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 Analysis; DIN:DOC ratio;

peptidase:phosphatase ratio; conductivity; shallow lakes

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 meso-

euhaline 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

𝑏𝐺1vs. 𝐺2 = ට 𝑟· 𝑠 𝑟+ 𝑠ln(ς 𝑥𝑗𝑥𝑗∈𝐺1 )1 𝑟Τ(ς 𝑥𝑙𝑥𝑙∈𝐺2 )1 𝑠Τ

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 = ඨ3 · 13+ 1 ln(NH4+ · NO2− · NO3−)1 3ΤSRP

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 Pawlowsky-

Glahn (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) (Díaz-

Delgado, 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. AcknowledgementsThe 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).

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Tables

Table 1.

code T (ºC) Conductivity

(mS/cm)

DIN:SRP DIN:DOC

PEP:PHO

Empordà WetlandsWinter

Ànser AN1 10.9 1.4 91.4 39.9 12.5Basses d’en Coll BC1 12.4 1.7 147.9 26.9 8.3Ter Vell TV1 12.5 4.5 37.8 1.14 4.4Litoral LI1 10.1 17.1 0.3 0.02 160.0Turies TU1 8.1 17.6 5.3 0.05 8.2Fra Ramon FR1 13.5 25.0 9.6 0.14 8.7

SummerÀnser AN2 21.0 1.2 56.3 11.08 5.1Basses d’en Coll BC2 19.0 2.3 33.8 4.12 6.7Ter Vell TV2 24.7 5.4 0.7 0.16 8.4Fra Ramon FR2 29.9 23.1 0.3 0.01 9.4Turies TU2 21.1 31.3 0.8 0.142 13.6Litoral LI2 20.1 37.0 0.1 0.03 21.3

Doñana WetlandsWinter

La Dulce DU1 8.5 1.0 13.5 0.07 0.4Santa Olalla SO1 9.7 3.7 128.2 0.74 1.9Lucio cangrejo LU1 7.5 7.2 24.9 1.83 3.6Tarelo TA1 12.0 16.1 40.3 6.66 2.0Algaida AL1 8.2 28.3 54.1 12.37 0.6

SummerLa Dulce DU2 17.9 0.8 10.8 0.11 0.8Santa Olalla SO2 19.4 2.8 3.5 0.27 2.2Lucio cangrejo LU2 21.7 4.8 33.7 0.62 1.1Tarelo TA2 22.2 14.7 69.4 0.18 6.3Algaida AL2 24.4 24.7 0.9 0.33 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.

Conductivity NO3- 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

Organic phosphorus (TOP -µM) 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

<0.0

1 - 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 0.584 <0.01 21

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

Table 2. Pearson coefficients between the significant raw data variables obtained by the RDA

analysis and the rest of the raw variables.

Table 3.

Conductivity PEP: PHO DIN:DOC

r p r p r p n

Conductivity 1 - - n.s. - n.s. 21bPEP vs PHO - n.s. 1 - - n.s. 21bDINvsDOC - n.s. - n.s. 1 - 21

Temperature - n.s. - n.s. -0.435 0.049 21bPEP vs BB -0.628 <0.01 -0.433 0.05 - n.s. 21bPHO vs BB -0.532 0.01 -0.905 <0.01 - n.s. 21bDIN vs SRP -0.433 0.05 - n.s. 0.741 <0.01 21bNO2+NO3 vs NH4 - n.s. - n.s. 0.725 <0.01 21bDIN vs TON - n.s. - n.s. 0.937 <0.01 21bNO2 vs NO3 - n.s. - n.s. -0.502 0.02 21bSRP vs TOP - n.s. - n.s. - n.s. 21bTON vs DOC - n.s. - n.s. - n.s. 21bTON vs POC - n.s. - n.s. - n.s. 21bTOP vs POC - n.s. - n.s. - n.s. 21bTOP vs DOC - n.s. 0.493 0.02 - n.s. 21bDOC vs POC - n.s. - n.s. - n.s. 21bNIT vs DEN Sed - n.s. - n.s. 0.571 0.02 16bNIT 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).