JPR Advance Access originally published online on July 25, 2008
Journal of Plankton Research 2008 30(11):1233-1243; doi:10.1093/plankt/fbn079
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Functional groups in marine phytoplankton assemblages dominated by diatoms in fjords of southern Chile
1 Instituto de Biología Marina, Universidad Austral de Chile, Campus Isla Teja, PO Box 567, Valdivia, Chile
2 Centro "i 
mar", Universidad de Los Lagos, Camino Chinquihue Km 6, Puerto Montt, Chile
3 Instituto de Investigaciones Oceanológicas, Universidad de Antofagasta, PO Box 117, Antofagasta, Chile
4 Instituto de Acuicultura, Centro de Investigación de Ecosistemas de la Patagonia (CIEP), Núcleo Milenio FORECOS and Programa Basal-COPAS, Universidad Austral de Chile, Campus Puerto Montt, Los Pinos S/N, Puerto Montt, Chile
* CORRESPONDING AUTHOR: catharinaalves{at}uach.cl
Received on May 12, 2008; accepted on July 23, 2008
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODRESULTSDISCUSSIONREFERENCES |
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The co-existence of phytoplankton assemblages under similar environmental conditions has allowed the identification of functional groups made up of species with similar morphological and physiological characteristics. Species belonging to similar functional groups in turn can be grouped in the three basic adaptive strategies C (colonist-invasives), S (stress-tolerants) and R (ruderals), these strategies being based on the species tolerances to different combinations of the degree of mixing and nutrient availability. In this study, we evaluated the applicability of the C–S–R strategies approach to marine diatom-dominated phytoplankton assemblages from fjords of southern Chile. Surface/volume ratios (S/V) and maximum linear dimensions were used to group the species in the three strategies regardless of their phylogenetic relationships. Multivariate statistical analyses (multiple correspondence and canonical correspondence) allowed us to identify three diatom groups. Group D1, made up of species with S/V > 1.5 µm–1 (Pseudo-nitzschia spp. group delicatissima, Cylindrotheca closterium, Leptocylindrus minimus), was correlated mainly with nitrate concentrations. In group D2, several species of the genus Chaetoceros (S/V
1 µm–1) were also correlated with nitrate. Species with attenuated forms and S/V ranging between 0.5 and 0.8 µm–1 (Skeletonema costatum, Talassionema nitzschioides, Rhizosolenia setigera) made up group D3, which was associated with stratified conditions and high silicate. |
INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONREFERENCES |
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The structure of the phytoplankton assemblages as well as the abundance of their species varies greatly according to the environmental conditions, mainly light and nutrient availability (Reynolds, 1997
). The broad range of shapes and cell sizes observed in species inhabiting similar environments (Margalef, 1978; Smayda, 1980) indicates a direct relationship between phytoplankton morphology and physiology (Lewis, 1976; Sournia, 1982). As phytoplankton population distributions are consequences of interactions between the species' life cycles and the system's selective properties, Margalef (Margalef, 1978) conceived a model in which nutrient concentrations and turbulence are the main factors determining marine phytoplankton species composition. This model, now well known as the "Margalef Mandala", reveals a sequence of species successions in phytoplankton assemblages in which diatoms dominate periods of mixing and high nutrient concentrations (r-strategists) and dinoflagellates prevail under oligotrophic and stratified conditions (K-strategists).
This model adequately explains the classic pattern of seasonal diatom-dinoflagellate succession observed in many temperate systems. However, it fails to explain the proliferation of certain species under conditions of stratification and high nutrient concentrations (Smayda and Reynolds, 2001). Inspired by the model proposed by Grime (Grime, 1979) for terrestrial vegetation, Reynolds (Reynolds, 1988, 1996, 1997) and Reynolds et al. (Reynolds et al., 2002) proposed an approach for freshwater phytoplankton similar to that formulated by Margalef (Margalef, 1978) in which the combination of nutrient availability and degree of mixing also determines the assemblage structure. However, this model includes several important modifications: (i) it considers the two axes, turbulence and nutrients, to be independent variables; (ii) the turbulence axis refers mainly to the vertical extent of mixing relative to decreasing light with depth, (iii) the convergence of morphological and physiological properties of the species characterize functional groups in which the phylogenetic origin of the species components is irrelevant and (iv) based on the maximum linear dimension (MLD) and surface/volume ratio (S/V) of the cells, typical representatives of each functional group present similar coordinates on a plot of MLD S/V against S/V (Reynolds, 1996, Fig. 4b).
Another important innovation of this approach is that the functional groups are divided into three primary strategies (C–S–R) rather than two (r–K). According to Reynolds (Reynolds, 1988), the C-strategists (colonist-invasives) are small, fast-growing species with high S/V ratios. They are very susceptible to grazing and are expected to dominate in stratified waters with high concentrations of nutrients and high light availability. The R-strategists (ruderals) are elongated in shape and, despite their large dimensions, have a high S/V ratio that affords them the advantage of harvesting light energy under high mixing conditions, but with high nutrient concentrations. The S-strategists (stress-tolerants) are large species with low S/V ratios and slow growth. These organisms are thought to dominate oligotrophic, high light conditions in which they can use strategies like mixotrophy and vertical migrations to obtain nutrients.
The Reynolds scheme of functional groups has been successfully applied to both freshwater phytoplankton (Huszar and Caraco, 1998; Padisák and Reynolds, 1998; Kruk et al., 2002; Alves-de-Souza et al., 2006) and marine dinoflagellates producing harmful algal blooms (Smayda and Reynolds, 2001, 2003). However, to the best of our knowledge, no attempt has been made to apply this model to planktonic marine diatoms. The cold waters of Chile's temperate southern coastal region offer an excellent opportunity to apply this model to marine phytoplankton assemblages dominated by diatoms. In this area, the interplay of nutrients and light limitation has been suggested to strongly influence primary production since phytoplankton assemblages may experience rapid changes in the type of limitation (light and nutrients) due to seasonal variability of climate and mixing conditions (Saggiomo et al., 1994; Iriarte et al., 2007).
Given the highly heterogeneous oceanographic conditions observed in the fjords and channels of southern Chile's inland seas, the different species of diatoms dominating the different sites are expected to use distinct strategies in order to adapt to the diverse trophic conditions and mixing levels. By correlating the species composition of phytoplankton assemblages with oceanographic conditions (mainly the degree of mixing and nutrient concentrations), we expect to be able to identify different functional groups within the large phylogenetic diatom group. In this study, we analyzed phytoplankton abundance data collected from fjords of southern Chile in order to (i) evaluate the applicability of the three basic adaptive strategies (C–S–R) proposed by Reynolds (Reynolds, 1988) to diatom-dominated marine phytoplankton assemblages, (ii) identify groups of species that could correspond to Reynolds functional groups in the assemblages and (iii) determine whether these groups are associated with environmental variables.
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METHOD |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONREFERENCES |
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Study area
The inland seas of southern Chile (41–56°S) include a variety of channel and fjord systems that are characterized by estuarine circulation which is determined by an offshore surface flow of fresh water over an onshore flow of oceanic water (Silva et al., 1995). The onshore flow delivers oceanic nutrients to the nearshore region below the surface waters, which are usually devoid of nutrients other than silicate (Silva and Neshyba, 1979; Iriarte et al., 2007). Then, mixing processes are necessary to bring nutrients up into the euphotic zone, which is usually stratified due the strong salinity gradient (Montecino et al., 2004). Except at some sites, the phytoplankton in this area has a heterogeneous spatial and temporal distributional pattern that is not characterized by the classic pattern of mixed waters dominated by diatoms versus stratified waters dominated by dinoflagellates (Iriarte et al., 2001). Regardless of the mixing conditions, diatoms are usually more abundant and constitute the main phytoplankton group throughout the year (Uribe, 1992; Iriarte et al., 1993; Lembeye et al., 1997). Dinoflagellate abundances are only important when they form blooms (Clément and Guzmán, 1989; Guzmán et al., 2002).
The study was restricted to the Magallanes Region at the southernmost part of Chile because it is the area for which the most information about the phytoplankton community is available. Owing to its large geographic extension, the study area (47°43'S–55°19'S) was divided into northern and southern regions (Fig. 1). To the best of our knowledge, there is no single data set for this area that covers both the spatial and seasonal distribution of phytoplankton and their relationship with nutrient concentrations. Therefore, we compiled data from two different complementary research studies which were analyzed independently with two different multivariate statistical analyses. We divided the study in four steps, which are described below.
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C–S–R strategies
Based on the past research on phytoplankton in the study area, we selected the most important phytoplankton species and according to their surface/volume ratio (S/V) and MLD assigned them to one of the three strategies (C–S–R) (Reynolds, 1988, 1996). The criterion for the selection of these species was based on their importance, considering their frequencies and/or abundances (Cabrini and Umani, 1991; Uribe, 1992; Iriarte et al., 1993; Avaria et al., 1999). The cell volume and surface area were calculated using equations for similar geometric shapes of the cell (Hillebrand et al., 1999) considering the cellular dimensions reported in the general literature for these species (Tomas, 1997). The values obtained were plotted on a dispersion graph with a logarithmic scale; the two axes were MLD S/V and S/V (Reynolds, 1996, 1997). We followed the guidelines indicated by Reynolds (Reynolds, 1988; see Table 10-2) for separating the species into the three strategies.
Detection of spatial and seasonal patterns
Considering that species with similar environmental requirements are expected to occur together, we recognized spatial and seasonal patterns in the co-occurrence of the species selected in the first step. We used phytoplankton abundance data published by Uribe et al. (Uribe et al., 1997) that cover an annual cycle (March 1996–March 1997) of the phytoplankton community at 20 sites in the Magallanes Region (Fig. 1). The integrated samples (0–20 m) were taken with a monthly or bimonthly frequency using a hose and preserved with acetic Lugol's solution (1%); the phytoplankton (>5 µm) abundances were then estimated according to Utermöhl (Utermöhl, 1958). Considering probable environmental differences between the seasons of the year and the latitudinal gradients of light and temperature observed along the study area (Uribe, 1992), a multiple correspondence analysis (MCA) was carried out to evaluate the possible association between the species and the seasons of the year (summer, autumn, winter, spring) and/or the geographical areas (northern, southern) (n = 139). The analysis was done using Statistica 6.0 (StatSoft). Prior to the analysis, the abundance data of these species were transformed logarithmically [ln (x+1)].
Relationships between species and environmental variables
To establish which environmental variables could determine the predominance of the different phytoplankton assemblages, we performed an additional analysis using another data set which includes phytoplankton abundance with simultaneous nutrient measurements in several localities of study area. This data set was collected as part of the Chilean CIMAR-Fjords Program (http://www.shoa.cl/cendhoc/index.htm) and has been partially published (Avaria et al., 1999, 2003; Silva et al., 2002; Sievers et al., 2002; Valdenegro and Silva, 2003). Each one of the 60 sampling stations were sampled once in October and November 1996 (southern area) and 1997 (northern area) (Fig. 1). The samples were taken with 5-L Niskin bottles at depths ranging from 5 to 10 m and preserved with Lugol's solution (1%). The phytoplankton (>5 µm) abundances were determined according to Utermöhl (Utermöhl, 1958). Simultaneously, measurements of temperature, salinity, water stability and dissolved inorganic nutrients (nitrate, orthophosphate and silicate) were obtained. The stability was used here as a measure of the degree of mixing and was calculated based on the water-column temperature and density.
Possible correlations between phytoplankton species abundance and environmental variables (n = 60), were evaluated with a canonical correspondence analysis (CCA) using CANOCO 4.0 software (ter Braak, 1995). The data were previously transformed logarithmically [ln (x+1)] and organized in a "biological" matrix that included the abundance of species present in at least 10% of the samples and an "explanatory" matrix including the measured environmental variables. Monte Carlo permutation testing (500 permutations; CANOCO 4.0) was used to determine the significance of the variables and the first two ordination axes.
Identification of functional groups
The functional groups were identified by considering the species that grouped together in all of the three first steps (C–S–R scheme, MCA, CCA), based mainly on the species that represented at least 5% of the total phytoplankton abundances in both data sets.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONREFERENCES |
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C–S–R strategies
According to the literature and the two data sets used in this study, the phytoplankton in the inland seas of southern Chile is composed of about 131 species; 74% of these species are diatoms, 19% dinoflagellates and 7% nanoflagellates. Of the recorded phytoplankton species, 36 predominated by their frequency and/or abundance; their wide range of shapes and sizes was reflected in their S/V ratios, which varied from 0.01 to 2.02 µm–1 (Table I). The dispersion graph of the S/V and MLD of these 36 species (Fig. 2) showed that they were grouped in the three strategies (C–S–R) regardless of their phylogenetic relationships. Most of the species (72.5%) were classified as R-strategists; although all diatoms (except Coscinodiscus sp.) were included in this strategy, they were distributed along a diagonal line, producing a continuum between small species (high S/V) and large species (low S/V) (Fig. 2). Beside the diatoms, the R-strategy also included some dinoflagellates (Alexandrium catenella, Ceratium fusus, Ceratium lineatum, Ceratium pentagonum) and one nanoflagellate (Dictyocha speculum). The rest of the species were distributed in the C (Eutreptiella sp., Heterocapsa triquetra, Scrippsiella trochoidea, Gymnodinium sp., nanoflagellates) and S strategies (Dinophysis acuminata, Dinophysis acuta, Coscinodiscus sp.).
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Spatial and seasonal patterns of phytoplankton assemblages
Considering traditional functional groups (i.e. dinoflagellates, diatoms and nanoflagellates), the data analyzed by Uribe et al. (Uribe et al., 1997), demonstrated a heterogeneous temporal and spatial phytoplankton distribution in terms of abundance and composition (Fig. 3). Phytoplankton concentrations were highest (182 cells L–1 104) in the northern area mainly in spring, whereas in the southern area, the highest concentrations (98 cells L–1 104) were observed in spring–summer. In general, diatoms constituted the most frequent and abundant group year-round in both areas; dinoflagellates were only important on some occasions in spring–summer. The nanoflagellates showed the largest spatial differences in the phytoplankton composition with the highest abundances in summer and autumn in the southern area. Of the total recorded species, 32 were present in 20% of the samples for at least one season of the year; of these, only five contributed with 5% of the total abundance: Pseudo-nitzschia spp. group delicatissima, Skeletonema costatum, Leptocylindrus minimus, Chaetoceros socialis, and Chaetoceros radicans (Table I).
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Although the diatom and dinoflagellate species compositions were very similar, with most of the species observed in both areas during the whole year, the species that contributed the most to the relative abundances differed considerably between the two areas and among the seasons of the year. These differences were evident in the MCA (Fig. 4A), whose spatial and temporal ordination of the species explained 23.7% of the total inertia (variance). The first dimension (16.6% of inertia) was correlated with the latitudinal gradient between the northern and southern areas, whereas the second dimension (8.1% of inertia) indicated the distribution of several phytoplankton assemblages according to the seasons of year. The species distribution revealed that, in autumn-winter, the assemblages were dominated by S. costatum, Thalassionema nitzschioides, Thalassiosira minuscula, Coscinodiscus sp., D. acuminata, and C. pentagonun in the northern area and by Rhizosolenia setigera, S. trochoideae, C. lineatum, and D. speculum in the southern area. In spring-summer, several species of Chaetoceros, and C. fusus were associated mainly with the northern area, whereas Pseudo-nitzschia spp. group delicatissima, Pseudo-nitzschia spp. group seriata, L. minimus, Leptocylindrus danicus, Cylindrotheca closterium, H. triquetra, Eutreptiella sp., and nanoflagellates were associated with the southern area.
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Relationships between phytoplankton assemblages and environmental variables
A preliminary CCA verified that salinity correlated positively with the nitrate concentration and negatively with the water stability. Hence, in order to eliminate the possible confounding effect on the model, salinity was excluded from a posterior CCA. Together, the eigenvalues of the first two canonical axes (0.078 and 0.061, respectively) explained 66% of the total variance. The species and environmental variables showed correlation values of 0.83 and 0.63 on canonical axes 1 and 2, respectively. The compositional axis 1 (SPEC AX1) was correlated mainly with water column stability, whereas, on the compositional axis 2 (SPEC AX2), nitrate and phosphate showed the highest correlations (Table II). Both axes were statistically significant (Monte Carlo testing, P = 0.002). In the forward stepwise model, nitrate (P = 0.008) and water stability (P = 0.002) were the only individual significant explanatory variables and there was no interaction between them (P = 0.225). The ordination diagram with the scores obtained confirmed that water stability and nitrate were the main variables responsible for the species distribution on the two axes (Fig. 4B). On axis 1, S. trochoidea, R. setigera, S. costatum, T. nitzschioides, and Pseudo-nitzschia spp. group seriata were associated with stratified conditions, whereas Thalassiosira decipiens, C. closterium, Coscinodiscus sp., C. radicans, and L. danicus were correlated with periods of mixing. The other species were distributed along axis 2, most of them associated with low nitrate levels.
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Identification of functional groups
Functional groups were identified by the consistent grouping of species in the three analyses performed, i.e. to consider that two species belong to the same functional group they must group together in the C–S–R scheme, MCA and CCA. Three diatom groups (D1, D2, D3) were identified. Group D1 was made up of species with S/V> 1.5 µm–1 (Pseudo-nitzschia spp. group delicatissima, C. closterium, L. minimus); they were predominant in the spring–summer in southern area and correlated mainly with nitrate concentrations. In group D2, several species of the genus Chaetoceros (S/V 1 µm–1) were also correlated with nitrate and had the highest abundances in spring–summer in the northern area. Species with attenuated forms and a S/V ranging between 0.5 and 0.8 µm–1 (S. costatum, T. nitzschioides, R. setigera) made up group D3, which had the highest abundances in autumn–winter and was associated with stratified conditions and high silicate.
The identification of dinoflagellate groups was hindered because only two species, C. pentagonum and S. trochoidea, were present at the same time in the two data sets used for the MCA and CCA. However, the fact that both species were distributed separately in the three analyses might suggest that they belong to different functional groups. Ceratium pentagonum, identified as an R-strategist species, was associated mainly with nitrate, whereas S. trochoidea was grouped with the C-strategist species and associated with water column stability.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONREFERENCES |
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The present work constitutes the first attempt at applying the scheme proposed by Reynolds (Reynolds, 1988
, 1996, 1997) and Reynolds et al. (Reynolds et al., 2002) to identify functional groups of marine planktonic diatoms, considering their occurrence in relation to different levels of nutrients and mixing conditions and independent of their seasonal characterteristics. According to Reynolds et al. (Reynolds et al., 2002), phytoplankton functional groups consist of species with morphological and physiological traits that have a similar ecology. The recognition of such functional groups per this definition implies that the species should share, at the same time, three key elements: shape, ecology and function. In this sense, our approach fits well with the definition of trait suggested by Violle et al. (Violle et al., 2007), i.e. any morphological or phenological feature measurable at the individual level, without reference to the environment or any other level of organization. Thus, the combined use of the three analyses performed in this work allowed us to objectively recognize phytoplankton functional groups: the C–S–R diagram allows us to identify the groups of species based on their morphological traits, independently of the environmental conditions, whereas with the MCA and CCA we could identify groups of species that co-occur under similar environmental conditions and probably present similar ecologies and physiologies.
The C–S–R diagram obtained in this study supports the suggestion of Reynolds (Reynolds, 1988) that phytoplankton species can be assigned to one of the three C–S–R strategies independent of their phylogenetic origin. Our results are also consistent with the Smayda and Reynolds proposal (Smayda and Reynolds, 2001) that indicates dinoflagellates are distributed in a distinctive way among these three strategies. Furthermore, our results indicate that the C–S–R model can be applied to marine phytoplankton in general. The fact that the dinoflagellate species were distributed among the three strategies but the diatoms (except for Coscinodiscus sp.) were recognized as R-strategists suggests an opposite tendency in the ecology of these two microalgal groups. These differences could be explained partly by the greater ecophysiological diversity of dinoflagellates when compared with diatoms and coccolithophorids (Iglesias-Rodríguez et al., 2002; Smayda, 2002; de Vargas et al., 2007, Kooistra et al., 2007; Litchman et al., 2007).
As in two previous studies carried out in this region (Uribe, 1992; Iriarte et al., 2001), diatoms constituted the main group during the whole year; the classic diatom-dinoflagellate succession, typical of other temperate systems, was not observed. Additionally, the treatment of the species according to traditional groups (Fig. 3) did not allow us to detect a valid general pattern for the whole study area. On the other hand, the results of the three analyses we used allowed us to identify three functional diatom groups correlated mainly with water stability (used here as a measure of the degree of mixing) and nitrate or silicate concentrations. The salinity, which has been previously identified as one of the main variables associated with the occurrence of phytoplankton assemblages in the fjords of southern Chile (Avaria et al., 1999, 2004; Pizarro et al., 2000), was also highly correlated with the phytoplankton distribution. However, since this variable showed a strong correlation with nitrate and water stability, it probably constitutes a co-variable. In fact, when salinity was discarded from the CCA model, the axes were highly significant and a better definition of the species distribution was observed.
Although both groups D1 and D2 were correlated to nitrate in the CCA, their different grouping in the ACM and their different S/V ratios indicate that they should be considered as separate groups. The lack of resolution in the distribution of these two groups associated with nitrate in the CCA could be explained because, in spite of the great importance of nutrient concentrations as a decisive factor in the phytoplankton species composition, it is often difficult to evaluate the relationship between nutrients and phytoplankton since the state of the assemblages is a consequence of the nutrient concentrations prior to sampling. Further studies on a short temporal scale focused on the physiology of the main species of D1 and D2 groups are then fundamental in order to establish the environmental factors determining the predominance of one group or another. The main species of group D3, S. costatum, apparently prefers semi-enclosed waters (Smayda, 1957) and was previously correlated with stratified conditions in other fjord systems (Tett et al., 1986; Haigh et al., 1992). To the north of the study area, blooms of this species can reach 24 cells L–1 106 (Avaria et al., 2004) and are commonly recorded at the heads of fjords with strong haline stratification and with high silicate in the upper meters due to the large freshwater contribution (Silva, 2007). Indeed, an experiment performed by Yoder (Yoder, 1979) demonstrated that silicate is the main nutrient limiting the growth of S. costatum which is also supported by field evidence (Pratt, 1965; Huo et al., 2001). In addition, the inverse relationship between the sedimentation rate and silicate has been recorded in this species, whereas no relationship was found between the sedimentation rate and nitrate or orthophosphate (Bienfang et al., 1982).
Although the three diatom groups were concentrated in the R-strategy, their distribution along a diagonal line is reminiscent of the r–K gradient proposed by Margalef (Margalef, 1978), which is indicated by the arrow in Fig. 2. This agrees well with the suggestion of Smayda and Reynolds (Smayda and Reynolds, 2003), who stated that dinoflagellates employ the three basic C–S–R strategies to exploit abiotic conditions of the habitat with r-and K-selected species in each strategy. Another interesting discovery observed in the C–S–R diagram is that the predominant diatom species in the study area were concentrated on the right side of the previously mentioned gradient. This confirms the observations of Avaria et al. (Avaria et al., 1999, 2003) that the predominant diatoms from inland seas of southern Chile area are r-selected species. Since groups D1 and D2 were correlated with nitrate and group D2 with silicate, it is possible to speculate that the species’ distributions on this diagonal line are given by a gradient in the Si:N ratio, which is highly dependent on the mixing conditions in these systems since these two nutrients come from different sources, i.e. nitrate comes from the onshore oceanic flow while the silicate comes from the offshore freshwater flow (Silva and Neshyba, 1979; Iriarte et al., 2007). This could explain the adjustment of the observed pattern to Margalef's model (Margalef, 1978), in which the nutrient concentration is directly correlated to mixing intensity. However, it is necessary to stress that the arrow in Fig. 2 does not represent a sequence of obligatory stages in a succession of species; rather, the functional groups could indicate mixing and nutrient conditions.
Dinoflagellates are usually considered to be indicators of stratified waters and low nutrients. However, Smayda and Reynolds (Smayda and Reynolds, 2001, 2003) demonstrated the possibility of identifying nine types of life forms in this phylogenetic group according to the C–S–R strategies. These life forms are, in turn, indicative of different habitats with several trophic and mixing conditions. According to the scheme proposed by these authors, the dinoflagellate species present in the study area might be classified as life forms II–IV and VII (Table III). At first glance, these results might seem to contradict the Smayda and Reynolds scheme (Smayda and Reynolds, 2001, 2003) because life forms that are characteristic of different types of habitats are found in the same geographical area. However, due to the strong environmental heterogeneity observed in the study area, local conditions vary in terms of mixing conditions and nutrient availability (Silva et al., 1995; Silva, 2007). Thus, these species can be correctly classified according to the Smayda and Reynolds scheme (Smayda and Reynolds, 2001). Our analysis demonstrates that dinoflagellates can be classified in the three C–S–R strategies and that this classification agrees with that of the above authors for the same species in question (to compare, see results of Fig. 3 and Table III). Moreover, this study and other previously published information for the study area and other systems located to the north (Clément and Guzmán, 1989; Uribe and Ruiz, 2001; Molinet et al., 2003; Iriarte et al., 2005) allow us to infer that the model outlined by Smayda and Reynolds (Smayda and Reynolds, 2001) can be applied relatively well to the harmful dinoflagellate blooms in the fjord and channel systems of the inland seas off southern Chile.
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In summary, our results suggest that the scheme proposed by Reynolds (Reynolds, 1988
, 1996, 1997) and Reynolds et al. (Reynolds et al., 2002) for freshwater phytoplankton can be successfully applied to marine phytoplankton, including the assemblages dominated by diatoms. Moreover, our results suggest that both Reynolds's C–S–R scheme and Margalef's r–K continuum are valid and, therefore, both models can be used as complementary approaches. The abundance data for the principal species indicated that diatoms are not a uniform group in their ecological responses; instead, they can be classified in different functional groups, which, in this study, were correlated mainly to the degree of water stability and the predominant available nutrient (nitrate or silicate). These conclusions suggest that the treatment of phytoplankton assemblages in traditional functional groups (i.e. dinoflagellates, diatoms and nanoflagellates) could results in a loss of information about the environmental factors determining community dynamics since the different species in each of these groups frequently have different ecological and physiological requirements. However, we want to emphasize that the approach we have used the needs to be improved, since there were several species that could not be classified as belonging to one group or another. In order to define the existing functional diatom groups and the environmental variables determining their predominance, more physiological studies, mainly about the nutrient uptake of the species, are required. The development of the approach for diatoms proposed in this study could be of great value for understanding the factors determining the high spatial heterogeneity of primary production in the fjords systems of southern Chile. |
ACKNOWLEDGEMENTS |
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We are grateful to the Fondo de Investigación Pesquera (FIP) and the Chilean CIMAR-Fjords Program (Comisión Oceanográfica Nacional, CONA) for allowing us access to the data used in this review. We especially wish to thank all the researchers who were involved in the generation of this information. We are also appreciative of Dr Lucia Helena Sampaio da Silva who read and commented on the manuscript and to Dr Colin S. Reynolds for his motivation and helpful comments.
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Notes |
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Corresponding editor: William Li
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