004 | -0.056 | 0.471* | 0.773*** | 1 |
Phytoplankton displayed a seasonal cycle characterised by minima in late autumn-winter (from December to February, with the monthly median always lower than 8.0 × 105 cells L-1 at all depths) (Fig.
Monthly medians (black lines) and first and third quartiles (blue and red circles, respectively) of phytoplankton abundance and the relative contribution of the main phytoplankton groups (flagellates, diatoms, dinoflagellates, and coccolithophores) (bars) from 2010 to 2017 at the four sampled depths (0.5, 5, 10 and 15 m) at the C1-LTER station.
In March, abundances started to increase, reaching the main peak in spring (May), with a median value of about 4 × 106 cells L-1 in the 0.5–5 m layer and about 2.3 × 106 cells L-1 at 10–15 m, although these differences among depths were not statistically significant. From June, phytoplankton abundance gradually decreased and was low throughout the summer. Slightly higher abundances were observed at 10 m during this period. A further slight increase was observed in October (Fig.
Considering the whole dataset, the phytoplankton community was dominated by flagellates (66%) and diatoms (29%), followed by coccolithophores and dinoflagellates (3 and 2%, respectively), with all groups showing a marked seasonal cycle (Fig.
Seasonal cycle of flagellates (A–B), diatoms (C–D), coccolithophores (E–F) and dinoflagellates (G–H) at 0.5 and 5 m depth (A, C, E, G) and at 10 and 15 m depth (B, D, F, H). In the box plot, the bold line represents the median, the box the 25th and 75th percentiles of the distribution, the whisker the non-outlier range, the circle the outliers and the triangle the extremes. Note: y-scales are different for diatoms/flagellates and dinoflagellates/coccolithophores.
On average, flagellates showed the highest abundances in spring-summer (H = 127.46, p < 0.001), from April to July, and minima in winter (Fig.
Diatoms showed minima from December to February (monthly median of about 104 cells L-1), then increased in late winter and peaked in spring (H = 40.39, p < 0.001), in May (monthly median 1.6 × 106 cells L-1) (Fig.
Coccolithophores were most abundant in autumn-winter (H = 53.42, p < 0.001) (Fig.
Dinoflagellates showed higher abundances in spring and late summer (H = 94.18, p < 0.001) (Fig.
Diatoms were negatively correlated with DIN and silicates (p < 0.001), while dinoflagellates positively with temperature (p < 0.001) and negatively with DIN (p < 0.05) (Table
Spearman rank correlations among oceanographic variables with water column integrated phytoplankton groups and taxa. Significant values (< 5%) are marked in bold. *p < 0.05; **p < 0.01; ***p < 0.001.
Temperature | Salinity | DIN | P-PO4 | Si-Si(OH)4 | Diatoms | Dinoflagellates | Coccolithophores | Flagellates | Total phytoplankton | Chaetoceros spp. | Skeletonema spp. | |
Temperature | 1 | |||||||||||
Salinity | -0.455*** | 1 | ||||||||||
DIN | -0.332*** | -0.161 | 1 | |||||||||
P-PO4 | -0.063 | -0.022 | 0.404*** | 1 | ||||||||
Si-Si(OH)4 | -0.069 | 0.084 | 0.469*** | 0.416*** | 1 | |||||||
Diatoms | 0.130 | -0.151 | -0.308** | -0.122 | -0.516*** | 1 | ||||||
Dinoflagellates | 0.380*** | -0.199 | -0.248* | -0.187 | 0.004 | 0.336*** | 1 | |||||
Coccolithophores | -0.165 | 0.081 | 0.126 | 0.347*** | 0.286** | -0.105 | -0.006 | 1 | ||||
Flagellates | 0.290** | -0.306** | -0.373*** | -0.317** | -0.214* | 0.497*** | 0.765*** | -0.084 | 1 | |||
Total phytoplankton | 0.200 | -0.292** | -0.373*** | -0.246* | -0.373*** | 0.752*** | 0.640*** | -0.046 | 0.906*** | 1 | ||
Chaetoceros spp. | 0.095 | -0.085 | -0.384*** | -0.164 | -0.406*** | 0.712*** | 0.311** | -0.162 | 0.377*** | 0.531*** | 1 | |
Skeletonema spp. | -0.451*** | 0.156 | 0.216* | -0.071 | -0.003 | 0.125 | -0.238* | -0.122 | -0.149 | -0.070 | 0.098 | 1 |
Phytoplankton assemblages were significantly diverse among seasons (Pseudo-F = 9.59, p < 0.01) and the IndVal calculation gave an indication of which species characterised different seasons (Table
List of phytoplankton taxa characterised by the highest and significant IndVal for each season (win: winter; spr: spring; sum: summer; aut: autumn).
Group | Indval | p-value | |
---|---|---|---|
Skeletonema spp. | win | 0.592 | 0.004 |
Und. choanoflagellates | win | 0.491 | 0.014 |
Octactis octonaria | win | 0.409 | 0.001 |
Protoperidinium bipes | win | 0.292 | 0.04 |
Cyclotella spp. | spr | 0.800 | 0.001 |
Prorocentrum micans | spr | 0.734 | 0.001 |
Chaetoceros throndsenii | spr | 0.724 | 0.001 |
Chaetoceros spp. | spr | 0.608 | 0.004 |
Torodinium sp. | spr | 0.590 | 0.001 |
Und. Cryptophyceae | spr | 0.576 | 0.001 |
Prorocentrum cordatum | spr | 0.517 | 0.006 |
Leucocryptos marina | spr | 0.516 | 0.002 |
Commation sp. | spr | 0.514 | 0.001 |
Ollicola vangoorii | spr | 0.507 | 0.001 |
Lessardia elongata | spr | 0.500 | 0.001 |
Meringosphaera mediterranea | spr | 0.484 | 0.001 |
Und. coccolithophores | spr | 0.427 | 0.001 |
Und. naked dinoflagellates | spr | 0.423 | 0.005 |
Diplopsalis group | spr | 0.414 | 0.001 |
Protoperidinium steinii | spr | 0.391 | 0.001 |
Alexandrium spp. | spr | 0.322 | 0.037 |
Und. Prymnesiophyceae | spr | 0.279 | 0.047 |
Dinobryon faculiferum | spr | 0.227 | 0.034 |
Proboscia alata | sum | 0.888 | 0.001 |
Hermesinum adriaticum | sum | 0.847 | 0.001 |
Ceratoperidinium falcatum | sum | 0.824 | 0.001 |
Guinardia flaccida | sum | 0.710 | 0.001 |
Und. pennate diatoms | sum | 0.668 | 0.001 |
Rhizosolenia spp. | sum | 0.611 | 0.001 |
Asteromphalus spp. | sum | 0.586 | 0.001 |
Pseudoscourfieldia marina | sum | 0.571 | 0.002 |
Rhabdolithes claviger | sum | 0.508 | 0.001 |
Thalassionema spp. | sum | 0.498 | 0.013 |
Und. thecate dinoflagellates | sum | 0.453 | 0.016 |
Dinophysis fortii | sum | 0.451 | 0.001 |
Cerataulina pelagica | sum | 0.434 | 0.050 |
Tripos furca | sum | 0.395 | 0.005 |
Gyrodinium spp. | sum | 0.387 | 0.026 |
Hemiaulus hauckii | sum | 0.364 | 0.004 |
Gonyaulax polygramma | sum | 0.340 | 0.005 |
Chaetoceros lorenzianus | sum | 0.320 | 0.008 |
Prorocentrum dactylus | sum | 0.312 | 0.010 |
Dinophysis caudata | sum | 0.300 | 0.009 |
Leptocylindrus mediterraneus | sum | 0.280 | 0.021 |
Bacteriastrum jadranum | sum | 0.269 | 0.016 |
Phalacroma rotundatum | sum | 0.268 | 0.025 |
Syracosphaera pulchra | aut | 0.776 | 0.001 |
Calciosolenia murrayi | aut | 0.700 | 0.001 |
Diploneis spp. | aut | 0.638 | 0.001 |
Dactyliosolen blavyanus | aut | 0.594 | 0.001 |
Lioloma pacificum | aut | 0.546 | 0.001 |
Ophiaster spp. | aut | 0.505 | 0.001 |
Guinardia striata | aut | 0.447 | 0.002 |
Asterionellopsis glacialis | aut | 0.435 | 0.001 |
Dictyocha fibula | aut | 0.369 | 0.011 |
Chaetoceros socialis | aut | 0.363 | 0.001 |
Und. Euglenophyceae | aut | 0.352 | 0.027 |
Paralia sulcata | aut | 0.260 | 0.029 |
The dbRDA analysis also revealed temporal differences of phytoplankton assemblages (Fig.
Distance-based redundancy analysis (dbRDA) plot of phytoplankton assemblages. Vectors of abiotic variables (temp: temperature; sal: salinity; precip: total precipitations in the three days preceding the sampling; Isonzo: Isonzo River discharge in the seven days preceding the sampling; DIN: dissolved inorganic nitrogen concentration; PO4: phosphate concentration; Si(OH)4: silicate concentration) affect the construction of the constrained ordination picture; the longer the vector, the bigger the effect of the variable.
The temporal distribution from January 2010 to December 2017 of water column integrated abundance values of the main phytoplankton groups showed interannual variability for maximum values and occurrence of these maxima (Fig.
Temporal variations from January 2010 to December 2017 of depth integrated abundances of total phytoplankton (A), flagellates (B), diatoms (C), coccolithophores (D) and dinoflagellates (E).
The two main taxonomic groups were flagellates and diatoms during the whole analysed period, with a predominance of flagellates (annual medians > 6 × 105 cells L-1 for flagellates, < 2 × 105 cells L-1 for diatoms). Two exceptions were observed in 2011 and 2012, when flagellates showed particularly low abundances (annual median 1.7 and 4.5 × 105 cells L-1, respectively) (Fig.
Coccolithophores displayed the typical seasonal cycle, with the highest abundances in autumn-winter, in all investigated years (Fig.
Dinoflagellates were always present in spring-summer (Fig.
This study presents the temporal dynamics, over eight years, of the phytoplankton community at a coastal station located in the north-eastern Adriatic, a highly variable environment. A marked seasonality, with warm summers and cool winters, was observed, which is typical of the area (
Phytoplankton attained minimum values in winter, in contrast to a previous study (
In late summer, the increase of silicates in deeper waters, under the pycnocline, as also observed in previous works (
In autumn, the high nutrient availability due to the riverine discharges and mixing of the water column and the still favourable light conditions triggered a second phytoplankton increase, although much smaller. Autumn blooms have been reported in the Gulf of Trieste (
In recent decades, the Gulf of Trieste has experienced considerable changes in its oceanographic, biogeochemical and biological features (
Changes in phytoplankton abundances have been extensively reported at both global (e.g.
The most striking change recorded during this study was the strong decrease of the phytoplankton annual maximum in late winter-early spring, typical of the study area (
Long-term sampling offers a unique opportunity to analyse multiannual datasets and describe complete seasonal cycles, thus unveiling possible changes occurring in phytoplankton community structure in highly variable environments such as coastal ecosystems where the distinction between natural variability and temporal trends is more difficult due to local disturbances. In the north-eastern part of the Gulf of Trieste, phytoplankton displayed a marked seasonal cycle strongly influenced by temperature and salinity, as revealed by multivariate analysis. This cycle was characterised by a spring peak dominated by nanoflagellates and small diatoms, triggered by high nutrient availability due to riverine discharges, and a second small increase in autumn dominated by nanoflagellates, larger diatoms and coccolithophores, possibly favoured by higher nutrient availability deriving from the mixing of the water column. In summer, stormy events could cause episodic diatom increases. At interannual scale, a strong decrease of the late winter-early spring bloom was observed in recent years, with the spring bloom becoming the main peak during the year. If the role of temperature will be confirmed with further analyses, it may have significant implications in the view of climate changes as drivers of long-term changes in phytoplankton dynamics. However, because long-term data series are considered necessary to determine whether the changes are actual ongoing trends, or fall within the interannual variability of phytoplankton communities, continuous monitoring of these alterations is very important. Therefore, LTER sites offer ideal study fields for this purpose and provide data for defining the environmental status, as required by the Marine Strategy. Additionally, the LTER-Italy network allows for sharing methodologies, ecological data and knowledge which would provide the opportunity to establish collaborations at the national (with other LTER-Italy sites), regional (LTER- Europe) and international (LTER-International) levels.
The Gulf of Trieste site is part of the national and international Long Term Ecological Research networks (LTER-Italy, LTER-Europe, ILTER). The authors would like to thank Paola Del Negro for continuing to promote long-term research in the Gulf of Trieste, Bruno Cataletto, Cinzia Comici and Edvino Cociancich for sampling and performing CTD measurements and the Riserva Marina di Miramare and ARPA-FVG for providing the vessels. The authors are grateful to ISMAR-CNR Trieste for providing data on total precipitations and to “Regione Autonoma Friuli Venezia Giulia, Direzioni centrali dell’amministrazione regionale, Direzione centrale ambiente ed energia, Area tutela geologico-idrico-ambientale, Servizio disciplina servizio idrico integrato, gestione risorse idriche, tutela acque da inquinamento” for providing Isonzo River hydrometric data. The authors also thank the subject editor and reviewers for their constructive comments and suggestions that helped to improve the paper.