Phytoplankton Time-Series in a LTER Site of the Adriatic Sea: Methodological Approach to Decipher Community Structure and Indicative Taxa

Abstract

In the shallow and landlocked northeast Adriatic Sea, environmental factors have changed in recent decades. Their influence on seasonal and inter-annual variability of phytoplankton has been documented in the recent literature. Here, we decipher the long-term variability of phytoplankton phenology at a Long-Term Ecological Research site (Gulf of Trieste, Slovenia). Structural changes in the phytoplankton community (period 2005–2017) were analysed using a multivariate protocol based on Bayesian clustering. The protocol was modified from the literature to fit the needs of the study, using correspondence analysis and k-means clustering. A novel index for ordination and selection of taxa based on frequency and evenness was developed. The Total Inertia analysis showed that this index better preserved the available information. Typical seasonal assemblages were highlighted by applying the Indicative Value index in conjunction with likelihood ratio values. We obtained a rough picture of the seasonal separation of the diatom-dominated community from the mixed community and a refined picture of the phenology of the assemblages and bloom events. The spring diatom peak proved to be inconstant and short-lived, while the autumn bloom was generally long and diverse. As expected for nearshore environments, the average life span of the assemblages was found to be short-periodic (2–4 months). The second part of the year and the last part of the series were more prone to changes in terms of typical assemblages.

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Article
Phytoplankton Time-Series in a LTER Site of the Adriatic Sea:
Methodological Approach to Decipher Community Structure
and Indicative Taxa
Ivano Vascotto 1,2, *, Patricija Mozetiˇc 1and Janja Francé1


Citation: Vascotto, I.; Mozetiˇc, P.;
Francé, J. Phytoplankton Time-Series
in a LTER Site of the Adriatic Sea:
Methodological Approach to
Decipher Community Structure and
Indicative Taxa. Water 2021,13, 2045.
https://doi.org/10.3390/w13152045
Academic Editor: Genuario Belmonte
Received: 10 June 2021
Accepted: 21 July 2021
Published: 27 July 2021
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Copyright: © 2021 by the authors.
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Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1National Institute of Biology, Marine Biology Station Piran, Fornaˇce 41, 6330 Piran, Slovenia;
patricija.mozetic@nib.si (P.M.); janja.france@nib.si (J.F.)
2Jozef Stefan International Postgraduate School, Jamova Cesta 39, 1000 Ljubljana, Slovenia
*Correspondence: ivano.vascotto@nib.si; Tel.: +356-(0)59-232-935
Abstract:
In the shallow and landlocked northeast Adriatic Sea, environmental factors have changed
in recent decades. Their influence on seasonal and inter-annual variability of phytoplankton has been
documented in the recent literature. Here, we decipher the long-term variability of phytoplankton
phenology at a Long-Term Ecological Research site (Gulf of Trieste, Slovenia). Structural changes in
the phytoplankton community (period 2005–2017) were analysed using a multivariate protocol based
on Bayesian clustering. The protocol was modified from the literature to fit the needs of the study,
using correspondence analysis and k-means clustering. A novel index for ordination and selection of
taxa based on frequency and evenness was developed. The Total Inertia analysis showed that this
index better preserved the available information. Typical seasonal assemblages were highlighted by
applying the Indicative Value index in conjunction with likelihood ratio values. We obtained a rough
picture of the seasonal separation of the diatom-dominated community from the mixed community
and a refined picture of the phenology of the assemblages and bloom events. The spring diatom peak
proved to be inconstant and short-lived, while the autumn bloom was generally long and diverse.
As expected for nearshore environments, the average life span of the assemblages was found to be
short-periodic (2–4 months). The second part of the year and the last part of the series were more
prone to changes in terms of typical assemblages.
Keywords: phytoplankton; phenology; assemblages; LTER; Adriatic Sea
1. Introduction
The theory behind the spatio-temporal distribution of phytoplankton species in the
pelagic habitat [
1
] has long been debated and remains unresolved. Variations in coastal
ecosystem properties play an important role in the distribution of phytoplankton taxa [
2
]
and one of the most evident features of seasonal phytoplankton dynamics is cyclic be-
haviour [
3
–
6
]. Nevertheless, the core problem remains that phytoplankton taxa take on
some paradoxical aspects when we consider their distribution in their hyperspace-niche [
1
].
The excess in phytoplankton taxa richness—the so-called Paradox of Plankton—has been as-
sociated with the permanent failure to reach the equilibrium state within the pelagic habitat,
as the habitat properties vary at a similar rate as the phytoplankton reproduce [
7
]. Numer-
ical simulations [
8
] have shown that nested competition for multiple limiting resources
can lead to an unstable equilibrium, as described for phytoplankton by Hutchinson [
1
].
However, it is still unknown how much of the observed coexistence can be attributed to
environmental heterogeneity and how much arises within the community [9].
In the 1970s, Margalef [
10
] introduced the concept of phytoplankton succession,
the so-called Mandala, in which the main stages of succession are driven by turbulence
and nutrient availability. Succession starts in highly turbulent waters where diatoms
successfully proliferate (r strategy) and culminates in calm oligotrophic waters where
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Water 2021,13, 2045 2 of 26
dinoflagellates dominate (K strategy). Coccolithophores thrive in intermediate conditions,
while red tides occur in calm but nutrient-enriched waters. Successive attempts have been
made to revise the original mandala, but with doubtful success [11].
For our understanding of oceanic biogeochemical cycles, trophic interactions and, in
general, the ecology of the marine environment, it is important to know how phytoplankton
diversity is distributed in the pelagic habitat [
12
,
13
]. In the marine environment, it is
particularly difficult to define areas or, better, defined spatial and temporal units on which
diversity is then calculated and assemblages defined. These difficulties arise from the
dynamic nature of the sea [
14
]. Phytoplankton phenology patterns vary in the time domain
from stable annual fluctuations in certain biomes to the absence of a repeating pattern in
others [
15
]. The usual pattern for estuarine and coastal environments is a short period with
fluctuations on the order of 2–4 months [
16
]. Although it is often difficult to access data
with spatial and temporal coverage, data from LTER (Long-Term Ecological Research) sites
are ideal for studying phytoplankton phenology because they cover large time windows.
Such data allow us to capture the basic community structure and its variability [16].
In the northernmost part of the Adriatic Sea, the Gulf of Trieste (GoT), phytoplankton
community composition has been described as nanoflagellate-dominated, with seasonal
outbursts of diatoms in spring and autumn in correlation with freshet periods and water
column mixing [
5
,
17
]. This community is also sensitive to freshwater inputs [
3
], and
some characteristic shifts in taxa composition have been observed following freshwater
pulses [18,19].
One approach to analysing phytoplankton phenology is to use time series of species
composition coupled with fidelity-specificity indices to describe indicative species phenol-
ogy. Such an analysis has already been applied in studies in the northern Adriatic [
5
,
20
–
22
],
based on fixed time intervals (seasons, months, years) used to find indicative species and
describe the phenology of co-occurring species (assemblages). In this work, we propose a
different approach with the reverse order of steps: species composition within time series
is first used to define time intervals, and then it is possible to find the groups of indicative
species within time intervals. For this purpose, we applied and modified the protocol
proposed by Souissi et al. [
23
] and used by Anneville et al. [
24
] in a study on the phyto-
plankton community of Lake Geneva and tested for marine phytoplankton communities in
the GoT [25,26].
In addition to co-occurrences, we were also interested in describing the major abun-
dance peaks and the taxa responsible for these peaks. To estimate the distributional
structure of a group of taxa, diversity indices of various kinds can usually be used to
estimate, for each sample, how much abundance is concentrated or even across taxa [
27
].
Again, we used the same approach, but in reverse, to estimate how much the abundance is
concentrated or uniform across samples for each taxon. The taxa that would be uniform
would be those that have constant abundance across samples; the more they would be
uneven the more their abundance would be concentrated in a few samples. In this way, we
tried to be careful not to discard any important taxa during the selection process.
Upon re-analysis of the long-term phytoplankton data series from the Slovenian
LTER station, we found that several problematic passages resulted in the loss of relevant
information about community structure, introduced incorrect ordinations of the data, and
caused difficulties in assigning indicative taxa. Therefore, one of the two goals of this study
is to refine the critical passages in the protocol of the analysis. The other objective of this
study is to use the refined analysis steps to better characterize the temporal dynamics of
phytoplankton with the determination of characteristic periods and species at the Slovenian
marine LTER station in the GoT.
2. Materials and Methods
2.1. Area of Study
The Gulf of Trieste (GoT) is a basin surrounded by land at the northeastern tip of
Adriatic Sea. Due to its shallow depth, 21 m on average [
28
], the GoT is largely influenced

Water 2021,13, 2045 3 of 26
by climatic conditions that cause variations in salinity and temperature. The GoT is
seasonally stratified, and its euphotic zone significantly exceeds the depth of the upper
mixed layer for most of the year [
29
]. The basin is under the influence of two main winds,
the “bora” and the “jugo”, which blow from the northeast and southeast, respectively.
Bora is a strong catabatic wind whose effect on the water column is twofold: mixing and
cooling, while Jugo is a constant wind that is thought to have a chaotic effect on the current
circulation in the Gulf [30].
The Slovenian LTER station (45.53833
◦
N, 13.55
◦
E; 21 m depth) is located at the
southern entrance to the GoT (Figure 1), where direct impact from freshwater inputs and
other pressures are minimal. The waters around the station are usually traversed by the
current North Adriatic Dense Water (NAdDW). Only occasionally, they are reached by
the plume of the Soˇca (Isonzo) River (SO, Figure 1), one of the local freshwater sources
that has the greatest influence on the basin [
30
]. The Slovenian LTER station represents
the reference station for national monitoring purposes, but the bias of the representation
should be taken into account when generalizing the results to the whole Gulf.
Water 2021, 13, x FOR PEER REVIEW 3 of 26
2. Materials and Methods
2.1. Area of Study
The Gulf of Trieste (GoT) is a basin surrounded by land at the northeastern tip of
Adriatic Sea. Due to its shallow depth, 21 m on average [28], the GoT is largely influenced
by climatic conditions that cause variations in salinity and temperature. The GoT is sea-
sonally stratified, and its euphotic zone significantly exceeds the depth of the upper mixed
layer for most of the year [29]. The basin is under the influence of two main winds, the
“bora” and the “jugo”, which blow from the northeast and southeast, respectively. Bora
is a strong catabatic wind whose effect on the water column is twofold: mixing and cool-
ing, while Jugo is a constant wind that is thought to have a chaotic effect on the current
circulation in the Gulf [30].
The Slovenian LTER station (45.53833° N, 13.55° E; 21 m depth) is located at the
southern entrance to the GoT (Figure 1), where direct impact from freshwater inputs and
other pressures are minimal. The waters around the station are usually traversed by the
current North Adriatic Dense Water (NAdDW). Only occasionally, they are reached by
the plume of the Soča (Isonzo) River (SO, Figure 1), one of the local freshwater sources
that has the greatest influence on the basin [30]. The Slovenian LTER station represents
the reference station for national monitoring purposes, but the bias of the representation
should be taken into account when generalizing the results to the whole Gulf.
Figure 1. The map of the study site: The Slovenian LTER station 000F is marked with a black dot
and a horizontal white bar. The dotted black line marks the 20 m isobath, the dotted white lines mark
international borders while the blue lines mark the main rivers. SO Soča River, TA Tagliamento River,
RI Rižana River, DR Dragonja River, MI Mirna River, IT Italy, SLO Slovenia, HR Croatia.
2.2. Data
Monthly data from Slovenian LTER sampling station 000F (Figure 1) were collected
and stored as part of routine sampling in the Slovenian National Monitoring Program. In
this work, a twelve-year time series from 2005 to 2017 was analysed. Phytoplankton was
sampled with Niskin bottles (5 L) at different depths (0 m, 5 m, 15 m and near the bottom
at 21 m). Phytoplankton samples were fixed with neutralized formaldehyde and stored
Figure 1.
The map of the study site: The Slovenian LTER station 000F is marked with a black dot and
a horizontal white bar. The dotted black line marks the 20 m isobath, the dotted white lines mark
international borders while the blue lines mark the main rivers. SO Soˇca River, TA Tagliamento River,
RI Rižana River, DR Dragonja River, MI Mirna River, IT Italy, SLO Slovenia, HR Croatia.
2.2. Data
Monthly data from Slovenian LTER sampling station 000F (Figure 1) were collected
and stored as part of routine sampling in the Slovenian National Monitoring Program. In
this work, a twelve-year time series from 2005 to 2017 was analysed. Phytoplankton was
sampled with Niskin bottles (5 L) at different depths (0 m, 5 m, 15 m and near the bottom
at 21 m). Phytoplankton samples were fixed with neutralized formaldehyde and stored
until analysis. Subsamples of 50 mL were left to settle in a sedimentation chamber for
48 h and then examined using an inverted microscope ZEISS Axiovert 135, according to
the Utermöhl method [
31
]. Phytoplankton taxa were determined to the lowest possible
taxonomic level and counted in 50 to 100 microscope fields at 400
×
magnification. The final

Water 2021,13, 2045 4 of 26
dataset of phytoplankton composition and abundance included more than 100,000 entries.
Taxonomic names were verified according to recent changes and consistency was checked
for synonyms [32,33].
2.3. Analysis
Data analysis was performed using R-studio software (Version 1.1.456—
©
2021–2018
RStudio, Inc., Boston, MA, USA). Table A1 in Appendix Ashows the list of packages used
in the analysis. Prior to analysis, data from different depths were merged in the Integrated
Abundance (IA) using the trapezoidal rule. On the resulting matrix of taxa IAs (matrix X;
Figure 2), a series of statistical methods for clustering the samples proposed by Anneville
et al. [
24
] were applied (Figure 2A) and then modified (Figure 2B). The core idea was to
create a clustering system of the samples based on the distribution of taxa and then obtain
the indicative taxa for each cluster by the index of Fidelity and Specificity (IndVal) [34].
Water 2021, 13, x FOR PEER REVIEW 4 of 26
until analysis. Subsamples of 50 mL were left to settle in a sedimentation chamber for 48
h and then examined using an inverted microscope ZEISS Axiovert 135, according to the
Utermöhl method [31]. Phytoplankton taxa were determined to the lowest possible taxo-
nomic level and counted in 50 to 100 microscope fields at 400× magnification. The final
dataset of phytoplankton composition and abundance included more than 100,000 entries.
Taxonomic names were verified according to recent changes and consistency was checked
for synonyms [32,33].
2.3. Analysis
Data analysis was performed using R-studio software (Version 1.1.456—© 2021–2018
RStudio, Inc., Boston, MA, USA). Table A1 in Appendix A shows the list of packages used
in the analysis. Prior to analysis, data from different depths were merged in the Integrated
Abundance (IA) using the trapezoidal rule. On the resulting matrix of taxa IAs (matrix X;
Figure 2), a series of statistical methods for clustering the samples proposed by Anneville
et al. [24] were applied (Figure 2A) and then modified (Figure 2B). The core idea was to
create a clustering system of the samples based on the distribution of taxa and then obtain
the indicative taxa for each cluster by the index of Fidelity and Specificity (IndVal) [34].
Figure 2. Flowchart of the (A) original and (B) modified protocol of data analysis.
Figure 2. Flowchart of the (A) original and (B) modified protocol of data analysis.
2.4. Original Protocol
The first step consisted of the selection of the taxa on which to perform the successive
analysis. Following Anneville et al. [
24
], taxa present in less than 12% of the samples were
excluded (matrix A; Figure 2A, step 1a). A Principal Component Analysis (PCA) was

Water 2021,13, 2045 5 of 26
then applied to the log-transformed data (Figure 2A, step 1b). Principal components that
accounted for 90% of the variance were retained and used to calculate PCA scores (matrix
A’; Figure 2A). Multinormality was tested using the Dagnelie method [
23
]. From the matrix
of PCA scores, hierarchical classification of samples (dates) was performed using Euclidean
distance with a flexible clustering strategy (Figure 2A, step 2). Then, the probability that
each sample belongs to each of the obtained clusters was calculated using Bayes’ theorem.
Here, the frequency of the cluster was used as the prior probability and the conditional
probability was calculated using the
χ2
distribution estimator of the Mahalanobis distance
of the object from the centroid of the cluster [
24
]. Since the probability was obtained for
each samples’ possible cluster, each sample could be reallocated to the cluster in which it
had the maximum probability. Finally, new clusters were obtained and step 3 (calculation
of P and reallocation of samples, Figure 2A) was repeated until the composition of the
clusters remained stable and the final partition of the samples was obtained (matrix Pg,
Figure 2A). Since it is possible to divide the samples into n clusters, where n varies from 1
to the total number of samples, a probability-based criterion was applied to characterize
each partition (Figure 2A, step 4) as follows: a vector P
max
(k) representing the maximum
probability for each sample was calculated. Each partition was evaluated on the basis of
the median of the values of P
max
(k) This median was interpreted as the average measure of
the within-cluster homogeneity [
23
]. The final clusters were used to create the map of taxa
assemblages, in which each sample (i.e., each month) was assigned to a cluster (Figure 2A,
step 4).
In each of these clusters, the IndVal index [
34
] was calculated for each taxon using the
IA matrix obtained at the beginning (matrix A; Figure 2A). This index is a multiplication of
two independently calculated values: the fidelity (FI
j,t
) and the specificity (SP
j,t
) of taxon t
in the cluster of samples Gj(Equations (1) and (2), respectively).
FIj,t =NSj,t
NSj+(1)
SPj,t =NIj,t
NI+j(2)
where NS
j,t
is the number of samples in cluster G
j
containing taxon t, NS
j+
is the total
number of samples in G
j
, NI
j,t
is the mean abundance of taxon t in the samples belonging
to G
j
and NI
+j
is the sum of the mean abundances of taxon t in all clusters. The fidelity
of a taxon for a cluster is 1 if that taxon is present in all samples of the cluster, while the
specificity of a taxon for a cluster is 1 if that taxon is present only in the cluster under
consideration. The IndVal is calculated as in Equation (3) and has a range between 0 and 1.
IndValj,t =FIj,t ×SPj,t (3)
As established by the authors in [
34
], an IndVal value greater than 0.25 is considered a
threshold to describe a taxon as indicative of a particular cluster.
2.5. Modified Protocol
2.5.1. Frequency Selection
In step 1a (Figure 2A), taxa were selected for analysis based on their frequency with
the threshold of 12%. The distribution of log cumulative abundances of the taxa along the
frequency values is shown in Figure 3A. The dashed line representing the 12% presence
shows no clear discontinuity in the data to support the setting of the threshold. Furthermore,
any value of frequency used as a threshold would be arbitrary as there are no discontinuities
along the frequency values. This is also supported by the frequency histogram showing
the relative abundances (Figure 3B), where some of the rarely occurring taxa (<12%)
showed relatively high abundances (darker colour). In contrast, some of the taxa with high
frequency showed relatively low abundance (lighter colour).

Water 2021,13, 2045 6 of 26
Water 2021, 13, x FOR PEER REVIEW 6 of 26
showing the relative abundances (Figure 3B), where some of the rarely occurring taxa
(<12%) showed relatively high abundances (darker colour). In contrast, some of the taxa
with high frequency showed relatively low abundance (lighter colour).
Figure 3. The rationale for the selection of taxa: (A) Frequency vs. log cumulative abundance distri-
bution of taxa (points), dashed line represents the 12% threshold; (B) Frequency histogram of taxa,
dashed line represents the 12% threshold, grey scale represents relative abundance of taxa; (C)
FREVE index vs. log cumulative abundance distribution of taxa (points), dashed line represents the
value of 0.28; (D) Histogram of FREVE index, the dashed line represents the 0.28 value of fluctuation
index, grey scale represents the relative abundance of taxa.
The lower bound of the distribution of points in Figure 3A resembles the logarithmic
function. A similar shape is obtained by the log transformation of the vector of positive
naturals N
+
[1, 2, 3, …]. This distribution results from the taxa identification and counting
method, during which some of the taxa were found only once per sample. This value of
1cell was then transformed per volume of sample and integrated. The resulting abun-
dances were thus generated by a categorical process of presence/absence discrimination,
even though the values look quantitative. The taxa abundances that occupy the lower limit
of the distribution (those that are close to the ln(x) function, Figure 3A) are therefore the
result of a scaled version of the log transformation of the presences’ vector of rare taxa. This
particular distribution of some taxa led to the postulation of the co-presence of two types of
rarity and four types of distribution of taxa in our dataset. A taxon may be rare in terms of
frequency (i.e., observed in a small number of samples) or rare in terms of abundance (i.e.,
having low abundances). The combination of rarities results in four distribution types: taxa
that are rare in both presence and abundance (type 1), taxa that are rare in presence but not
in abundance (type 2), taxa that are common in presence and abundance (type 3), and taxa
that are common in presence and rare in abundance (type 4).
To measure the evenness of the abundances of each taxon, in order to exclude the
type 1 (rare and even) taxa, we chose the Pielou Evenness index λ [27]. The idea was to
scale the taxa frequency (f) to the taxa evenness (λ). In this way, we obtained a new index
for the taxa, which we tentatively called FREVE (frequency and evenness), and which is
shown in Equation (4):
FREVE = f

(4)
Figure 3.
The rationale for the selection of taxa: (
A
) Frequency vs. log cumulative abundance
distribution of taxa (points), dashed line represents the 12% threshold; (
B
) Frequency histogram
of taxa, dashed line represents the 12% threshold, grey scale represents relative abundance of taxa;
(
C
) FREVE index vs. log cumulative abundance distribution of taxa (points), dashed line represents
the value of 0.28; (
D
) Histogram of FREVE index, the dashed line represents the 0.28 value of
fluctuation index, grey scale represents the relative abundance of taxa.
The lower bound of the distribution of points in Figure 3A resembles the logarithmic
function. A similar shape is obtained by the log transformation of the vector of positive
naturals N
+
[1, 2, 3,
. . .
]. This distribution results from the taxa identification and counting
method, during which some of the taxa were found only once per sample. This value of
1cell was then transformed per volume of sample and integrated. The resulting abundances
were thus generated by a categorical process of presence/absence discrimination, even
though the values look quantitative. The taxa abundances that occupy the lower limit of
the distribution (those that are close to the ln(x) function, Figure 3A) are therefore the result
of a scaled version of the log transformation of the presences’ vector of rare taxa. This
particular distribution of some taxa led to the postulation of the co-presence of two types
of rarity and four types of distribution of taxa in our dataset. A taxon may be rare in terms
of frequency (i.e., observed in a small number of samples) or rare in terms of abundance
(i.e., having low abundances). The combination of rarities results in four distribution types:
taxa that are rare in both presence and abundance (type 1), taxa that are rare in presence
but not in abundance (type 2), taxa that are common in presence and abundance (type 3),
and taxa that are common in presence and rare in abundance (type 4).
To measure the evenness of the abundances of each taxon, in order to exclude the type
1 (rare and even) taxa, we chose the Pielou Evenness index
λ
[
27
]. The idea was to scale the
taxa frequency (f) to the taxa evenness (
λ
). In this way, we obtained a new index for the
taxa, which we tentatively called FREVE (frequency and evenness), and which is shown in
Equation (4):
FREVE =fλ(4)
and have the limits as in Equations (5)–(8):
lim
(f, λ)→(0+,1−)fλ=0 (5)

Water 2021,13, 2045 7 of 26
lim
(f, λ)→(0+,0+)fλ=1 (6)
lim
(f, λ)→(1−,0+)fλ=1 (7)
lim
(f, λ)→(1−,1−)fλ=1 (8)
Infrequent taxa with evenly distributed abundance would have frequency (f) close
to 0 and evenness (
λ
) close to 1, then FREVE is close to 0 (Equation (5)). The other three
cases, corresponding to distributions of type 2 (Equation (6)), type 3 (Equation (7)), and
type 4 (Equation (8)), would yield FREVE values close to 1 (Figure 3C). The histogram
of the FREVE index (Figure 3D) shows how the index shifted the taxa with the highest
abundances to the right half. The cutting threshold we set to select taxa was the composite
of two arbitrary thresholds. Because we wanted to retain taxa that were present at least once
per year (f = 1/12) and whose abundance was less uniform than the level of intermediate
evenness (
λ
= 0.5) [
35
], we obtained the threshold using the Equation (4) (0.28). Finally,
taxa with FREVE > 0.28 were retained for subsequent analysis.
2.5.2. Component Space and Clustering
The choice of Principal Component Analysis (PCA) in the original protocol implies
the preservation of Euclidean Distance between sample objects. The use of symmetric
coefficients (as Euclidean distance) for the analysis of sample-taxa datasets is not the right
choice in most cases [
36
]. This is due to the different information that a double presence or
double absence of a given taxa brings, namely the “double-zero” problem [
36
]. Instead of
PCA, a Correspondence Analysis (CA), which preserves
χ2
distance, was performed on the
dataset after FREVE taxa selection (Figure 2B, step 1b). CA was also used to perform Inertia
analysis [
36
] on the datasets selected by both protocols (Figure 2A, matrix A’ and 2B, matrix
B’) to evaluate and confront the two selection methods (frequency-based vs. FREVE). To
obtain clusters of samples, non-hierarchical k-means clustering [
36
] was then computed
instead of Bayesian reallocation (Figure 2B, step 2). Each n-partition was evaluated using
Calinski pseudo-F [36,37] and Ratkowsky index [38] (Figure 2B, step 3).
2.5.3. Indicative Taxa
The selection of the best clustering of samples was followed, as in the original protocol,
by the application of the IndVal index (Figure 2B, step 4a). Dufrene and Legendre stated
that “the use of IndVal removes any effect of the number of sites in the various clusters
and also differences in abundance among sites belonging to the cluster” [
34
]. However,
with the way IndVal is calculated, it is possible for a taxon to have the same IndVal value
in two completely different hypothetical situations. In the first situation, the taxon whose
cumulative abundance is split into two clusters and is present in half of the samples in both
clusters would have the same IndVal value in both clusters (0.25). The IndVal value would
also be the same (0.25) in a second situation where the taxon is present in two clusters,
where in one it is present in every sample (FI = 1) and has 25% of the abundance (
SP = 0.25
)
and in the other it is present in one third of the samples (FI = 0.33) and has 75% of the
abundance (SP = 0.75). Theoretically, it should be possible to disentangle these biased cases
using the p-values calculated according to the permutative method proposed by Dufrene
and Legendre [
34
]. However, in the multivariate situation where each cluster is defined
around several taxa, many if not all taxa are suboptimally described. As a result, many
of the possible cluster permutations will produce higher IndVal values for each taxon,
reducing the discriminatory power of the p-value. To facilitate interpretation of the IndVal
results, the centroids of the clusters (CA centroids) obtained from k-means were projected
onto the space of taxa (CA columns) (Figure 2B, step 4b), resulting in a vector of taxa
likelihood ratios (Observed/Expected) for the centroids. This is justified in Appendix B.
The higher the projected value of the taxa (Xproj), which represents the likelihood of the
centroid, the stronger the association (Xproj > 0; Likelihood ratio > 1) between the column

Water 2021,13, 2045 8 of 26
(taxa) and the centroid (cluster). Xproj was used as the association index and the threshold
was set to Xproj 1 (Likelihood ratio 2). A taxon with Xproj > 1 is strongly associated with
the cluster, indicating that its observed abundance is more than twice as high as expected.
The similarity of the two indicator matrices (IndVal and Xproj) was tested using the Mantel
test over ranked indices (999 permutations) [36].
2.5.4. Log-Transformation
A final remark concerns the log transformation made at the beginning of the original
protocol. In the modified protocol (Figure 2B), the raw data were not log-transformed
because, as pointed out in the literature [
39
], this method, used on count data, is either
redundant or wrong in most cases. In the context of extrapolating probabilities from
Mahalanobis distances, multinormality was an issue and indeed, taxa abundances were log-
transformed (see Section 2.4) and multinormality was tested using the Dagnelie method [
23
]
before proceeding. However, no normality assumptions are required when using raw
distances for clustering.
3. Results
3.1. Phytoplankton Community Composition and Taxa Selection
In the phytoplankton community at the Slovenian LTER station, a total of 130 taxa
were determined during 2005–2017, including 53 diatom taxa, 50 dinoflagellate taxa
and 15 coccolithophore taxa. The remaining 12 taxa were distributed among the classes
Cryptophyceae, Chlorophyceae, Euglenophyceae, Prasinophyceae, Chrysophyceae, Dic-
tyochophyceae and other undetermined nanoflagellates. Nanoflagellates accounted for
the largest proportion of abundance (57%), while diatom cells accounted for 36%, coccol-
ithophores 4% and dinoflagellates 3% of total abundance.
Abundance peaks above 1
×
10
6
cells L
−1
were mainly recorded in late winter/spring
and autumn and were mainly attributable to diatoms. Five diatom blooms were dominated by
Chaetoceros spp. in February 2007 (2.2
×
10
6
cells L
−1
), November 2011 (
1.2 ×106cells L−1
),
November and December 2012 (up to 1.6
×
10
6
cells L
−1
) and July 2015 (
1.7 ×106cells L−1
),
while Skeletonema species were responsible for two February blooms in 2011 and 2012
(
1.2 ×106cells L−1
). The largest diatom bloom was recorded in November 2010, when
species from the Pseudo-nitzschia delicatissima group reached 3.7
×
10
6
cells L
−1
. Nanoflag-
ellates caused two abundance peaks in July 2005 (1.5
×
10
6
cells L
−1
) and in May 2016
(1.6 ×106cells L−1).
Out of a total of 130 taxa, 57 taxa were selected for further analysis based on the
proposed FREVE (Table 1). Of these, 56 were already included in the frequency-based
selection and one rare taxon was rescued. The major difference between the two selection
methods was 17 common taxa that were discarded by FREVE but would have been retained
based on frequency.
Table 1.
Comparison of the number of retained and discarded taxa between two selection methods:
frequency-based and FREVE.
Frequency Selection
Common (f > 0.12) Rare (f < 0.12)
73 57
FREVE selection (FREVE > 0.28) 57 56 1
(FREVE < 0.28) 73 17 56
A comparison between selection methods was made using Inertia Analysis. Total
Inertia (TI) was calculated as the sum of the eigenvalues of the
χ2
dissimilarity matrix.
Taxa were sequentially removed from less abundant to abundant and TI was recalculated
(Figure 4). The initial TI’s value of the complete dataset (2.89) is closer to the TI value for
the dataset obtained by FREVE selection (2.81) than the TI values obtained by frequency

Water 2021,13, 2045 9 of 26
selection (2.46). This means that FREVE retains more information from the original dataset.
The one species rescued by FREVE (Table 1) was responsible for about 13% of the data TI,
more than the 17 discarded taxa.
Water 2021, 13, x FOR PEER REVIEW 9 of 26
The one species rescued by FREVE (Table 1) was responsible for about 13% of the data TI,
more than the 17 discarded taxa.
Table 1. Comparison of the number of retained and discarded taxa between two selection meth-
ods: frequency-based and FREVE.
Frequency Selection
Common (f > 0.12) Rare (f < 0.12)
73 57
FREVE selection (FREVE > 0.28) 57 56 1
(FREVE < 0.28) 73 17 56
Figure 4. Inertia analysis for three datasets, the full dataset (All taxa), the dataset resulting from
frequency selection, and the dataset resulting from FREVE selection. The taxa ordered by abun-
dance were removed at each step and the Total Inertia was recalculated.
3.2. Evaluation of n-Partition
Among all possible partitions, the Calinski pseudo-F values peaked for the partition
with four clusters and reached a second maximum for the partition with 18 clusters (Fig-
ure 5, left). The Ratkowsky Between-Sum-of-square increase rate was highest between par-
titions with three and four clusters (Figure 5, right), indicating that the best number of
clusters was four. Between 18 and 20 clusters, the Ratkowsky’s index reached the plateau.
Finally, two partitions with 4 and 18 clusters were chosen to obtain a broad and a fine
resolution in the data discontinuities, respectively.
Figure 5. (left) Calinski Pseudo-F results plotted for each n-clustered possible partition and (right)
the Ratkowsky index for each n-clustered possible partition.
Figure 4.
Inertia analysis for three datasets, the full dataset (All taxa), the dataset resulting from
frequency selection, and the dataset resulting from FREVE selection. The taxa ordered by abundance
were removed at each step and the Total Inertia was recalculated.
3.2. Evaluation of n-Partition
Among all possible partitions, the Calinski pseudo-F values peaked for the partition
with four clusters and reached a second maximum for the partition with 18 clusters
(Figure 5
, left). The Ratkowsky Between-Sum-of-square increase rate was highest between
partitions with three and four clusters (Figure 5, right), indicating that the best number of
clusters was four. Between 18 and 20 clusters, the Ratkowsky’s index reached the plateau.
Finally, two partitions with 4 and 18 clusters were chosen to obtain a broad and a fine
resolution in the data discontinuities, respectively.
Water 2021, 13, x FOR PEER REVIEW 9 of 26
The one species rescued by FREVE (Table 1) was responsible for about 13% of the data TI,
more than the 17 discarded taxa.
Table 1. Comparison of the number of retained and discarded taxa between two selection meth-
ods: frequency-based and FREVE.
Frequency Selection
Common (f > 0.12) Rare (f < 0.12)
73 57
FREVE selection (FREVE > 0.28) 57 56 1
(FREVE < 0.28) 73 17 56
Figure 4. Inertia analysis for three datasets, the full dataset (All taxa), the dataset resulting from
frequency selection, and the dataset resulting from FREVE selection. The taxa ordered by abun-
dance were removed at each step and the Total Inertia was recalculated.
3.2. Evaluation of n-Partition
Among all possible partitions, the Calinski pseudo-F values peaked for the partition
with four clusters and reached a second maximum for the partition with 18 clusters (Fig-
ure 5, left). The Ratkowsky Between-Sum-of-square increase rate was highest between par-
titions with three and four clusters (Figure 5, right), indicating that the best number of
clusters was four. Between 18 and 20 clusters, the Ratkowsky’s index reached the plateau.
Finally, two partitions with 4 and 18 clusters were chosen to obtain a broad and a fine
resolution in the data discontinuities, respectively.
Figure 5. (left) Calinski Pseudo-F results plotted for each n-clustered possible partition and (right)
the Ratkowsky index for each n-clustered possible partition.
Figure 5.
(
left
) Calinski Pseudo-F results plotted for each n-clustered possible partition and (
right
) the Ratkowsky index for
each n-clustered possible partition.
3.3. Original Protocol Temporal Map
For the temporal map obtained with the original protocol (Figure 2A), the best of
possible partitions with six clusters were used and the IndVal index was applied to select
the indicative taxa of the clusters (Figure 6). A total of 43 out of 73 taxa were found to
be indicative of at least one cluster (Table 2). The cluster map revealed a rough seasonal
pattern except for the first two years (2005 and 2006). These two years and parts of
2007 and 2008 belonged entirely to Cluster I, in which no taxa exceeded the threshold
(
IndVal = 0.25
) to be defined as indicative. From 2009 onwards, the winter (January–March)
was mostly characterized by Cluster II with two indicative species, the diatom Skeletonema

Water 2021,13, 2045 10 of 26
costatum s.l. and the coccolithophore Ophiaster hydroideus. In 2009–2012, Cluster II extended
into spring, whereas in recent years, it had already appeared in autumn. Cluster III
almost always appeared before Cluster II, usually in autumn (October to December).
This cluster was described by many indicative taxa, predominantly diatoms. Seven taxa
had IndVal > 0.5: the diatoms Asterionellopsis glacialis,Cylindrotheca closterium, Eucampia
spp., Lauderia annulata, Pleurosigma normanii and the Pseudo-nitzschia seriata group, as
well as a coccolithophore Calciosolenia murrayi. Cluster III was generally anticipated by
Cluster VI, typical of the summer (July to September) and occasionally extended into the
autumn, which contained ten indicative taxa, mostly diatoms. Three taxa had
IndVal > 0.5
:
the diatoms Proboscia alata and Rhizosolenia spp. and a coccolithophore Rhabdosphaera
stylifera. Cluster IV was intermittently present in spring and summer and best described the
temporal distribution for two diatom taxa: Cyclotella spp. and Cerataulina pelagica. Finally,
Cluster V appeared in 2009 and characterized the spring from March to June. Cluster
V was represented by a mixed phytoplankton community, but only one coccolithophore
(Calyptrosphaera oblonga) had IndVal > 0.5.
Table 2.
Composition of taxa in clusters derived from the original protocol sensu Anneville et al., (2002) with corresponding
IndVal value. Only taxa with IndVal value higher than 0.25 are shown. The taxa are organized in descending order of IndVal
value.
Cluster Taxon IndVal Cluster Taxon IndVal
I
Water 2021, 13, x FOR PEER REVIEW 11 of 26
Table 2. Composition of taxa in clusters derived from the original protocol sensu Anneville et al., (2002) with correspond-
ing IndVal value. Only taxa with IndVal value higher than 0.25 are shown. The taxa are organized in descending order of
IndVal value.
Cluster Taxon IndVal Cluster Taxon IndVal
/ / Cyclotella spp. 0.46
Skeletonema costatum s.l. 0.33 Prorocentrum compressum 0.44
Ophiaster hydroideus 0.26 Euglenophyceae 0.42
Eucampia spp. 0.80 Bacteriastrum delicatulum 0.42
Pseudo-nitzschia seriata gr. 0.62 Prorocentrum balticum 0.40
Lauderia annulata 0.62 Prorocentrum micans 0.39
Calciosolenia murrayi 0.61 Prasinophyceae 0.38
Pleurosigma normanii 0.54 Prorocentrum cordatum 0.33
Cylindrotheca closterium 0.53 Alexandrium minutum 0.32
Asterionellopsis glacialis 0.50 Diatoms non ident. 0.31
Leptocylindrus mediterraneus 0.49 Leptocylindrus danicus 0.29
Thalassiosira spp. 0.43 Nitzschia longissima 0.29
Chaetoceros spp. 0.43 Cryptophyceae 0.29
Nitzschia spp. 0.43 Gymnodinium spp. 0.29
Proboscia indica 0.41 Prorocentrum triestinum 0.28
Cerataulina pelagica 0.37 Amphora spp. 0.28
Pseudo-nitzschia delicatissima gr. 0.37 Rhizosolenia spp. 0.54
Leptocylindrus danicus 0.34 Proboscia alata 0.53
Emiliania huxleyi 0.32 Rhabdosphaera stylifera 0.51
Guinardia flaccida 0.31 Pseudo-nitzschia delicatissima gr. 0.49
Thalassionema nitzschioides 0.28 Dactyliosolen fragilissimus 0.40
Cyclotella spp. 0.29 Syracosphaera pulchra 0.37
Cerataulina pelagica 0.25 Thalassionema nitzschioides 0.34
Calyptrosphaera oblonga 0.63 Hemiaulas hauckii 0.29
Heterocapsa gr. 0.49 Tripos fusus 0.29
Chlorophyceae 0.46 Guinardia striata 0.28
3.4. Modified Protocol Temporal Map
3.4.1. Four-Clustered Partition
The temporal map showing four clusters obtained with the modified protocol (Figure
2B) is shown in Figure 7 (left), while indicative taxa with Xproj and IndVal values for these
clusters are summarized in Table 3. Two clusters showed some degree of seasonality. The
“winter” Cluster II was strongly associated with the diatoms Skeletonema costatum s.l. and
Chaetoceros simplex (Xproj 22.0 and 5.23, respectively, and IndVal > 0.5), but was restricted
to February 2005, 2011, and 2012. The “summer and autumn” Cluster IV, which was also
occurred sporadically in the early spring, was indicative of diatom blooms. Six diatom
taxa had a meaningful Xproj > 1 for this cluster, while a variety of other taxa in a different
order (though mainly diatoms) were indicated as typical by IndVal ≥ 0.25. Interestingly,
the centroid of the Cluster IV was most associated with Lauderia annulata, which had
IndVal < 0.25. Cluster III was present only in February 2017 and was associated with the
diatoms Chaetoceros curvisetus, Leptocylindrus danicus and L. annulata. The largest of the
clusters, Cluster I, was distributed across all other months and represented the mixed
phytoplankton community of nanoflagellates, small diatoms and some coccolithophore
taxa, although none of the Xproj or IndVal were particularly high. The Mantel test be-
tween the two ordination indices (Xproj and IndVal) showed a significant correlation (r =
0.78; p-value = 0.03).
/ /
V
Water 2021, 13, x FOR PEER REVIEW 11 of 26
Table 2. Composition of taxa in clusters derived from the original protocol sensu Anneville et al., (2002) with correspond-
ing IndVal value. Only taxa with IndVal value higher than 0.25 are shown. The taxa are organized in descending order of
IndVal value.
Cluster Taxon IndVal Cluster Taxon IndVal
/ / Cyclotella spp. 0.46
Skeletonema costatum s.l. 0.33 Prorocentrum compressum 0.44
Ophiaster hydroideus 0.26 Euglenophyceae 0.42
Eucampia spp. 0.80 Bacteriastrum delicatulum 0.42
Pseudo-nitzschia seriata gr. 0.62 Prorocentrum balticum 0.40
Lauderia annulata 0.62 Prorocentrum micans 0.39
Calciosolenia murrayi 0.61 Prasinophyceae 0.38
Pleurosigma normanii 0.54 Prorocentrum cordatum 0.33
Cylindrotheca closterium 0.53 Alexandrium minutum 0.32
Asterionellopsis glacialis 0.50 Diatoms non ident. 0.31
Leptocylindrus mediterraneus 0.49 Leptocylindrus danicus 0.29
Thalassiosira spp. 0.43 Nitzschia longissima 0.29
Chaetoceros spp. 0.43 Cryptophyceae 0.29
Nitzschia spp. 0.43 Gymnodinium spp. 0.29
Proboscia indica 0.41 Prorocentrum triestinum 0.28
Cerataulina pelagica 0.37 Amphora spp. 0.28
Pseudo-nitzschia delicatissima gr. 0.37 Rhizosolenia spp. 0.54
Leptocylindrus danicus 0.34 Proboscia alata 0.53
Emiliania huxleyi 0.32 Rhabdosphaera stylifera 0.51
Guinardia flaccida 0.31 Pseudo-nitzschia delicatissima gr. 0.49
Thalassionema nitzschioides 0.28 Dactyliosolen fragilissimus 0.40
Cyclotella spp. 0.29 Syracosphaera pulchra 0.37
Cerataulina pelagica 0.25 Thalassionema nitzschioides 0.34
Calyptrosphaera oblonga 0.63 Hemiaulas hauckii 0.29
Heterocapsa gr. 0.49 Tripos fusus 0.29
Chlorophyceae 0.46 Guinardia striata 0.28
3.4. Modified Protocol Temporal Map
3.4.1. Four-Clustered Partition
The temporal map showing four clusters obtained with the modified protocol (Figure
2B) is shown in Figure 7 (left), while indicative taxa with Xproj and IndVal values for these
clusters are summarized in Table 3. Two clusters showed some degree of seasonality. The
“winter” Cluster II was strongly associated with the diatoms Skeletonema costatum s.l. and
Chaetoceros simplex (Xproj 22.0 and 5.23, respectively, and IndVal > 0.5), but was restricted
to February 2005, 2011, and 2012. The “summer and autumn” Cluster IV, which was also
occurred sporadically in the early spring, was indicative of diatom blooms. Six diatom
taxa had a meaningful Xproj > 1 for this cluster, while a variety of other taxa in a different
order (though mainly diatoms) were indicated as typical by IndVal ≥ 0.25. Interestingly,
the centroid of the Cluster IV was most associated with Lauderia annulata, which had
IndVal < 0.25. Cluster III was present only in February 2017 and was associated with the
diatoms Chaetoceros curvisetus, Leptocylindrus danicus and L. annulata. The largest of the
clusters, Cluster I, was distributed across all other months and represented the mixed
phytoplankton community of nanoflagellates, small diatoms and some coccolithophore
taxa, although none of the Xproj or IndVal were particularly high. The Mantel test be-
tween the two ordination indices (Xproj and IndVal) showed a significant correlation (r =
0.78; p-value = 0.03).
Cyclotella spp. 0.46
II
Water 2021, 13, x FOR PEER REVIEW 11 of 26
Table 2. Composition of taxa in clusters derived from the original protocol sensu Anneville et al., (2002) with correspond-
ing IndVal value. Only taxa with IndVal value higher than 0.25 are shown. The taxa are organized in descending order of
IndVal value.
Cluster Taxon IndVal Cluster Taxon IndVal
/ / Cyclotella spp. 0.46
Skeletonema costatum s.l. 0.33 Prorocentrum compressum 0.44
Ophiaster hydroideus 0.26 Euglenophyceae 0.42
Eucampia spp. 0.80 Bacteriastrum delicatulum 0.42
Pseudo-nitzschia seriata gr. 0.62 Prorocentrum balticum 0.40
Lauderia annulata 0.62 Prorocentrum micans 0.39
Calciosolenia murrayi 0.61 Prasinophyceae 0.38
Pleurosigma normanii 0.54 Prorocentrum cordatum 0.33
Cylindrotheca closterium 0.53 Alexandrium minutum 0.32
Asterionellopsis glacialis 0.50 Diatoms non ident. 0.31
Leptocylindrus mediterraneus 0.49 Leptocylindrus danicus 0.29
Thalassiosira spp. 0.43 Nitzschia longissima 0.29
Chaetoceros spp. 0.43 Cryptophyceae 0.29
Nitzschia spp. 0.43 Gymnodinium spp. 0.29
Proboscia indica 0.41 Prorocentrum triestinum 0.28
Cerataulina pelagica 0.37 Amphora spp. 0.28
Pseudo-nitzschia delicatissima gr. 0.37 Rhizosolenia spp. 0.54
Leptocylindrus danicus 0.34 Proboscia alata 0.53
Emiliania huxleyi 0.32 Rhabdosphaera stylifera 0.51
Guinardia flaccida 0.31 Pseudo-nitzschia delicatissima gr. 0.49
Thalassionema nitzschioides 0.28 Dactyliosolen fragilissimus 0.40
Cyclotella spp. 0.29 Syracosphaera pulchra 0.37
Cerataulina pelagica 0.25 Thalassionema nitzschioides 0.34
Calyptrosphaera oblonga 0.63 Hemiaulas hauckii 0.29
Heterocapsa gr. 0.49 Tripos fusus 0.29
Chlorophyceae 0.46 Guinardia striata 0.28
3.4. Modified Protocol Temporal Map
3.4.1. Four-Clustered Partition
The temporal map showing four clusters obtained with the modified protocol (Figure
2B) is shown in Figure 7 (left), while indicative taxa with Xproj and IndVal values for these
clusters are summarized in Table 3. Two clusters showed some degree of seasonality. The
“winter” Cluster II was strongly associated with the diatoms Skeletonema costatum s.l. and
Chaetoceros simplex (Xproj 22.0 and 5.23, respectively, and IndVal > 0.5), but was restricted
to February 2005, 2011, and 2012. The “summer and autumn” Cluster IV, which was also
occurred sporadically in the early spring, was indicative of diatom blooms. Six diatom
taxa had a meaningful Xproj > 1 for this cluster, while a variety of other taxa in a different
order (though mainly diatoms) were indicated as typical by IndVal ≥ 0.25. Interestingly,
the centroid of the Cluster IV was most associated with Lauderia annulata, which had
IndVal < 0.25. Cluster III was present only in February 2017 and was associated with the
diatoms Chaetoceros curvisetus, Leptocylindrus danicus and L. annulata. The largest of the
clusters, Cluster I, was distributed across all other months and represented the mixed
phytoplankton community of nanoflagellates, small diatoms and some coccolithophore
taxa, although none of the Xproj or IndVal were particularly high. The Mantel test be-
tween the two ordination indices (Xproj and IndVal) showed a significant correlation (r =
0.78; p-value = 0.03).
Skeletonema costatum s.l. 0.33 Prorocentrum compressum 0.44
Ophiaster hydroideus 0.26 Euglenophyceae 0.42
III
Water 2021, 13, x FOR PEER REVIEW 11 of 26
Table 2. Composition of taxa in clusters derived from the original protocol sensu Anneville et al., (2002) with correspond-
ing IndVal value. Only taxa with IndVal value higher than 0.25 are shown. The taxa are organized in descending order of
IndVal value.
Cluster Taxon IndVal Cluster Taxon IndVal
/ / Cyclotella spp. 0.46
Skeletonema costatum s.l. 0.33 Prorocentrum compressum 0.44
Ophiaster hydroideus 0.26 Euglenophyceae 0.42
Eucampia spp. 0.80 Bacteriastrum delicatulum 0.42
Pseudo-nitzschia seriata gr. 0.62 Prorocentrum balticum 0.40
Lauderia annulata 0.62 Prorocentrum micans 0.39
Calciosolenia murrayi 0.61 Prasinophyceae 0.38
Pleurosigma normanii 0.54 Prorocentrum cordatum 0.33
Cylindrotheca closterium 0.53 Alexandrium minutum 0.32
Asterionellopsis glacialis 0.50 Diatoms non ident. 0.31
Leptocylindrus mediterraneus 0.49 Leptocylindrus danicus 0.29
Thalassiosira spp. 0.43 Nitzschia longissima 0.29
Chaetoceros spp. 0.43 Cryptophyceae 0.29
Nitzschia spp. 0.43 Gymnodinium spp. 0.29
Proboscia indica 0.41 Prorocentrum triestinum 0.28
Cerataulina pelagica 0.37 Amphora spp. 0.28
Pseudo-nitzschia delicatissima gr. 0.37 Rhizosolenia spp. 0.54
Leptocylindrus danicus 0.34 Proboscia alata 0.53
Emiliania huxleyi 0.32 Rhabdosphaera stylifera 0.51
Guinardia flaccida 0.31 Pseudo-nitzschia delicatissima gr. 0.49
Thalassionema nitzschioides 0.28 Dactyliosolen fragilissimus 0.40
Cyclotella spp. 0.29 Syracosphaera pulchra 0.37
Cerataulina pelagica 0.25 Thalassionema nitzschioides 0.34
Calyptrosphaera oblonga 0.63 Hemiaulas hauckii 0.29
Heterocapsa gr. 0.49 Tripos fusus 0.29
Chlorophyceae 0.46 Guinardia striata 0.28
3.4. Modified Protocol Temporal Map
3.4.1. Four-Clustered Partition
The temporal map showing four clusters obtained with the modified protocol (Figure
2B) is shown in Figure 7 (left), while indicative taxa with Xproj and IndVal values for these
clusters are summarized in Table 3. Two clusters showed some degree of seasonality. The
“winter” Cluster II was strongly associated with the diatoms Skeletonema costatum s.l. and
Chaetoceros simplex (Xproj 22.0 and 5.23, respectively, and IndVal > 0.5), but was restricted
to February 2005, 2011, and 2012. The “summer and autumn” Cluster IV, which was also
occurred sporadically in the early spring, was indicative of diatom blooms. Six diatom
taxa had a meaningful Xproj > 1 for this cluster, while a variety of other taxa in a different
order (though mainly diatoms) were indicated as typical by IndVal ≥ 0.25. Interestingly,
the centroid of the Cluster IV was most associated with Lauderia annulata, which had
IndVal < 0.25. Cluster III was present only in February 2017 and was associated with the
diatoms Chaetoceros curvisetus, Leptocylindrus danicus and L. annulata. The largest of the
clusters, Cluster I, was distributed across all other months and represented the mixed
phytoplankton community of nanoflagellates, small diatoms and some coccolithophore
taxa, although none of the Xproj or IndVal were particularly high. The Mantel test be-
tween the two ordination indices (Xproj and IndVal) showed a significant correlation (r =
0.78; p-value = 0.03).
Eucampia spp. 0.80 Bacteriastrum delicatulum 0.42
Pseudo-nitzschia seriata gr. 0.62 Prorocentrum balticum 0.40
Lauderia annulata 0.62 Prorocentrum micans 0.39
Calciosolenia murrayi 0.61 Prasinophyceae 0.38
Pleurosigma normanii 0.54 Prorocentrum cordatum 0.33
Cylindrotheca closterium 0.53 Alexandrium minutum 0.32
Asterionellopsis glacialis 0.50 Diatoms non ident. 0.31
Leptocylindrus mediterraneus 0.49 Leptocylindrus danicus 0.29
Thalassiosira spp. 0.43 Nitzschia longissima 0.29
Chaetoceros spp. 0.43 Cryptophyceae 0.29
Nitzschia spp. 0.43 Gymnodinium spp. 0.29
Proboscia indica 0.41 Prorocentrum triestinum 0.28
Cerataulina pelagica 0.37 Amphora spp. 0.28
Pseudo-nitzschia delicatissima gr. 0.37
VI
Water 2021, 13, x FOR PEER REVIEW 11 of 26
Table 2. Composition of taxa in clusters derived from the original protocol sensu Anneville et al., (2002) with correspond-
ing IndVal value. Only taxa with IndVal value higher than 0.25 are shown. The taxa are organized in descending order of
IndVal value.
Cluster Taxon IndVal Cluster Taxon IndVal
/ / Cyclotella spp. 0.46
Skeletonema costatum s.l. 0.33 Prorocentrum compressum 0.44
Ophiaster hydroideus 0.26 Euglenophyceae 0.42
Eucampia spp. 0.80 Bacteriastrum delicatulum 0.42
Pseudo-nitzschia seriata gr. 0.62 Prorocentrum balticum 0.40
Lauderia annulata 0.62 Prorocentrum micans 0.39
Calciosolenia murrayi 0.61 Prasinophyceae 0.38
Pleurosigma normanii 0.54 Prorocentrum cordatum 0.33
Cylindrotheca closterium 0.53 Alexandrium minutum 0.32
Asterionellopsis glacialis 0.50 Diatoms non ident. 0.31
Leptocylindrus mediterraneus 0.49 Leptocylindrus danicus 0.29
Thalassiosira spp. 0.43 Nitzschia longissima 0.29
Chaetoceros spp. 0.43 Cryptophyceae 0.29
Nitzschia spp. 0.43 Gymnodinium spp. 0.29
Proboscia indica 0.41 Prorocentrum triestinum 0.28
Cerataulina pelagica 0.37 Amphora spp. 0.28
Pseudo-nitzschia delicatissima gr. 0.37 Rhizosolenia spp. 0.54
Leptocylindrus danicus 0.34 Proboscia alata 0.53
Emiliania huxleyi 0.32 Rhabdosphaera stylifera 0.51
Guinardia flaccida 0.31 Pseudo-nitzschia delicatissima gr. 0.49
Thalassionema nitzschioides 0.28 Dactyliosolen fragilissimus 0.40
Cyclotella spp. 0.29 Syracosphaera pulchra 0.37
Cerataulina pelagica 0.25 Thalassionema nitzschioides 0.34
Calyptrosphaera oblonga 0.63 Hemiaulas hauckii 0.29
Heterocapsa gr. 0.49 Tripos fusus 0.29
Chlorophyceae 0.46 Guinardia striata 0.28
3.4. Modified Protocol Temporal Map
3.4.1. Four-Clustered Partition
The temporal map showing four clusters obtained with the modified protocol (Figure
2B) is shown in Figure 7 (left), while indicative taxa with Xproj and IndVal values for these
clusters are summarized in Table 3. Two clusters showed some degree of seasonality. The
“winter” Cluster II was strongly associated with the diatoms Skeletonema costatum s.l. and
Chaetoceros simplex (Xproj 22.0 and 5.23, respectively, and IndVal > 0.5), but was restricted
to February 2005, 2011, and 2012. The “summer and autumn” Cluster IV, which was also
occurred sporadically in the early spring, was indicative of diatom blooms. Six diatom
taxa had a meaningful Xproj > 1 for this cluster, while a variety of other taxa in a different
order (though mainly diatoms) were indicated as typical by IndVal ≥ 0.25. Interestingly,
the centroid of the Cluster IV was most associated with Lauderia annulata, which had
IndVal < 0.25. Cluster III was present only in February 2017 and was associated with the
diatoms Chaetoceros curvisetus, Leptocylindrus danicus and L. annulata. The largest of the
clusters, Cluster I, was distributed across all other months and represented the mixed
phytoplankton community of nanoflagellates, small diatoms and some coccolithophore
taxa, although none of the Xproj or IndVal were particularly high. The Mantel test be-
tween the two ordination indices (Xproj and IndVal) showed a significant correlation (r =
0.78; p-value = 0.03).
Rhizosolenia spp. 0.54
Leptocylindrus danicus 0.34 Proboscia alata 0.53
Emiliania huxleyi 0.32 Rhabdosphaera stylifera 0.51
Guinardia flaccida 0.31 Pseudo-nitzschia delicatissima gr. 0.49
Thalassionema nitzschioides 0.28 Dactyliosolen fragilissimus 0.40
IV
Water 2021, 13, x FOR PEER REVIEW 11 of 26
Table 2. Composition of taxa in clusters derived from the original protocol sensu Anneville et al., (2002) with correspond-
ing IndVal value. Only taxa with IndVal value higher than 0.25 are shown. The taxa are organized in descending order of
IndVal value.
Cluster Taxon IndVal Cluster Taxon IndVal
/ / Cyclotella spp. 0.46
Skeletonema costatum s.l. 0.33 Prorocentrum compressum 0.44
Ophiaster hydroideus 0.26 Euglenophyceae 0.42
Eucampia spp. 0.80 Bacteriastrum delicatulum 0.42
Pseudo-nitzschia seriata gr. 0.62 Prorocentrum balticum 0.40
Lauderia annulata 0.62 Prorocentrum micans 0.39
Calciosolenia murrayi 0.61 Prasinophyceae 0.38
Pleurosigma normanii 0.54 Prorocentrum cordatum 0.33
Cylindrotheca closterium 0.53 Alexandrium minutum 0.32
Asterionellopsis glacialis 0.50 Diatoms non ident. 0.31
Leptocylindrus mediterraneus 0.49 Leptocylindrus danicus 0.29
Thalassiosira spp. 0.43 Nitzschia longissima 0.29
Chaetoceros spp. 0.43 Cryptophyceae 0.29
Nitzschia spp. 0.43 Gymnodinium spp. 0.29
Proboscia indica 0.41 Prorocentrum triestinum 0.28
Cerataulina pelagica 0.37 Amphora spp. 0.28
Pseudo-nitzschia delicatissima gr. 0.37 Rhizosolenia spp. 0.54
Leptocylindrus danicus 0.34 Proboscia alata 0.53
Emiliania huxleyi 0.32 Rhabdosphaera stylifera 0.51
Guinardia flaccida 0.31 Pseudo-nitzschia delicatissima gr. 0.49
Thalassionema nitzschioides 0.28 Dactyliosolen fragilissimus 0.40
Cyclotella spp. 0.29 Syracosphaera pulchra 0.37
Cerataulina pelagica 0.25 Thalassionema nitzschioides 0.34
Calyptrosphaera oblonga 0.63 Hemiaulas hauckii 0.29
Heterocapsa gr. 0.49 Tripos fusus 0.29
Chlorophyceae 0.46 Guinardia striata 0.28
3.4. Modified Protocol Temporal Map
3.4.1. Four-Clustered Partition
The temporal map showing four clusters obtained with the modified protocol (Figure
2B) is shown in Figure 7 (left), while indicative taxa with Xproj and IndVal values for these
clusters are summarized in Table 3. Two clusters showed some degree of seasonality. The
“winter” Cluster II was strongly associated with the diatoms Skeletonema costatum s.l. and
Chaetoceros simplex (Xproj 22.0 and 5.23, respectively, and IndVal > 0.5), but was restricted
to February 2005, 2011, and 2012. The “summer and autumn” Cluster IV, which was also
occurred sporadically in the early spring, was indicative of diatom blooms. Six diatom
taxa had a meaningful Xproj > 1 for this cluster, while a variety of other taxa in a different
order (though mainly diatoms) were indicated as typical by IndVal ≥ 0.25. Interestingly,
the centroid of the Cluster IV was most associated with Lauderia annulata, which had
IndVal < 0.25. Cluster III was present only in February 2017 and was associated with the
diatoms Chaetoceros curvisetus, Leptocylindrus danicus and L. annulata. The largest of the
clusters, Cluster I, was distributed across all other months and represented the mixed
phytoplankton community of nanoflagellates, small diatoms and some coccolithophore
taxa, although none of the Xproj or IndVal were particularly high. The Mantel test be-
tween the two ordination indices (Xproj and IndVal) showed a significant correlation (r =
0.78; p-value = 0.03).
Cyclotella spp. 0.29 Syracosphaera pulchra 0.37
Cerataulina pelagica 0.25 Thalassionema nitzschioides 0.34
V
Water 2021, 13, x FOR PEER REVIEW 11 of 26
Table 2. Composition of taxa in clusters derived from the original protocol sensu Anneville et al., (2002) with correspond-
ing IndVal value. Only taxa with IndVal value higher than 0.25 are shown. The taxa are organized in descending order of
IndVal value.
Cluster Taxon IndVal Cluster Taxon IndVal
/ / Cyclotella spp. 0.46
Skeletonema costatum s.l. 0.33 Prorocentrum compressum 0.44
Ophiaster hydroideus 0.26 Euglenophyceae 0.42
Eucampia spp. 0.80 Bacteriastrum delicatulum 0.42
Pseudo-nitzschia seriata gr. 0.62 Prorocentrum balticum 0.40
Lauderia annulata 0.62 Prorocentrum micans 0.39
Calciosolenia murrayi 0.61 Prasinophyceae 0.38
Pleurosigma normanii 0.54 Prorocentrum cordatum 0.33
Cylindrotheca closterium 0.53 Alexandrium minutum 0.32
Asterionellopsis glacialis 0.50 Diatoms non ident. 0.31
Leptocylindrus mediterraneus 0.49 Leptocylindrus danicus 0.29
Thalassiosira spp. 0.43 Nitzschia longissima 0.29
Chaetoceros spp. 0.43 Cryptophyceae 0.29
Nitzschia spp. 0.43 Gymnodinium spp. 0.29
Proboscia indica 0.41 Prorocentrum triestinum 0.28
Cerataulina pelagica 0.37 Amphora spp. 0.28
Pseudo-nitzschia delicatissima gr. 0.37 Rhizosolenia spp. 0.54
Leptocylindrus danicus 0.34 Proboscia alata 0.53
Emiliania huxleyi 0.32 Rhabdosphaera stylifera 0.51
Guinardia flaccida 0.31 Pseudo-nitzschia delicatissima gr. 0.49
Thalassionema nitzschioides 0.28 Dactyliosolen fragilissimus 0.40
Cyclotella spp. 0.29 Syracosphaera pulchra 0.37
Cerataulina pelagica 0.25 Thalassionema nitzschioides 0.34
Calyptrosphaera oblonga 0.63 Hemiaulas hauckii 0.29
Heterocapsa gr. 0.49 Tripos fusus 0.29
Chlorophyceae 0.46 Guinardia striata 0.28
3.4. Modified Protocol Temporal Map
3.4.1. Four-Clustered Partition
The temporal map showing four clusters obtained with the modified protocol (Figure
2B) is shown in Figure 7 (left), while indicative taxa with Xproj and IndVal values for these
clusters are summarized in Table 3. Two clusters showed some degree of seasonality. The
“winter” Cluster II was strongly associated with the diatoms Skeletonema costatum s.l. and
Chaetoceros simplex (Xproj 22.0 and 5.23, respectively, and IndVal > 0.5), but was restricted
to February 2005, 2011, and 2012. The “summer and autumn” Cluster IV, which was also
occurred sporadically in the early spring, was indicative of diatom blooms. Six diatom
taxa had a meaningful Xproj > 1 for this cluster, while a variety of other taxa in a different
order (though mainly diatoms) were indicated as typical by IndVal ≥ 0.25. Interestingly,
the centroid of the Cluster IV was most associated with Lauderia annulata, which had
IndVal < 0.25. Cluster III was present only in February 2017 and was associated with the
diatoms Chaetoceros curvisetus, Leptocylindrus danicus and L. annulata. The largest of the
clusters, Cluster I, was distributed across all other months and represented the mixed
phytoplankton community of nanoflagellates, small diatoms and some coccolithophore
taxa, although none of the Xproj or IndVal were particularly high. The Mantel test be-
tween the two ordination indices (Xproj and IndVal) showed a significant correlation (r =
0.78; p-value = 0.03).
Calyptrosphaera oblonga 0.63 Hemiaulas hauckii 0.29
Heterocapsa gr. 0.49 Tripos fusus 0.29
Chlorophyceae 0.46 Guinardia striata 0.28
Five taxa were indicative of more than one cluster. Excluding the first two years,
the typical sequence of phytoplankton during the study period was Cluster II—Cluster
V—Cluster IV—Cluster VI—Cluster III.

Water 2021,13, 2045 11 of 26
Water 2021, 13, x FOR PEER REVIEW 10 of 26
3.3. Original Protocol Temporal Map
For the temporal map obtained with the original protocol (Figure 2A), the best of
possible partitions with six clusters were used and the IndVal index was applied to select
the indicative taxa of the clusters (Figure 6). A total of 43 out of 73 taxa were found to be
indicative of at least one cluster (Table 2). The cluster map revealed a rough seasonal pat-
tern except for the first two years (2005 and 2006). These two years and parts of 2007 and
2008 belonged entirely to Cluster I, in which no taxa exceeded the threshold (IndVal =
0.25) to be defined as indicative. From 2009 onwards, the winter (January–March) was
mostly characterized by Cluster II with two indicative species, the diatom Skeletonema cos-
tatum s.l. and the coccolithophore Ophiaster hydroideus. In 2009–2012, Cluster II extended
into spring, whereas in recent years, it had already appeared in autumn. Cluster III almost
always appeared before Cluster II, usually in autumn (October to December). This cluster
was described by many indicative taxa, predominantly diatoms. Seven taxa had IndVal >
0.5: the diatoms Asterionellopsis glacialis, Cylindrotheca closterium, Eucampia spp., Lauderia
annulata, Pleurosigma normanii and the Pseudo-nitzschia seriata group, as well as a coccolith-
ophore Calciosolenia murrayi. Cluster III was generally anticipated by Cluster VI, typical of
the summer (July to September) and occasionally extended into the autumn, which con-
tained ten indicative taxa, mostly diatoms. Three taxa had IndVal > 0.5: the diatoms Pro-
boscia alata and Rhizosolenia spp. and a coccolithophore Rhabdosphaera stylifera. Cluster IV
was intermittently present in spring and summer and best described the temporal distri-
bution for two diatom taxa: Cyclotella spp. and Cerataulina pelagica. Finally, Cluster V ap-
peared in 2009 and characterized the spring from March to June. Cluster V was repre-
sented by a mixed phytoplankton community, but only one coccolithophore (Calyptro-
sphaera oblonga) had IndVal > 0.5.
Five taxa were indicative of more than one cluster. Excluding the first two years, the
typical sequence of phytoplankton during the study period was Cluster II—Cluster V—
Cluster IV—Cluster VI—Cluster III.
Figure 6. Temporal map of phytoplankton assemblages based on the original protocol sensu
Anneville et al. (2002). The white area indicates missing data.
Figure 6.
Temporal map of phytoplankton assemblages based on the original protocol sensu An-
neville et al. (2002). The white area indicates missing data.
3.4. Modified Protocol Temporal Map
3.4.1. Four-Clustered Partition
The temporal map showing four clusters obtained with the modified protocol
(Figure 2B)
is shown in Figure 7(left), while indicative taxa with Xproj and IndVal values
for these clusters are summarized in Table 3. Two clusters showed some degree of sea-
sonality. The “winter” Cluster II was strongly associated with the diatoms Skeletonema
costatum s.l. and Chaetoceros simplex (Xproj 22.0 and 5.23, respectively, and IndVal > 0.5),
but was restricted to February 2005, 2011, and 2012. The “summer and autumn” Cluster IV,
which was also occurred sporadically in the early spring, was indicative of diatom blooms.
Six diatom taxa had a meaningful Xproj > 1 for this cluster, while a variety of other taxa
in a different order (though mainly diatoms) were indicated as typical by IndVal
≥
0.25.
Interestingly, the centroid of the Cluster IV was most associated with Lauderia annulata,
which had IndVal < 0.25. Cluster III was present only in February 2017 and was associated
with the diatoms Chaetoceros curvisetus, Leptocylindrus danicus and L. annulata. The largest of
the clusters, Cluster I, was distributed across all other months and represented the mixed
phytoplankton community of nanoflagellates, small diatoms and some coccolithophore
taxa, although none of the Xproj or IndVal were particularly high. The Mantel test between
the two ordination indices (Xproj and IndVal) showed a significant correlation (r = 0.78;
p-value = 0.03).
Table 3.
Taxa composition of the 4-clustered partition with corresponding Likelihood ratios (Xproj) for the centroids and
IndVal values (IndVal). Taxa with Xproj > 1 or IndVal value > 0.25 are shown in bold. Taxa in both columns are organized in
descending order of IndVal and Xproj. The term “Nanoflagellates” stands for flagellates in the nanoplankton size fraction
that have not been identified as prasinophytes, cryptophytes, etc.
Cluster Taxon (Ranked by Xproj) Xproj Taxon (Ranked by IndVal) IndVal
I
Water 2021, 13, x FOR PEER REVIEW 12 of 26
Figure 7. (left) Temporal map of phytoplankton assemblages of the 4-clustered partition. (right) Temporal map of phyto-
plankton assemblages of the 18-clustered partition. The white area indicates missing data.
Table 3. Taxa composition of the 4-clustered partition with corresponding Likelihood ratios (Xproj) for the centroids and
IndVal values (IndVal). Taxa with Xproj > 1 or IndVal value > 0.25 are shown in bold. Taxa in both columns are organized
in descending order of IndVal and Xproj. The term “Nanoflagellates” stands for flagellates in the nanoplankton size frac-
tion that have not been identified as prasinophytes, cryptophytes, etc.
Cluster Taxon (Ranked by Xproj) Xproj Taxon (Ranked by IndVal) IndVal
Meringosphaera mediterranea 0.78 Prasinophyceae 0.42
Diploneis crabro 0.68 Diatoms non ident. 0.41
Diatoms non ident. 0.61 Meringosphaera mediterranea 0.39
Cylindrotheca Closterium 0.56 Diploneis crabro 0.36
Ophiaster hydroideus 0.53 Cryptophyceae 0.33
Emiliania huxleyi 0.48 Cylindrotheca closterium 0.33
Cryptophyceae 0.44 Heterocapsa gr. 0.3
Dictyocha fibula 0.41 Cyclotella spp. 0.29
Prorocentrum cordatum 0.39 nanoflagellates 0.29
Nanoflagellates 0.37 Ophiaster hydroideus 0.26
Gonyaulax spp. 0.37 Emiliania huxleyi 0.26
Skeletonema costatum s.l. 22.00 Skeletonema costatum s.l. 0.98
Chaetoceros simplex 5.23 Chaetoceros simplex 0.55
Prorocentrum compressum −0.25 Emiliania huxleyi 0.29
Scrippsiella trochoidea −0.25 Scrippsiella trochoidea 0.28
Chaetoceros curvisetus 76.01 Chaetoceros curvisetus 1
Leptocylindrus danicus 9.61 Leptocylindrus danicus 0.87
Lauderia annulata 4.33 Prorocentrum micans 0.75
Prorocentrum compressum 2.92 Lauderia annulata 0.75
Asterionellopsis glacialis 2.85 Asterionellopsis glacialis 0.68
Prorocentrum micans 2.51 Prorocentrum compressum 0.66
Prorocentrum cordatum 2.22 Prorocentrum cordatum 0.66
Prorocentrum balticum 1.68 Prorocentrum balticum 0.64
Bacteriastrum delicatulum 1.42 Bacteriastrum delicatulum 0.58
Meringosphaera mediterranea 0.78 Prasinophyceae 0.42
Diploneis crabro 0.68 Diatoms non ident. 0.41
Diatoms non ident. 0.61 Meringosphaera mediterranea 0.39
Cylindrotheca Closterium 0.56 Diploneis crabro 0.36
Ophiaster hydroideus 0.53 Cryptophyceae 0.33
Emiliania huxleyi 0.48 Cylindrotheca closterium 0.33
Cryptophyceae 0.44 Heterocapsa gr. 0.3
Dictyocha fibula 0.41 Cyclotella spp. 0.29
Prorocentrum cordatum 0.39 nanoflagellates 0.29
Nanoflagellates 0.37 Ophiaster hydroideus 0.26
Gonyaulax spp. 0.37 Emiliania huxleyi 0.26

Water 2021,13, 2045 12 of 26
Table 3. Cont.
Cluster Taxon (Ranked by Xproj) Xproj Taxon (Ranked by IndVal) IndVal
II
Water 2021, 13, x FOR PEER REVIEW 12 of 26
Figure 7. (left) Temporal map of phytoplankton assemblages of the 4-clustered partition. (right) Temporal map of phyto-
plankton assemblages of the 18-clustered partition. The white area indicates missing data.
Table 3. Taxa composition of the 4-clustered partition with corresponding Likelihood ratios (Xproj) for the centroids and
IndVal values (IndVal). Taxa with Xproj > 1 or IndVal value > 0.25 are shown in bold. Taxa in both columns are organized
in descending order of IndVal and Xproj. The term “Nanoflagellates” stands for flagellates in the nanoplankton size frac-
tion that have not been identified as prasinophytes, cryptophytes, etc.
Cluster Taxon (Ranked by Xproj) Xproj Taxon (Ranked by IndVal) IndVal
Meringosphaera mediterranea 0.78 Prasinophyceae 0.42
Diploneis crabro 0.68 Diatoms non ident. 0.41
Diatoms non ident. 0.61 Meringosphaera mediterranea 0.39
Cylindrotheca Closterium 0.56 Diploneis crabro 0.36
Ophiaster hydroideus 0.53 Cryptophyceae 0.33
Emiliania huxleyi 0.48 Cylindrotheca closterium 0.33
Cryptophyceae 0.44 Heterocapsa gr. 0.3
Dictyocha fibula 0.41 Cyclotella spp. 0.29
Prorocentrum cordatum 0.39 nanoflagellates 0.29
Nanoflagellates 0.37 Ophiaster hydroideus 0.26
Gonyaulax spp. 0.37 Emiliania huxleyi 0.26
Skeletonema costatum s.l. 22.00 Skeletonema costatum s.l. 0.98
Chaetoceros simplex 5.23 Chaetoceros simplex 0.55
Prorocentrum compressum −0.25 Emiliania huxleyi 0.29
Scrippsiella trochoidea −0.25 Scrippsiella trochoidea 0.28
Chaetoceros curvisetus 76.01 Chaetoceros curvisetus 1
Leptocylindrus danicus 9.61 Leptocylindrus danicus 0.87
Lauderia annulata 4.33 Prorocentrum micans 0.75
Prorocentrum compressum 2.92 Lauderia annulata 0.75
Asterionellopsis glacialis 2.85 Asterionellopsis glacialis 0.68
Prorocentrum micans 2.51 Prorocentrum compressum 0.66
Prorocentrum cordatum 2.22 Prorocentrum cordatum 0.66
Prorocentrum balticum 1.68 Prorocentrum balticum 0.64
Bacteriastrum delicatulum 1.42 Bacteriastrum delicatulum 0.58
Skeletonema costatum s.l. 22.00 Skeletonema costatum s.l. 0.98
Chaetoceros simplex 5.23 Chaetoceros simplex 0.55
Prorocentrum compressum −0.25 Emiliania huxleyi 0.29
Scrippsiella trochoidea −0.25 Scrippsiella trochoidea 0.28
III
Water 2021, 13, x FOR PEER REVIEW 12 of 26
Figure 7. (left) Temporal map of phytoplankton assemblages of the 4-clustered partition. (right) Temporal map of phyto-
plankton assemblages of the 18-clustered partition. The white area indicates missing data.
Table 3. Taxa composition of the 4-clustered partition with corresponding Likelihood ratios (Xproj) for the centroids and
IndVal values (IndVal). Taxa with Xproj > 1 or IndVal value > 0.25 are shown in bold. Taxa in both columns are organized
in descending order of IndVal and Xproj. The term “Nanoflagellates” stands for flagellates in the nanoplankton size frac-
tion that have not been identified as prasinophytes, cryptophytes, etc.
Cluster Taxon (Ranked by Xproj) Xproj Taxon (Ranked by IndVal) IndVal
Meringosphaera mediterranea 0.78 Prasinophyceae 0.42
Diploneis crabro 0.68 Diatoms non ident. 0.41
Diatoms non ident. 0.61 Meringosphaera mediterranea 0.39
Cylindrotheca Closterium 0.56 Diploneis crabro 0.36
Ophiaster hydroideus 0.53 Cryptophyceae 0.33
Emiliania huxleyi 0.48 Cylindrotheca closterium 0.33
Cryptophyceae 0.44 Heterocapsa gr. 0.3
Dictyocha fibula 0.41 Cyclotella spp. 0.29
Prorocentrum cordatum 0.39 nanoflagellates 0.29
Nanoflagellates 0.37 Ophiaster hydroideus 0.26
Gonyaulax spp. 0.37 Emiliania huxleyi 0.26
Skeletonema costatum s.l. 22.00 Skeletonema costatum s.l. 0.98
Chaetoceros simplex 5.23 Chaetoceros simplex 0.55
Prorocentrum compressum −0.25 Emiliania huxleyi 0.29
Scrippsiella trochoidea −0.25 Scrippsiella trochoidea 0.28
Chaetoceros curvisetus 76.01 Chaetoceros curvisetus 1
Leptocylindrus danicus 9.61 Leptocylindrus danicus 0.87
Lauderia annulata 4.33 Prorocentrum micans 0.75
Prorocentrum compressum 2.92 Lauderia annulata 0.75
Asterionellopsis glacialis 2.85 Asterionellopsis glacialis 0.68
Prorocentrum micans 2.51 Prorocentrum compressum 0.66
Prorocentrum cordatum 2.22 Prorocentrum cordatum 0.66
Prorocentrum balticum 1.68 Prorocentrum balticum 0.64
Bacteriastrum delicatulum 1.42 Bacteriastrum delicatulum 0.58
Chaetoceros curvisetus 76.01 Chaetoceros curvisetus 1
Leptocylindrus danicus 9.61 Leptocylindrus danicus 0.87
Lauderia annulata 4.33 Prorocentrum micans 0.75
Prorocentrum compressum 2.92 Lauderia annulata 0.75
Asterionellopsis glacialis 2.85 Asterionellopsis glacialis 0.68
Prorocentrum micans 2.51 Prorocentrum compressum 0.66
Prorocentrum cordatum 2.22 Prorocentrum cordatum 0.66
Prorocentrum balticum 1.68 Prorocentrum balticum 0.64
Bacteriastrum delicatulum 1.42 Bacteriastrum delicatulum 0.58
Hemiaulas hauckii 1.26 Hemiaulas hauckii 0.56
Nitzschia longissima 0.4 Nitzschia longissima 0.53
Pleurosigma normanii 0.2 Dictyocha fibula 0.52
Prorocentrum triestinum 0.19 Prorocentrum triestinum 0.47
Guinardia striata 0.15 Pleurosigma normanii 0.44
Dictyocha fibula 0.11 Prorocentrum gracile 0.39
Pseudo-nitzschia seriata gr. −0.08 Guinardia striata 0.38
Cerataulina pelagica −0.13 Cyclotella spp. 0.37
Prorocentrum gracile −0.27 Pseudo-nitzschia seriata gr. 0.32
Gyrodinium. spp. −0.27 Gyrodinium spp. 0.32
Cyclotella spp. −0.37 Cerataulina pelagica 0.31
Thalassionema nitzschioides −0.37 Thalassionema nitzschioides 0.3
Rhizosolenia spp. −0.38 Rhizosolenia spp. 0.25
IV
Water 2021, 13, x FOR PEER REVIEW 13 of 26
Hemiaulas hauckii 1.26 Hemiaulas hauckii 0.56
Nitzschia longissima 0.4 Nitzschia longissima 0.53
Pleurosigma normanii 0.2 Dictyocha fibula 0.52
Prorocentrum triestinum 0.19 Prorocentrum triestinum 0.47
Guinardia striata 0.15 Pleurosigma normanii 0.44
Dictyocha fibula 0.11 Prorocentrum gracile 0.39
Pseudo-nitzschia seriata gr. −0.08 Guinardia striata 0.38
Cerataulina pelagica −0.13 Cyclotella spp. 0.37
Prorocentrum gracile −0.27 Pseudo-nitzschia seriata gr. 0.32
Gyrodinium. spp. −0.27 Gyrodinium spp. 0.32
Cyclotella spp. −0.37 Cerataulina pelagica 0.31
Thalassionema nitzschioides −0.37 Thalassionema nitzschioides 0.3
Rhizosolenia spp. −0.38 Rhizosolenia spp. 0.25
Lauderia annulata 2.17 Pseudo-nitzschia delicatissima gr. 0.86
Asterionellopsis glacialis 1.38 Chaetoceros spp. 0.81
Guinardia striata 1.34 Proboscia alata 0.79
Leptocylindrus mediterraneus 1.18 Dactyliosolen fragilissimus 0.64
Pseudo-nitzschia seriata gr. 1.08 Nitzschia spp. 0.6
Bacteriastrum delicatulum 1.05 Cerataulina pelagica 0.6
Chaetoceros spp. 0.92 Syracosphaera pulchra 0.55
Cerataulina pelagica 0.8 Euglenophyceae 0.54
Pleurosigma normanii 0.76 Rhizosolenia spp. 0.49
Rhabdosphaera stylifera 0.75 Rhabdosphaera stylifera 0.46
Dactyliosolen fragilissimus 0.75 Tripos fusus 0.44
Hemiaulas hauckii 0.7 Pseudo-nitzschia seriata gr. 0.41
Leptocylindrus danicus 0.67 Guinardia flaccida 0.41
Guinardia flaccida 0.59 Heterocapsa gr. 0.41
Coccolith. non ident. 0.59 Cylindrotheca closterium 0.41
Pseudo-nitzschia delicatissima gr. 0.59 Leptocylindrus mediterraneus 0.39
Proboscia alata 0.55 Prasinophyceae 0.39
Syracosphaera pulchra 0.49 Calciosolenia murrayi 0.38
Thalassiosira spp. 0.49 Thalassionema nitzschioides 0.37
Tripos fusus 0.35 Guinardia striata 0.36
Calciosolenia murrayi 0.3 Diploneis crabro 0.35
Rhizosolenia spp. 0.3 Thalassiosira spp. 0.34
Euglenophyceae 0.28 Gymnodinium spp. 0.32
Nitzschia longissima 0.21 nanoflagellates 0.3
Prorocentrum triestinum 0.21 Calciosolenia brasiliensis 0.29
Dinobryon spp. 0.18 Ophiaster hydroideus 0.26
Oxytoxum spp. 0.1 Pleurosigma normanii 0.26
Protoperidinium spp. 0.08 Diatoms non ident. 0.26
Calciosolenia brasiliensis 0.05 Cryptophyceae 0.25
Nitzschia spp. 0.04 Emiliania huxleyi 0.25
3.4.2. Eighteen-Clustered Partition
The temporal map of 18 clusters obtained with the modified protocol is shown in
Figure 7 (right), with the corresponding Xproj and IndVal values summarized in Table 4.
It shows a more complex picture of seasonality, especially in autumn. Ten clusters in-
cluded three or more samples/months, while the remaining eight included only one sam-
ple/month.
Lauderia annulata 2.17 Pseudo-nitzschia delicatissima gr. 0.86
Asterionellopsis glacialis 1.38 Chaetoceros spp. 0.81
Guinardia striata 1.34 Proboscia alata 0.79
Leptocylindrus mediterraneus 1.18 Dactyliosolen fragilissimus 0.64
Pseudo-nitzschia seriata gr. 1.08 Nitzschia spp. 0.6
Bacteriastrum delicatulum 1.05 Cerataulina pelagica 0.6
Chaetoceros spp. 0.92 Syracosphaera pulchra 0.55
Cerataulina pelagica 0.8 Euglenophyceae 0.54
Pleurosigma normanii 0.76 Rhizosolenia spp. 0.49
Rhabdosphaera stylifera 0.75 Rhabdosphaera stylifera 0.46
Dactyliosolen fragilissimus 0.75 Tripos fusus 0.44
Hemiaulas hauckii 0.7 Pseudo-nitzschia seriata gr. 0.41
Leptocylindrus danicus 0.67 Guinardia flaccida 0.41
Guinardia flaccida 0.59 Heterocapsa gr. 0.41
Coccolith. non ident. 0.59 Cylindrotheca closterium 0.41
Pseudo-nitzschia delicatissima gr. 0.59 Leptocylindrus mediterraneus 0.39
Proboscia alata 0.55 Prasinophyceae 0.39
Syracosphaera pulchra 0.49 Calciosolenia murrayi 0.38
Thalassiosira spp. 0.49 Thalassionema nitzschioides 0.37
Tripos fusus 0.35 Guinardia striata 0.36
Calciosolenia murrayi 0.3 Diploneis crabro 0.35
Rhizosolenia spp. 0.3 Thalassiosira spp. 0.34
Euglenophyceae 0.28 Gymnodinium spp. 0.32
Nitzschia longissima 0.21 nanoflagellates 0.3
Prorocentrum triestinum 0.21 Calciosolenia brasiliensis 0.29
Dinobryon spp. 0.18 Ophiaster hydroideus 0.26
Oxytoxum spp. 0.1 Pleurosigma normanii 0.26
Protoperidinium spp. 0.08 Diatoms non ident. 0.26
Calciosolenia brasiliensis 0.05 Cryptophyceae 0.25
Nitzschia spp. 0.04 Emiliania huxleyi 0.25

Water 2021,13, 2045 13 of 26
Water 2021, 13, x FOR PEER REVIEW 12 of 26
Figure 7. (left) Temporal map of phytoplankton assemblages of the 4-clustered partition. (right) Temporal map of phyto-
plankton assemblages of the 18-clustered partition. The white area indicates missing data.
Table 3. Taxa composition of the 4-clustered partition with corresponding Likelihood ratios (Xproj) for the centroids and
IndVal values (IndVal). Taxa with Xproj > 1 or IndVal value > 0.25 are shown in bold. Taxa in both columns are organized
in descending order of IndVal and Xproj. The term “Nanoflagellates” stands for flagellates in the nanoplankton size frac-
tion that have not been identified as prasinophytes, cryptophytes, etc.
Cluster Taxon (Ranked by Xproj) Xproj Taxon (Ranked by IndVal) IndVal
I
Meringosphaera mediterranea 0.78 Prasinophyceae 0.42
Diploneis crabro 0.68 Diatoms non ident. 0.41
Diatoms non ident. 0.61 Meringosphaera mediterranea 0.39
Cylindrotheca Closterium 0.56 Diploneis crabro 0.36
Ophiaster hydroideus 0.53 Cryptophyceae 0.33
Emiliania huxleyi 0.48 Cylindrotheca closterium 0.33
Cryptophyceae 0.44 Heterocapsa gr. 0.3
Dictyocha fibula 0.41 Cyclotella spp. 0.29
Prorocentrum cordatum 0.39 nanoflagellates 0.29
Nanoflagellates 0.37 Ophiaster hydroideus 0.26
Gonyaulax spp. 0.37 Emiliania huxleyi 0.26
II
Skeletonema costatum s.l. 22.00 Skeletonema costatum s.l. 0.98
Chaetoceros simplex 5.23 Chaetoceros simplex 0.55
Prorocentrum compressum −0.25 Emiliania huxleyi 0.29
Scrippsiella trochoidea −0.25 Scrippsiella trochoidea 0.28
III
Chaetoceros curvisetus 76.01 Chaetoceros curvisetus 1
Leptocylindrus danicus 9.61 Leptocylindrus danicus 0.87
Lauderia annulata 4.33 Prorocentrum micans 0.75
Prorocentrum compressum 2.92 Lauderia annulata 0.75
Asterionellopsis glacialis 2.85 Asterionellopsis glacialis 0.68
Prorocentrum micans 2.51 Prorocentrum compressum 0.66
Prorocentrum cordatum 2.22 Prorocentrum cordatum 0.66
Prorocentrum balticum 1.68 Prorocentrum balticum 0.64
Bacteriastrum delicatulum 1.42 Bacteriastrum delicatulum 0.58
Figure 7.
(
left
) Temporal map of phytoplankton assemblages of the 4-clustered partition. (
right
) Temporal map of
phytoplankton assemblages of the 18-clustered partition. The white area indicates missing data.
3.4.2. Eighteen-Clustered Partition
The temporal map of 18 clusters obtained with the modified protocol is shown in
Figure 7(right), with the corresponding Xproj and IndVal values summarized in
Table 4
.
It shows a more complex picture of seasonality, especially in autumn. Ten clusters in-
cluded three or more samples/months, while the remaining eight included only one
sample/month.
Table 4.
Taxa composition of the 18-clustered partition with corresponding Likelihood ratios (Xproj) for the centroids and
IndVal values (IndVal). Taxa with Xproj > 1 or IndVal > 0.25 are in bold. Taxa in both columns are organized in descending
order of IndVal and Xproj. The term “Nanoflagellates” stands for flagellates in the nanoplankton size fraction that have not
been identified as prasinophytes, cryptophytes, etc.
Cluster Taxon (Ranked by Xproj) Xproj Taxon (Ranked by IndVal) IndVal
I
Water 2021, 13, x FOR PEER REVIEW 14 of 26
During winter, the most important clusters were Cluster VII, strongly associated with
the coccolithophore Emiliania huxleyi, and Cluster IX associated with the chrysophycean
species Meringosphaera mediterranea. Cluster IX was also scattered in the other seasons.
In spring, the main clusters were IV and XVIII. From 2005 to 2012, late spring was
characterized by Cluster IV associated with the diatoms Cyclotella spp. and C. simplex and
the dinoflagellates Prorocentrum gracile, Prorocentrum cordatum and the Heterocapsa group.
In 2009 and 2013, another late spring cluster (Cluster XVIII) formed, most strongly associ-
ated with the diatom Bacteriastrum delicatulum, as well as dinoflagellates from the genus
Prorocentrum, the coccolitophore Calyptrosphaera oblonga, and undetermined eugleno-
phytes.
The most important summer cluster, typical mainly of July during 2005–2012, was
Cluster XI, which was also sporadically present in other seasons, especially in late au-
tumn. This cluster was associated with the diatoms Chaetoceros spp. and Proboscia alata.
From 2010, another summer cluster (Cluster II) appeared, characterized by a diverse com-
munity of diatoms (e.g., Guinardia flaccida, Dactyliosolen fragilissimus and undetermined
species), dinoflagellates (e.g., Prorocentrum triestinum, P. gracile) and chlorophytes.
Autumn months were the most varied and richest for the occurrence of various clus-
ters. Besides scattered occurrences of Cluster IX and Cluster XI, this season was character-
ized by three clusters: Cluster XII, Cluster III and Cluster XVII. Cluster XII was most typ-
ical in October and November and was associated with undetermined coccolithophores
and various diatoms such as Guinardia striata, Hemiaulus hauckii and D. fragilissimus. From
2010, the Cluster III appeared, associated with coccolithophores Calciosolenia murrayi and
Syracosphaera pulchra, diatoms from the Pseudo-nitzschia delicatissima group and some di-
noflagellates. Finally, Cluster XVII was associated with diatoms such as species from
Pseudo-nitzschia seriata group, Asterionellopsis glacialis, Nitzschia longissima and Pleurosigma
normanii.
A correlation of 0.87 with p-value = 0.001 was obtained between the two ordination
indices (Xproj and IndVal).
Table 4. Taxa composition of the 18-clustered partition with corresponding Likelihood ratios (Xproj) for the centroids and
IndVal values (IndVal). Taxa with Xproj > 1 or IndVal > 0.25 are in bold. Taxa in both columns are organized in descending
order of IndVal and Xproj. The term “Nanoflagellates” stands for flagellates in the nanoplankton size fraction that have
not been identified as prasinophytes, cryptophytes, etc.
Cluste
r
Taxon (Ranked by Xproj) Xproj Taxon (Ranked by IndVal) IndVal
Skeletonema costatum s.l. 22.0 Skeletonema costatum s.l. 0.92
Chaetoceros simplex 5.23 Chaetoceros simplex 0.23
Diatoms non ident. 10.1 Diatoms non ident. 0.38
Guinardia flaccida 5.01 Dactyliosolen fragilissimus 0.18
Dactyliosolen fragilissimus 4.20 Heterocapsa gr. 0.12
Prorocentrum triestinum 4.12 Prorocentrum triestinum 0.12
Oxytoxum spp. 1.82 Chlorophyceae 0.11
Heterocapsa gr. 1.69 Guinardia flaccida 0.10
Pleurosigma normanii 1.58 Cyclotella spp. 0.07
Prorocentrum gracile 1.49 Thalassionema nitzschioides 0.06
Guinardia striata 1.17 Prorocentrum balticum 0.06
Chlorophyceae 1.16 Gyrodinium spp. 0.06
Thalassionema nitzschioides 1.14 Prorocentrum gracile 0.06
Hemiaulas hauckii 1.07 Cryptophyceae 0.06
Calciosolenia murrayi 4.75 Pseudo-nitzschia delicatissima gr. 0.65
Pseudo-nitzschia delicatissima gr. 4.37 Nitzschia spp. 0.28
Syracosphaera pulchra 2.11 Syracosphaera pulchra 0.28
Tripos fusus 1.87 Calciosolenia murrayi 0.24
Dactyliosolen fragilissimus 1.63 Dactyliosolen fragilissimus 0.20
Tripos furca 1.59 Gyrodinium spp. 0.18
Skeletonema costatum s.l. 22.0 Skeletonema costatum s.l. 0.92
Chaetoceros simplex 5.23 Chaetoceros simplex 0.23
II
Water 2021, 13, x FOR PEER REVIEW 14 of 26
During winter, the most important clusters were Cluster VII, strongly associated with
the coccolithophore Emiliania huxleyi, and Cluster IX associated with the chrysophycean
species Meringosphaera mediterranea. Cluster IX was also scattered in the other seasons.
In spring, the main clusters were IV and XVIII. From 2005 to 2012, late spring was
characterized by Cluster IV associated with the diatoms Cyclotella spp. and C. simplex and
the dinoflagellates Prorocentrum gracile, Prorocentrum cordatum and the Heterocapsa group.
In 2009 and 2013, another late spring cluster (Cluster XVIII) formed, most strongly associ-
ated with the diatom Bacteriastrum delicatulum, as well as dinoflagellates from the genus
Prorocentrum, the coccolitophore Calyptrosphaera oblonga, and undetermined eugleno-
phytes.
The most important summer cluster, typical mainly of July during 2005–2012, was
Cluster XI, which was also sporadically present in other seasons, especially in late au-
tumn. This cluster was associated with the diatoms Chaetoceros spp. and Proboscia alata.
From 2010, another summer cluster (Cluster II) appeared, characterized by a diverse com-
munity of diatoms (e.g., Guinardia flaccida, Dactyliosolen fragilissimus and undetermined
species), dinoflagellates (e.g., Prorocentrum triestinum, P. gracile) and chlorophytes.
Autumn months were the most varied and richest for the occurrence of various clus-
ters. Besides scattered occurrences of Cluster IX and Cluster XI, this season was character-
ized by three clusters: Cluster XII, Cluster III and Cluster XVII. Cluster XII was most typ-
ical in October and November and was associated with undetermined coccolithophores
and various diatoms such as Guinardia striata, Hemiaulus hauckii and D. fragilissimus. From
2010, the Cluster III appeared, associated with coccolithophores Calciosolenia murrayi and
Syracosphaera pulchra, diatoms from the Pseudo-nitzschia delicatissima group and some di-
noflagellates. Finally, Cluster XVII was associated with diatoms such as species from
Pseudo-nitzschia seriata group, Asterionellopsis glacialis, Nitzschia longissima and Pleurosigma
normanii.
A correlation of 0.87 with p-value = 0.001 was obtained between the two ordination
indices (Xproj and IndVal).
Table 4. Taxa composition of the 18-clustered partition with corresponding Likelihood ratios (Xproj) for the centroids and
IndVal values (IndVal). Taxa with Xproj > 1 or IndVal > 0.25 are in bold. Taxa in both columns are organized in descending
order of IndVal and Xproj. The term “Nanoflagellates” stands for flagellates in the nanoplankton size fraction that have
not been identified as prasinophytes, cryptophytes, etc.
Cluste
r
Taxon (Ranked by Xproj) Xproj Taxon (Ranked by IndVal) IndVal
Skeletonema costatum s.l. 22.0 Skeletonema costatum s.l. 0.92
Chaetoceros simplex 5.23 Chaetoceros simplex 0.23
Diatoms non ident. 10.1 Diatoms non ident. 0.38
Guinardia flaccida 5.01 Dactyliosolen fragilissimus 0.18
Dactyliosolen fragilissimus 4.20 Heterocapsa gr. 0.12
Prorocentrum triestinum 4.12 Prorocentrum triestinum 0.12
Oxytoxum spp. 1.82 Chlorophyceae 0.11
Heterocapsa gr. 1.69 Guinardia flaccida 0.10
Pleurosigma normanii 1.58 Cyclotella spp. 0.07
Prorocentrum gracile 1.49 Thalassionema nitzschioides 0.06
Guinardia striata 1.17 Prorocentrum balticum 0.06
Chlorophyceae 1.16 Gyrodinium spp. 0.06
Thalassionema nitzschioides 1.14 Prorocentrum gracile 0.06
Hemiaulas hauckii 1.07 Cryptophyceae 0.06
Calciosolenia murrayi 4.75 Pseudo-nitzschia delicatissima gr. 0.65
Pseudo-nitzschia delicatissima gr. 4.37 Nitzschia spp. 0.28
Syracosphaera pulchra 2.11 Syracosphaera pulchra 0.28
Tripos fusus 1.87 Calciosolenia murrayi 0.24
Dactyliosolen fragilissimus 1.63 Dactyliosolen fragilissimus 0.20
Tripos furca 1.59 Gyrodinium spp. 0.18
Diatoms non ident. 10.1 Diatoms non ident. 0.38
Guinardia flaccida 5.01 Dactyliosolen fragilissimus 0.18
Dactyliosolen fragilissimus 4.20 Heterocapsa gr. 0.12
Prorocentrum triestinum 4.12 Prorocentrum triestinum 0.12
Oxytoxum spp. 1.82 Chlorophyceae 0.11
Heterocapsa gr. 1.69 Guinardia flaccida 0.10
Pleurosigma normanii 1.58 Cyclotella spp. 0.07
Prorocentrum gracile 1.49 Thalassionema nitzschioides 0.06
Guinardia striata 1.17 Prorocentrum balticum 0.06
Chlorophyceae 1.16 Gyrodinium spp. 0.06
Thalassionema nitzschioides 1.14 Prorocentrum gracile 0.06
Hemiaulas hauckii 1.07 Cryptophyceae 0.06

Water 2021,13, 2045 14 of 26
Table 4. Cont.
Cluster Taxon (Ranked by Xproj) Xproj Taxon (Ranked by IndVal) IndVal
III
Water 2021, 13, x FOR PEER REVIEW 14 of 26
During winter, the most important clusters were Cluster VII, strongly associated with
the coccolithophore Emiliania huxleyi, and Cluster IX associated with the chrysophycean
species Meringosphaera mediterranea. Cluster IX was also scattered in the other seasons.
In spring, the main clusters were IV and XVIII. From 2005 to 2012, late spring was
characterized by Cluster IV associated with the diatoms Cyclotella spp. and C. simplex and
the dinoflagellates Prorocentrum gracile, Prorocentrum cordatum and the Heterocapsa group.
In 2009 and 2013, another late spring cluster (Cluster XVIII) formed, most strongly associ-
ated with the diatom Bacteriastrum delicatulum, as well as dinoflagellates from the genus
Prorocentrum, the coccolitophore Calyptrosphaera oblonga, and undetermined eugleno-
phytes.
The most important summer cluster, typical mainly of July during 2005–2012, was
Cluster XI, which was also sporadically present in other seasons, especially in late au-
tumn. This cluster was associated with the diatoms Chaetoceros spp. and Proboscia alata.
From 2010, another summer cluster (Cluster II) appeared, characterized by a diverse com-
munity of diatoms (e.g., Guinardia flaccida, Dactyliosolen fragilissimus and undetermined
species), dinoflagellates (e.g., Prorocentrum triestinum, P. gracile) and chlorophytes.
Autumn months were the most varied and richest for the occurrence of various clus-
ters. Besides scattered occurrences of Cluster IX and Cluster XI, this season was character-
ized by three clusters: Cluster XII, Cluster III and Cluster XVII. Cluster XII was most typ-
ical in October and November and was associated with undetermined coccolithophores
and various diatoms such as Guinardia striata, Hemiaulus hauckii and D. fragilissimus. From
2010, the Cluster III appeared, associated with coccolithophores Calciosolenia murrayi and
Syracosphaera pulchra, diatoms from the Pseudo-nitzschia delicatissima group and some di-
noflagellates. Finally, Cluster XVII was associated with diatoms such as species from
Pseudo-nitzschia seriata group, Asterionellopsis glacialis, Nitzschia longissima and Pleurosigma
normanii.
A correlation of 0.87 with p-value = 0.001 was obtained between the two ordination
indices (Xproj and IndVal).
Table 4. Taxa composition of the 18-clustered partition with corresponding Likelihood ratios (Xproj) for the centroids and
IndVal values (IndVal). Taxa with Xproj > 1 or IndVal > 0.25 are in bold. Taxa in both columns are organized in descending
order of IndVal and Xproj. The term “Nanoflagellates” stands for flagellates in the nanoplankton size fraction that have
not been identified as prasinophytes, cryptophytes, etc.
Cluste
r
Taxon (Ranked by Xproj) Xproj Taxon (Ranked by IndVal) IndVal
Skeletonema costatum s.l. 22.0 Skeletonema costatum s.l. 0.92
Chaetoceros simplex 5.23 Chaetoceros simplex 0.23
Diatoms non ident. 10.1 Diatoms non ident. 0.38
Guinardia flaccida 5.01 Dactyliosolen fragilissimus 0.18
Dactyliosolen fragilissimus 4.20 Heterocapsa gr. 0.12
Prorocentrum triestinum 4.12 Prorocentrum triestinum 0.12
Oxytoxum spp. 1.82 Chlorophyceae 0.11
Heterocapsa gr. 1.69 Guinardia flaccida 0.10
Pleurosigma normanii 1.58 Cyclotella spp. 0.07
Prorocentrum gracile 1.49 Thalassionema nitzschioides 0.06
Guinardia striata 1.17 Prorocentrum balticum 0.06
Chlorophyceae 1.16 Gyrodinium spp. 0.06
Thalassionema nitzschioides 1.14 Prorocentrum gracile 0.06
Hemiaulas hauckii 1.07 Cryptophyceae 0.06
Calciosolenia murrayi 4.75 Pseudo-nitzschia delicatissima gr. 0.65
Pseudo-nitzschia delicatissima gr. 4.37 Nitzschia spp. 0.28
Syracosphaera pulchra 2.11 Syracosphaera pulchra 0.28
Tripos fusus 1.87 Calciosolenia murrayi 0.24
Dactyliosolen fragilissimus 1.63 Dactyliosolen fragilissimus 0.20
Tripos furca 1.59 Gyrodinium spp. 0.18
Calciosolenia murrayi 4.75 Pseudo-nitzschia delicatissima gr. 0.65
Pseudo-nitzschia delicatissima gr. 4.37 Nitzschia spp. 0.28
Syracosphaera pulchra 2.11 Syracosphaera pulchra 0.28
Tripos fusus 1.87 Calciosolenia murrayi 0.24
Dactyliosolen fragilissimus 1.63 Dactyliosolen fragilissimus 0.20
Tripos furca 1.59 Gyrodinium spp. 0.18
Calciosolenia brasiliensis 1.55 Tripos fusus 0.18
Nitzschia spp. 1.39 Calciosolenia brasiliensis 0.17
Prorocentrum triestinum 1.24 Ophiaster hydroideus 0.16
Rhabdosphaera stylifera 1.12 Rhizosolenia spp. 0.13
Gyrodinium spp. 1.12 Tripos furca 0.13
Leptocylindrus mediterraneus 1.09 Pseudo-nitzschia seriata gr. 0.11
IV
Water 2021, 13, x FOR PEER REVIEW 15 of 26
Calciosolenia brasiliensis 1.55 Tripos fusus 0.18
Nitzschia spp. 1.39 Calciosolenia brasiliensis 0.17
Prorocentrum triestinum 1.24 Ophiaster hydroideus 0.16
Rhabdosphaera stylifera 1.12 Rhizosolenia spp. 0.13
Gyrodinium spp. 1.12 Tripos furca 0.13
Leptocylindrus mediterraneus 1.09 Pseudo-nitzschia seriata gr. 0.11
Cyclotella spp. 5.28 Cyclotella spp. 0.44
Chaetoceros simplex 4.02 Prorocentrum gracile 0.20
Prorocentrum gracile 2.97 Prorocentrum cordatum 0.13
Heterocapsa gr. 1.08 Chaetoceros simplex 0.13
Prorocentrum cordatum 1.03 Prasinophyceae 0.12
Chaetoceros curvisetus 76.0 Chaetoceros curvisetus 0.95
Leptocylindrus danicus 9.61 Prorocentrum cordatum 0.31
Lauderia annulata 4.33 Prorocentrum balticum 0.24
Prorocentrum compressum 2.92 Leptocylindrus danicus 0.23
Asterionellopsis glacialis 2.85 Hemiaulas hauckii 0.22
Prorocentrum micans 2.51 Prorocentrum micans 0.21
Prorocentrum cordatum 2.22 Prorocentrum compressum 0.20
Prorocentrum balticum 1.68 Bacteriastrum delicatulum 0.12
Bacteriastrum delicatulum 1.42 Guinardia striata 0.11
Hemiaulas hauckii 1.26 Nitzschia longissima 0.11
Lauderia annulata 114. Lauderia annulata 0.83
Leptocylindrus mediterraneus 22.7 Leptocylindrus mediterraneus 0.54
Thalassiosira spp. 7.94 Thalassiosira spp. 0.28
Guinardia flaccida 5.54 Gonyaulax spp. 0.20
Cerataulina pelagica 4.35 Guinardia flaccida 0.18
Leptocylindrus danicus 2.89 Protoperidinium spp. 0.12
Thalassionema nitzschioides 2.03 Thalassionema nitzschioides 0.10
Gonyaulax spp. 1.76 Calciosolenia brasiliensis 0.08
Protoperidinium spp. 1.63 Ophiaster hydroideus 0.08
Calciosolenia brasiliensis 1.35 Pleurosigma normanii 0.08
Pleurosigma normanii 1.34 Dactyliosolen fragilissimus 0.08
Nitzschia longissima 1.01 Gyrodinium spp. 0.07
Emiliania huxleyi 3.71 Emiliania huxleyi 0.11
Ophiaster hydroideus 1.74 Ophiaster hydroideus 0.07
Diploneis crabro 1.73 Diploneis crabro 0.07
Gonyaulax spp. 1.66 Prasinophyceae 0.06
Cylindrotheca closterium 1.24 Meringosphaera mediterranea 0.06
Dictyocha fibula 97.2 Dictyocha fibula 0.92
Rhabdosphaera stylifera 10.2 Rhabdosphaera stylifera 0.54
Oxytoxum spp. 10.1 Oxytoxum spp. 0.44
Calciosolenia brasiliensis 5.17 Calciosolenia brasiliensis 0.27
Prorocentrum micans 4.36 Tripos furca 0.20
Prorocentrum triestinum 4.14 Prorocentrum triestinum 0.18
Diatoms non ident. 3.04 Diatoms non ident. 0.18
Diploneis crabro 2.90 Prorocentrum micans 0.17
Prorocentrum compressum 2.71 Diploneis crabro 0.17
Tripos furca 2.53 Heterocapsa gr. 0.14
Thalassionema nitzschioides 2.25 Scrippsiella trochoidea 0.14
Scrippsiella trochoidea 2.20 Thalassionema nitzschioides 0.13
Heterocapsa gr. 1.85 Chlorophyceae 0.12
Cylindrotheca closterium 1.10 Prorocentrum compressum 0.10
Meringosphaera mediterranea 1.05 Cryptophyceae 0.08
Asterionellopsis glacialis 61.3 Asterionellopsis glacialis 0.79
Cyclotella spp. 5.28 Cyclotella spp. 0.44
Chaetoceros simplex 4.02 Prorocentrum gracile 0.20
Prorocentrum gracile 2.97 Prorocentrum cordatum 0.13
Heterocapsa gr. 1.08 Chaetoceros simplex 0.13
Prorocentrum cordatum 1.03 Prasinophyceae 0.12
V
Water 2021, 13, x FOR PEER REVIEW 15 of 26
Calciosolenia brasiliensis 1.55 Tripos fusus 0.18
Nitzschia spp. 1.39 Calciosolenia brasiliensis 0.17
Prorocentrum triestinum 1.24 Ophiaster hydroideus 0.16
Rhabdosphaera stylifera 1.12 Rhizosolenia spp. 0.13
Gyrodinium spp. 1.12 Tripos furca 0.13
Leptocylindrus mediterraneus 1.09 Pseudo-nitzschia seriata gr. 0.11
Cyclotella spp. 5.28 Cyclotella spp. 0.44
Chaetoceros simplex 4.02 Prorocentrum gracile 0.20
Prorocentrum gracile 2.97 Prorocentrum cordatum 0.13
Heterocapsa gr. 1.08 Chaetoceros simplex 0.13
Prorocentrum cordatum 1.03 Prasinophyceae 0.12
Chaetoceros curvisetus 76.0 Chaetoceros curvisetus 0.95
Leptocylindrus danicus 9.61 Prorocentrum cordatum 0.31
Lauderia annulata 4.33 Prorocentrum balticum 0.24
Prorocentrum compressum 2.92 Leptocylindrus danicus 0.23
Asterionellopsis glacialis 2.85 Hemiaulas hauckii 0.22
Prorocentrum micans 2.51 Prorocentrum micans 0.21
Prorocentrum cordatum 2.22 Prorocentrum compressum 0.20
Prorocentrum balticum 1.68 Bacteriastrum delicatulum 0.12
Bacteriastrum delicatulum 1.42 Guinardia striata 0.11
Hemiaulas hauckii 1.26 Nitzschia longissima 0.11
Lauderia annulata 114. Lauderia annulata 0.83
Leptocylindrus mediterraneus 22.7 Leptocylindrus mediterraneus 0.54
Thalassiosira spp. 7.94 Thalassiosira spp. 0.28
Guinardia flaccida 5.54 Gonyaulax spp. 0.20
Cerataulina pelagica 4.35 Guinardia flaccida 0.18
Leptocylindrus danicus 2.89 Protoperidinium spp. 0.12
Thalassionema nitzschioides 2.03 Thalassionema nitzschioides 0.10
Gonyaulax spp. 1.76 Calciosolenia brasiliensis 0.08
Protoperidinium spp. 1.63 Ophiaster hydroideus 0.08
Calciosolenia brasiliensis 1.35 Pleurosigma normanii 0.08
Pleurosigma normanii 1.34 Dactyliosolen fragilissimus 0.08
Nitzschia longissima 1.01 Gyrodinium spp. 0.07
Emiliania huxleyi 3.71 Emiliania huxleyi 0.11
Ophiaster hydroideus 1.74 Ophiaster hydroideus 0.07
Diploneis crabro 1.73 Diploneis crabro 0.07
Gonyaulax spp. 1.66 Prasinophyceae 0.06
Cylindrotheca closterium 1.24 Meringosphaera mediterranea 0.06
Dictyocha fibula 97.2 Dictyocha fibula 0.92
Rhabdosphaera stylifera 10.2 Rhabdosphaera stylifera 0.54
Oxytoxum spp. 10.1 Oxytoxum spp. 0.44
Calciosolenia brasiliensis 5.17 Calciosolenia brasiliensis 0.27
Prorocentrum micans 4.36 Tripos furca 0.20
Prorocentrum triestinum 4.14 Prorocentrum triestinum 0.18
Diatoms non ident. 3.04 Diatoms non ident. 0.18
Diploneis crabro 2.90 Prorocentrum micans 0.17
Prorocentrum compressum 2.71 Diploneis crabro 0.17
Tripos furca 2.53 Heterocapsa gr. 0.14
Thalassionema nitzschioides 2.25 Scrippsiella trochoidea 0.14
Scrippsiella trochoidea 2.20 Thalassionema nitzschioides 0.13
Heterocapsa gr. 1.85 Chlorophyceae 0.12
Cylindrotheca closterium 1.10 Prorocentrum compressum 0.10
Meringosphaera mediterranea 1.05 Cryptophyceae 0.08
Asterionellopsis glacialis 61.3 Asterionellopsis glacialis 0.79
Chaetoceros curvisetus 76.0 Chaetoceros curvisetus 0.95
Leptocylindrus danicus 9.61 Prorocentrum cordatum 0.31
Lauderia annulata 4.33 Prorocentrum balticum 0.24
Prorocentrum compressum 2.92 Leptocylindrus danicus 0.23
Asterionellopsis glacialis 2.85 Hemiaulas hauckii 0.22
Prorocentrum micans 2.51 Prorocentrum micans 0.21
Prorocentrum cordatum 2.22 Prorocentrum compressum 0.20
Prorocentrum balticum 1.68 Bacteriastrum delicatulum 0.12
Bacteriastrum delicatulum 1.42 Guinardia striata 0.11
Hemiaulas hauckii 1.26 Nitzschia longissima 0.11
VI
Water 2021, 13, x FOR PEER REVIEW 15 of 26
Calciosolenia brasiliensis 1.55 Tripos fusus 0.18
Nitzschia spp. 1.39 Calciosolenia brasiliensis 0.17
Prorocentrum triestinum 1.24 Ophiaster hydroideus 0.16
Rhabdosphaera stylifera 1.12 Rhizosolenia spp. 0.13
Gyrodinium spp. 1.12 Tripos furca 0.13
Leptocylindrus mediterraneus 1.09 Pseudo-nitzschia seriata gr. 0.11
Cyclotella spp. 5.28 Cyclotella spp. 0.44
Chaetoceros simplex 4.02 Prorocentrum gracile 0.20
Prorocentrum gracile 2.97 Prorocentrum cordatum 0.13
Heterocapsa gr. 1.08 Chaetoceros simplex 0.13
Prorocentrum cordatum 1.03 Prasinophyceae 0.12
Chaetoceros curvisetus 76.0 Chaetoceros curvisetus 0.95
Leptocylindrus danicus 9.61 Prorocentrum cordatum 0.31
Lauderia annulata 4.33 Prorocentrum balticum 0.24
Prorocentrum compressum 2.92 Leptocylindrus danicus 0.23
Asterionellopsis glacialis 2.85 Hemiaulas hauckii 0.22
Prorocentrum micans 2.51 Prorocentrum micans 0.21
Prorocentrum cordatum 2.22 Prorocentrum compressum 0.20
Prorocentrum balticum 1.68 Bacteriastrum delicatulum 0.12
Bacteriastrum delicatulum 1.42 Guinardia striata 0.11
Hemiaulas hauckii 1.26 Nitzschia longissima 0.11
Lauderia annulata 114. Lauderia annulata 0.83
Leptocylindrus mediterraneus 22.7 Leptocylindrus mediterraneus 0.54
Thalassiosira spp. 7.94 Thalassiosira spp. 0.28
Guinardia flaccida 5.54 Gonyaulax spp. 0.20
Cerataulina pelagica 4.35 Guinardia flaccida 0.18
Leptocylindrus danicus 2.89 Protoperidinium spp. 0.12
Thalassionema nitzschioides 2.03 Thalassionema nitzschioides 0.10
Gonyaulax spp. 1.76 Calciosolenia brasiliensis 0.08
Protoperidinium spp. 1.63 Ophiaster hydroideus 0.08
Calciosolenia brasiliensis 1.35 Pleurosigma normanii 0.08
Pleurosigma normanii 1.34 Dactyliosolen fragilissimus 0.08
Nitzschia longissima 1.01 Gyrodinium spp. 0.07
Emiliania huxleyi 3.71 Emiliania huxleyi 0.11
Ophiaster hydroideus 1.74 Ophiaster hydroideus 0.07
Diploneis crabro 1.73 Diploneis crabro 0.07
Gonyaulax spp. 1.66 Prasinophyceae 0.06
Cylindrotheca closterium 1.24 Meringosphaera mediterranea 0.06
Dictyocha fibula 97.2 Dictyocha fibula 0.92
Rhabdosphaera stylifera 10.2 Rhabdosphaera stylifera 0.54
Oxytoxum spp. 10.1 Oxytoxum spp. 0.44
Calciosolenia brasiliensis 5.17 Calciosolenia brasiliensis 0.27
Prorocentrum micans 4.36 Tripos furca 0.20
Prorocentrum triestinum 4.14 Prorocentrum triestinum 0.18
Diatoms non ident. 3.04 Diatoms non ident. 0.18
Diploneis crabro 2.90 Prorocentrum micans 0.17
Prorocentrum compressum 2.71 Diploneis crabro 0.17
Tripos furca 2.53 Heterocapsa gr. 0.14
Thalassionema nitzschioides 2.25 Scrippsiella trochoidea 0.14
Scrippsiella trochoidea 2.20 Thalassionema nitzschioides 0.13
Heterocapsa gr. 1.85 Chlorophyceae 0.12
Cylindrotheca closterium 1.10 Prorocentrum compressum 0.10
Meringosphaera mediterranea 1.05 Cryptophyceae 0.08
Asterionellopsis glacialis 61.3 Asterionellopsis glacialis 0.79
Lauderia annulata 114. Lauderia annulata 0.83
Leptocylindrus mediterraneus 22.7 Leptocylindrus mediterraneus 0.54
Thalassiosira spp. 7.94 Thalassiosira spp. 0.28
Guinardia flaccida 5.54 Gonyaulax spp. 0.20
Cerataulina pelagica 4.35 Guinardia flaccida 0.18
Leptocylindrus danicus 2.89 Protoperidinium spp. 0.12
Thalassionema nitzschioides 2.03 Thalassionema nitzschioides 0.10
Gonyaulax spp. 1.76 Calciosolenia brasiliensis 0.08
Protoperidinium spp. 1.63 Ophiaster hydroideus 0.08
Calciosolenia brasiliensis 1.35 Pleurosigma normanii 0.08
Pleurosigma normanii 1.34 Dactyliosolen fragilissimus 0.08
Nitzschia longissima 1.01 Gyrodinium spp. 0.07
VII
Water 2021, 13, x FOR PEER REVIEW 15 of 26
Calciosolenia brasiliensis 1.55 Tripos fusus 0.18
Nitzschia spp. 1.39 Calciosolenia brasiliensis 0.17
Prorocentrum triestinum 1.24 Ophiaster hydroideus 0.16
Rhabdosphaera stylifera 1.12 Rhizosolenia spp. 0.13
Gyrodinium spp. 1.12 Tripos furca 0.13
Leptocylindrus mediterraneus 1.09 Pseudo-nitzschia seriata gr. 0.11
Cyclotella spp. 5.28 Cyclotella spp. 0.44
Chaetoceros simplex 4.02 Prorocentrum gracile 0.20
Prorocentrum gracile 2.97 Prorocentrum cordatum 0.13
Heterocapsa gr. 1.08 Chaetoceros simplex 0.13
Prorocentrum cordatum 1.03 Prasinophyceae 0.12
Chaetoceros curvisetus 76.0 Chaetoceros curvisetus 0.95
Leptocylindrus danicus 9.61 Prorocentrum cordatum 0.31
Lauderia annulata 4.33 Prorocentrum balticum 0.24
Prorocentrum compressum 2.92 Leptocylindrus danicus 0.23
Asterionellopsis glacialis 2.85 Hemiaulas hauckii 0.22
Prorocentrum micans 2.51 Prorocentrum micans 0.21
Prorocentrum cordatum 2.22 Prorocentrum compressum 0.20
Prorocentrum balticum 1.68 Bacteriastrum delicatulum 0.12
Bacteriastrum delicatulum 1.42 Guinardia striata 0.11
Hemiaulas hauckii 1.26 Nitzschia longissima 0.11
Lauderia annulata 114. Lauderia annulata 0.83
Leptocylindrus mediterraneus 22.7 Leptocylindrus mediterraneus 0.54
Thalassiosira spp. 7.94 Thalassiosira spp. 0.28
Guinardia flaccida 5.54 Gonyaulax spp. 0.20
Cerataulina pelagica 4.35 Guinardia flaccida 0.18
Leptocylindrus danicus 2.89 Protoperidinium spp. 0.12
Thalassionema nitzschioides 2.03 Thalassionema nitzschioides 0.10
Gonyaulax spp. 1.76 Calciosolenia brasiliensis 0.08
Protoperidinium spp. 1.63 Ophiaster hydroideus 0.08
Calciosolenia brasiliensis 1.35 Pleurosigma normanii 0.08
Pleurosigma normanii 1.34 Dactyliosolen fragilissimus 0.08
Nitzschia longissima 1.01 Gyrodinium spp. 0.07
Emiliania huxleyi 3.71 Emiliania huxleyi 0.11
Ophiaster hydroideus 1.74 Ophiaster hydroideus 0.07
Diploneis crabro 1.73 Diploneis crabro 0.07
Gonyaulax spp. 1.66 Prasinophyceae 0.06
Cylindrotheca closterium 1.24 Meringosphaera mediterranea 0.06
Dictyocha fibula 97.2 Dictyocha fibula 0.92
Rhabdosphaera stylifera 10.2 Rhabdosphaera stylifera 0.54
Oxytoxum spp. 10.1 Oxytoxum spp. 0.44
Calciosolenia brasiliensis 5.17 Calciosolenia brasiliensis 0.27
Prorocentrum micans 4.36 Tripos furca 0.20
Prorocentrum triestinum 4.14 Prorocentrum triestinum 0.18
Diatoms non ident. 3.04 Diatoms non ident. 0.18
Diploneis crabro 2.90 Prorocentrum micans 0.17
Prorocentrum compressum 2.71 Diploneis crabro 0.17
Tripos furca 2.53 Heterocapsa gr. 0.14
Thalassionema nitzschioides 2.25 Scrippsiella trochoidea 0.14
Scrippsiella trochoidea 2.20 Thalassionema nitzschioides 0.13
Heterocapsa gr. 1.85 Chlorophyceae 0.12
Cylindrotheca closterium 1.10 Prorocentrum compressum 0.10
Meringosphaera mediterranea 1.05 Cryptophyceae 0.08
Asterionellopsis glacialis 61.3 Asterionellopsis glacialis 0.79
Emiliania huxleyi 3.71 Emiliania huxleyi 0.11
Ophiaster hydroideus 1.74 Ophiaster hydroideus 0.07
Diploneis crabro 1.73 Diploneis crabro 0.07
Gonyaulax spp. 1.66 Prasinophyceae 0.06
Cylindrotheca closterium 1.24 Meringosphaera mediterranea 0.06

Water 2021,13, 2045 15 of 26
Table 4. Cont.
Cluster Taxon (Ranked by Xproj) Xproj Taxon (Ranked by IndVal) IndVal
VIII
Water 2021, 13, x FOR PEER REVIEW 15 of 26
Calciosolenia brasiliensis 1.55 Tripos fusus 0.18
Nitzschia spp. 1.39 Calciosolenia brasiliensis 0.17
Prorocentrum triestinum 1.24 Ophiaster hydroideus 0.16
Rhabdosphaera stylifera 1.12 Rhizosolenia spp. 0.13
Gyrodinium spp. 1.12 Tripos furca 0.13
Leptocylindrus mediterraneus 1.09 Pseudo-nitzschia seriata gr. 0.11
Cyclotella spp. 5.28 Cyclotella spp. 0.44
Chaetoceros simplex 4.02 Prorocentrum gracile 0.20
Prorocentrum gracile 2.97 Prorocentrum cordatum 0.13
Heterocapsa gr. 1.08 Chaetoceros simplex 0.13
Prorocentrum cordatum 1.03 Prasinophyceae 0.12
Chaetoceros curvisetus 76.0 Chaetoceros curvisetus 0.95
Leptocylindrus danicus 9.61 Prorocentrum cordatum 0.31
Lauderia annulata 4.33 Prorocentrum balticum 0.24
Prorocentrum compressum 2.92 Leptocylindrus danicus 0.23
Asterionellopsis glacialis 2.85 Hemiaulas hauckii 0.22
Prorocentrum micans 2.51 Prorocentrum micans 0.21
Prorocentrum cordatum 2.22 Prorocentrum compressum 0.20
Prorocentrum balticum 1.68 Bacteriastrum delicatulum 0.12
Bacteriastrum delicatulum 1.42 Guinardia striata 0.11
Hemiaulas hauckii 1.26 Nitzschia longissima 0.11
Lauderia annulata 114. Lauderia annulata 0.83
Leptocylindrus mediterraneus 22.7 Leptocylindrus mediterraneus 0.54
Thalassiosira spp. 7.94 Thalassiosira spp. 0.28
Guinardia flaccida 5.54 Gonyaulax spp. 0.20
Cerataulina pelagica 4.35 Guinardia flaccida 0.18
Leptocylindrus danicus 2.89 Protoperidinium spp. 0.12
Thalassionema nitzschioides 2.03 Thalassionema nitzschioides 0.10
Gonyaulax spp. 1.76 Calciosolenia brasiliensis 0.08
Protoperidinium spp. 1.63 Ophiaster hydroideus 0.08
Calciosolenia brasiliensis 1.35 Pleurosigma normanii 0.08
Pleurosigma normanii 1.34 Dactyliosolen fragilissimus 0.08
Nitzschia longissima 1.01 Gyrodinium spp. 0.07
Emiliania huxleyi 3.71 Emiliania huxleyi 0.11
Ophiaster hydroideus 1.74 Ophiaster hydroideus 0.07
Diploneis crabro 1.73 Diploneis crabro 0.07
Gonyaulax spp. 1.66 Prasinophyceae 0.06
Cylindrotheca closterium 1.24 Meringosphaera mediterranea 0.06
Dictyocha fibula 97.2 Dictyocha fibula 0.92
Rhabdosphaera stylifera 10.2 Rhabdosphaera stylifera 0.54
Oxytoxum spp. 10.1 Oxytoxum spp. 0.44
Calciosolenia brasiliensis 5.17 Calciosolenia brasiliensis 0.27
Prorocentrum micans 4.36 Tripos furca 0.20
Prorocentrum triestinum 4.14 Prorocentrum triestinum 0.18
Diatoms non ident. 3.04 Diatoms non ident. 0.18
Diploneis crabro 2.90 Prorocentrum micans 0.17
Prorocentrum compressum 2.71 Diploneis crabro 0.17
Tripos furca 2.53 Heterocapsa gr. 0.14
Thalassionema nitzschioides 2.25 Scrippsiella trochoidea 0.14
Scrippsiella trochoidea 2.20 Thalassionema nitzschioides 0.13
Heterocapsa gr. 1.85 Chlorophyceae 0.12
Cylindrotheca closterium 1.10 Prorocentrum compressum 0.10
Meringosphaera mediterranea 1.05 Cryptophyceae 0.08
Asterionellopsis glacialis 61.3 Asterionellopsis glacialis 0.79
Dictyocha fibula 97.2 Dictyocha fibula 0.92
Rhabdosphaera stylifera 10.2 Rhabdosphaera stylifera 0.54
Oxytoxum spp. 10.1 Oxytoxum spp. 0.44
Calciosolenia brasiliensis 5.17 Calciosolenia brasiliensis 0.27
Prorocentrum micans 4.36 Tripos furca 0.20
Prorocentrum triestinum 4.14 Prorocentrum triestinum 0.18
Diatoms non ident. 3.04 Diatoms non ident. 0.18
Diploneis crabro 2.90 Prorocentrum micans 0.17
Prorocentrum compressum 2.71 Diploneis crabro 0.17
Tripos furca 2.53 Heterocapsa gr. 0.14
Thalassionema nitzschioides 2.25 Scrippsiella trochoidea 0.14
Scrippsiella trochoidea 2.20 Thalassionema nitzschioides 0.13
Heterocapsa gr. 1.85 Chlorophyceae 0.12
Cylindrotheca closterium 1.10 Prorocentrum compressum 0.10
IX
Water 2021, 13, x FOR PEER REVIEW 15 of 26
Calciosolenia brasiliensis 1.55 Tripos fusus 0.18
Nitzschia spp. 1.39 Calciosolenia brasiliensis 0.17
Prorocentrum triestinum 1.24 Ophiaster hydroideus 0.16
Rhabdosphaera stylifera 1.12 Rhizosolenia spp. 0.13
Gyrodinium spp. 1.12 Tripos furca 0.13
Leptocylindrus mediterraneus 1.09 Pseudo-nitzschia seriata gr. 0.11
Cyclotella spp. 5.28 Cyclotella spp. 0.44
Chaetoceros simplex 4.02 Prorocentrum gracile 0.20
Prorocentrum gracile 2.97 Prorocentrum cordatum 0.13
Heterocapsa gr. 1.08 Chaetoceros simplex 0.13
Prorocentrum cordatum 1.03 Prasinophyceae 0.12
Chaetoceros curvisetus 76.0 Chaetoceros curvisetus 0.95
Leptocylindrus danicus 9.61 Prorocentrum cordatum 0.31
Lauderia annulata 4.33 Prorocentrum balticum 0.24
Prorocentrum compressum 2.92 Leptocylindrus danicus 0.23
Asterionellopsis glacialis 2.85 Hemiaulas hauckii 0.22
Prorocentrum micans 2.51 Prorocentrum micans 0.21
Prorocentrum cordatum 2.22 Prorocentrum compressum 0.20
Prorocentrum balticum 1.68 Bacteriastrum delicatulum 0.12
Bacteriastrum delicatulum 1.42 Guinardia striata 0.11
Hemiaulas hauckii 1.26 Nitzschia longissima 0.11
Lauderia annulata 114. Lauderia annulata 0.83
Leptocylindrus mediterraneus 22.7 Leptocylindrus mediterraneus 0.54
Thalassiosira spp. 7.94 Thalassiosira spp. 0.28
Guinardia flaccida 5.54 Gonyaulax spp. 0.20
Cerataulina pelagica 4.35 Guinardia flaccida 0.18
Leptocylindrus danicus 2.89 Protoperidinium spp. 0.12
Thalassionema nitzschioides 2.03 Thalassionema nitzschioides 0.10
Gonyaulax spp. 1.76 Calciosolenia brasiliensis 0.08
Protoperidinium spp. 1.63 Ophiaster hydroideus 0.08
Calciosolenia brasiliensis 1.35 Pleurosigma normanii 0.08
Pleurosigma normanii 1.34 Dactyliosolen fragilissimus 0.08
Nitzschia longissima 1.01 Gyrodinium spp. 0.07
Emiliania huxleyi 3.71 Emiliania huxleyi 0.11
Ophiaster hydroideus 1.74 Ophiaster hydroideus 0.07
Diploneis crabro 1.73 Diploneis crabro 0.07
Gonyaulax spp. 1.66 Prasinophyceae 0.06
Cylindrotheca closterium 1.24 Meringosphaera mediterranea 0.06
Dictyocha fibula 97.2 Dictyocha fibula 0.92
Rhabdosphaera stylifera 10.2 Rhabdosphaera stylifera 0.54
Oxytoxum spp. 10.1 Oxytoxum spp. 0.44
Calciosolenia brasiliensis 5.17 Calciosolenia brasiliensis 0.27
Prorocentrum micans 4.36 Tripos furca 0.20
Prorocentrum triestinum 4.14 Prorocentrum triestinum 0.18
Diatoms non ident. 3.04 Diatoms non ident. 0.18
Diploneis crabro 2.90 Prorocentrum micans 0.17
Prorocentrum compressum 2.71 Diploneis crabro 0.17
Tripos furca 2.53 Heterocapsa gr. 0.14
Thalassionema nitzschioides 2.25 Scrippsiella trochoidea 0.14
Scrippsiella trochoidea 2.20 Thalassionema nitzschioides 0.13
Heterocapsa gr. 1.85 Chlorophyceae 0.12
Cylindrotheca closterium 1.10 Prorocentrum compressum 0.10
Meringosphaera mediterranea 1.05 Cryptophyceae 0.08
Asterionellopsis glacialis 61.3 Asterionellopsis glacialis 0.79
Meringosphaera mediterranea 1.05 Cryptophyceae 0.08
X
Water 2021, 13, x FOR PEER REVIEW 16 of 26
Lauderia annulata 3.51 Ophiaster hydroideus 0.21
Thalassiosira spp. 2.66 Thalassiosira spp. 0.16
Cerataulina pelagica 2.63 Guinardia striata 0.13
Ophiaster hydroideus 2.14 Thalassionema nitzschioides 0.13
Leptocylindrus mediterraneus 1.98 Leptocylindrus mediterraneus 0.10
Thalassionema nitzschioides 1.69 Diploneis crabro 0.09
Guinardia striata 1.21 Calciosolenia murrayi 0.09
Calciosolenia murrayi 1.21 Cerataulina pelagica 0.07
Chaetoceros spp. 2.53 Proboscia alata 0.45
Proboscia alata 1.78 Chaetoceros spp. 0.38
Coccolith. non ident. 4.43 Nitzschia spp. 0.14
Guinardia striata 2.50 Guinardia striata 0.12
Oxytoxum spp. 1.78 Dactyliosolen fragilissimus 0.10
Hemiaulas hauckii 1.68 Syracosphaera pulchra 0.09
Dactyliosolen fragilissimus 1.48 Hemiaulas hauckii 0.09
Rhabdosphaera stylifera 1.27 Coccolith. non ident. 0.09
Nitzschia spp. 1.14 Proboscia alata 0.07
Pleurosigma normanii 1.05 Rhabdosphaera stylifera 0.06
Dinobryon spp. 39.1 Dinobryon spp. 0.84
Leptocylindrus danicus 19.9 Leptocylindrus danicus 0.46
Prorocentrum compressum 5.51 Prorocentrum compressum 0.34
Chaetoceros curvisetus 2.45 Scrippsiella trochoidea 0.28
Prorocentrum micans 2.40 Prorocentrum micans 0.21
Scrippsiella trochoidea 2.35 Nitzschia longissima 0.18
Nitzschia longissima 1.35 Prasinophyceae 0.18
Emiliania huxleyi 17.2 Emiliania huxleyi 0.41
Dinobryon spp. 16.2 Meringosphaera mediterranea 0.30
Prorocentrum gracile 6.36 Prorocentrum gracile 0.21
Meringosphaera mediterranea 5.11 Diploneis crabro 0.17
Diploneis crabro 4.91 Dinobryon spp. 0.12
Protoperidinium spp. 2.33 Protoperidinium spp. 0.12
Chaetoceros simplex 1.16 Chaetoceros simplex 0.07
Calciosolenia murrayi 1.01 Calciosolenia murrayi 0.05
Cerataulina pelagica 20.7 Cerataulina pelagica 0.65
Leptocylindrus danicus 7.56 Guinardia flaccida 0.26
Guinardia flaccida 3.12 Tripos furca 0.19
Cylindrotheca closterium 30.7 Cylindrotheca closterium 0.53
Calciosolenia murrayi 11.9 Calciosolenia murrayi 0.26
Pleurosigma normanii 9.42 Pleurosigma normanii 0.26
Nitzschia longissima 6.55 Nitzschia longissima 0.18
Thalassionema nitzschioides 5.11 Ophiaster hydroideus 0.14
Ophiaster hydroideus 3.36 Thalassionema nitzschioides 0.14
Calciosolenia brasiliensis 2.62 Tripos fusus 0.09
Diploneis crabro 2.56 Calciosolenia brasiliensis 0.09
Tripos fusus 1.80 Diploneis crabro 0.09
Diatoms non ident. 1.55 Syracosphaera pulchra 0.08
Guinardia flaccida 1.41 Nitzschia spp. 0.08
Syracosphaera pulchra 1.20 Meringosphaera mediterranea 0.07
Nitzschia spp. 1.17 Diatoms non ident. 0.06
Asterionellopsis glacialis 1.10 Guinardia striata 0.06
Chaetoceros curvisetus 1.07 Gyrodinium spp. 0.05
Pseudo-nitzschia seriata gr. 15.8 Pseudo-nitzschia seriata gr. 0.62
Protoperidinium spp. 5.80 Protoperidinium spp. 0.29
Asterionellopsis glacialis 4.03 Thalassiosira spp. 0.25
Nitzschia longissima 3.72 Guinardia striata 0.13
Asterionellopsis glacialis 61.3 Asterionellopsis glacialis 0.79
Lauderia annulata 3.51 Ophiaster hydroideus 0.21
Thalassiosira spp. 2.66 Thalassiosira spp. 0.16
Cerataulina pelagica 2.63 Guinardia striata 0.13
Ophiaster hydroideus 2.14 Thalassionema nitzschioides 0.13
Leptocylindrus mediterraneus 1.98 Leptocylindrus mediterraneus 0.10
Thalassionema nitzschioides 1.69 Diploneis crabro 0.09
Guinardia striata 1.21 Calciosolenia murrayi 0.09
Calciosolenia murrayi 1.21 Cerataulina pelagica 0.07
XI
Water 2021, 13, x FOR PEER REVIEW 16 of 26
Lauderia annulata 3.51 Ophiaster hydroideus 0.21
Thalassiosira spp. 2.66 Thalassiosira spp. 0.16
Cerataulina pelagica 2.63 Guinardia striata 0.13
Ophiaster hydroideus 2.14 Thalassionema nitzschioides 0.13
Leptocylindrus mediterraneus 1.98 Leptocylindrus mediterraneus 0.10
Thalassionema nitzschioides 1.69 Diploneis crabro 0.09
Guinardia striata 1.21 Calciosolenia murrayi 0.09
Calciosolenia murrayi 1.21 Cerataulina pelagica 0.07
Chaetoceros spp. 2.53 Proboscia alata 0.45
Proboscia alata 1.78 Chaetoceros spp. 0.38
Coccolith. non ident. 4.43 Nitzschia spp. 0.14
Guinardia striata 2.50 Guinardia striata 0.12
Oxytoxum spp. 1.78 Dactyliosolen fragilissimus 0.10
Hemiaulas hauckii 1.68 Syracosphaera pulchra 0.09
Dactyliosolen fragilissimus 1.48 Hemiaulas hauckii 0.09
Rhabdosphaera stylifera 1.27 Coccolith. non ident. 0.09
Nitzschia spp. 1.14 Proboscia alata 0.07
Pleurosigma normanii 1.05 Rhabdosphaera stylifera 0.06
Dinobryon spp. 39.1 Dinobryon spp. 0.84
Leptocylindrus danicus 19.9 Leptocylindrus danicus 0.46
Prorocentrum compressum 5.51 Prorocentrum compressum 0.34
Chaetoceros curvisetus 2.45 Scrippsiella trochoidea 0.28
Prorocentrum micans 2.40 Prorocentrum micans 0.21
Scrippsiella trochoidea 2.35 Nitzschia longissima 0.18
Nitzschia longissima 1.35 Prasinophyceae 0.18
Emiliania huxleyi 17.2 Emiliania huxleyi 0.41
Dinobryon spp. 16.2 Meringosphaera mediterranea 0.30
Prorocentrum gracile 6.36 Prorocentrum gracile 0.21
Meringosphaera mediterranea 5.11 Diploneis crabro 0.17
Diploneis crabro 4.91 Dinobryon spp. 0.12
Protoperidinium spp. 2.33 Protoperidinium spp. 0.12
Chaetoceros simplex 1.16 Chaetoceros simplex 0.07
Calciosolenia murrayi 1.01 Calciosolenia murrayi 0.05
Cerataulina pelagica 20.7 Cerataulina pelagica 0.65
Leptocylindrus danicus 7.56 Guinardia flaccida 0.26
Guinardia flaccida 3.12 Tripos furca 0.19
Cylindrotheca closterium 30.7 Cylindrotheca closterium 0.53
Calciosolenia murrayi 11.9 Calciosolenia murrayi 0.26
Pleurosigma normanii 9.42 Pleurosigma normanii 0.26
Nitzschia longissima 6.55 Nitzschia longissima 0.18
Thalassionema nitzschioides 5.11 Ophiaster hydroideus 0.14
Ophiaster hydroideus 3.36 Thalassionema nitzschioides 0.14
Calciosolenia brasiliensis 2.62 Tripos fusus 0.09
Diploneis crabro 2.56 Calciosolenia brasiliensis 0.09
Tripos fusus 1.80 Diploneis crabro 0.09
Diatoms non ident. 1.55 Syracosphaera pulchra 0.08
Guinardia flaccida 1.41 Nitzschia spp. 0.08
Syracosphaera pulchra 1.20 Meringosphaera mediterranea 0.07
Nitzschia spp. 1.17 Diatoms non ident. 0.06
Asterionellopsis glacialis 1.10 Guinardia striata 0.06
Chaetoceros curvisetus 1.07 Gyrodinium spp. 0.05
Pseudo-nitzschia seriata gr. 15.8 Pseudo-nitzschia seriata gr. 0.62
Protoperidinium spp. 5.80 Protoperidinium spp. 0.29
Asterionellopsis glacialis 4.03 Thalassiosira spp. 0.25
Nitzschia longissima 3.72 Guinardia striata 0.13
Chaetoceros spp. 2.53 Proboscia alata 0.45
Proboscia alata 1.78 Chaetoceros spp. 0.38
XII
Water 2021, 13, x FOR PEER REVIEW 16 of 26
Lauderia annulata 3.51 Ophiaster hydroideus 0.21
Thalassiosira spp. 2.66 Thalassiosira spp. 0.16
Cerataulina pelagica 2.63 Guinardia striata 0.13
Ophiaster hydroideus 2.14 Thalassionema nitzschioides 0.13
Leptocylindrus mediterraneus 1.98 Leptocylindrus mediterraneus 0.10
Thalassionema nitzschioides 1.69 Diploneis crabro 0.09
Guinardia striata 1.21 Calciosolenia murrayi 0.09
Calciosolenia murrayi 1.21 Cerataulina pelagica 0.07
Chaetoceros spp. 2.53 Proboscia alata 0.45
Proboscia alata 1.78 Chaetoceros spp. 0.38
Coccolith. non ident. 4.43 Nitzschia spp. 0.14
Guinardia striata 2.50 Guinardia striata 0.12
Oxytoxum spp. 1.78 Dactyliosolen fragilissimus 0.10
Hemiaulas hauckii 1.68 Syracosphaera pulchra 0.09
Dactyliosolen fragilissimus 1.48 Hemiaulas hauckii 0.09
Rhabdosphaera stylifera 1.27 Coccolith. non ident. 0.09
Nitzschia spp. 1.14 Proboscia alata 0.07
Pleurosigma normanii 1.05 Rhabdosphaera stylifera 0.06
Dinobryon spp. 39.1 Dinobryon spp. 0.84
Leptocylindrus danicus 19.9 Leptocylindrus danicus 0.46
Prorocentrum compressum 5.51 Prorocentrum compressum 0.34
Chaetoceros curvisetus 2.45 Scrippsiella trochoidea 0.28
Prorocentrum micans 2.40 Prorocentrum micans 0.21
Scrippsiella trochoidea 2.35 Nitzschia longissima 0.18
Nitzschia longissima 1.35 Prasinophyceae 0.18
Emiliania huxleyi 17.2 Emiliania huxleyi 0.41
Dinobryon spp. 16.2 Meringosphaera mediterranea 0.30
Prorocentrum gracile 6.36 Prorocentrum gracile 0.21
Meringosphaera mediterranea 5.11 Diploneis crabro 0.17
Diploneis crabro 4.91 Dinobryon spp. 0.12
Protoperidinium spp. 2.33 Protoperidinium spp. 0.12
Chaetoceros simplex 1.16 Chaetoceros simplex 0.07
Calciosolenia murrayi 1.01 Calciosolenia murrayi 0.05
Cerataulina pelagica 20.7 Cerataulina pelagica 0.65
Leptocylindrus danicus 7.56 Guinardia flaccida 0.26
Guinardia flaccida 3.12 Tripos furca 0.19
Cylindrotheca closterium 30.7 Cylindrotheca closterium 0.53
Calciosolenia murrayi 11.9 Calciosolenia murrayi 0.26
Pleurosigma normanii 9.42 Pleurosigma normanii 0.26
Nitzschia longissima 6.55 Nitzschia longissima 0.18
Thalassionema nitzschioides 5.11 Ophiaster hydroideus 0.14
Ophiaster hydroideus 3.36 Thalassionema nitzschioides 0.14
Calciosolenia brasiliensis 2.62 Tripos fusus 0.09
Diploneis crabro 2.56 Calciosolenia brasiliensis 0.09
Tripos fusus 1.80 Diploneis crabro 0.09
Diatoms non ident. 1.55 Syracosphaera pulchra 0.08
Guinardia flaccida 1.41 Nitzschia spp. 0.08
Syracosphaera pulchra 1.20 Meringosphaera mediterranea 0.07
Nitzschia spp. 1.17 Diatoms non ident. 0.06
Asterionellopsis glacialis 1.10 Guinardia striata 0.06
Chaetoceros curvisetus 1.07 Gyrodinium spp. 0.05
Pseudo-nitzschia seriata gr. 15.8 Pseudo-nitzschia seriata gr. 0.62
Protoperidinium spp. 5.80 Protoperidinium spp. 0.29
Asterionellopsis glacialis 4.03 Thalassiosira spp. 0.25
Nitzschia longissima 3.72 Guinardia striata 0.13
Coccolith. non ident. 4.43 Nitzschia spp. 0.14
Guinardia striata 2.50 Guinardia striata 0.12
Oxytoxum spp. 1.78 Dactyliosolen fragilissimus 0.10
Hemiaulas hauckii 1.68 Syracosphaera pulchra 0.09
Dactyliosolen fragilissimus 1.48 Hemiaulas hauckii 0.09
Rhabdosphaera stylifera 1.27 Coccolith. non ident. 0.09
Nitzschia spp. 1.14 Proboscia alata 0.07
Pleurosigma normanii 1.05 Rhabdosphaera stylifera 0.06
XIII
Water 2021, 13, x FOR PEER REVIEW 16 of 26
Lauderia annulata 3.51 Ophiaster hydroideus 0.21
Thalassiosira spp. 2.66 Thalassiosira spp. 0.16
Cerataulina pelagica 2.63 Guinardia striata 0.13
Ophiaster hydroideus 2.14 Thalassionema nitzschioides 0.13
Leptocylindrus mediterraneus 1.98 Leptocylindrus mediterraneus 0.10
Thalassionema nitzschioides 1.69 Diploneis crabro 0.09
Guinardia striata 1.21 Calciosolenia murrayi 0.09
Calciosolenia murrayi 1.21 Cerataulina pelagica 0.07
Chaetoceros spp. 2.53 Proboscia alata 0.45
Proboscia alata 1.78 Chaetoceros spp. 0.38
Coccolith. non ident. 4.43 Nitzschia spp. 0.14
Guinardia striata 2.50 Guinardia striata 0.12
Oxytoxum spp. 1.78 Dactyliosolen fragilissimus 0.10
Hemiaulas hauckii 1.68 Syracosphaera pulchra 0.09
Dactyliosolen fragilissimus 1.48 Hemiaulas hauckii 0.09
Rhabdosphaera stylifera 1.27 Coccolith. non ident. 0.09
Nitzschia spp. 1.14 Proboscia alata 0.07
Pleurosigma normanii 1.05 Rhabdosphaera stylifera 0.06
Dinobryon spp. 39.1 Dinobryon spp. 0.84
Leptocylindrus danicus 19.9 Leptocylindrus danicus 0.46
Prorocentrum compressum 5.51 Prorocentrum compressum 0.34
Chaetoceros curvisetus 2.45 Scrippsiella trochoidea 0.28
Prorocentrum micans 2.40 Prorocentrum micans 0.21
Scrippsiella trochoidea 2.35 Nitzschia longissima 0.18
Nitzschia longissima 1.35 Prasinophyceae 0.18
Emiliania huxleyi 17.2 Emiliania huxleyi 0.41
Dinobryon spp. 16.2 Meringosphaera mediterranea 0.30
Prorocentrum gracile 6.36 Prorocentrum gracile 0.21
Meringosphaera mediterranea 5.11 Diploneis crabro 0.17
Diploneis crabro 4.91 Dinobryon spp. 0.12
Protoperidinium spp. 2.33 Protoperidinium spp. 0.12
Chaetoceros simplex 1.16 Chaetoceros simplex 0.07
Calciosolenia murrayi 1.01 Calciosolenia murrayi 0.05
Cerataulina pelagica 20.7 Cerataulina pelagica 0.65
Leptocylindrus danicus 7.56 Guinardia flaccida 0.26
Guinardia flaccida 3.12 Tripos furca 0.19
Cylindrotheca closterium 30.7 Cylindrotheca closterium 0.53
Calciosolenia murrayi 11.9 Calciosolenia murrayi 0.26
Pleurosigma normanii 9.42 Pleurosigma normanii 0.26
Nitzschia longissima 6.55 Nitzschia longissima 0.18
Thalassionema nitzschioides 5.11 Ophiaster hydroideus 0.14
Ophiaster hydroideus 3.36 Thalassionema nitzschioides 0.14
Calciosolenia brasiliensis 2.62 Tripos fusus 0.09
Diploneis crabro 2.56 Calciosolenia brasiliensis 0.09
Tripos fusus 1.80 Diploneis crabro 0.09
Diatoms non ident. 1.55 Syracosphaera pulchra 0.08
Guinardia flaccida 1.41 Nitzschia spp. 0.08
Syracosphaera pulchra 1.20 Meringosphaera mediterranea 0.07
Nitzschia spp. 1.17 Diatoms non ident. 0.06
Asterionellopsis glacialis 1.10 Guinardia striata 0.06
Chaetoceros curvisetus 1.07 Gyrodinium spp. 0.05
Pseudo-nitzschia seriata gr. 15.8 Pseudo-nitzschia seriata gr. 0.62
Protoperidinium spp. 5.80 Protoperidinium spp. 0.29
Asterionellopsis glacialis 4.03 Thalassiosira spp. 0.25
Nitzschia longissima 3.72 Guinardia striata 0.13
Dinobryon spp. 39.1 Dinobryon spp. 0.84
Leptocylindrus danicus 19.9 Leptocylindrus danicus 0.46
Prorocentrum compressum 5.51 Prorocentrum compressum 0.34
Chaetoceros curvisetus 2.45 Scrippsiella trochoidea 0.28
Prorocentrum micans 2.40 Prorocentrum micans 0.21
Scrippsiella trochoidea 2.35 Nitzschia longissima 0.18
Nitzschia longissima 1.35 Prasinophyceae 0.18
XIV
Water 2021, 13, x FOR PEER REVIEW 16 of 26
Lauderia annulata 3.51 Ophiaster hydroideus 0.21
Thalassiosira spp. 2.66 Thalassiosira spp. 0.16
Cerataulina pelagica 2.63 Guinardia striata 0.13
Ophiaster hydroideus 2.14 Thalassionema nitzschioides 0.13
Leptocylindrus mediterraneus 1.98 Leptocylindrus mediterraneus 0.10
Thalassionema nitzschioides 1.69 Diploneis crabro 0.09
Guinardia striata 1.21 Calciosolenia murrayi 0.09
Calciosolenia murrayi 1.21 Cerataulina pelagica 0.07
Chaetoceros spp. 2.53 Proboscia alata 0.45
Proboscia alata 1.78 Chaetoceros spp. 0.38
Coccolith. non ident. 4.43 Nitzschia spp. 0.14
Guinardia striata 2.50 Guinardia striata 0.12
Oxytoxum spp. 1.78 Dactyliosolen fragilissimus 0.10
Hemiaulas hauckii 1.68 Syracosphaera pulchra 0.09
Dactyliosolen fragilissimus 1.48 Hemiaulas hauckii 0.09
Rhabdosphaera stylifera 1.27 Coccolith. non ident. 0.09
Nitzschia spp. 1.14 Proboscia alata 0.07
Pleurosigma normanii 1.05 Rhabdosphaera stylifera 0.06
Dinobryon spp. 39.1 Dinobryon spp. 0.84
Leptocylindrus danicus 19.9 Leptocylindrus danicus 0.46
Prorocentrum compressum 5.51 Prorocentrum compressum 0.34
Chaetoceros curvisetus 2.45 Scrippsiella trochoidea 0.28
Prorocentrum micans 2.40 Prorocentrum micans 0.21
Scrippsiella trochoidea 2.35 Nitzschia longissima 0.18
Nitzschia longissima 1.35 Prasinophyceae 0.18
Emiliania huxleyi 17.2 Emiliania huxleyi 0.41
Dinobryon spp. 16.2 Meringosphaera mediterranea 0.30
Prorocentrum gracile 6.36 Prorocentrum gracile 0.21
Meringosphaera mediterranea 5.11 Diploneis crabro 0.17
Diploneis crabro 4.91 Dinobryon spp. 0.12
Protoperidinium spp. 2.33 Protoperidinium spp. 0.12
Chaetoceros simplex 1.16 Chaetoceros simplex 0.07
Calciosolenia murrayi 1.01 Calciosolenia murrayi 0.05
Cerataulina pelagica 20.7 Cerataulina pelagica 0.65
Leptocylindrus danicus 7.56 Guinardia flaccida 0.26
Guinardia flaccida 3.12 Tripos furca 0.19
Cylindrotheca closterium 30.7 Cylindrotheca closterium 0.53
Calciosolenia murrayi 11.9 Calciosolenia murrayi 0.26
Pleurosigma normanii 9.42 Pleurosigma normanii 0.26
Nitzschia longissima 6.55 Nitzschia longissima 0.18
Thalassionema nitzschioides 5.11 Ophiaster hydroideus 0.14
Ophiaster hydroideus 3.36 Thalassionema nitzschioides 0.14
Calciosolenia brasiliensis 2.62 Tripos fusus 0.09
Diploneis crabro 2.56 Calciosolenia brasiliensis 0.09
Tripos fusus 1.80 Diploneis crabro 0.09
Diatoms non ident. 1.55 Syracosphaera pulchra 0.08
Guinardia flaccida 1.41 Nitzschia spp. 0.08
Syracosphaera pulchra 1.20 Meringosphaera mediterranea 0.07
Nitzschia spp. 1.17 Diatoms non ident. 0.06
Asterionellopsis glacialis 1.10 Guinardia striata 0.06
Chaetoceros curvisetus 1.07 Gyrodinium spp. 0.05
Pseudo-nitzschia seriata gr. 15.8 Pseudo-nitzschia seriata gr. 0.62
Protoperidinium spp. 5.80 Protoperidinium spp. 0.29
Asterionellopsis glacialis 4.03 Thalassiosira spp. 0.25
Nitzschia longissima 3.72 Guinardia striata 0.13
Emiliania huxleyi 17.2 Emiliania huxleyi 0.41
Dinobryon spp. 16.2 Meringosphaera mediterranea 0.30
Prorocentrum gracile 6.36 Prorocentrum gracile 0.21
Meringosphaera mediterranea 5.11 Diploneis crabro 0.17
Diploneis crabro 4.91 Dinobryon spp. 0.12
Protoperidinium spp. 2.33 Protoperidinium spp. 0.12
Chaetoceros simplex 1.16 Chaetoceros simplex 0.07
Calciosolenia murrayi 1.01 Calciosolenia murrayi 0.05
XV
Water 2021, 13, x FOR PEER REVIEW 16 of 26
Lauderia annulata 3.51 Ophiaster hydroideus 0.21
Thalassiosira spp. 2.66 Thalassiosira spp. 0.16
Cerataulina pelagica 2.63 Guinardia striata 0.13
Ophiaster hydroideus 2.14 Thalassionema nitzschioides 0.13
Leptocylindrus mediterraneus 1.98 Leptocylindrus mediterraneus 0.10
Thalassionema nitzschioides 1.69 Diploneis crabro 0.09
Guinardia striata 1.21 Calciosolenia murrayi 0.09
Calciosolenia murrayi 1.21 Cerataulina pelagica 0.07
Chaetoceros spp. 2.53 Proboscia alata 0.45
Proboscia alata 1.78 Chaetoceros spp. 0.38
Coccolith. non ident. 4.43 Nitzschia spp. 0.14
Guinardia striata 2.50 Guinardia striata 0.12
Oxytoxum spp. 1.78 Dactyliosolen fragilissimus 0.10
Hemiaulas hauckii 1.68 Syracosphaera pulchra 0.09
Dactyliosolen fragilissimus 1.48 Hemiaulas hauckii 0.09
Rhabdosphaera stylifera 1.27 Coccolith. non ident. 0.09
Nitzschia spp. 1.14 Proboscia alata 0.07
Pleurosigma normanii 1.05 Rhabdosphaera stylifera 0.06
Dinobryon spp. 39.1 Dinobryon spp. 0.84
Leptocylindrus danicus 19.9 Leptocylindrus danicus 0.46
Prorocentrum compressum 5.51 Prorocentrum compressum 0.34
Chaetoceros curvisetus 2.45 Scrippsiella trochoidea 0.28
Prorocentrum micans 2.40 Prorocentrum micans 0.21
Scrippsiella trochoidea 2.35 Nitzschia longissima 0.18
Nitzschia longissima 1.35 Prasinophyceae 0.18
Emiliania huxleyi 17.2 Emiliania huxleyi 0.41
Dinobryon spp. 16.2 Meringosphaera mediterranea 0.30
Prorocentrum gracile 6.36 Prorocentrum gracile 0.21
Meringosphaera mediterranea 5.11 Diploneis crabro 0.17
Diploneis crabro 4.91 Dinobryon spp. 0.12
Protoperidinium spp. 2.33 Protoperidinium spp. 0.12
Chaetoceros simplex 1.16 Chaetoceros simplex 0.07
Calciosolenia murrayi 1.01 Calciosolenia murrayi 0.05
Cerataulina pelagica 20.7 Cerataulina pelagica 0.65
Leptocylindrus danicus 7.56 Guinardia flaccida 0.26
Guinardia flaccida 3.12 Tripos furca 0.19
Cylindrotheca closterium 30.7 Cylindrotheca closterium 0.53
Calciosolenia murrayi 11.9 Calciosolenia murrayi 0.26
Pleurosigma normanii 9.42 Pleurosigma normanii 0.26
Nitzschia longissima 6.55 Nitzschia longissima 0.18
Thalassionema nitzschioides 5.11 Ophiaster hydroideus 0.14
Ophiaster hydroideus 3.36 Thalassionema nitzschioides 0.14
Calciosolenia brasiliensis 2.62 Tripos fusus 0.09
Diploneis crabro 2.56 Calciosolenia brasiliensis 0.09
Tripos fusus 1.80 Diploneis crabro 0.09
Diatoms non ident. 1.55 Syracosphaera pulchra 0.08
Guinardia flaccida 1.41 Nitzschia spp. 0.08
Syracosphaera pulchra 1.20 Meringosphaera mediterranea 0.07
Nitzschia spp. 1.17 Diatoms non ident. 0.06
Asterionellopsis glacialis 1.10 Guinardia striata 0.06
Chaetoceros curvisetus 1.07 Gyrodinium spp. 0.05
Pseudo-nitzschia seriata gr. 15.8 Pseudo-nitzschia seriata gr. 0.62
Protoperidinium spp. 5.80 Protoperidinium spp. 0.29
Asterionellopsis glacialis 4.03 Thalassiosira spp. 0.25
Nitzschia longissima 3.72 Guinardia striata 0.13
Cerataulina pelagica 20.7 Cerataulina pelagica 0.65
Leptocylindrus danicus 7.56 Guinardia flaccida 0.26
Guinardia flaccida 3.12 Tripos furca 0.19

Water 2021,13, 2045 16 of 26
Table 4. Cont.
Cluster Taxon (Ranked by Xproj) Xproj Taxon (Ranked by IndVal) IndVal
XVI
Water 2021, 13, x FOR PEER REVIEW 16 of 26
Lauderia annulata 3.51 Ophiaster hydroideus 0.21
Thalassiosira spp. 2.66 Thalassiosira spp. 0.16
Cerataulina pelagica 2.63 Guinardia striata 0.13
Ophiaster hydroideus 2.14 Thalassionema nitzschioides 0.13
Leptocylindrus mediterraneus 1.98 Leptocylindrus mediterraneus 0.10
Thalassionema nitzschioides 1.69 Diploneis crabro 0.09
Guinardia striata 1.21 Calciosolenia murrayi 0.09
Calciosolenia murrayi 1.21 Cerataulina pelagica 0.07
Chaetoceros spp. 2.53 Proboscia alata 0.45
Proboscia alata 1.78 Chaetoceros spp. 0.38
Coccolith. non ident. 4.43 Nitzschia spp. 0.14
Guinardia striata 2.50 Guinardia striata 0.12
Oxytoxum spp. 1.78 Dactyliosolen fragilissimus 0.10
Hemiaulas hauckii 1.68 Syracosphaera pulchra 0.09
Dactyliosolen fragilissimus 1.48 Hemiaulas hauckii 0.09
Rhabdosphaera stylifera 1.27 Coccolith. non ident. 0.09
Nitzschia spp. 1.14 Proboscia alata 0.07
Pleurosigma normanii 1.05 Rhabdosphaera stylifera 0.06
Dinobryon spp. 39.1 Dinobryon spp. 0.84
Leptocylindrus danicus 19.9 Leptocylindrus danicus 0.46
Prorocentrum compressum 5.51 Prorocentrum compressum 0.34
Chaetoceros curvisetus 2.45 Scrippsiella trochoidea 0.28
Prorocentrum micans 2.40 Prorocentrum micans 0.21
Scrippsiella trochoidea 2.35 Nitzschia longissima 0.18
Nitzschia longissima 1.35 Prasinophyceae 0.18
Emiliania huxleyi 17.2 Emiliania huxleyi 0.41
Dinobryon spp. 16.2 Meringosphaera mediterranea 0.30
Prorocentrum gracile 6.36 Prorocentrum gracile 0.21
Meringosphaera mediterranea 5.11 Diploneis crabro 0.17
Diploneis crabro 4.91 Dinobryon spp. 0.12
Protoperidinium spp. 2.33 Protoperidinium spp. 0.12
Chaetoceros simplex 1.16 Chaetoceros simplex 0.07
Calciosolenia murrayi 1.01 Calciosolenia murrayi 0.05
Cerataulina pelagica 20.7 Cerataulina pelagica 0.65
Leptocylindrus danicus 7.56 Guinardia flaccida 0.26
Guinardia flaccida 3.12 Tripos furca 0.19
Cylindrotheca closterium 30.7 Cylindrotheca closterium 0.53
Calciosolenia murrayi 11.9 Calciosolenia murrayi 0.26
Pleurosigma normanii 9.42 Pleurosigma normanii 0.26
Nitzschia longissima 6.55 Nitzschia longissima 0.18
Thalassionema nitzschioides 5.11 Ophiaster hydroideus 0.14
Ophiaster hydroideus 3.36 Thalassionema nitzschioides 0.14
Calciosolenia brasiliensis 2.62 Tripos fusus 0.09
Diploneis crabro 2.56 Calciosolenia brasiliensis 0.09
Tripos fusus 1.80 Diploneis crabro 0.09
Diatoms non ident. 1.55 Syracosphaera pulchra 0.08
Guinardia flaccida 1.41 Nitzschia spp. 0.08
Syracosphaera pulchra 1.20 Meringosphaera mediterranea 0.07
Nitzschia spp. 1.17 Diatoms non ident. 0.06
Asterionellopsis glacialis 1.10 Guinardia striata 0.06
Chaetoceros curvisetus 1.07 Gyrodinium spp. 0.05
Pseudo-nitzschia seriata gr. 15.8 Pseudo-nitzschia seriata gr. 0.62
Protoperidinium spp. 5.80 Protoperidinium spp. 0.29
Asterionellopsis glacialis 4.03 Thalassiosira spp. 0.25
Nitzschia longissima 3.72 Guinardia striata 0.13
Cylindrotheca closterium 30.7 Cylindrotheca closterium 0.53
Calciosolenia murrayi 11.9 Calciosolenia murrayi 0.26
Pleurosigma normanii 9.42 Pleurosigma normanii 0.26
Nitzschia longissima 6.55 Nitzschia longissima 0.18
Thalassionema nitzschioides 5.11 Ophiaster hydroideus 0.14
Ophiaster hydroideus 3.36 Thalassionema nitzschioides 0.14
Calciosolenia brasiliensis 2.62 Tripos fusus 0.09
Diploneis crabro 2.56 Calciosolenia brasiliensis 0.09
Tripos fusus 1.80 Diploneis crabro 0.09
Diatoms non ident. 1.55 Syracosphaera pulchra 0.08
Guinardia flaccida 1.41 Nitzschia spp. 0.08
Syracosphaera pulchra 1.20 Meringosphaera mediterranea 0.07
Nitzschia spp. 1.17 Diatoms non ident. 0.06
Asterionellopsis glacialis 1.10 Guinardia striata 0.06
Chaetoceros curvisetus 1.07 Gyrodinium spp. 0.05
XVII
Water 2021, 13, x FOR PEER REVIEW 16 of 26
Lauderia annulata 3.51 Ophiaster hydroideus 0.21
Thalassiosira spp. 2.66 Thalassiosira spp. 0.16
Cerataulina pelagica 2.63 Guinardia striata 0.13
Ophiaster hydroideus 2.14 Thalassionema nitzschioides 0.13
Leptocylindrus mediterraneus 1.98 Leptocylindrus mediterraneus 0.10
Thalassionema nitzschioides 1.69 Diploneis crabro 0.09
Guinardia striata 1.21 Calciosolenia murrayi 0.09
Calciosolenia murrayi 1.21 Cerataulina pelagica 0.07
Chaetoceros spp. 2.53 Proboscia alata 0.45
Proboscia alata 1.78 Chaetoceros spp. 0.38
Coccolith. non ident. 4.43 Nitzschia spp. 0.14
Guinardia striata 2.50 Guinardia striata 0.12
Oxytoxum spp. 1.78 Dactyliosolen fragilissimus 0.10
Hemiaulas hauckii 1.68 Syracosphaera pulchra 0.09
Dactyliosolen fragilissimus 1.48 Hemiaulas hauckii 0.09
Rhabdosphaera stylifera 1.27 Coccolith. non ident. 0.09
Nitzschia spp. 1.14 Proboscia alata 0.07
Pleurosigma normanii 1.05 Rhabdosphaera stylifera 0.06
Dinobryon spp. 39.1 Dinobryon spp. 0.84
Leptocylindrus danicus 19.9 Leptocylindrus danicus 0.46
Prorocentrum compressum 5.51 Prorocentrum compressum 0.34
Chaetoceros curvisetus 2.45 Scrippsiella trochoidea 0.28
Prorocentrum micans 2.40 Prorocentrum micans 0.21
Scrippsiella trochoidea 2.35 Nitzschia longissima 0.18
Nitzschia longissima 1.35 Prasinophyceae 0.18
Emiliania huxleyi 17.2 Emiliania huxleyi 0.41
Dinobryon spp. 16.2 Meringosphaera mediterranea 0.30
Prorocentrum gracile 6.36 Prorocentrum gracile 0.21
Meringosphaera mediterranea 5.11 Diploneis crabro 0.17
Diploneis crabro 4.91 Dinobryon spp. 0.12
Protoperidinium spp. 2.33 Protoperidinium spp. 0.12
Chaetoceros simplex 1.16 Chaetoceros simplex 0.07
Calciosolenia murrayi 1.01 Calciosolenia murrayi 0.05
Cerataulina pelagica 20.7 Cerataulina pelagica 0.65
Leptocylindrus danicus 7.56 Guinardia flaccida 0.26
Guinardia flaccida 3.12 Tripos furca 0.19
Cylindrotheca closterium 30.7 Cylindrotheca closterium 0.53
Calciosolenia murrayi 11.9 Calciosolenia murrayi 0.26
Pleurosigma normanii 9.42 Pleurosigma normanii 0.26
Nitzschia longissima 6.55 Nitzschia longissima 0.18
Thalassionema nitzschioides 5.11 Ophiaster hydroideus 0.14
Ophiaster hydroideus 3.36 Thalassionema nitzschioides 0.14
Calciosolenia brasiliensis 2.62 Tripos fusus 0.09
Diploneis crabro 2.56 Calciosolenia brasiliensis 0.09
Tripos fusus 1.80 Diploneis crabro 0.09
Diatoms non ident. 1.55 Syracosphaera pulchra 0.08
Guinardia flaccida 1.41 Nitzschia spp. 0.08
Syracosphaera pulchra 1.20 Meringosphaera mediterranea 0.07
Nitzschia spp. 1.17 Diatoms non ident. 0.06
Asterionellopsis glacialis 1.10 Guinardia striata 0.06
Chaetoceros curvisetus 1.07 Gyrodinium spp. 0.05
Pseudo-nitzschia seriata gr. 15.8 Pseudo-nitzschia seriata gr. 0.62
Protoperidinium spp. 5.80 Protoperidinium spp. 0.29
Asterionellopsis glacialis 4.03 Thalassiosira spp. 0.25
Nitzschia longissima 3.72 Guinardia striata 0.13
Pseudo-nitzschia seriata gr. 15.8 Pseudo-nitzschia seriata gr. 0.62
Protoperidinium spp. 5.80 Protoperidinium spp. 0.29
Asterionellopsis glacialis 4.03 Thalassiosira spp. 0.25
Nitzschia longissima 3.72 Guinardia striata 0.13
Pleurosigma normanii 2.98 Syracosphaera pulchra 0.10
Thalassiosira spp. 2.57 Dactyliosolen fragilissimus 0.09
Cylindrotheca closterium 2.10 Nitzschia longissima 0.09
Prorocentrum triestinum 2.00 Pleurosigma normanii 0.09
Calciosolenia murrayi 1.57 Nitzschia spp. 0.09
Diploneis crabro 1.35 Calciosolenia murrayi 0.08
Guinardia striata 1.27 Chaetoceros spp. 0.08
Oxytoxum spp. 1.11 Asterionellopsis glacialis 0.07
XVIII
Water 2021, 13, x FOR PEER REVIEW 17 of 26
Pleurosigma normanii 2.98 Syracosphaera pulchra 0.10
Thalassiosira spp. 2.57 Dactyliosolen fragilissimus 0.09
Cylindrotheca closterium 2.10 Nitzschia longissima 0.09
Prorocentrum triestinum 2.00 Pleurosigma normanii 0.09
Calciosolenia murrayi 1.57 Nitzschia spp. 0.09
Diploneis crabro 1.35 Calciosolenia murrayi 0.08
Guinardia striata 1.27 Chaetoceros spp. 0.08
Oxytoxum spp. 1.11 Asterionellopsis glacialis 0.07
Bacteriastrum delicatulum 21.4 Bacteriastrum delicatulum 0.67
Prorocentrum micans 4.60 Euglenophyceae 0.26
Prorocentrum compressum 2.85 Heterocapsa gr. 0.24
Heterocapsa gr. 2.52 Prorocentrum balticum 0.18
Euglenophyceae 2.30 Prorocentrum micans 0.17
Prorocentrum balticum 2.21 Prorocentrum triestinum 0.13
Prorocentrum gracile 1.90 Prorocentrum compressum 0.12
Prorocentrum triestinum 1.74 Scrippsiella trochoidea 0.11
Oxytoxum spp. 1.28 Cyclotella spp. 0.10
Scrippsiella trochoidea 1.04 Chlorophyceae 0.10
4. Discussion
4.1. Review of the Critical Steps of Analysis
4.1.1. Taxa Selection, Ordination and Clustering
Plankton communities have complex and dynamic structure [40]. To unveil the tem-
poral patterns of these communities it is often necessary to reduce the number of variates
(i.e., taxa); however, preserving a representative amount of information is key when
choosing different methods of taxa selection. Our results show that Total Inertia (TI),
which was used as an indicator of the total amount of information present in the dataset
[36] was higher when FREVE was used compared to the selection based on frequency of
appearance. In other words, FREVE was more effective in preserving information (Figure
4). In the “real life” scenario of the GoT coastal ecosystem, the four distribution types we
postulate can be interpreted as follows: Type 1 taxa can be considered occasional, e.g.,
only occasionally driven by currents or otherwise introduced; Type 2 taxa are also infre-
quent but may reproduce and bloom under favourable conditions; Type 3 taxa are com-
mon and bloom regularly; and Type 4 taxa are commonly present but always with low
abundances. For the interpretation of phenology of the phytoplankton typical of the basin,
the taxa of types 2 and 3 seemed to be more interesting because they contribute to the
seasonal dynamics more than taxa of types 1 and 4. However, because taxa of type 4 are
common, helping to define the baseline of the community and tending to structure the
abundance matrix, the objective was then to eliminate only taxa of type 1 from the dataset.
Taxa of types 1 and 4 share a common feature: in the samples in which they were present,
they exhibited constant or quasi-constant abundance and are therefore found near or on
the log distribution (Figure 3A). Taxa of types 2 and 3 also share a common feature: they
exhibited fluctuating abundance in the samples in which they were present, so that the
majority of abundance is concentrated in only a fraction of the samples. Considering again
the FREVE index, instead of evenness we could have chosen dominance indices (like
Simpson), which are invariant across the number of classes [41] but we were more inter-
ested in evaluating the space occupied by the classes (samples) over the disposable space
(the whole time series). Moreover, Pielou’s characteristic of variation between 0 and 1
made it ideal for coupling with frequency compared to other entropy indices.
The differences between temporal maps obtained by PCA (Figure 6) and CA (Figure
7) cannot be associated only with the selected number of clusters in which to divide the
matrix (6 clusters vs. 4 clusters or 18 clusters) or the different taxa selection methods used
in the two protocols, but they mostly express a qualitative difference between the two
types of analysis. Legendre and Legendre [36] argue that Euclidean distance on which
Bacteriastrum delicatulum 21.4 Bacteriastrum delicatulum 0.67
Prorocentrum micans 4.60 Euglenophyceae 0.26
Prorocentrum compressum 2.85 Heterocapsa gr. 0.24
Heterocapsa gr. 2.52 Prorocentrum balticum 0.18
Euglenophyceae 2.30 Prorocentrum micans 0.17
Prorocentrum balticum 2.21 Prorocentrum triestinum 0.13
Prorocentrum gracile 1.90 Prorocentrum compressum 0.12
Prorocentrum triestinum 1.74 Scrippsiella trochoidea 0.11
Oxytoxum spp. 1.28 Cyclotella spp. 0.10
Scrippsiella trochoidea 1.04 Chlorophyceae 0.10
During winter, the most important clusters were Cluster VII, strongly associated with
the coccolithophore Emiliania huxleyi, and Cluster IX associated with the chrysophycean
species Meringosphaera mediterranea. Cluster IX was also scattered in the other seasons.
In spring, the main clusters were IV and XVIII. From 2005 to 2012, late spring was
characterized by Cluster IV associated with the diatoms Cyclotella spp. and C. simplex and
the dinoflagellates Prorocentrum gracile,Prorocentrum cordatum and the Heterocapsa group.
In 2009 and 2013, another late spring cluster (Cluster XVIII) formed, most strongly associ-
ated with the diatom Bacteriastrum delicatulum, as well as dinoflagellates from the genus
Prorocentrum, the coccolitophore Calyptrosphaera oblonga, and undetermined euglenophytes.
The most important summer cluster, typical mainly of July during 2005–2012, was
Cluster XI, which was also sporadically present in other seasons, especially in late autumn.
This cluster was associated with the diatoms Chaetoceros spp. and Proboscia alata. From
2010, another summer cluster (Cluster II) appeared, characterized by a diverse community
of diatoms (e.g., Guinardia flaccida,Dactyliosolen fragilissimus and undetermined species),
dinoflagellates (e.g., Prorocentrum triestinum, P. gracile) and chlorophytes.
Autumn months were the most varied and richest for the occurrence of various
clusters. Besides scattered occurrences of Cluster IX and Cluster XI, this season was charac-

Water 2021,13, 2045 17 of 26
terized by three clusters: Cluster XII, Cluster III and Cluster XVII. Cluster XII was most
typical in October and November and was associated with undetermined coccolithophores
and various diatoms such as Guinardia striata,Hemiaulus hauckii and D. fragilissimus. From
2010, the Cluster III appeared, associated with coccolithophores Calciosolenia murrayi and
Syracosphaera pulchra, diatoms from the Pseudo-nitzschia delicatissima group and some di-
noflagellates. Finally, Cluster XVII was associated with diatoms such as species from
Pseudo-nitzschia seriata group, Asterionellopsis glacialis,Nitzschia longissima and Pleurosigma
normanii.
A correlation of 0.87 with p-value = 0.001 was obtained between the two ordination
indices (Xproj and IndVal).
4. Discussion
4.1. Review of the Critical Steps of Analysis
4.1.1. Taxa Selection, Ordination and Clustering
Plankton communities have complex and dynamic structure [
40
]. To unveil the
temporal patterns of these communities it is often necessary to reduce the number of
variates (i.e., taxa); however, preserving a representative amount of information is key when
choosing different methods of taxa selection. Our results show that Total Inertia (TI), which
was used as an indicator of the total amount of information present in the dataset [
36
] was
higher when FREVE was used compared to the selection based on frequency of appearance.
In other words, FREVE was more effective in preserving information
(Figure 4)
. In the “real
life” scenario of the GoT coastal ecosystem, the four distribution types we postulate can be
interpreted as follows: Type 1 taxa can be considered occasional, e.g., only occasionally
driven by currents or otherwise introduced; Type 2 taxa are also infrequent but may
reproduce and bloom under favourable conditions; Type 3 taxa are common and bloom
regularly; and Type 4 taxa are commonly present but always with low abundances. For
the interpretation of phenology of the phytoplankton typical of the basin, the taxa of
types 2 and 3 seemed to be more interesting because they contribute to the seasonal
dynamics more than taxa of types 1 and 4. However, because taxa of type 4 are common,
helping to define the baseline of the community and tending to structure the abundance
matrix, the objective was then to eliminate only taxa of type 1 from the dataset. Taxa of
types 1 and 4 share a common feature: in the samples in which they were present, they
exhibited constant or quasi-constant abundance and are therefore found near or on the log
distribution (Figure 3A). Taxa of types 2 and 3 also share a common feature: they exhibited
fluctuating abundance in the samples in which they were present, so that the majority of
abundance is concentrated in only a fraction of the samples. Considering again the FREVE
index, instead of evenness we could have chosen dominance indices (like Simpson), which
are invariant across the number of classes [
41
] but we were more interested in evaluating
the space occupied by the classes (samples) over the disposable space (the whole time
series). Moreover, Pielou’s characteristic of variation between 0 and 1 made it ideal for
coupling with frequency compared to other entropy indices.
The differences between temporal maps obtained by PCA (Figure 6) and CA
(Figure 7)
cannot be associated only with the selected number of clusters in which to divide the
matrix (6 clusters vs. 4 clusters or 18 clusters) or the different taxa selection methods used
in the two protocols, but they mostly express a qualitative difference between the two
types of analysis. Legendre and Legendre [
36
] argue that Euclidean distance on which PCA
relies is not a good distance method for studying species frequency tables because of the
double-zero problem. We detected an increase in diversity over the years using Pielou’s
evenness index (data not shown). This increase could be due to a real increase in diversity
or to better taxonomical skills of the expert who analysed the samples. Regardless of the
reason, we observed a concentration of presences inside fewer taxa in the first years of the
data series, which also means a high number of zero or near zero abundance values for
other taxa. The large cluster covering the first years (2005–2006) of the series (see Cluster

Water 2021,13, 2045 18 of 26
I in Figure 6) with no seasonal structure and no indicative taxa was indeed the dataset
region that contained most of the zeros.
With the use of a more adequate ordination method, i.e., CA that resulted in tem-
poral maps in which diverse seasonal clusters were observed throughout the data series
(Figure 7)
, we also confirmed the existence of seasonal patterns of phytoplankton commu-
nity during the first years. The seasonality of the clusters was found in both partition levels
(4 and 18) for this first part of the temporal series. The main difference between the two
was that the 4-clustered and 18-clustered partitioning levels allowed us to observe seasonal
patterns at two different degrees of complexity: a baseline structure of the phytoplankton
community in the Gulf of Trieste (Figure 7, left) and a more detailed one (Figure 7, right),
which will be further discussed.
As regards the clustering algorithm, we consider the method based on k-means clus-
tering better than the method based on Bayes’ rule proposed in the original protocol. Bayes’
rule forces the clusters to achieve an equilibrium between dimension and homogeneity,
but this would come at the cost of cutting off part of the complexity of the matrix. With the
k-means method, the clusters are not weighted according to their relative dimension but
only based on the fraction of variance that they explain. Thus, when using the k-means
method, blooming events can form small outlying clusters easier than using Bayes’ rule.
Moreover, the k-means algorithm dictates the use of multiple random starts to avoid local
maxima in the objective function, a problem that is not tackled in the original protocol.
Finally, the use of the Calinski pseudo-F and Ratkowsky indices, which are both based on
the analysis of variance, is preferable to the use of the probability vector
Pmax(k)
proposed
in the original protocol. This is for the same reason that the k-means method is preferable
to Bayes’ rule.
4.1.2. Indicative Taxa
Characterization of the indicative species of clusters using the IndVal index resulted
in some taxa that were indicative of more than one cluster. Examples include Cyclotella sp.
in Clusters IV and V of the original protocol (IndVal 0.29 and 0.46, Table 2) and Emiliania
huxleyi in Clusters I, II and IV of the modified protocol (IndVal 0.26, 0.29 and 0.25, Table 3).
The fact that “IndVal removes any effect of the number of the sites in the various clusters
and also differences in abundance among sites belonging to the cluster” [
34
] was in some
cases a serious flaw of the method. Both the taxa in the example (Cyclotella sp. and Emiliania
huxleyi) are very common in the phytoplankton community and consequently the fidelity
term (Equation (1)) of the IndVal formula (Equation (3)) is, for many of the possible cluster,
close to 1. The other term (Equation (2)), known as specificity, should counterbalance this
by giving more weight to clusters that contain most of the average taxon’s abundance.
However, it is possible that the average abundance is quasi-constant between clusters, in
particular when the clusters describe taxa distribution sub-optimally, which is often the
case when the number of taxa is high while the number of clusters is low [36].
Another example of an IndVal computational flaw is given by the single-sample
Cluster III (Table 3) and the corresponding Cluster V (Table 4). Those two clusters belong
to two different partitioning levels of the same taxa selection (modified protocol) and were
both indicative only for February 2017. However, they exhibit different species selection
with high IndVal values. While more than 20 taxa were indicative of Cluster III of the
4-clustered partition, only two were indicative of Cluster V of the 18-clustered partition.
This is explained by the way in which the IndVal index is calculated in both cases. The
fidelity term for the taxa present in February 2017 is, in both cases, equal to 1 (Equation (1)).
Instead, the denominator
NI+j
(Equation (2)) of the specificity term varies since it is the
sum of the mean abundances of all clusters [
34
]. With further refining in the 18-clustered
partition and the division of big clusters from the 4-clustered partition into smaller clusters,
the abundance peaks of more taxa were better defined by some of these smaller clusters.
Consequently, the denominator
NI+j
(Equation (2)) increased, the specificity term dropped,

Water 2021,13, 2045 19 of 26
and many taxa were no longer defined as indicative for February 2017, despite the fact that
the cluster remained unchanged.
The likelihood ratio method (Xproj—Appendix B) served as a supplementary estima-
tor of indicative taxa for a cluster because the arbitrary threshold (0.25) of the IndVal index
was ambiguous. K-means clustering minimizes the within-cluster variance [
36
] which
being expressed in
χ2
metric tends to separate similarly deviant samples from average
profile samples [
36
]. In this way, the taxa with higher likelihood ratios are those most
responsible for the formation of deviant clusters, while taxa with likelihood values close to
zero are responsible for large average clusters. The likelihood ratios
(Observed/Expected)
present an advantage because they are not constrained between 0 and 1, which allows
more precise identification of the taxa that are most important for the definition of a cluster.
Below zero Xproj values indicate taxa that were present less than expected in a cluster and
they also play a role in the definition of clusters. The single-sampled cluster of February
2017 can again serve as a good example. Two diatom species, Chaetoceros curivesetus and
Leptocylindrus danicus, had similarly high IndVal values for Cluster III in 4-clustered par-
tition (1 and 0.87, respectively, Table 3), which reveals that both are strongly indicative
of this cluster. On the other hand, Xproj results show a substantial difference between
the importances of the two species: in February 2017, the abundance of C. curivesetus was
more than 70 times higher than expected (Xproj 76.0), while it was just ten times higher
than expected for L. danicus (Xproj 9.61). Another important advantage of (Xproj) values is
that the value for a taxon does not change between the different n-clustered partitions as
long the centroid of the cluster remains the same (e.g., Cluster III in Table 3and Cluster
V in Table 4). As stated in the definition of methods, the deviation values (Xproj) are
dependent only on the centroid of a cluster, which in this case is the same for both clusters
(Appendix B).
Although the two indexing systems (IndVal and Xproj) have organized the indicative
taxa in a similar way (as shown by highly significant correlation), substantial advantages
have emerged for Xproj suggesting it as a preferred option in defining indicative taxa.
In summary, from an analytical point of view, for a correct representation of the
assemblages we recommend to use (i) non log-transformed data, (ii) a selection method that
preserves the information (like FREVE), (iii) distances that are not sensitive to the double-
zero problem (such as the chi-square distance), (iv) a clustering method that minimizes the
variance within clusters (such as k-means), and (v) more indices to define indicative taxa
(the pair IndVal and Xproj seems effective).
4.2. Phytoplankton Phenology in the Period 2005–2017
In the 4-clustered partition (Figure 7, left), the main distinction that can be made be-
tween clusters represent two phytoplankton communities which are known to occur under
different environmental conditions in the Gulf of Trieste [
40
]. In fact, the largest Cluster I is
indicative of a mixed community composed mainly of nanoflagellates, diatoms and coc-
colithophores, while the remaining clusters (II, III and IV) represent the period of the year
when diatom abundances increase and dominate the community. This alternation between
the dominance of different phytoplankton groups is typical of the Gulf of Trieste [
5
,
20
]
and the wider basin of the northern Adriatic [
21
,
22
,
42
], where nonetheless nanoflagellates
contribute up to three thirds of total phytoplankton abundance [
40
]. The fact that in Cluster
I none of the taxa exceeded the threshold pre-set for Xproj is not surprising, since in this
large cluster the taxa are close to their expected (average) profile. In this sense Cluster I
represents the “baseline” of the phytoplankton community in the GoT.
Cluster II and Cluster III both describe winter diatoms outbursts. For Cluster II, the
highest Xproj was calculated for Skeletonema costatum s.l., which was identified as one
of the characteristic taxa of the winter period in the northern Adriatic [
5
,
20
,
21
,
43
]. Since
the taxonomy of the genus Skeletonema has not been resolved yet for our samples, all
individuals were treated as S. costatum s.l. Possibly, most of the individuals belonged to
Skeletonema marinoi, which was identified in the northern Adriatic [
44
], but more cryptic

Water 2021,13, 2045 20 of 26
species could be present [
43
]. Cluster II appeared only in three years, suggesting a very
scattered appearance of Skeletonema species. This is in line with the observations of Cerino
et al., in 2019 [
5
] who signalled a decrease in abundance of Skeletonema in the GoT after
2013. Some species from the genus Chaetoceros are also typical of the winter period in the
area [
20
]. In our analysis, this was the case for C. simplex in Cluster II and C. curvisetus
in the single-sample Cluster III. C. curvisetus was probably pooled with Chaetoceros spp.
when present in low abundances, but during its bloom in February 2017 it was identified
at species level, which then formed a specific cluster.
Cluster IV that was characterised by the highest diatom diversity was roughly divided
into three periods: early spring, summer (mainly July) and autumn. At this level of
partitioning, Cluster IV signals the phenology of diatom blooms, which during the study
period had two conspicuous peaks, in July and in autumn [
17
,
40
]. Interestingly, diatoms
at the LTER station in the Italian part of the GoT showed a different phenology in recent
years, where between the years 2013 and 2017 the late spring peak of diatoms became
the main one during the year, replacing the late winter-early spring bloom of Skeletonema
spp. [
5
]. This type of shift was not observed in our data series, indicating differences in
environmental conditions that determine community structure at the scale of a few km.
The division of the Cluster IV by 18-clustered partition (Figure 7, right) not only into
large clusters, such as clusters III, XI, XII, XVII and XVIII, but also some of the single-sample
clusters, such as VI, X, XIII and XV, helped to detail the phenology of diatoms. Pennate
diatoms of the genus Pseudo-nitzschia, which are often mentioned as community-forming in
the northern Adriatic [
5
,
42
,
43
] were characteristic of two autumn clusters, namely, Cluster
III and Cluster XVII. Although species of this genus can bloom during different times
of the year, they are mainly typical of autumn and winter [
45
,
46
]. Specifically, species
from genus Pseudo-nitzschia, in our study period, lacked the early spring bloom described
in other areas of the Mediterranean Sea [
47
]. The prolonged presence of Cluster III in
Autumn 2010 describes an unusually long and intense bloom of species belonging to
the P. delicatissima group, observed that year throughout the GoT [
5
]. The presence of
P. delicatissima was recorded also in other coastal areas of the Mediterranean and were
associated to higher concentration of silicate and nitrate [
48
]. In our study, Pseudo-nitzschia
species were determined only at the level of two groups, the P. seriata group and the P.
delicatissima group, while a recent study helped to resolve the diversity of Pseudo-nitzschia
species in the GoT through integrative taxonomy [
45
]. The difficulties associated with
the identification of Pseudo-nitzschia species as well as other taxa such as Chaetoceros in
routine monitoring constitute a significant drawback for phenology studies. In fact, the
consequent divergence of results when considering entire complexes or single species
produces uncertainty in the description of taxa niches and community assemblages [
45
].
The last broad autumn cluster in the 18-clustered partition was represented by Cluster XII,
which was characterised by undetermined coccolithophores in addition to large diatoms
such as Guinardia striata and Hemiaulus hauckii. Similar co-dominance of large diatoms and
coccolithophores during autumn has been reported by other studies carried out in the same
area [
20
,
21
]. Finally, two single-sample clusters occurred twice in the month of December;
Cluster X in 2009 with a peak of the diatom Asterionellopsis glacialis, and Cluster VI in 2013
with Lauderia annulata, which otherwise occurs rarely and in low numbers. Both species
are considered as important community components in the western part of the northern
Adriatic in the autumn and winter period [21,22].
Diatom dominance in autumn months was succeeded by winter Cluster VII, which
emerged from Cluster I, i.e., the “baseline” community of the 4-clustered partition. Cluster
VII was associated with the coccolithophores E. huxleyi and Ophiaster hydroideus and the
diatom Diploneis crabro, all species also found elsewhere in the northern Adriatic in the
autumn-winter period [
5
,
21
,
42
]. E. huxleyi, a cosmopolitan species that often forms blooms
in the worlds’ oceans [
49
], was found in most of samples of our time series but was the
most abundant during winter. The only time when this cluster diverged from winter
phenology was in May 2014, when a peculiar increase of E. huxleyi was also reported in

Water 2021,13, 2045 21 of 26
the neighbouring area [
5
]. Occasional blooms outside the winter period were described
also in other areas of the Mediterranean [
47
], while a decrease in the winter blooms of E.
huxleyi after 2002 was observed in the southern part of the northern Adriatic [
22
]. The
“Skeletonema” Cluster I that succeeded Cluster VII during 2005, 2011 and 2012, was the
same in both partitions.
Late winter and early spring were mostly defined by the remnants of the largest
cluster in the 4-clustered partition, namely Cluster IX, which in this case consisted of only
one indicative species (Meringosphaera mediterranea). The taxa with a higher-than-expected
abundance (data not shown in Table 4) were similar to those of Cluster I in the 4-clustered
partition, which means that Cluster IX can be interpreted as a mixed community with
the dominance of nanoflagellates, which have a known spring peak in the GoT [
40
]. The
fact that this cluster was less present in 2011 and 2012 than in other years seems to agree
with the results of Cerino et al., 2019 [
5
], who described a low nanoflagellate density at the
Italian LTER station in those two years.
Spring clusters Cluster IV and XVIII followed Cluster IX until 2013, thus signalling
the late spring peak of dinoflagellates in the GoT [
5
,
40
]. Different species of the genus
Prorocentrum and Heterocapsa group, indicative of these clusters are characteristic spring
species in the northern Adriatic [
21
,
22
,
42
]. Similar to other areas of the Mediterranean [
50
],
the Bacteriastrum genus was also found in association with dinoflagellates (Cluster XVIII).
In mid-summer, Cluster XI emerged, especially in the first part of the time series, domi-
nated by diatoms Chaetoceros spp. and Proboscia alata. This July peak of diatoms became
a recurrent feature after the shift in the plankton community observed in 2002/2003 [
17
].
These summer blooms were tentatively linked to higher precipitations in June and July. A
similar assemblage developed also in colder months when substantial precipitation is more
common. The co-occurrence of P. alata with taxa from the genus Chaetoceros during summer
has also been described in other coastal areas of the Mediterranean [
51
]. Both taxa repre-
senting the Cluster XI have been described to produce resting stages [
52
]. In addition to
the consideration that they might bloom in response to summer precipitation, the recurrent
July occurrence of Cluster XI could also be explained as a diapause phenomenon [
53
], e.g.,
germination of dormant spores after the summer irradiance peak. Another summer cluster
(Cluster II) composed of diatoms, dinoflagellates and chlorophytes emerged alongside
Cluster XI in 2010, eventually replacing the latter from 2013 onwards.
The diatom spring bloom was not constant and was short living, while the autumn
bloom was usually longer and diverse. Dinoflagellates increased typically at the end
of spring and in the summer, usually co-occurring with diatoms. The typical mixed
community of the GoT, composed mainly of nano-sized phytoflagellates, was usually
dominant the first part of the year, while coccolithophores were mostly present during
the second part of the year with the exception of typically winter taxa. Considering the
whole time series, we noticed a change in the middle part of the series (between year 2010
and 2013). In fact, in autumn, the importance of clusters associated with nanoflagellates
decreased, while clusters associated with diatoms increased in number (see Figure 7,
right). Brush et al., (2021) discuss these changes in relation to high inter-annual variability
and alternation of drought periods with periods of higher freshwater discharge, which
is connected with climate change at mesoscale in the northern Adriatic. It is possible
that precipitation variability was also linked to the scattered presence in the summer of
clusters associated with diatoms after 2013. Moreover, the occurrence of small clusters
increased in number towards the end of the series. In fact, four of the eight existing
single-sample clusters were present during the last two years 2016–2017 (Figure 7, right).
This phenomenon could be explained by increased instability in community structure,
possibly related to increased environmental disorder [
22
]. The results seem to be consistent
with the irregularities expected for North Adriatic, which has been described as one of
the less seasonal areas of the Mediterranean and more prone to irregular and interannual
patterns [6].

Water 2021,13, 2045 22 of 26
The presence of resting stages has been described for many diatom species and for
species in many other phytoplankton groups [
52
,
53
]. This reproductive strategy has been
linked to seasonal succession in diatoms and to mechanisms of resilience in phytoplankton
in general [
53
,
54
]. When considering the 4-clustered partition, Margalef’s concept seems to
be suitable to explain the succession of the two main assemblages (Cluster I vs. Clusters II,
III, and IV), with diatoms thriving in nutrient enriched waters [
10
]. However, considering
the detailed structure of the assemblages in the 18-clustered partition, the succession
model based on resting stages may be better suited to reflect the dynamics for some taxa,
for example, the indicative taxa of Cluster XI. This model is considered crucial for the
demography of phytoplankton in confined coastal environments [
53
] and could represent
a piece of the mosaic in explaining Hutchinson’s plankton paradox [1].
As expected for coastal environments, the average lifetime of an assemblage was
short, i.e., 2–4 months [16]; in fact, for the 4-clustered partition a cluster lasted 2.3 months
on average (variance = 3.8 months), while it was shorter for the 18-clustered partition
(
mean = 1.4 months
, variance = 0.68 months). January, March, April, May, August, Septem-
ber, and October were equal in terms of number of typical assemblages, and the most
stable among all, since, during the thirteen years of the series, there were at most four
different clusters for each of them (Figure 7, right). For August, September, and October,
the assemblages sequence appeared to be more cyclical inter-annually, while for the rest
of them it seems that a dominant cluster alternated sporadically with others. In general,
the fact that a cluster appeared several times in the same month or season could signal
recurring environmental conditions and underline the link between phytoplankton phe-
nology and seasonality. This seems to be particularly true for January, March, April, and
May. In these months, our data did not describe major changes possibly indicating stability
either climatically, in connection with drivers such as river discharges, nutrients, etc., or
other factors (physical properties, biotic interactions). In August, September, and October
these conditions possibly alternated from year to year. For late autumn clusters, seasonality
was harder to depict. Nonetheless, high diatom diversity and lack of a repetitive pattern
of clusters can be considered a distinctive mark for this part of the year in the GoT. The
variability of clusters became higher in two directions. On the one hand, the phytoplankton
community became increasingly unstable from the beginning to the end of the time series.
On the other, the second part of the year was more prone to changes in terms of typical
assemblages following either cyclical or complex non-repetitive patterns, while the first
part of the year was generally more stable.
5. Conclusions
The aim of our study was to analyse the phytoplankton community of the Gulf of
Trieste and identify specific patterns of seasonal occurrence, which we assumed to be
variable in time, thus mirroring variable environmental conditions [
40
]. We searched for
characteristic bloom taxa, specific seasonal assemblages, and their variability in time. It
was critical to use appropriate methodology for analysing such a complex dataset so to
avoid losing important information. By modifying the original protocol, we approached a
more realistic seasonal pattern.
The phytoplankton community at the Slovenian LTER station between years 2005
and 2017 showed a complex seasonal pattern. Some taxa maintained their phenology
throughout the whole series and represented a more stable part of the phytoplankton
community, e.g., winter and spring taxa. On the other hand, a high diversity of clusters
and indicative taxa observed in autumn may indicate high variability of environmental
conditions during this part of the year and/or higher interspecies competition for resources.
The occurrence of small clusters towards the end of the series reduced the predictabil-
ity of phytoplankton phenology. A switch from more predictive to more irregular phyto-
plankton community dynamics was observed recently not only in the GoT but also in the
entire northern Adriatic [
22
,
55
], probably triggered by climatic and hydrological drivers at
mesoscale.

Water 2021,13, 2045 23 of 26
Author Contributions:
Conceptualization, I.V. and J.F.; Data curation, I.V. and J.F.; Formal analysis,
I.V.; Methodology, I.V.; Supervision, P.M. and J.F.; Writing—original draft, I.V.; Writing—review
and editing, I.V., P.M. and J.F. All authors have read and agreed to the published version of the
manuscript.
Funding:
This research was funded by Slovenian Research Agency (ARRS), grant number P1-0237
and by the ARRS program for young researcher 51986.
Data Availability Statement:
Phytoplankton data originates from the national monitoring program
financed by the Slovenian Environment Agency of the Ministry of Environment and Spatial Planning.
Acknowledgments: Authors thank Milijan Šiško for microscopic analysis.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. List of R-packages and functions used in the analysis.
Package Functions Goal
vegan [56] diversity Pielou λ
ade4 [57]dagnelie.test Multinormality
dudi.coa CA
cluster [58] agnes Hierarchical classification
Morpho [59] typprobClass Probability from Mahalanobis
cclust [38] clustIndex Calinski & Ratkowsky
labdsv [60] indval IndVal indexes
ape [61] mantel.randtest Mantel test
Appendix B
The following proves that Xprojare the likelihood ratio of the centroids: given that
X is our sample-taxa matrix
r×c
, and Q the derived matrix of chi-square components
χij [36,57] with:
Q=∑χij =∑Observedij −Expectedij
qExpectedij
(A1)
we can apply singular value decomposition on Q:
Q=ˆ
UWU0(A2)
where
ˆ
U
and
U0
are the orthogonal matrices and W is the diagonal matrix of singular
values. The position of the rows r in CA space is equal to the matrix F (Equation (A3)) and
the position of the columns c is equal to matrix V (Equation (A4)) with:
F=D(pi+)−1/2 ˆ
UW (A3)
V=D(p+j)−1/2 U (A4)
where
D(pi+)−1/2
and
D(p+j)−1/2
are diagonal matrices of square roots of row weights and
column weights, respectively. Those weights are the row (i+) and column (+j) components

Water 2021,13, 2045 24 of 26
of the
qExpectedij
term of the Equation (A1). Then the projection (Xproj) of the rows
(samples) in respect to the columns (taxa) is given by the projection of F onto V:
Xproj =D(pi+)−1/2 ˆ
UW (D(p+j)−1/2 U)0
=D(pi+)−1/2 ˆ
UWU0D(p+j)−1/2
=D(pi+)−1/2 QD(p+j)−1/2
=∑1
√Expectedi+×Observedij−Expectedij
√Expectedij ×1
√Expected+j
=Observedij
Expectedij −1
(A5)
The matrix F used here corresponds to the centroid matrix when each sample is a
cluster, so each row of F is a centroid. Instead, when we use the centroids matrix obtained
from k-means clustering on F we obtain the likelihood ratios for the clusters’ centroids.
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