Title: A compositional analysis approach to phytoplankton composition in coastal
Mediterranean wetlands: influence of salinity and nutrient availability
Authors: Rocío López-Flores1, Xavier D. Quintana2, Anna M. Romaní2, Lluís
Bañeras2, Olaya Ruiz-Rueda2, Jordi Compte3, Andy J. Green4 and Juan J. Egozcue5.
1
Área de Ecología. Departamento de Ciencias Agrarias y del Medio Natural. Escuela
Politécnica Superior de Huesca. Instituto de Investigación en Ciencias Ambientales
(IUCA), Universidad de Zaragoza. Carretera de Cuarte s/n. 22071. Huesca. Spain.
2
Institute of Aquatic Ecology and Department of Environmental Sciences. University
of Girona. Facultat de Ciències. Av. Mª Aurèlia Capmany, 69. 17071. Girona. Spain.
3
Dipartimento di Scienze Botaniche, Ecologiche e Geologiche, Università di Sassari,
Via Piandanna, I-07100 Sassari, Italy.
4
Department of Wetland Ecology, Estación Biológica de Doñana, EBD-CSIC, Americo
Vespucio s/n, 41092 Seville, Spain
5
Matemàtica Aplicada III. Edifici C2 (ETSECCPB). Planta 2, Despatx 211b. C/Jordi
Girona, 1-3. 08034. Barcelona. Spain
* email: rocio.lopez@unizar.es
telephone: + 34 974 292685
fax: + 34 974 239302
KEY
WORDS:
RDA;
Compositional
Data
peptidase:phosphatase ratio; conductivity; shallow lakes
Analysis;
DIN:DOC
ratio;
Abstract:
Communities in Mediterranean wetlands are strongly constrained by the hydrological
perturbations and the water flow regime. Salinity and nutrient availability have often
been considered the most important variables determining changes in the phytoplankton
community of coastal wetlands. Often, ratios between the main environmental variables
have more relevance than the absolute values of each variable; however, in most cases
ratios are not suitable for use in multivariate models commonly used by limnologists.
The main objective of the present work was to identify the main variables or variable
ratios that are the driving forces of the major phytoplankton taxonomic groups in
Mediterranean coastal wetlands, using compositional data analysis techniques (CoDa).
With this aim, eleven shallow wetlands (6 in Empordà, 5 in Doñana, NE and SW of
Spain respectively) were sampled in winter and spring 2007. Two approaches were
used: the first one using raw data and the second one using CoDa techniques to
transform data. Our results show that differences in hydrological patterns led to three
main community assemblages, ranging from communities dominated by typical marine
taxa (diatoms and dinoflagellates) when marine influence was high, to communities
dominated by cyanobacteria during confinement and when inorganic nitrogen was
scarce. In freshwaters with a high turnover rate, the community was dominated by
opportunistic chlorophytes and cryptophytes that need inorganic nitrogen availability.
When the raw data and CoDa approaches were compared, the CoDa approach permitted
a better ecological interpretation of the phytoplankton community and the main
ecological processes. Salinity was the main environmental factor with both approaches,
while the second CoDa RDA axis was related with the balance between the peptidase
and phosphatase enzyme activities, confirming the relevance of nutrient retrieval
processes in determining phytoplankton composition. We recommend the use of CoDa
techniques for analyses of planktonic communities, such as the one presented here, in
order to improve the interpretation of both existing and future datasets.
1. Introduction
Mediterranean wetlands are highly variable environments in terms of biogeochemical
and physical conditions, mainly due to their irregular hydrology (Alvarez-Cobelas et
al., 2005; Beklioglu et al., 2007). For this reason, they have a high taxonomical and
functional diversity, but are also very vulnerable (Mitsch and Gosselink, 1993;
Perennou et al., 2012). The phytoplankton communities in these wetlands have to
recurrently adapt to a changing environment, and are strongly constrained by the
hydrological perturbations and the water flow regime (López-Flores et al., 2006b).
Changes in the hydrology of these systems are largely determined by influence of the
sea or rivers, together with anthropogenic factors (Serrano, 2006; Badosa et al., 2008).
Research into the main environmental factors determining changes in the phytoplankton
community of coastal wetlands is complex and requires the inclusion of physical,
chemical and biological factors which change through time and are interrelated. The
environmental factors determining changes in the phytoplankton community
composition in coastal ecosystems include the chemical characteristics of the water (i.e.
nutrient content, conductivity, pH, dissolved organic carbon), its physical properties
(temperature, turnover time) as well as interactions between phytoplankton and the rest
of the biota (bacterioplankton, zooplankton) (Beklioglu et al., 2007). Of all these
factors, salinity and nutrient availability have often been considered to be the most
important (Comín and Valiela, 1993; Romo and Miracle, 1995; López-Flores et al.,
2006a; Reyes et al., 2007; Specchiulli et al., 2008).
Often, the proportions between different environmental variables have more relevance
than the absolute values of each variable. Relationships among relevant environmental
variables might reveal causal relationships which are difficult to demonstrate when
working with raw data. In the specific case of nutrients, while total nutrient
concentration determines the total phytoplankton biomass, nutrient proportions may
determine the viability and the growth of certain specific groups and therefore, the
phytoplankton species composition. For instance, the inorganic:organic nitrogen ratio
has been described as a key factor in the development of certain algal blooms (Glibert et
al., 2007) and has also been suggested as a determinant of mixotrophic strategies
(Jones, 2000; López-Flores et al., 2006a). The threshold of the Redfield nutrient ratios
(C:N:P, 106:16:1, Redfield, 1934) have been traditionally used to identify potential
nutrient limitations for the development of specific groups (Redfield, 1934; Falkowski
and Davis, 2004; van der Molen and Perissinotto, 2011). For example, it is well known
that the relative abundance of cyanobacteria is strongly related to a low N:P ratio (Romo
et al., 1996). In the case of the heterotrophic use of organic matter, the ratio between
peptidase and phosphatase extracellular enzyme activities has been described as an
indicator of either nitrogen or phosphorus limitation, and is more informative than the
plain enzymatic activities (Sala et al., 2001). Finally, ratios between physical variables
such as lagoon surface and the catchment area have been found to be better descriptors
of the driving force determining the heterotrophic-autotrophic contribution to the
plankton community than either variable alone (López-Flores et al., 2009). Thus, the
identification of the factors driving the phytoplankton community composition in
wetlands requires multivariate analyses including both single variables and ratios
between variables. However, in most cases ratios are not suitable for use in
multivariable models commonly used by limnologists, such as redundancy analysis
(RDA) or canonical correlation analysis (CCA), due to methodological exigencies.
Aitchison (1986) developed the compositional data analysis techniques (CoDa) to be
applied in any data matrix where data represent parts of a whole, thus only carrying
relative information. This is the case of proportions for different taxonomic groups. The
use of CoDa analysis has several advantages (Aitchison and Egozcue, 2005): 1) results
are scale invariant, i.e. they do not depend on the units in which they are expressed
(proportions, percentages, ppm, relative abundances or weight per liter or per kilogram);
2) it avoids spurious correlations, such as those found when using conventional statistics
with proportions, where the increase of one component necessarily means the decrease
of others; 3) it uses log contrasts, assuring the symmetry in any further statistical
analysis, since the correlation between any environmental variables and a log contrast of
two components x/y is exactly the same as the correlation found with the opposite log
contrast y/x, but with a change in sign. In this paper, the term CoDa has been always
used as an abbreviation of compositional data analysis techniques and do not have to be
confused with the terms codon or coda used in limnology (i.e. (Reynolds et al., 2002;
Padisák et al., 2006) to denominate phytoplankton functional groups.
The main objective of the present work was to identify the main variables or variable
ratios that are the driving forces of the major phytoplankton taxonomic groups in
Mediterranean coastal wetlands. The study included the analysis of eleven water bodies
from two coastal wetland complexes located in the north and south of Spain, where the
phytoplankton community composition and a complete set of environmental parameters
were measured. Since nutrient content exerts an important role on determining changes
in the phytoplankton community composition, rates of nitrification and denitrification
were also measured in both areas in order to provide more information on nitrogen
dynamics. A second objective was to test the suitability of using CoDa techniques for
multivariable analysis in aquatic ecology. Two analytical approaches were used and
compared: a first approach using conventional statistics on raw data and a second one,
where variables were previously transformed using CoDa techniques. To our
knowledge, the application of CoDa approach in this data set is the first use of CoDa in
studies of phytoplankton ecology.
2. Methods
2.1. Study sites
We selected two different coastal wetland complexes, the Empordà Wetlands and the
Doñana Natural Space, both located in Spain with a Mediterranean climate (Figure 1).
The former are located in the Mediterranean coast (NE Spain), the latter in the Atlantic
coast (SW Spain). Several waterbodies with differences in salinity were selected in both
sites. Environments were grouped as oligohaline (EC25 <7 mS/cm) and meso-euhaline
(EC25 >7 mS/cm) following (Lucena-Moya et al., 2009).
2.1.1. Empordà wetlands
The Empordà wetlands include a group of Mediterranean coastal wetlands and salt
marshes, with variable depths (average depth of 0.60 m and maximum depth
approximately 2 m), located in Girona (NE Spain, +42° 15', +3° 6', Figure 1). They
have a typical Mediterranean hydrology, which is greatly affected by the proximity to
the sea, but with no tidal influence (Quintana et al., 1998). Three meso-euhaline salt
marshes (Turies, Litoral and Fra Ramon) and three permanent oligohaline shallow lakes
(Ter Vell, Basses d’en Coll and Bassa Ànser) were sampled.The hydrology of mesoeuhaline waterbodies depend mainly on sudden and irregular intrusions of seawater
during sea storms, and fresh water during periods of intense rainfall. Despite sea storms,
rainfall, or the entry of fresh water from rivers, the marshes lack a continuous water
supply for long periods of time and tend towards desiccation (Brucet et al., 2005;
Badosa et al., 2006; López-Flores et al., 2006a; López-Flores et al., 2009). Freshwater
inputs in oligohaline waterbodies are strongly controlled by irrigation activities (Badosa
et al 2006)
2.1.2. Doñana wetlands
The Doñana Natural Space is an extensive protected area within the Guadalquivir delta,
located in Huelva, Sevilla and Cádiz provinces (SW Spain, +36° 58’, -6° 24’, Figure 1).
Various kinds of wetlands are present, the largest being surface-fed temporary marshes
which flood in winter and dry out in summer. However, there are also groundwater-fed
lagoons and permanent wetlands (Serrano et al., 1999; Serrano, 2006; Reyes et al.,
2008; Espinar and Serrano, 2009; Díaz-Delgado, 2010). In this study two oligohaline
peridunal semi-permanent lagoons (la Dulce and Santa Olalla, López-Archilla et al.,
(2012)), two seasonal salt marshes with some tidal influence from the Guadalquivir
river (Lucio de Cangrejo, oligohaline, Reyes et al., (2007)) and Algaida, meso-euhaline,
Gallego Fernández and García Novo (2007), +36° 53' 53.27", -6° 18' 22.24"), and one
agricultural-impacted meso-euhaline permanent lagoon (el Tarelo, Serrano et al.,
(2004)) were sampled. These sites were partly selected owing to the ease of access
compared with other parts of Doñana.
2.2. Sampling and analysis
All shallow lakes (6 in Empordà Wetlands, 5 in Doñana Wetlands) were sampled in
winter (January) and spring (May) 2007. Water samples were collected from a central
point of each basin at 15-30 cm depth. Three replicates for every measure and sample
were taken. The composition of the phytoplankton community was determined by
analysis of the relative abundance of class-target pigments to total chlorophyll.
Phytoplankton pigments were analysed following López-Flores et al. (2006a), and used
to calculate the contribution of chemotaxonomic phytoplankton classes by means of the
Chemtax program (Mackey et al., 1996). Temperature and electrical conductivity
(EC25) were measured in situ. Filtered samples (Whatman GF/F) were frozen for NH4+,
NO2-, NO3- and soluble reactive phosphorus (SRP) determinations. Dissolved Inorganic
Nitrogen (DIN) was calculated as the sum of the three nitrogen inorganic forms (DIN =
NH4+ + NO2- + NO3-). Unfiltered samples were either frozen for later analysis of total
nitrogen and total phosphorus, or refrigerated for total organic carbon (TOC) analysis.
Nutrient analyses followed Grasshoff et al. (1983), and total and dissolved organic
carbon were measured using a TOC analyser (TOC 5000 Shimadzu, Shimadzu Scientic
Instruments, Columbia, USA). The particulate organic carbon (POC) was calculated
from the difference between TOC and DOC. The organic nitrogen (TON) and
phosphorus (TOP) were calculated from the difference between the total and the
inorganic nutrient. The extracellular enzyme activities of leucine-aminopeptidase (EC
3.4.11.1) and phosphatase (EC 3.1.3.1–2) were measured spectrofluorometrically by
incubating the samples with artificial substrate analogues, following Romaní and
Sabater (1999). Bacterioplankton biovolume was calculated with a FACSCalibur
(Becton & Dickinson) flow cytometer, following the methods described in López-Flores
et al. (2009). Biovolume was then transformed into biomass (carbon content) using the
equation from Loferer-Krößbacher et al. (1998).
Potential nitrification and denitrification rates were measured in the sediment and
rhizosphere of the dominant plant species. Five to ten shoots and rhizomes of the
dominant plant species were harvested at every sampling point until a sufficient amount
of living, non-senescent roots was obtained. The sediment was sampled in triplicate by
collecting three randomly distributed samples in a square meter surface area using a 7
cm diameter Plexiglas tube mounted in a manual core sampler in monospecific stands of
the dominant plants. Only the upper part of the sediment, down to 4 cm depth, was used
for analysis. Sample preparation methods, dominant plant species and details of activity
measurements were described by Ruiz-Rueda et al. (2009).
2.3. Data transformation and statistical analyses
In order to quantify the influence of different variables on the phytoplankton classes, we
carried out a redundancy analysis (RDA). All canonical axes were used to evaluate the
significant variables under analysis by means of a Monte Carlo test (1000
permutations). RDA tests were performed using CANOCO v4.5 (ter Braak and
Smilauer, 2002). The correlations were carried out using PASW Statistic® for Windows
18.0.0 (SPSS, Chicago, Illinois).
Two data matrices were used. One included the abundance of phytoplankton taxonomic
groups, and a second one included the environmental variables that may affect
phytoplankton
variability
between
waterbodies
and
sampling
periods.
The
environmental matrix included physical data (temperature), chemical data (conductivity,
soluble reactive phosphorus (SRP), nitrite (NO2-), nitrate (NO3-), ammonia (NH4+),
organic nitrogen (TON), organic phosphorus (TOP), dissolved organic carbon (DOC)
and particulate organic carbon (POC)) and the activities of the extracellular enzymes
phosphatase and leucine-aminopeptidase (PHO, PEP and PEP/BB, peptidase per
bacterial biomass). These latter variables were included within the environmental matrix
as indicators of either phosphorus or nitrogen limitation of the plankton community.
Data were examined using two approaches. First, RDA was done with raw data,
previously log (x+1) transformed (from hereon, the “raw data” approach). Then, a
second RDA was performed, using the compositional data analysis techniques of
Aitchison (1986) (from hereon, the “CoDa” approach). Here, the environmental matrix
was transformed using log-contrasts, specifically balances (bi), a particular case of log-
contrasts. Log-contrasts are typical scale invariant log-ratios of components of a
composition. bi is defined as the natural logarithm of a ratio of geometric means of two
groups of the variables that are being compared. The form of a balance between two
groups of components in a composition (G1 and G2) is
𝑏𝐺1 vs . 𝐺2 =
𝑟·𝑠 (
ln
𝑟+𝑠 (
𝑥 𝑗 ∈𝐺1
𝑥𝑗 )1
𝑟
𝑥 𝑙 ∈𝐺2
𝑥𝑙 )1
𝑠
where r and s are the numbers of components in the numerator (G1) and in the
denominator (G2), respectively. Thus, a balance bG1 vs. G2 is the natural logarithm of a
ratio of geometric means between two groups of components that we want to compare
multiplied by a normalizing coefficient that depends of the number of components
(Egozcue et al., 2003; Egozcue and Pawlowsky-Glahn, 2005). Components included in
the numerator or in the denominator are user-defined.
As an example, if we want to investigate the balance between inorganic nitrogen forms
and soluble inorganic phosphorus, we use the balance
𝑏𝐷𝐼𝑁 𝑣𝑠.𝑆𝑅𝑃 =
− 1
3·1
(NH4+ · NO−
2 · NO3 )
ln
3+1
SRP
3
Using these CoDa procedures, spurious correlations do not appear and other difficulties,
such as asymmetric distributions of raw components, may become more accessible.
In both approaches, the biological data were log (x+1) transformed and centred, so that
we used clr (centred log-ratio) variables, as described by Egozcue and PawlowskyGlahn (2011).
All environmental parameters available (12 variables and 14 balances, repectively) were
included in the raw data and CoDa approach databases. The environmental parameters
retained for the analysis were identified by a forward selection procedure available in
CANOCO 4.5 (ter Braak and Smilauer, 2002), using a cut-off point of 0.10 (Magnan et
al., 1994).
3. Results
3.1. Nutrient and enzymatic activity regularities
In the Empordà wetlands, the waterbodies with higher freshwater inputs (and lower
conductivity) were also the ones with higher DIN:SRP and DIN:DOC ratios, while the
waterbodies with higher conductivity were those with lower DIN:SRP and DIN:DOC
ratios (Table 1). That pattern was observed both in winter and in summer. Taking as a
reference the Redfield ratio of 16:1 for nitrogen and phosphorus and 16:106 for nitrogen
and carbon, values found for DIN:SRP and DIN:DOC exceeded Redfield ratios in
freshwater waterbodies and were below them in brackish ones. There was only one
exception in Ter Vell in summer where DIN:SRP and DIN:DOC ratios were under the
Redfield ratio. The enzyme activity ratio, PEP:PHO followed the same pattern of
conductivity in summer, but no pattern was observed in winter, when Litoral lagoon
registered the maximum ratio value (Table 1).
In the Doñana wetlands there was no consistent association between the decrease in salt
content and the increase in the DIN:SRP or DIN:DOC ratios. Values below Redfield
ratios were found mainly in summer in the waterbodies with the lowest (Santa Olalla,
La Dulce), and the highest (Algaida) conductivity. All the values of the PEP:PHO
enzyme activity ratio observed in the five Doñana wetlands were lower than those found
in the Empordà wetlands, only increasing at Tarelo lagoon in summer.
3.2. Multivariable analyses: Main factors driving the community composition
The raw data approach (Figure 2) yielded four environmental variables (conductivity,
PHO, TOP and NO3-) that contributed significantly (p<0.1) to the distribution of
phytoplankton groups, explaining 47.2% of the variation of the data set. The first two
axes of the RDA explained 71.0% and 22.8% of the taxa–environment relationship. In
the CoDa approach (Figure 4), once more, conductivity was significant as were also the
enzyme activity balance (bPEPvsPHO) and bDINvsDOC (p<0.1). In this second solution,
significant variables explained 45.2% of the variation of the taxonomic data set. In this
case, the first two axes of the RDA explained 70.9% and 24.8% of the taxa–
environment relationship.
In both solutions, the first axis was closely and negatively correlated with conductivity.
The taxonomic groups showed a similar response to this axis in both analyses.
Chlorophytes and cryptophytes dominated the community when the conductivity was
low, while the other taxonomic groups dominated at high conductivities.
The second axis revealed some differences between the raw data and the CoDa
approaches. In the raw data approach (Figure 2), the TOP was the variable most
correlated with this second axis. The NO3- concentration and the PHO activity were
correlated with both first RDA axes. In the CoDa approach (Figure 3), the second axis
was mainly negatively correlated with the enzyme activity balance (bPEPvsPHO).
Conductivity and bPEPvsPHO were almost orthogonal and very close to the first and the
second RDA axis, respectively. The balance bDIN:DOC correlated with the two first RDA
axes.
Of the taxonomic groups, cyanobacteria were the most closely related with the second
axis in both approaches, and their high relative abundance coincided with high TOP
concentration and low NO3- availability (Figures 2 and 3). Moreover, according to the
CoDa approach RDA, cyanobacteria were favoured by a high bPEPvsPHO. Diatoms were
correlated with the second axis, in the opposite direction to cyanobacteria.
Chlorophytes, dinoflagellates, haptophytes and cryptophytes showed weak correlations
with this second axis, of which only chlorophytes shared the orientation of
cyanobacteria.
In the CoDa approach RDA biplot (Figure 3), a differentiated spatial distribution around
the two axes can be observed for the Empordà and Doñana samples. To illustrate this,
two wide arrows showing the change from freshwater to brackish water have been
added to the figure 3. The light arrow shows the range of the environmental gradient
found in Doñana. It starts in the waterbodies with the highest freshwater influence,
during summer (Dulce), and it finishes in waterbodies with the highest marine influence
during winter (Algaida), showing a strong relationship with bPEPvsPHO. The freshwater
samples were those with a higher bPEPvsPHO. The dark arrow, showing the Empordà
gradient, describes a curve. Between freshwater waterbodies, differences were mainly
due to the increase of conductivity. On the other hand, samples from higher
conductivities became closely related with the bDIN:DOC and the bPEPvsPHO. At increasing
salinities, DIN availability was lower and relative peptidase activity was higher. This
pattern is hard to observe in the raw data approach RDA (Figure 2).
Due to covariation, several variables (in the raw data approach) or balances (in the
CoDa approach) were eliminated from the RDA. However, their relationship with the
significant variables or balances can help to explain how phytoplankton taxonomic
groups respond to environmental conditions. Tables 2 and 3 present the correlations
between the significant variables or balances included in the raw data or CoDa RDAs
and the other variables excluded from the RDA analyses. Some variables and ratios
were correlated with conductivity, of which bacterial biomass, bPEP:BB and bPHOvsBB had
the strongest correlations (negatively in the case of the two balances). TOP was
positively correlated with peptidase activity, SRP, bacterial biomass, temperature, and
some of the other organic nutrients. The phosphatase activity was positively correlated
with peptidase activity and ON. Likewise, bPEPvsPHO showed a positive correlation with
bTOPvsDOC and a negative one with bPEPvsBB and bPHOvsBB.
3.3. Nitrification and denitrification balance
Potential nitrification and denitrification rates were not included in the RDA analyses
since this was measured only for some of the studied shallow lakes (Figure 4). When
these variables were correlated with those significant in the raw data RDA, nitrification
in sediment was correlated with NO3- (r=0.62; p<0.01) and denitrification in rhizomes
was correlated with TOP (r=0.67; p<0.01). Moreover, some other correlations occurred
between nitrification and denitrification rates and non-significant variables. Thus,
denitrification in rhizomes was correlated with bacterial biomass (r=0.84; p<0.001), ON
(r=0.57; p<0.001), peptidase activity (r=0.48; p<0.05) and POC (r=0.50; p<0.05).
Denitrification in sediment was correlated with NO2- (r=0.53; p<0.05) and peptidase
activity (r=0.59; p<0.05). Furthermore, the balances between nitrification and
denitrification in rhizomes and in sediment (bNITvsDEN sed and bNITvsDEN rhi, respectively)
were included in Table 3 (correlations with significant balances from CoDa approach).
In this case, bNITvsDEN sed showed a positive correlation with bDINvsDOC (Table 3) and also
with bDINvsSRP (r=0.55; p<0.05).
Denitrification was always higher than nitrification both in rhizome and in sediment
(Figure 4). However, values of both rates were higher in rhizomes. The highest
denitrification values were found in Tarelo lagoon (Doñana), both in summer and in
winter. The highest nitrification values were found in Bassa Ànser (Empordà).
4. Discussion
The raw data approach RDA multivariate analysis showed salinity and nutrient
concentration as the main environmental factors driving phytoplankton composition in
Mediterranean coastal shallow lakes. This role of salinity and nutrients has often been
reported for phytoplankton assemblages in coastal ecosystems (Comín and Valiela,
1993; Romo and Miracle, 1995; López-Flores et al., 2006a; Reyes et al., 2007;
Specchiulli et al., 2008), as well as for other planktonic organisms (Brucet et al., 2006;
Badosa et al., 2007). We found diatoms, haptophytes and dinoflagellates to dominate in
brackish water and chlorophytes and cryptophytes to dominate in freshwater
environments. The dominance of freshwater opportunistic organisms, such as
chlorophytes and cryptophytes (Reynolds, 2006) also coincided with situations of high
inorganic nitrogen availability. In contrast, cyanobacteria were found in situations of
high TOP concentration and low nitrate availability. Finally, diatoms, haptophytes and
dinoflagellates had a weak relationship with TOP and nitrate, being prolific in those
waterbodies closest to the sea, such as Fra Ramon in Empordá and Algaida in Doñana.
High conductivity and a high phosphatase activity rate are conditions similar to those
found in the sea (Smith et al., 1992) and associated with low inorganic phosphorus
availability (Frutos et al., 2004; Bogé et al., 2012). Therefore, probably both the water
and the main phytoplankter in those lagoons entered directly via connections with the
sea. However, a steady temporal shift in the community is expected, once any lagoon
becomes isolated from the sea (López-Flores et al., 2006b).
Conductivity and bPEPvsPHO arose as almost orthogonal variables in the CoDa approach
of the RDA, suggesting an independent relationship between salinity and the balance of
enzymatic activities. Both these variables in turn arose as independent of bDINvsDOC.
However, careful analysis of the CoDa approach biplot reveals interesting patterns
related to hydrology, that are very different between Doñana and Empordà. In Empordà,
freshwater ecosystems are fed by a relatively continuous freshwater supply, which also
supplies continuous inputs of inorganic nitrogen (López-Flores et al., 2003; Badosa et
al., 2007). In contrast, brackish water ecosystems are fed by sudden inputs of both
marine and/or freshwater, after which they are subjected to an intense confinement in
summer, without further inputs of water and nutrients. During this confinement, there
are significant nitrogen losses due to denitrification (Golterman, 2000), while
phosphorus tend to accumulate, causing a strong decrease in the N:P ratio (Quintana et
al., 1998). The inverse relationship found between temperature and the bDINvsDOC is also
indicative of the influence of this hydrological pattern. Flowing and freshwater
waterbodies are rich in inorganic nitrogen, while, the brackish waterbodies where water
remains confined are poor in inorganic nitrogen with a high bPEPvsPHO (see the darker
arrow in Figure 4).
In Doñana, according to the RDA, the bPEPvsPHO balance decreases with increasing
conductivity, indicating that freshwater ecosystems are more nitrogen limited than
brackish ones. Brackish waterbodies analysed in Doñana are mainly under periodical
saline influence (through tidal currents, saline groundwater seepage and spray) (DíazDelgado, 2010). On the other hand, freshwater waterbodies are peridunar ponds, located
on sandy soils, mainly fed by surface runoff or rainfall infiltration after precipitation
(Serrano and Toja, 1995; Serrano, 2006). After precipitation events, peridunar ponds
remain confined, loosing inorganic nitrogen and accumulating phosphorus. Comparing
both wetland complexes, the general finding is that in these coastal ecosystems water
flow (even fresh or brackish) provide inorganic nitrogen inputs, while water
confinement causes strong nitrogen loses by denitrification in freshwater lagoons. The
low value of the nitrification: denitrification ratio in both substrates and in all the
waterbodies analysed (Figure 4) agrees with this interpretation, and suggests that water
inputs supply the nitrogen surplus found in non-confined ecosystems.
Others have discussed the relevance of denitrification in Mediterranean wetlands
(Golterman, 1983; Currin et al., 1996; Pavel et al., 1996), but there are few data on this
subject. In this study, we found higher potential denitrification activities in rhizomes
compared (on a dry weight basis) to the bulk sediment. Reasons for these higher rates
are complex and are due to a mixture of several factors, such as the production of root
exudates, the increase of nitrogen oxides, or complex plant-bacteria associations, which
may significantly alter the microbial composition (Jaeger et al., 1999; Orwin et al.,
2006; Henry et al., 2008; Ruiz-Rueda et al., 2009; Trias et al., 2012). Meanwhile, the
nitrification rate was always lower, regardless of substrate or period. The denitrification
was more intense in Doñana, especially in summer. The high denitrification rates
registered in Tarelo lagoon in both sampling periods were probably due to the major
availability of nitrogen in inorganic forms, since this lagoon is surrounded by
greenhouses and mainly fed by the salinized and nitrate-polluted ground water seepage
of the Guadalquivir river (Serrano et al., 2004). The positive relationship found between
bDINvsDOC and bNITvsDEN suggests a higher relative denitrification activity in the bulk
sediment when organic carbon is present, and agrees with Fulweiler et al. (2008), who
found that, at mid-term (days), denitrification was stimulated by the increase of organic
matter, after an initial short period of intense cyanobacterial nitrification.
The dissimilarity in the saltwater origin can help to explain why, during the summer, the
community was dominated by diatoms in Doñana and by dinoflagellates and
cyanobacteria in Empordà. Diatoms have high growth rates (Stolte and Garcés, 2006)
and often show a quick response to nutrient availability (Margalef, 1978), e.g. due to
sea water inputs during high tides. On the other hand, cyanobacteria and dinoflagellates
have adapted to nutrient scarcity, either through their low growth (Stolte and Garcés,
2006) or their ability to incorporate atmospheric or organic nitrogen (Burkholder et al.,
2006) during confinement periods. Similarly, the direct relationship between the
enzymatic balance (bPEPvsPHO) and the bOPvsDOC reveal the effort of the phytoplankton
community to find nitrogen during periods of phosphorus surplus (Nausch, 2000;
Francoeur and Wetzel, 2003).
Phosphatase and peptidase enzymatic activities have been linked with salinity by
various authors. In general, the variation in ectoenzymatic activities was significantly
related to bacterial abundance, which in turn, was highly associated with variations in
salinity and phytoplankton biomass (Cunha et al., 2000; Caruso, 2010). In our study, the
phosphatase activity was not correlated with conductivity and bPHOvsBB had an inverse
link with it. The phosphatase activity was higher in freshwater ecosystems and during
freshwater inputs, when free-living bacterial biomasses were the lowest and the
bacterial community reacted in response to the inflow of nitrogen; with intensification
of their phosphatase activity. Thus, Caruso (2010) and
Chróst and Siuda (2002),
concluded that in freshwater flows the phosphatase activity was higher since the
bacteria attached to particulate organic matter were highly effective (3.4-4.9 times
higher) compared with the free-living bacteria. In our study, peptidase was directly
correlated with conductivity, but this relationship became reversed when the peptidase
activity was relative to bacterial biomass (bPEPvsBB). This partially agrees with Cunha
(2000), who found a direct relationship between peptidase activity and the total amount
of bacteria which, at the same time, were directly related with salinity and
phytoplankton biomass. Thus, in the saltiest environments, there was a high peptidase
activity, probably enhanced by the high phytoplankton biomass and conductivity
(Cunha et al., 2000; Chróst and Siuda, 2002) but a reduced efficiency of bacteria
recycling organic nitrogen.
In our study, the CoDa approach permitted a better ecological interpretation of the
phytoplankton community. When the raw data and CoDa approaches were compared,
the CoDa approach allowed an improved interpretation of the processes dominating the
study environments: while salinity remained as the main environmental factor, strongly
related to the first RDA axis, the second CoDa RDA axis was related with the balance
between the peptidase and phosphatase enzyme activities, suggesting that the N:P
imbalance and the consequent N or P limitation are major determinants of the
phytoplankton composition. This supports Sala et al. (2001) who pointed out that the
PEP:PHO ratio is more informative of this N or P limitation than the simple enzyme
activities, because the simple amount of both enzymes is mainly related to total
microbial biomass. The importance of other ratios between environmental variables in
aquatic ecosystems has previously been reported by several authors (Kisand et al.,
2001; Rubin and Leff, 2007; López-Flores et al., 2009; Barlett and Leff, 2010).
In this study, differences in hydrological patterns led to three main community
assemblages, ranging from communities dominated by typical marine taxa (diatoms and
dinoflagellates) when marine influence was high, to communities dominated by
cyanobacteria during confinement and when inorganic nitrogen was scarce. Finally, in
freshwaters with a high turnover rate, the community was dominated by opportunistic
chlorophytes and cryptophytes that need inorganic nitrogen availability. After a change
in the phytoplankton community, a change in the whole food web is likely, since the
nutritive properties (Picard and Lair, 2000; Muller-Navarra, 2008) and the competitive
strategies (nitrogen fixation capability, Quesada et al. (1998); mixotrophy, Jones
(2000); toxins production Colin and Dam (2002), etc.) of the phytoplankton components
in the three main communities observed are clearly different. For this reason, the effects
of flow regulations, land-use and climatic change on phytoplankton must be considered
carefully in order to maintain resilience and diversity in the food webs in Mediterranean
shallow lakes. As we expected, results were not totally different between the two
approaches (raw and CoDa) and the conclusions we could arrive were similar, but
working with CoDa and, consequently, with balances, the information we could include
in the model was more, so the interpretation of samples tendency was easier and the
final conclusion reached were more robust. The CoDa techniques should also be more
widely adapted for analyses of planktonic communities, as the one presented here, in
order to improve the interpretation of both new and existing data. Authors do not
expect that other planktonic studies obtain a different ecological interpretation after a
CoDa approach, but probably they are going to have a more complete one. The fact is
that compositional data approaches allow a whole analysis of data, where the
combination of variable in balances (ratios) is permitted. And sometimes, as explained
in Pierotti and Martín-Fernández (2011), this approach might provide a lot more insight
to the biological phenomena hidden in the data.
5. Acknowledgements
The authors wish to thank staff of the EBD (Estación Biologica de Doñana) and Doñana
ICTS (Infraestructura Científica y Tecnológica Singular) for their logistic support
during sampling.
Funding
This work was funded by a grant from the Ministerio de Ciencia e Innovación (ref.
CGL2011-23907); and also by an ICTS project from the Estación Biológica de Doñana
(CSIC) and the Spanish Ministerio de Educación y Ciencia (ref: ICTS 24/2007).
References
Aitchison, J. (1986). The statistical analysis of compositional data. London, Chapman &
Hall Ltd. 416 pp.
Aitchison, J., Egozcue, J. J. (2005). Compositional Data Analysis: Where Are We and
Where Should We Be Heading? Mathematical Geology. 37(7): 829-850.
Alvarez-Cobelas, M., Rojo, C., Angeler, D. G. (2005). Mediterranean limnology:
current status, gaps and the future. Journal of limnology. 64(1): 13-29.
Badosa, A., Boix, D., Brucet, S., López-Flores, R., Quintana, X. D. (2006). Nutrients
and zooplankton composition and dynamics in relation to the hydrological pattern in a
confined Mediterranean salt marsh (NE Iberian Peninsula). Estuarine, Coastal and Shelf
Science. 66(3-4): 513-522.
Badosa, A., Boix, D., Brucet, S., López-Flores, R., Quintana, X. D. (2007). Short-term
effects of changes in water management on the limnological characteristics and
zooplankton of a eutrophic Mediterranean coastal lagoon (NE Iberian Peninsula).
Marine Pollution Bulletin. 54(8): 1273-1284.
Badosa, A., Boix, D., Brucet, S., López-Flores, R., Quintana, X. D. (2008). Short-term
variation in the ecological status of a Mediterranean coastal lagoon (NE Iberian
Peninsula) after a man-made change of hydrological regime. Aquatic Conservation:
Marine and Freshwater Ecosystems. 18(7): 1078-1090.
Barlett, M., Leff, L. (2010). Planktonic bacterial responses to nutrient amendments in
wetland mesocosms. Wetlands. 30(6): 1161-1170.
Beklioglu, M., Romo, S., Kagalou, I., Quintana, X. D., B‚cares, E. (2007). State of the
art in the functioning of shallow Mediterranean lakes: workshop conclusions.
Hydrobiologia. 584: 317-326.
Bogé, G., Lespilette, M., Jamet, D., Jamet, J. L. (2012). Role of sea water DIP and DOP
in controlling bulk alkaline phosphatase activity in N.W. Mediterranean Sea (Toulon,
France). Marine Pollution Bulletin. 64(10): 1989-1996.
Brucet, S., Boix, D., López-Flores, R., Badosa, A., Moreno-Amich, R., Quintana, X. D.
(2005). Zooplankton structure and dynamics in permanent and temporary Mediterranean
salt marshes: taxon-based and size-based approaches. Archiv für Hydrobiologie. 162(4):
535-555.
Brucet, S., Boix, D., López-Flores, R., Badosa, A., Quintana, X. D. (2006). Size and
species diversity of zooplankton communities in fluctuating Mediterranean salt
marshes. Estuarine, Coastal and Shelf Science. 67: 424-432.
Burkholder, J. M., Azanza, R. V., Sako, Y. (2006). The ecology of harmful
dinoflagellates. In: Granéli, E. and Turner, J. T. (eds.). Ecology of harmful algae.
Heidelberg, Springer-Verlag Berlin. 189 pp. 53-66.
Caruso, G. (2010). Leucine aminopeptidase, β-glucosidase and alkaline phosphatase
activity rates and their significance in nutrient cycles in some coastal Mediterranean
sites. Marine Drugs. 8(4): 916–940.
Colin, S. P., Dam, H. G. (2002). Testing for toxic effects of prey on zooplankton using
sole versus mixed diets. Limnol.Oceanogr. 47(5): 1430-1437.
Comín, F. A., Valiela, I. (1993). On the controls of phytoplankton abundance and
production in coastal lagoons. Journal of Coastal Research. 9(4): 895-906.
Cunha, M. A., Almeida, M. A., Alcântara, F. (2000). Patterns of ectoenzymatic and
heterotrophic bacterial activities along a salinity gradient in a shallow tidal estuary.
Marine Ecology Progress Series. 204: 1-12.
Currin, C. A., Joye, S. B., Paerl, H. W. (1996). Diel rates of N2-fixation and
denitrification in a transplanted Spartina alterniflora marsh: Implications for N-flux
dynamics. Estuarine, Coastal and Shelf Science. 42: 597-616.
Chróst, R. J., Siuda, W. (2002). Microbial enzymes in lake ecosystems. In: Burns, R. G.
and Dick, R. P. (eds.). Enzymes in the Environment: Activity, Ecology, and
Applications. New York, Marcel Dekker Inc. pp. 34-72.
Díaz-Delgado, R. (2010). An integrated monitoring programme for Doñana Natural
space: The set-up and implementation. In: Hurford, C., Scheneider, M. and Cowx, I.
(eds.). Conservation Monitoring in Freshwater Habitats: A Practical Guide and Case
Studies. Dordrecht, Springer pp. 75-386.
Egozcue, J. J., Pawlowsky-Glahn, V. (2005). Groups of parts and their balances in
compositional data analysis. Mathematical Geology 37(7): 795-828.
Egozcue, J. J., Pawlowsky-Glahn, V. (2011). Basic Concepts and Procedures. (eds.).
Compositional Data Analysis, John Wiley & Sons, Ltd pp. 12-28.
Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G., Barcelo-Vidal, C. (2003).
Isometric logratio transformations for compositional data analysis. Mathematical
Geology 35(3): 279-300.
Espinar, J. L., Serrano, L. (2009). A quantitative hydrogeomorphic approach to the
classification of temporary wetlands in the Doñana National Park (SW Spain). Aquatic
Ecology. 43(2): 323-334.
Falkowski, P. G., Davis, C. S. (2004). Natural Proportions. Nature 431(7005): 131.
Francoeur, S. N., Wetzel, R. G. (2003). Regulation of periphytic leucine-aminopeptidase
activity. Aquatic Microbial Ecology. 31: 249-258.
Frutos, M. D., Blasco, J., Gómez-Parra, A. (2004). Phosphatase activity in salt-ponds of
the Bay of Cádiz. Ciencias Marinas. 30(3): 403-416.
Fulweiler, R., Nixon, S., Buckley, B., Granger, S. (2008). Net Sediment N2 Fluxes in a
Coastal Marine System—Experimental Manipulations and a Conceptual Model.
Ecosystems. 11(7): 1168-1180.
Gallego Fernández, J. B., García Novo, F. (2007). High-intensity low-intensity
restoration alternatives tidal marsh Guadalquivir estuary, SW Spain. Ecological
Engineering. 30(2): 112-121.
Glibert, P. M., Wazniak, C. E., Hall, M. R., Sturgis, B. (2007). Seasonal and interannual
trends in nitrogen and brown tide in Maryland's coastal bays. Ecological Applications.
17(sp5): S79-S87.
Golterman, H. L. (1983). Algal bioassays and algal growth controlling factors in
eutrophic shallow lakes. Hydrobiologia (Historical Archive). 100(1): 59-64.
Golterman, H. L. (2000). Denitrification and a numerical modelling approach for
shallow waters. Hydrobiologia. 431(1): 93-104.
Grasshoff, K., Ehrhardt, M., Kremling, K. (1983). Methods of sea water analysis.
Weiheim, Verlag Chemie. 317 pp.
Henry, S., Texier, S., Hallet, S., Bru, D., Dambreville, C., Chèneby, D., Bizouard, F.,
Germon, J. C., Philippot, L. (2008). Disentangling the rhizosphere effect on nitrate
reducers and denitrifiers: insight into the role of root exudates. Environmental
Microbiology. 10(11): 3082-3092.
Jaeger, C. H., Lindow, S. E., Miller, W., Clark, E., Firestone, M. K. (1999). Mapping of
Sugar and Amino Acid Availability in Soil around Roots with Bacterial Sensors of
Sucrose and Tryptophan. Applied and Environmental Microbiology. 65(6): 2685-2690.
Jones, R. I. (2000). Mixotrophy in planktonic protists: an overview. Freshwater Biology.
45(2): 219-226.
Kisand, V., Tuvikene, L., Nõges, T. (2001). Role of phosphorus and nitrogen for bacteria
and phytopankton development in a large shallow lake. Hydrobiologia. 457(1): 187197.
Loferer-Krößbacher, M., Klima, J., Psenner, R. (1998). Determination of bacterial cell
dry mass by transmission electron microscopy and densitometric image analysis.
Applied and Environmental Microbiology. 62(2): 688-694.
López-Archilla, A. I., Coleto, M. C., Montes, C., Peñin, I., Guerrero, M. C. (2012).
Temporal variation of phytoplankton in two neighbouring Mediterranean shallow lakes
in Doñana National Park (Spain). Limnetica. 31(2): 289-304.
López-Flores, R., Boix, D., Badosa, A., Brucet, S., Quintana, X. (2006a). Pigment
composition and size distribution of phytoplankton in a confined Mediterranean salt
marsh ecosystem. Marine Biology. 149: 1313-1324.
López-Flores, R., Boix, D., Badosa, A., Brucet, S., Quintana, X. D. (2009).
Environmental factors affecting bacterioplankton or phytoplankton dominance and
relationships in a mediterranean salt marsh. Journal of Experimental Marine Biology
and Ecology. 369: 118-126.
López-Flores, R., Garcés, E., Boix, D., Badosa, A., Brucet, S., Masó, M., Quintana, X.
D. (2006b). Comparative composition and dynamics of harmful dinoflagellates in
Mediterranean salt marshes and nearby external marine waters. Harmful Algae. 5(6):
637-648.
López-Flores, R., Quintana, X. D., Salvadó, V., Hidalgo, M., Sala, L., Moreno-Amich,
R. (2003). Comparison of nutrient and contaminant fluxes in two areas with different
hydrological regimes (Emporda Wetlands, NE Spain). Water Research. 37(12): 30343046.
Lucena-Moya, P., Pardo, I., Álvarez, M. (2009). Development of a typology for
transitional waters in the Mediterranean ecoregion: The case of the islands. Estuarine,
Coastal and Shelf Science. 82(1): 61-72.
Mackey, M. D., Mackey, D. J., Higgins, H. W., Wright, S. W. (1996). CHEMTAX - A
program for estimating class abundances from chemical markers: application to HPLC
measurements of phytoplankton. Marine Ecology Progress Series. 144: 265-283.
Magnan, P., Rodriguez, M. A., Legendre, P., Lacasse, S. (1994). Dietary variation in
freshwater fish species: relative contribution of biotic interactions, abiotic factors and
spatial structure. Can. J. Fish. Aquat. Sci. 51: 2856–2865.
Margalef, R. (1978). Life-forms of phytoplankton as survival alternatives in an unstable
environment. Oceanol. Acta. 1(4): 493-509.
Mitsch, W. J., Gosselink, J. G. (1993). Wetlands. New York, Van Nostrand Reinhold.
722 pp.
Muller-Navarra, D. C. (2008). Food Web Paradigms: The Biochemical View on Trophic
Interactions. International Review of Hydrobiology. 93(4-5): 489-505.
Nausch, M. (2000). Experimental evidence for interactions between bacterial peptidase
and alkaline phosphatase activity in the Baltic Sea. Aquatic Ecology. 34(4): 331-343.
Orwin, K. H., Wardle, D. A., Greenfield, L. G. (2006). Ecological consequences of
carbon substrate identity and diversity in a laboratory study. Ecology. 87(3): 580-593.
Padisák, J., Borics, G., Grigorszky, I., Soróczki-Pintér, É. (2006). Use of phytoplankton
assemblages for monitoring ecological status of lakes within the Water Framework
Directive: The Assemblage Index. Hydrobiologia. 553(1): 1-14.
Pavel, E. W., Reneau, R. B., Berry, D. F., Smith, E. P., Mostaghimi, S. (1996).
Denitrification potential of nontidal riparian Wetland soils in the Virginia coastal plain.
Wat.Res. 30(11): 2798-2804.
Perennou, C., Beltrame, C., Guelmami, A., Vives, P. T., Caessteker, P. (2012). Existing
areas and past changes of wetland extent in the Mediterranean region: an overview.
Ecologia Mediterranea. 38(2): 53-66.
Picard, V., Lair, N. (2000). The influence of autotrophic and heterotrophic foods on the
demography of Daphnia longispina under starved, semi-natural and enriched conditions.
Journal of Plankton Research 22(10): 1925-1944.
Pierotti, M. E. R., Martín-Fernández, J. A. (2011). Compositional Analysis in
Behavioural and Evolutionary Ecology. (eds.). Compositional Data Analysis, John
Wiley & Sons, Ltd pp. 218-234.
Quesada, A., Nieva, M., Leganés, F., Ucha, A., Martín, M., Fernández-Valiente, E.
(1998). Acclimation of cyanobacterial communities in rice fields and response of
nitrogenase activity to light regime. Microbial Ecology. 35(2): 147-155.
Quintana, X. D., Moreno-Amich, R., Comín, F. A. (1998). Nutrient and plankton
dynamics in a Mediterranean salt marsh dominated by incidents of flooding. Part.I.
Differential confinement of nutrients. Journal of Plankton Research. 20(11): 2089-2107.
Redfield, A. C. (1934). On the proportions of organic derivatives in sea water and their
relation to the composition of plankton. I. In: Daniel, R. J. (eds.). James Johnstone
Memorial Volume, University Press of Liverpool pp. 176-192.
Reyes, I., Casco, M., Toja, J., Serrano, L. (2008). Hydrological complexity supports
high phytoplankton richness in the Doñana marshland (SW Spain). Hydrobiologia.
614(1): 47-54.
Reyes, I., Martín, G., Reina, M., Arechederra, A., Serrano, L., Casco, M. A., Toja, J.
(2007). Phytoplankton from NE Doñana marshland (“El Cangrejo Grande”, Doñana
Natural Park, Spain). Limnetica. 26(2).
Reynolds, C. S. (2006). Ecology of phytoplankton. Cambridge, Cambridge University
Press. 552 pp.
Reynolds, C. S., Huszar, V., Kruk, C., Naselli-Flores, L., Melo, S. (2002). Towards as
functional classification of the freswater phytoplankton. Journal of Plankton Research.
24(5): 417-428.
Romaní, A. M., Sabater, S. (1999). Epilithic ectoenzyme activity in a nutrient- rich
Mediterranean river. Aquatic Sciences 61: 122-132.
Romo, S., Miracle, M. R. (1995). Diversity of the phytoplankton assemblages of a
polymictic hypertrophic lake. Archiv für Hydrobiologie. 132(3): 363-384.
Romo, S., Van Donk, E., Gylstra, R., Gulati, R. (1996). A multivariate analysis of
phytoplankton and food web changes in a shallow biomanipulated lake. Freshwater
Biology. 36(3): 683-696.
Rubin, M., Leff, L. (2007). Nutrients and other abiotic factors affecting bacterial
communities in an Ohio River (USA). Microbial Ecology. 54(2): 374-383.
Ruiz-Rueda, O., Hallin, S., Bañeras, L. (2009). Structure and function of denitrifying
and nitrifying bacterial communities in relation to the plant species in a constructed
wetland. FEMS Microbiol. Ecol. 67: 308-319.
Sala, M. M., Karner, M., Arin, L., Marrasé, C. (2001). Measurement of ectoenzyme
activities as an indication of inorganic nutrient imbalance in microbial communities.
Aquatic Microbial Ecology. 23(3): 301-311.
Serrano, L., Burgos, M. D., Díaz-Espejo, A., Toja, J. (1999). Phosphorus inputs to
wetlands following storm events after drought. Wetlands. 19(2): 318-326.
Serrano, L., Reina, M., Arechederra, A., Casco, M. A., Toja, J. (2004). Limnological
description of the Tarelo lagoon (SW Spain). Limnetica. 23(1-2): 1-10.
Serrano, L., Toja, J. (1995). Limnological description of four temporary ponds in the
Doñana National Park (SW, Spain). Archiv für Hydrobiologie. 133: 497-516.
Serrano, L. M. R., G. Martín, I. Reyes, A. Arechederra, D. León & J. Toja (2006). The
aquatic systems of Doñana (SW Spain): watersheds and frontiers. Limnetica. 25(1-2):
11-32.
Smith, D. C., Simont, M., Alldredge, A. L., Azam, F. (1992). lntense hydrolytic enzyme
activity on marine aggregates and implications for rapid particle dissolution. Nature.
359: 139-142.
Specchiulli, A., Focardi, S., Renzi, M., Scirocco, T., Cilenti, L., Breber, P., Bastianoni,
S. (2008). Environmental heterogeneity patterns and assessment of trophic levels in two
Mediterranean lagoons: Orbetello and Varano, Italy. Science of The Total Environment.
402(2–3): 285-298.
Stolte, W., Garcés, E. (2006). Ecological aspects of harmful algal in situ population
growth rates. In: Granéli, E. and Turner, J. T. (eds.). Ecology of harmful algae.
Heidelberg, Springer-verlag Berlin. 189 pp. 139-152.
ter Braak, C. J. F., Smilauer, P. (2002). CANOCO reference manual and CanoDraw for
Windows user's guide: Software for Canonical Community Ordination (version 4.5).
Ithaca, NY, USA, Microcomputer Power. 500 pp.
Trias, R., Ruiz-Rueda, O., García-Lledó, A., Vilar-Sanz, A., López-Flores, R., Quintana,
X. D., Hallin, S., Bañeras, L. (2012). Emergent macrophytes act selectively on
ammonia-oxidizing bacteria and archaea. Appl Environ Microbiol. 78(17): 6352-6356.
van der Molen, J. S., Perissinotto, R. (2011). Microalgal productivity in an estuarine
lake during a drought cycle: The St. Lucia Estuary, South Africa. Estuarine, Coastal and
Shelf Science. 92(1): 1-9.
Tables
Table 1.
code
Ànser
Basses d’en Coll
Ter Vell
Litoral
Turies
Fra Ramon
Ànser
Basses d’en Coll
Ter Vell
Fra Ramon
Turies
Litoral
La Dulce
Santa Olalla
Lucio cangrejo
Tarelo
Algaida
La Dulce
Santa Olalla
Lucio cangrejo
Tarelo
Algaida
T (ºC)
Conductivity
(mS/cm)
Empordà Wetlands
Winter
AN1 10.9
1.4
BC1
12.4
1.7
TV1
12.5
4.5
LI1
10.1
17.1
TU1
8.1
17.6
FR1
13.5
25.0
Summer
AN2 21.0
1.2
BC2
19.0
2.3
TV2
24.7
5.4
FR2
29.9
23.1
TU2
21.1
31.3
LI2
20.1
Doñana Wetlands
Winter
DU1
8.5
SO1
9.7
LU1
7.5
TA1
12.0
AL1
8.2
Summer
DU2 17.9
SO2
19.4
LU2
21.7
TA2
22.2
AL2
24.4
DIN:SRP
DIN:DOC
PEP:PHO
91.4
147.9
37.8
0.3
5.3
9.6
39.9
26.9
1.14
0.02
0.05
0.14
12.5
8.3
4.4
160.0
8.2
8.7
56.3
33.8
0.7
0.3
0.8
11.08
4.12
0.16
0.01
0.142
5.1
6.7
8.4
9.4
13.6
37.0
0.1
0.03
21.3
1.0
3.7
7.2
16.1
28.3
13.5
128.2
24.9
40.3
54.1
0.07
0.74
1.83
6.66
12.37
0.4
1.9
3.6
2.0
0.6
0.8
2.8
4.8
14.7
24.7
10.8
3.5
33.7
69.4
0.9
0.11
0.27
0.62
0.18
0.33
0.8
2.2
1.1
6.3
3.2
Table 1. Main environmental variables and ratios classified by site and sampling period. Mean
values (n=3) are shown for each variable and ratio. Ratios are calculated as a quotient between
variables, they are not balances as described in the methods. Sites are listed in order of
increasing conductivity.
Table 2.
NO3-
Conductivity
TOP
PHO
r
p
r
p
r
p
r
p
n
Conductivity (mS/cm)
1
-
-
n.s.
0.480
0.028
-
n.s.
21
Nitrate (NO3- - µM)
-
n.s.
1
-
-
n.s.
-
n.s.
21
0.480
0.028
-
n.s.
1
-
-
n.s.
21
Phosphatase (PHO - µM/h)
-
n.s.
-
n.s.
-
n.s.
1
-
21
Temperature (º C)
-
n.s.
-
n.s.
0.540
0.012
-
n.s.
21
Ammonia (NH4+ - µM)
-
n.s.
0.466
0.033
-
n.s.
-
n.s.
21
Nitrite (NO2- - µM)
-
n.s.
0.670
<0.01
-
n.s.
-
n.s.
21
Soluble Reactive Phosphorus (SRP - µM)
-
n.s.
-
n.s.
0.559
<0.01
-
n.s.
21
Organic Nitrogen (TON - µM)
-
n.s.
-
n.s.
0.546
<0.01
0.544
0.011
21
Dissolved Organic Carbon (DOC - µM)
-
n.s.
-
n.s.
-
0.055
-
n.s.
21
Particulated Organic Carbon (POC - µM)
-
n.s.
-
n.s.
0.724
<0.01
-
n.s.
21
Peptidase (PEP - µM/h)
0.558
<0.01
-
n.s.
0.687
<0.01
Bacterial Biomass (BB - mg C/ L)
0.570
<0.01
-
n.s.
0.839
<0.01
-
n.s.
21
Nitrification Rhizome
n.s.
-
n.s.
-
n.s.
-
n.s.
16
Denitrification Rhizome
n.s.
-
n.s.
0.674
<0.01
-
n.s.
220
Nitrification Sediment
n.s.
0.624
<0.01
-
n.s.
-
n.s.
20
Denitrification Sediment
n.s.
-
n.s.
-
n.s.
-
n.s.
16
Organic phosphorus (TOP -µM)
0.584 <0.01
Table 2. Pearson coefficients between the significant raw data variables obtained by the RDA
analysis and the rest of the raw variables.
21
Table 3.
Conductivity
PEP: PHO
DIN:DOC
r
p
r
p
r
p
n
Conductivity
1
-
-
n.s.
-
n.s.
21
bPEP vs PHO
-
n.s.
1
-
-
n.s.
21
bDINvsDOC
-
n.s.
-
n.s.
1
-
21
Temperature
-
n.s.
-
n.s.
-0.435
0.049
21
bPEP vs BB
-0.628
<0.01
-0.433
0.05
-
n.s.
21
bPHO vs BB
-0.532
0.01
-0.905
<0.01
-
n.s.
21
bDIN vs SRP
-0.433
0.05
-
n.s.
0.741
<0.01
21
bNO2+NO3 vs NH4
-
n.s.
-
n.s.
0.725
<0.01
21
bDIN vs TON
-
n.s.
-
n.s.
0.937
<0.01
21
bNO2 vs NO3
-
n.s.
-
n.s.
-0.502
0.02
21
bSRP vs TOP
-
n.s.
-
n.s.
-
n.s.
21
bTON vs DOC
-
n.s.
-
n.s.
-
n.s.
21
bTON vs POC
-
n.s.
-
n.s.
-
n.s.
21
bTOP vs POC
-
n.s.
-
n.s.
-
n.s.
21
bTOP vs DOC
-
n.s.
0.493
0.02
-
n.s.
21
bDOC vs POC
-
n.s.
-
n.s.
-
n.s.
21
bNIT vs DEN Sed
-
n.s.
-
n.s.
0.571
0.02
16
bNIT vs DEN Rhi
-
n.s.
-
n.s.
-
n.s.
16
Table 3. Pearson correlation coefficients between the significant environmental balances
obtained by the RDA analysis and the rest of the balances calculated.
Figures
Figure 1. Location of the two study areas. Five and six sample sites were sampled twice in 2007
in Doñana and in Empordà Wetlands.
Figure 2. Ordination triplot based on the redundancy analysis (RDA) of phytoplankton data and
environmental variables of the studied wetlands. Only significant variables were represented
(p<0.1). The arrow lengths are proportional to the individual variables influence in the
ordination. Filled circles represent Empordà sites and empty circles represent Doñana sites.
Empordà wetlands: Bassa Ànser:AN; Basses d’en Coll:BC; Ter Vell:TV; Litoral:LI; Turies:TU;
Fra Ramon:FR and Doñana wetlands: La Dulce:DU; Santa Olalla:SO; Lucio cangrejo:LU;
Tarelo:TA; Algaida:AL. Sampling period 1: Winter and 2: Summer.
Figure 3. Ordination triplot based on the redundancy analysis (RDA) of phytoplankton data and
environmental variable ratios (log-contrasts) of the studied wetlands. Only significant variables
were represented (p<0.1). The arrow lengths are proportional to the individual variables
influence in the ordination. Filled circles represent Empordà sites and empty circles represent
Doñana sites. Wide arrows show the fresh-brackish water gradient found in every wetland.
Empordà: Bassa Ànser:AN; Basses d’en Coll:BC; Ter Vell:TV; Litoral:LI; Turies:TU; Fra
Ramon:FR and Doñana: La Dulce:DU; Santa Olalla:SO; Lucio cangrejo:LU; Tarelo:TA;
Algaida:AL. Sampling period 1: Winter and 2: Summer.
Figure 4. Nitrification and denitrification values of rhizomes and sediment in the different
sampled points, separated by marsh and by period. Empordà wetlands: Bassa Ànser:AN; Basses
d’en Coll:BC; Ter Vell:TV; Litoral:LI; Turies:TU; Fra Ramon:FR and Doñana wetlands: La
Dulce:DU; Santa Olalla:SO; Lucio cangrejo:LU; Tarelo:TA; Algaida:AL. Sampling period 1:
Winter and 2: Summer. Some data were not available due to methodological problems
(Rhizome data not available: denitrification in LI1 and LI2 and nitrification in AN1, AN2, AL1,
LI1 and TA1; Sediment data not available: denitrification in AN1, AN2, AL1, LI1 and TA1
and nitrification in LI1 and LI2).