TY - JOUR
AU - Espinoza-González,, Oscar
AB - Abstract Alexandrium catenella, the main species associated with harmful algal blooms, has progressively increased its distribution through one of the most extensive and highly variable fjord systems in the world. In order to understand this successful expansion, we evaluated the effects of different salinities, light intensity, temperatures, nitrogen (N) forms and nitrogen/phosphate (N:P) ratio levels on the growth performance, using clones isolated from different locations across its wide geographic distribution. Results showed that the growth responses were plastic and, in some cases, different reaction norms among clones were observed. Despite plasticity, the optimal growth of A. catenella (i.e. highest growth rate and highest maximal cells density) was reached within a narrow thermal range (12–15°C), while salinity (20–30 PSU) and light intensity (20–120 μmol m−2 s−1) ranges were wider. These results are partially consistent with the highest cell densities recorded in the field. Furthermore, optimal growth was reached using reduced forms of nitrogen (i.e. urea and NH4+) and in unbalanced N:P ratios (18:1 and 30:1). These characteristics likely allow A. catenella to grow in highly variable environmental conditions and might partly explain the recent expansion of this species. INTRODUCTION Alexandrium is one of the most studied dinoflagellate genera due to its diversity, worldwide distribution and the severity of the toxic outbreaks they generate (Anderson et al., 2012). Some of these species belong to the Alexandrium tamarense species complex (Lilly et al., 2007), previously formed by the morphospecies Alexandrium catenella, A. tamarense and Alexandrium fundyense but now segregated into five distinct genetic clades or cryptic species: A. catenella (Group I), Alexandrium mediterraneum (Group II), A. tamarense (Group III), Alexandrium pacificum (Group IV) and Alexandrium australiense (Group V) (John et al., 2014; Willem, 2017). These species live throughout the Northern and Southern hemispheres generating periodic bloom events in many coastal locations across the world, with apparent range expansions in some areas (Anderson et al., 1994; Sebastian et al., 2005; Persich et al., 2006; Nagai et al., 2007; Montoya et al., 2010). Thus, the capacity of the different species from the A. tamarense species complex to colonize new habitats seems to indicate that the vegetative cells have a high potential for growth within a highly variable environment. In Southern Chile, A. catenella, the main species associated with harmful algal blooms (HABs), has progressively increased its distribution through one of the most extensive fjord systems in the world (Iriarte et al., 2014). This species was first reported in 1972 at ca. 56° S; subsequent monitoring programs have recorded its progressive northward expansion over the past 40 years, reaching ca. 43° S in 2002 (Molinet et al., 2003; Varela et al., 2012). In 2016, a severe bloom of this species spread from the northern part of the Aysén Region (ca. 44.5° S) up to the Desertores Islands (ca. 42.6° S) in the Chiloé Inland Sea, and surprisingly for the first time the bloom was recorded along the exposed Pacific Ocean coast extending up to ca. 39.7° S (Paredes et al., 2019). The extensive area of the Chilean fjords is characterized by a heterogeneous and highly variable environment formed by an intricate coastline with many islands, peninsulas, fjords and channels (Iriarte et al., 2014). This covers a north–south extension of ca. 1000 km, from 41.51° S (Seno de Reloncaví) to 55.91° S (Cabo de Hornos), and a geographical area of ca. 240 000 km2 (Iriarte et al., 2014). The freshwater plume from the fjords, flowing into the adjacent oceanic waters, generates sharp latitudinal, longitudinal and vertical variability in salinity, temperature and nutrients (Iriarte et al., 2014). Thus, surface water with low salinity and poor nutrient content (0–8 μM nitrate and 0–0.8 μM phosphates), except for silicate (Torres et al., 2011), overlies the oceanic salty water rich in nutrients (12–24 μM NO3− and 1.2–2.4 μM phosphates), producing a strong vertical gradient (Silva, 2008; Schneider et al., 2014). Meanwhile, adjacent to the fjords, the oceanic water exhibits higher inorganic nutrient concentrations (NO3− and phosphate) with a pronounced north–south gradient, with the highest concentrations in the south (5–15 μM NO3− and 0.5–1 μM phosphates in the surface water between 43° S and 55° S; Iriarte et al., 2014). Similarly, the average sea surface temperatures decreases from 10°C down to <6°C between the northernmost section of Patagonia (ca. 41° S) and the Antarctic Polar Front (ca. 55° S) while salinity increases from ca. 32.7 to 34.5 PSU in the upper 100 m (Iriarte et al., 2014). Light availability increases during spring–summer, though light in the surface waters may be limited by the vertical light attenuation due to a sizable supply of sediment in the freshwater input from large rivers, ice melting and rainfall (2000–7000 mm year−1), in addition to the dissolved and particulate organic matter (Huovinen and Gomez, 2011; Iriarte et al., 2014). Thus, light for phytoplankton photosynthesis could vary from the surface down to the lower limit of the euphotic zone, 5–20 m deep, depending on the season and/or geographic area (Huovinen and Gomez, 2011; Montero et al., 2017). Considering that A. catenella cells are migrants, information concerning their growth performance is highly desirable in order to understand the range expansion of the species. The cell growth performance of phytoplankton species can be estimated based on growth rate and maximum cell density (Bell, 1991). The estimation of these growth responses can give us an insight into the ecological and evolutionary aspects of its life cycle (Bell, 1991; MacIntyre and Cullen, 2005). For example, the growth rate, estimated using a common garden experimental design (different strains cultured under the same conditions), would allow the reaction norms of the clones, i.e. the set of phenotypes that can be produced by an individual genotype when exposed to different environmental conditions (Fusco and Minelli, 2010), to be described and compared. This can allow identification of the factors that explain the growth rate variability, such as genotype (G), environment (E) and the genotype by environment interaction (G × E), to be assessed (e.g. Gsell et al., 2012; Strom et al., 2013; Sassenhagen et al., 2015). The width of the reaction norm with respect to a particular environmental driver can reveal the plasticity of an organism in a species, namely the ability of an individual genotype to produce different phenotypes when exposed to a particular environmental gradient (c.f. Fusco and Minelli, 2010). Furthermore, this kind of experiment allows the genetic variability in plasticity, i.e. non-parallel reaction norm for different genotypes of the same species to be observed (c.f. Fusco and Minelli, 2010). Different reaction norms based on genetic differences might suggest local adaptation of a genotype to a particular set of environmental condition (Falconer and Mackay, 1996). This mechanism has been invoked to explain different growth performances among strains of HABs species isolated from different localities (Jensen and Moestrup, 1997; Siu et al., 1997; Nguyen-Ngoc, 2004; Boer et al., 2005). Both the plasticity and the diversity of reaction norms are relevant traits in determining potential of adaptation and responses to a variable environment (Richards et al., 2006). On the other hand, knowing the growth performance of a species across an environmental gradient can give us an insight into the environmental window driving the specific physiological responses involved bloom initiation and development (e.g. Etheridge and Roesler, 2005; Jauzein et al., 2008; Bill et al., 2016). In the Chilean fjords, there are a number of studies on A. catenella that have assessed the effects of temperature (Navarro et al., 2006; Aguilera-Belmonte et al., 2013), salinity (Aguilera-Belmonte et al., 2013) and light (Montecino et al., 2001) on growth responses. Although, many of these experiments were performed using a single strain, collected from one locality and tested under a few environmental factors and different experimental designs that preclude comparisons among studies. The present work aims to describe the growth rate responses of different genotypes of A. catenella (i.e. reaction norms) and assess the influence of the explanatory factors (i.e. G, E and G × E) that may account for the observed variability in their responses. In addition, we determine the environmental ranges for the optimal growth of A. catenella, i.e. the range of conditions over which the growth is significantly higher, and discuss how these ranges could favor bloom development. To do this, clones of A. catenella isolated from different localities were cultured under different environmental gradients following a common garden experiment approach, considering different salinities, light intensities, temperatures, nitrogen/phosphate (N:P) ratios or nitrogen forms as factors for the gradients. METHOD Clone isolation The strains of A. catenella were isolated from different localities across their current geographic distribution (Fig. 1). The cultures were initiated from controlled cyst germination or from phytoplankton samples collected in different years (Table I). Strains Q06, Q09 and Q12 (from Quellón, Chiloé), SD01 (from Santo Domingo) and AY02 (from Bahía Low) were isolated from cysts, while ACC02 and ACC07 (from Canal Costa), A1 (from Puerto Edén) and K7 (from Canal Beagle) were isolated from phytoplankton samples. In both cases, the vegetative cells were isolated by capillary manipulation using an inverted microscope (Eclipse T100, Nikon Corp., Tokyo, Japan) and then transferred to culture plates (Nunclon D Surface, Nunc S/A, Denmark). After the initial growth, the clonal cultures were transferred to 125-mL Erlenmeyer flasks and kept in growth chamber under constant conditions, as indicate below, until the experiments in 2010. Fig. 1 Open in new tabDownload slide Map of Southern Chile indicating the sampling locations from where the clones of A. catenella were isolated (from sediment or water samples) and the administrative regions (Los Lagos, Aysén and Magallanes). Fig. 1 Open in new tabDownload slide Map of Southern Chile indicating the sampling locations from where the clones of A. catenella were isolated (from sediment or water samples) and the administrative regions (Los Lagos, Aysén and Magallanes). Table I Information on the A. catenella clones isolated from different localities in distinct regions within the Chilean fjords Strains . Locality . Lat/long . Region . Year . Life cycle . Q06 Quellón 43.1° S/73.4° W Los Lagos 2004 Cyst Q09 Quellón 43.1° S/73.4° W Los Lagos 2009 Cyst Q12 Quellón 43.1° S/73.4° W Los Lagos 2009 Cyst SD01 Santo Domingo 43.6° S/73.0° W Aysén 2004 Cyst ACC02 Canal Costa 45.5° S/73.6° W Aysén 1994 Vegetative cell ACC07 Canal Costa 45.5° S/73.6° W Aysén 1994 Vegetative cell AY02 Bahia Low 44.0° S/73.7° W Aysén 2009 Cyst A1 Puerto Edén 49.2° S/74.4° W Magallanes 2009 Vegetative cell K7 Canal Beagle 54.9° S/68.4° W Magallanes 2009 Vegetative cell Strains . Locality . Lat/long . Region . Year . Life cycle . Q06 Quellón 43.1° S/73.4° W Los Lagos 2004 Cyst Q09 Quellón 43.1° S/73.4° W Los Lagos 2009 Cyst Q12 Quellón 43.1° S/73.4° W Los Lagos 2009 Cyst SD01 Santo Domingo 43.6° S/73.0° W Aysén 2004 Cyst ACC02 Canal Costa 45.5° S/73.6° W Aysén 1994 Vegetative cell ACC07 Canal Costa 45.5° S/73.6° W Aysén 1994 Vegetative cell AY02 Bahia Low 44.0° S/73.7° W Aysén 2009 Cyst A1 Puerto Edén 49.2° S/74.4° W Magallanes 2009 Vegetative cell K7 Canal Beagle 54.9° S/68.4° W Magallanes 2009 Vegetative cell Open in new tab Table I Information on the A. catenella clones isolated from different localities in distinct regions within the Chilean fjords Strains . Locality . Lat/long . Region . Year . Life cycle . Q06 Quellón 43.1° S/73.4° W Los Lagos 2004 Cyst Q09 Quellón 43.1° S/73.4° W Los Lagos 2009 Cyst Q12 Quellón 43.1° S/73.4° W Los Lagos 2009 Cyst SD01 Santo Domingo 43.6° S/73.0° W Aysén 2004 Cyst ACC02 Canal Costa 45.5° S/73.6° W Aysén 1994 Vegetative cell ACC07 Canal Costa 45.5° S/73.6° W Aysén 1994 Vegetative cell AY02 Bahia Low 44.0° S/73.7° W Aysén 2009 Cyst A1 Puerto Edén 49.2° S/74.4° W Magallanes 2009 Vegetative cell K7 Canal Beagle 54.9° S/68.4° W Magallanes 2009 Vegetative cell Strains . Locality . Lat/long . Region . Year . Life cycle . Q06 Quellón 43.1° S/73.4° W Los Lagos 2004 Cyst Q09 Quellón 43.1° S/73.4° W Los Lagos 2009 Cyst Q12 Quellón 43.1° S/73.4° W Los Lagos 2009 Cyst SD01 Santo Domingo 43.6° S/73.0° W Aysén 2004 Cyst ACC02 Canal Costa 45.5° S/73.6° W Aysén 1994 Vegetative cell ACC07 Canal Costa 45.5° S/73.6° W Aysén 1994 Vegetative cell AY02 Bahia Low 44.0° S/73.7° W Aysén 2009 Cyst A1 Puerto Edén 49.2° S/74.4° W Magallanes 2009 Vegetative cell K7 Canal Beagle 54.9° S/68.4° W Magallanes 2009 Vegetative cell Open in new tab Genotypic characterization The taxonomic identity of the clones used in this study was morphological and genetically confirmed in previous study (Varela et al., 2012), but to assert that they represented distinct genotypes, the molecular marker amplified fragment length polymorphism (AFLP) was used. The DNA was extracted using a standard phenol–chloroform method (Sambrook et al., 1989), and AFLP procedure was carried out following Vos et al. (Vos et al., 1995) and Paredes et al. (Paredes et al., 2019), using 100 ng of DNA. Selective amplification was performed using FAM-labelled dye primers EcoRI (5′GACTGCGTACCAATTCXXX3′) and MseI (5′GATGAGTCCTGAGTAAXXX3′), in the following four combinations: EcoRIAAG × MseICTA, EcoRIAAG × MseICTT, EcoRIACC × MseICTA and EcoRIACC × MseICTT. AFLP amplification was performed by touchdown PCR program using a Thermo Px2 PCR thermocycler (Thermo Electron Corporation, MA, USA), and the resulting AFLP products were separated using an ABI PRISM 3500 xl (Applied Biosystems) sequencer. The presence/absence of comigrating AFLP fragments per clone was determined using GeneMarker software to establish the genotypes presence/absence matrix. The peak detection threshold ranged from 100 to 15 000 relative fluorescence units (RFUs), and fragments from 50 and 400 pb were considered. Then, a genetic distance among A. catenella clonal strains was estimated using Nei’s (Nei, 1978) corrected genetic distance. This analysis was performed using the R software (R Core Team, 2017) and the “poppr” package. Experimental design Following a common garden experimental design, i.e. several clones (genotypes) tested under the same environmental conditions (Wood et al., 2005), five independent experiments were conducted to evaluate the effects of salinity, temperature, light intensity, nitrogen forms and nitrogen/phosphate ratios on the growth rate and maximum cell density of A. catenella. In each experiment, a single factor was tested using at least four of the nine clones of A. catenella isolated. Additionally, previous to the experiment, all clones were acclimated for 1 week under the experimental conditions to minimize the effect of previous culture conditions (Lakeman et al., 2009). At the beginning of each experiment, 200 mL of culture L1 medium (Guillard and Ryther, 1962) was placed in Erlenmeyer flasks with an initial cell density of 200 cells/mL, and at least three replicate flasks were considered per level. During the experimental period, every other day, three subsamples of 1-mL culture were collected, and cells were counted using a Sedgewick Rafter plate (1 mL/1 μL) in an inverted microscope (Olympus CKX 42). For each replicate, the growth rate was determined through a linear regression model γi = α + βχi (Guillard and Hargraves, 1993), where γi = ln-transformed cell density (cells/mL); χi = time (days); α = intercept; and β = growth rate (cells div/day). These α and β parameters were estimated by the least squares method. The cell density at the end of the exponential phase (between 15 and 25 days, depending on the experiment) was used as the maximum cell density response (cells/mL). Table II Genetic comparison among A. catenella clones through Nei’s (Nei, 1978) genetic distance . Q06 . Q09 . Q12 . SD01 . AC007 . ACC02 . AY02 . A1 . K7 . Q06 0 Q09 0.585 0 Q12 0.523 0.426 0 SD01 0.614 0.699 0.650 0 C007 0.551 0.609 0.565 0.532 0 ACC02 0.738 0.580 0.767 0.560 0.537 0 AY02 0.672 0.609 0.556 0.672 0.645 0.585 0 A1 0.509 0.509 0.435 0.699 0.738 0.726 0.551 0 K7 0.654 0.686 0.578 0.549 0.559 0.540 0.531 0.754 0 . Q06 . Q09 . Q12 . SD01 . AC007 . ACC02 . AY02 . A1 . K7 . Q06 0 Q09 0.585 0 Q12 0.523 0.426 0 SD01 0.614 0.699 0.650 0 C007 0.551 0.609 0.565 0.532 0 ACC02 0.738 0.580 0.767 0.560 0.537 0 AY02 0.672 0.609 0.556 0.672 0.645 0.585 0 A1 0.509 0.509 0.435 0.699 0.738 0.726 0.551 0 K7 0.654 0.686 0.578 0.549 0.559 0.540 0.531 0.754 0 Open in new tab Table II Genetic comparison among A. catenella clones through Nei’s (Nei, 1978) genetic distance . Q06 . Q09 . Q12 . SD01 . AC007 . ACC02 . AY02 . A1 . K7 . Q06 0 Q09 0.585 0 Q12 0.523 0.426 0 SD01 0.614 0.699 0.650 0 C007 0.551 0.609 0.565 0.532 0 ACC02 0.738 0.580 0.767 0.560 0.537 0 AY02 0.672 0.609 0.556 0.672 0.645 0.585 0 A1 0.509 0.509 0.435 0.699 0.738 0.726 0.551 0 K7 0.654 0.686 0.578 0.549 0.559 0.540 0.531 0.754 0 . Q06 . Q09 . Q12 . SD01 . AC007 . ACC02 . AY02 . A1 . K7 . Q06 0 Q09 0.585 0 Q12 0.523 0.426 0 SD01 0.614 0.699 0.650 0 C007 0.551 0.609 0.565 0.532 0 ACC02 0.738 0.580 0.767 0.560 0.537 0 AY02 0.672 0.609 0.556 0.672 0.645 0.585 0 A1 0.509 0.509 0.435 0.699 0.738 0.726 0.551 0 K7 0.654 0.686 0.578 0.549 0.559 0.540 0.531 0.754 0 Open in new tab In each experiment, one environmental factor was modified at time, while the others were kept constant, i.e. 30 ± 5 μmol m−2 s−1 (provided by fluorescent lamps), photoperiod 16 L:8 D, salinity at 30 PSU, temperature at 12 ± 1°C and L1 medium (except when N and P were modified). Salinity effect on A. catenella was tested at four levels, 15, 20, 25 and 30 PSU, using clones, ACC07, AY02, Q06 and Q12, and with three replicates for each clone (n = 48). These clones were isolated from a geographic area where a strong salinity gradient has been described (Silva and Guzmán, 2006; Sievers, 2008; González et al., 2011). The different levels of salinity were achieved by diluting natural filtered seawater (0.2 μm) with distilled water or adding NaCl, before preparing L1 medium. Temperature effect was tested at four levels, 8, 12, 15 and 18°C, using the clones K7, A1, AY02 and Q09 and with four replicates for each clone (n = 64). The origin of these specific clones represents a wide latitudinal range from the geographic distribution of A. catenella throughout the Chilean fjords (Table I). The different temperatures were provided by culture chambers. Light intensity effect was tested at four levels, 20 ± 5, 50 ± 5, 120 ± 5 and 250 ± 5 μmol m−2 s−1, using the clones K7, A1, AY02 and Q09 and with four replicates for each clone (n = 64). The light intensity was adjusted by modifying the number and distance of the fluorescent lamps (white light). The light intensity was measured through quantum photometer (LI-250Q PAR System, LI-COR). N:P ratio was tested at five ratio levels, 3:1, 9:1, 18:1, 30:1 and 90:1, using K7, A1, AY02 and Q09 clones and with four replicates for each clone (n = 80). Each level of N:P ratio was achieved by keeping the nitrate concentration constant at 145 |$\mu$|M and varying the phosphate concentration at 50, 5, 8, 16 and 1.6 |$\mu$|M. Forms of nitrogen effects were evaluated with three nitrogen forms: NH4+ (NaH2PO4), NO3− (NaNO3) and urea using A1, AC02, AY02, Q12 and SD01 clones and with three replicates for each clone (n = 45). All the forms of nitrogen were used at 100-μM final concentrations. In nutrient experiments (N:P ratio and N forms), unlike the others, each culture was incubated in sterile L1 medium prepared with artificial seawater. Field data In order to explore how well our results for optimal growth conditions compared with the abundance of cells observed in the field, data from a monitoring program database of cell abundance was used. This database was built by monthly monitoring in 208 stations across the Chilean fjords system from 2006 to 2015 by the HAB Management and Monitoring Program of the Instituto de Fomento Pesquero (IFOP). This database is available in several reports at http://biblioteca.ifop.cl/F and can be accessed as “guest” (“invitado” in Spanish) and using “Alexandrium” as keyword. In this monitoring program, phytoplankton samples were collected along with measurements of water temperature and salinity (CTD-O Seabird 19 plus V2) at each station from the Los Lagos (n = 55), Aysén (n = 75) and Magallanes regions (n = 66). The phytoplankton samples were collected from the surface down to a depth of 10 m using integrating hose sampler (Lindahl, 1986). These samples (125 mL) were fixed with Lugol’s solution and dark stored. The Utermöhl (Utermöhl, 1958) method was followed for counting A. catenella cells, using 10 mL sedimentation chambers. The counts were made with a Zeiss AXIO VERT A1 inverted microscope at ×40 magnification. Statistical analyses The influence of genotype (G), environment (E) or the interaction between genotypes and environment (G × E) on the variability of growth rates were estimated through a general linear model (GLM) fitted for each experiment. Using this model, the growth rate variability can be partitioning into its variance components, VP(growth rate) = VG + VE + VG × E, where VP is the total phenotypic variance for growth rate; VG is the proportion of phenotypic variation attributable to genotypes (clones); VE is the proportion of phenotypic variation attributable to environment variation (plastic trait); and VG × E is the genotype by environment interaction (genetic variation for plasticity). Furthermore, when a G × E effect was found, and in order to visualize the differences in the reaction norms among clones, the main effect of the environmental factor was evaluated using a one-way ANOVA per genotype. These analyses were performed using a linear model (LM) framework, and the assumption of homoscedasticity and normality were tested by Shapiro–Wilk and Levene tests, respectively. On the other hand, in order to determine the optimal growth range of A. catenella under different conditions, a generalized linear mixed model (GLMM) was used (Venables and Ripley, 2002). In these cases, the clone factor was defined as a random effect, considering clones as samples of A. catenella, and the environmental factor was defined as a fixed effect, i.e. levels of interest for hypothesis testing. In the GLM and GLMM analyses a Gaussian residuals distributions model was fitted to the growth rate, while, for the maximum cell density response, a negative binomial residual distributions model was fitted. When a significant effect was found in the LM and GLMM, a Tukey’s HSD multiple comparisons test was used. In each case, the null statistical hypotheses were rejected under a significance level (α) of 0.05. All analyses were performed with R software (R Core Team, 2017), and GLMM was fitted using the R package “lme4” (Bates et al., 2015). RESULTS Genetic characterization Genetic characterization through AFLP showed no identical genotypes among the nine clones analyzed. The genetic analysis, considering the 367 loci from AFLP raw data, showed high levels of genetic distance among clones, ranging between 0.426 (Q09 vs Q12) and 0.767 (Q12 vs ACC02) (Table II). This confirms that the clones used in the present study correspond to different genotypes of A. catenella. Components of the growth rate variation In most of the experiments, all the explanatory components of the variance had a significant effect on the growth rate. The observed variance of growth rate was explained mainly by the variation in environmental factors (E), followed to a lesser extent by interaction (G × E) and genotype (Figs 2 and 3; Table III). The exceptions to these were observed in the light intensity experiment where no significant effect of genotype and G × E interaction were found, also in the temperature experiment for the genotype factor, and N form experiment for the G × E interaction (Table III). Furthermore, the main effect of the environment showed differences in the norm reaction among clones, where some of these were able to grow similarly across environmental gradient, while others had significantly higher growth rates at narrower ranges. The growth rates of ACC07, AY02 and Q6 genotypes did not show differences among salinity treatments. Similarly, the growth rate of A1 did not vary among the temperature and N:P ratio treatments (LM; P > 0.05. Fig. 2A–C). However, the other genotypes (i.e. Q12 in salinity or K7, AY02 and Q09 in temperature experiments) were significantly affected by the environmental variation (LM; P < 0.05. Fig. 2A–C). Furthermore, differences in the growth rate were observed among clones isolated from the same locality and/or region. For example, Q12 achieved a high growth rate in a narrower range along the salinity gradient (from 20 to 30 PSU) than Q06 clone, which did not change with salinity variation (Fig. 2A). Fig. 2 Open in new tabDownload slide Growth rates of A. catenella clones cultured in (A) different salinities (at 30 ± 5 μmol m−2 s−1 of light intensity and 12 ± 1°C), (B) temperatures (at 30 ± 5 μmol m−2 s−1 of light intensity and at 30 PSU) and (C) light intensity (at 30 PSU and 12 ± 1°C). Different letters above a boxplot denote significant differences of a single genotype among environmental levels (Tukey post hoc test, after one-way ANOVA). Fig. 2 Open in new tabDownload slide Growth rates of A. catenella clones cultured in (A) different salinities (at 30 ± 5 μmol m−2 s−1 of light intensity and 12 ± 1°C), (B) temperatures (at 30 ± 5 μmol m−2 s−1 of light intensity and at 30 PSU) and (C) light intensity (at 30 PSU and 12 ± 1°C). Different letters above a boxplot denote significant differences of a single genotype among environmental levels (Tukey post hoc test, after one-way ANOVA). Fig. 3 Open in new tabDownload slide Growth rates of A. catenella clones cultured in different N:P ratios (A) and nitrogen form (B). Different letters above boxplot denote significant differences of a single genotype among environmental levels (Tukey post hoc test, after one-way ANOVA). Fig. 3 Open in new tabDownload slide Growth rates of A. catenella clones cultured in different N:P ratios (A) and nitrogen form (B). Different letters above boxplot denote significant differences of a single genotype among environmental levels (Tukey post hoc test, after one-way ANOVA). Table III Two-way ANOVA for the growth rate Explanatory variables . Growth rate (cells div/day) . Df . Dr . Df . Rd . P . %Ov . Null 47 0.018 Salinity 3 0.006 44 0.013 <0.05 32 Clone 3 0.003 41 0.010 <0.05 14 S × C 9 0.004 32 0.006 <0.05 22 Null 63 0.016 Temperature 3 0.009 60 0.008 <0.05 53 Clone 3 0.000 57 0.008 >0.05 1 T × C 9 0.004 48 0.004 <0.05 22 Null 63 0.012 Light intensity 3 0.004 60 0.012 <0.05 29 Clone 3 0.000 57 0.009 >0.05 2 L × C 9 0.002 48 0.007 >0.05 13 Null 79 0.032 N:P ratio 3 0.013 76 0.018 <0.05 37 Clone 4 0.002 72 0.030 <0.05 7 N × C 12 0.008 60 0.011 <0.05 24 Null 44 0.031 N forms 4 0.009 40 0.022 <0.05 28 Clone 2 0.005 38 0.017 <0.05 16 N × C 8 0.004 30 0.013 >0.05 12 Explanatory variables . Growth rate (cells div/day) . Df . Dr . Df . Rd . P . %Ov . Null 47 0.018 Salinity 3 0.006 44 0.013 <0.05 32 Clone 3 0.003 41 0.010 <0.05 14 S × C 9 0.004 32 0.006 <0.05 22 Null 63 0.016 Temperature 3 0.009 60 0.008 <0.05 53 Clone 3 0.000 57 0.008 >0.05 1 T × C 9 0.004 48 0.004 <0.05 22 Null 63 0.012 Light intensity 3 0.004 60 0.012 <0.05 29 Clone 3 0.000 57 0.009 >0.05 2 L × C 9 0.002 48 0.007 >0.05 13 Null 79 0.032 N:P ratio 3 0.013 76 0.018 <0.05 37 Clone 4 0.002 72 0.030 <0.05 7 N × C 12 0.008 60 0.011 <0.05 24 Null 44 0.031 N forms 4 0.009 40 0.022 <0.05 28 Clone 2 0.005 38 0.017 <0.05 16 N × C 8 0.004 30 0.013 >0.05 12 %Ov, observed variation accounted for each phenotypic component; Df, degrees of freedom; Dr, deviance residual; Rd, residual deviance; P, probability. Open in new tab Table III Two-way ANOVA for the growth rate Explanatory variables . Growth rate (cells div/day) . Df . Dr . Df . Rd . P . %Ov . Null 47 0.018 Salinity 3 0.006 44 0.013 <0.05 32 Clone 3 0.003 41 0.010 <0.05 14 S × C 9 0.004 32 0.006 <0.05 22 Null 63 0.016 Temperature 3 0.009 60 0.008 <0.05 53 Clone 3 0.000 57 0.008 >0.05 1 T × C 9 0.004 48 0.004 <0.05 22 Null 63 0.012 Light intensity 3 0.004 60 0.012 <0.05 29 Clone 3 0.000 57 0.009 >0.05 2 L × C 9 0.002 48 0.007 >0.05 13 Null 79 0.032 N:P ratio 3 0.013 76 0.018 <0.05 37 Clone 4 0.002 72 0.030 <0.05 7 N × C 12 0.008 60 0.011 <0.05 24 Null 44 0.031 N forms 4 0.009 40 0.022 <0.05 28 Clone 2 0.005 38 0.017 <0.05 16 N × C 8 0.004 30 0.013 >0.05 12 Explanatory variables . Growth rate (cells div/day) . Df . Dr . Df . Rd . P . %Ov . Null 47 0.018 Salinity 3 0.006 44 0.013 <0.05 32 Clone 3 0.003 41 0.010 <0.05 14 S × C 9 0.004 32 0.006 <0.05 22 Null 63 0.016 Temperature 3 0.009 60 0.008 <0.05 53 Clone 3 0.000 57 0.008 >0.05 1 T × C 9 0.004 48 0.004 <0.05 22 Null 63 0.012 Light intensity 3 0.004 60 0.012 <0.05 29 Clone 3 0.000 57 0.009 >0.05 2 L × C 9 0.002 48 0.007 >0.05 13 Null 79 0.032 N:P ratio 3 0.013 76 0.018 <0.05 37 Clone 4 0.002 72 0.030 <0.05 7 N × C 12 0.008 60 0.011 <0.05 24 Null 44 0.031 N forms 4 0.009 40 0.022 <0.05 28 Clone 2 0.005 38 0.017 <0.05 16 N × C 8 0.004 30 0.013 >0.05 12 %Ov, observed variation accounted for each phenotypic component; Df, degrees of freedom; Dr, deviance residual; Rd, residual deviance; P, probability. Open in new tab Optimal growth responses Alexandrium catenella grew across a wide range of experimental conditions, with the growth rate varying from 0.142 to 0.197 cells div/day. The lowest growth rate was observed at the highest light intensity level (250 μmol m−2 s−1), whereas the highest growth rate was observed in the urea treatment (Table IV). In turn, the maximum cell density varied between 4464 and 15 677 cells/mL, with the lowest density values using NO3− as the N form and with the highest density values at the 50 μmol m−2 s−1 level (Table IV). Table IV Growth rate and maximum cell density averages, standard deviations (SD) for each experiment and multiple comparisons by a posteriori Tukey HSD test following a one-way ANOVA (GLMM) Explanatory variables . Levels . Growth rate (cells div/day) . Maximum cell density (cells/mL) . Average . SD . Tukey HSD . Average . SD . Tukey HSD . Salinity 15 0.153 0.017 a 10 925 3107 a 20 0.176 0.016 b 14 894 2400 b 25 0.172 0.010 b 15 399 2945 b 30 0.183 0.022 b 13 388 2232 ab Temperature 8 0.156 0.007 a 9103 1909 a 12 0.169 0.015 b 11 682 2559 b 15 0.181 0.007 c 14 422 2087 c 18 0.152 0.014 a 8541 1973 a Light intensity 20 0.161 0.011 b 12 867 1966 ab 50 0.159 0.017 b 15 677 3165 c 120 0.158 0.010 b 14 057 3645 bc 250 0.142 0.009 a 11 507 1967 a Nitrogen forms NH4+ 0.190 0.027 b 4912 2408 a NO3− 0.165 0.020 a 4 464 1898 a Urea 0.197 0.021 b 6772 2647 b N:P ratio 3:1 0.158 0.019 a 6389 2292 b 9:1 0.167 0.016 ab 9055 2975 c 18:1 0.180 0.012 bc 11 165 2178 dc 30:1 0.189 0.015 c 11 741 2227 d 90:1 0.162 0.017 a 4809 1517 a Explanatory variables . Levels . Growth rate (cells div/day) . Maximum cell density (cells/mL) . Average . SD . Tukey HSD . Average . SD . Tukey HSD . Salinity 15 0.153 0.017 a 10 925 3107 a 20 0.176 0.016 b 14 894 2400 b 25 0.172 0.010 b 15 399 2945 b 30 0.183 0.022 b 13 388 2232 ab Temperature 8 0.156 0.007 a 9103 1909 a 12 0.169 0.015 b 11 682 2559 b 15 0.181 0.007 c 14 422 2087 c 18 0.152 0.014 a 8541 1973 a Light intensity 20 0.161 0.011 b 12 867 1966 ab 50 0.159 0.017 b 15 677 3165 c 120 0.158 0.010 b 14 057 3645 bc 250 0.142 0.009 a 11 507 1967 a Nitrogen forms NH4+ 0.190 0.027 b 4912 2408 a NO3− 0.165 0.020 a 4 464 1898 a Urea 0.197 0.021 b 6772 2647 b N:P ratio 3:1 0.158 0.019 a 6389 2292 b 9:1 0.167 0.016 ab 9055 2975 c 18:1 0.180 0.012 bc 11 165 2178 dc 30:1 0.189 0.015 c 11 741 2227 d 90:1 0.162 0.017 a 4809 1517 a Different letters in Tukey HSD column denote significant differences in each experiment. Open in new tab Table IV Growth rate and maximum cell density averages, standard deviations (SD) for each experiment and multiple comparisons by a posteriori Tukey HSD test following a one-way ANOVA (GLMM) Explanatory variables . Levels . Growth rate (cells div/day) . Maximum cell density (cells/mL) . Average . SD . Tukey HSD . Average . SD . Tukey HSD . Salinity 15 0.153 0.017 a 10 925 3107 a 20 0.176 0.016 b 14 894 2400 b 25 0.172 0.010 b 15 399 2945 b 30 0.183 0.022 b 13 388 2232 ab Temperature 8 0.156 0.007 a 9103 1909 a 12 0.169 0.015 b 11 682 2559 b 15 0.181 0.007 c 14 422 2087 c 18 0.152 0.014 a 8541 1973 a Light intensity 20 0.161 0.011 b 12 867 1966 ab 50 0.159 0.017 b 15 677 3165 c 120 0.158 0.010 b 14 057 3645 bc 250 0.142 0.009 a 11 507 1967 a Nitrogen forms NH4+ 0.190 0.027 b 4912 2408 a NO3− 0.165 0.020 a 4 464 1898 a Urea 0.197 0.021 b 6772 2647 b N:P ratio 3:1 0.158 0.019 a 6389 2292 b 9:1 0.167 0.016 ab 9055 2975 c 18:1 0.180 0.012 bc 11 165 2178 dc 30:1 0.189 0.015 c 11 741 2227 d 90:1 0.162 0.017 a 4809 1517 a Explanatory variables . Levels . Growth rate (cells div/day) . Maximum cell density (cells/mL) . Average . SD . Tukey HSD . Average . SD . Tukey HSD . Salinity 15 0.153 0.017 a 10 925 3107 a 20 0.176 0.016 b 14 894 2400 b 25 0.172 0.010 b 15 399 2945 b 30 0.183 0.022 b 13 388 2232 ab Temperature 8 0.156 0.007 a 9103 1909 a 12 0.169 0.015 b 11 682 2559 b 15 0.181 0.007 c 14 422 2087 c 18 0.152 0.014 a 8541 1973 a Light intensity 20 0.161 0.011 b 12 867 1966 ab 50 0.159 0.017 b 15 677 3165 c 120 0.158 0.010 b 14 057 3645 bc 250 0.142 0.009 a 11 507 1967 a Nitrogen forms NH4+ 0.190 0.027 b 4912 2408 a NO3− 0.165 0.020 a 4 464 1898 a Urea 0.197 0.021 b 6772 2647 b N:P ratio 3:1 0.158 0.019 a 6389 2292 b 9:1 0.167 0.016 ab 9055 2975 c 18:1 0.180 0.012 bc 11 165 2178 dc 30:1 0.189 0.015 c 11 741 2227 d 90:1 0.162 0.017 a 4809 1517 a Different letters in Tukey HSD column denote significant differences in each experiment. Open in new tab Salinity Alexandrium catenella had a better growth performance within the 20–30 PSU than at 15 PSU where its performance declined significantly (GLMM; P < 0.05). At 15 PSU, both the growth rate and the maximum cell density showed the lowest values (0.153 cells div/day and 10 925 cells/mL). While the highest growth rate (0.183 cells div/day) and maximum cell density (15 399 cells/mL) were observed at 30 and 25 PSU, respectively, no significant differences were observed between 20, 25 and 30 PSU (Table IV). Temperature Alexandrium catenella showed similar responses in their growth rate and maximum cell density. The significant (GLMM; P < 0.05) highest growth rate (0.181 cells div/day) and maximum cell density (14.422 cells/mL) were observed at 15°C followed by the 12°C, whereas the significant (GLMM; P < 0.05) lowest performance of both variables was reached at 8 and 18°C (Table IV). Light intensity The light gradient affected the growth rate and the maximum cell density differently. The growth rate varied between 0.142 and 0.161 cells div/day, with a significant (GLMM; P < 0.05) lowest value at the highest light intensity of 250 μmol m−2 s−1; however, no significant differences were observed between the other levels (Table IV). Contrary to the growth rate, the maximum cell density showed significant (GLMM; P < 0.05) highest values at 50 and 120 μmol m−2 s−1 with 15 677 and 14 057 cells/mL, respectively, and the lowest values at 20 and 250 μmol m−2 s−1 with 12 867 and 11 507 cells/mL, respectively (Table IV). Nitrogen forms The nitrogen forms affected the growth rate and the maximum cell density differently. The growth rate varied between 0.165 and 0.197 cells div/day, with the lowest values using NO3− as the inorganic N form. However, no significant differences were observed for the highest values using either NH4+ or urea as the N source. Contrary to the growth rate, the highest maximum cell density (6772 cells/mL) was reached in cultures using urea (GLMM; P < 0.05), but no significant differences were observed for the lowest values using either NH4+ or NO3− as the inorganic N source, with 4920 and 4464 cells/mL, respectively (Table IV). N:P ratios The growth responses were similar for both growth rate and maximum cell density. In both cases, the significantly higher values were reached at a ratio of 30:1 (0.189 cells div/day and 11 741 cells/mL, respectively), although no significant differences were observed with an intermediate value at 18:1 ratio. The lower growth rates observed at N:P ratio of 3:1, 9:1 and 90:1 were not significantly different (Table IV). However, maximum cell density varied significantly (GLMM; P < 0.05) between the highest values achieved at ratio of 3:1 and 9:1 (i.e. 6389 and 9055 cells/mL, respectively), with the lowest values (4809 cells/mL) reached at a ratio of 90:1 (Table IV). Comparisons between laboratory growth responses and cell concentrations measured in the field Field data showed that the cells of A. catenella are distributed across a wide range of temperatures and salinities, but its highest abundances were observed in narrower ranges, and these were different among regions (Fig. 3B). In Los Lagos Region, the cells were observed between 9 and 13°C and between 32 and 35 PSU, with the high densities (1000–99 999 cells/L) between 10.5 and 12.5°C and between 33 and 34 PSU (Fig. 4A). In Aysén Region, the cells were observed in the broadest range of temperature and salinity (between 5.8 and 22°C and between 8 and 34 PSU) (Fig. 4B). The high cell densities (1000–99 999 cells/L) were observed between 8 and 16.5°C and between 15 and 34 PSU (Fig. 4B). However, the highest density in this region (10 000 to >1 000 000 cells/L) was reported during one of the most intense blooms of A. catenella in 2009 (Mardones et al., 2010), with narrower ranges of temperature (12–14°C) and salinity (22–34 PSU) (Fig. 4B). In the Magallanes Region, cells were observed between 5 and 12°C and between 12 and 34 PSU, but high densities (1000–99 999 cells/L) were observed between 5.5–10°C and 20–32.5 PSU (Fig. 4C). The range of salinity for optimal growth responses, observed in our study (20–30 PSU; that could be extended up to 35 PSU if other studies with strains isolated from Chilean fjords, e.g. Aguilera-Belmonte et al., 2013, are considered), could be considered as consistent with the range for the high cell density observed in the field (1000–99 999 cells/L) in each of the three regions. However, the range was wider (extending down to 15 PSU) in the Aysén and narrower (33–34 PSU) in the Los Lagos Region. Furthermore, if the highest cell abundances in the field (10 000 to > 1 000 000 cells/L) are considered, the experimental and the field ranges for salinity match. In contrast, the experimental thermal range for optimal growth falls within the wider field range for high cell density observed in the Aysén Region (from 9 to 16°C), but it is only partially consistent with the narrow field range from the Los Lagos Region (between 11 and 12°C) and different from the range observed in the Magallanes Region (from 6 and 10°C). Fig. 4 Open in new tabDownload slide Cell density (cells/L) variation of A. catenella with both temperature and salinity recorded in the field from 2006 to 2015 in the regions: (A) Los Lagos, (B) Aysén and (C) Magallanes. The bubbles encompass most of the highest concentrations. Fig. 4 Open in new tabDownload slide Cell density (cells/L) variation of A. catenella with both temperature and salinity recorded in the field from 2006 to 2015 in the regions: (A) Los Lagos, (B) Aysén and (C) Magallanes. The bubbles encompass most of the highest concentrations. DISCUSSION Growth rate response and plasticity The ability of A. catenella to grow under different ranges of salinity, light intensity, temperature, N:P ratio and nitrogen forms highlights the phenotypic plasticity of this species in response to different environmental conditions. This plasticity, or the niche width (sensuThomas et al., 2012), regarding distinct environmental drivers, could confer to the cell populations different attributes, e.g. (i) to inhabit zones beyond the reach of predators (Strom et al., 2013), (ii) to increase the range of responses in the face of different selective pressures (Gsell et al., 2012; Sassenhagen et al., 2015; Kremp et al., 2016), (iii) to maintain fitness in unfavorable environments and/or to increase fitness in favorable environments as observed in some invasive plants (Richards et al., 2006) and (iv) to promote the niche complementarity (Brandenburg et al., 2018). Different studies have demonstrated phenotypic plasticity in distinct Alexandrium species under different environmental drivers. Such studies have shown that these species can grow in a broad range of nitrate and phosphate concentrations (Chang and Mcclean, 1997; Siu et al., 1997; Parkhill and Cembella, 1999; Hwang and Lu, 2000; Collos et al., 2004) or under a wide range of light intensities (Parkhill and Cembella, 1999; Hwang and Lu, 2000; Shi et al., 2005). Experiments with thermal gradients have shown eurythermal attributes in many species (Jensen and Moestrup, 1997; Siu et al., 1997; Nguyen-Ngoc, 2004; Laabir et al., 2011). Regarding salinity, several species of Alexandrium are considered euryhaline (Jensen and Moestrup, 1997; Siu et al., 1997; Parkhill and Cembella, 1999; Grzebyk et al., 2003; Laabir et al., 2011), although stenohaline species have also been observed (Lim and Ogata, 2005). Therefore, plasticity is a trait that allows for growth in highly variable environments and may explain, in part, the successful range expansion of several Alexandrium species in the Northern and Southern hemispheres, becoming the most cosmopolitan HAB genus in the world (Anderson et al., 2012). The differences observed among clones of A. catenella in growth responses under certain environmental conditions (i.e. distinct reaction norms), such us with salinity, temperature or N:P ratio, reveals another potential attribute of this species in responding to environmental variation. These results seem to indicate that genetically distinct strains, with dissimilar growth responses to environmental variability, coexist within the population. Different studies on Alexandrium have consistently shown levels of genetic diversity coupled with a phenotypic variation in responses for both growth rate and saxitoxin proportion (Alpermann et al., 2010; Kremp et al., 2016; Brandenburg et al., 2018; Tobin et al., 2019). Perhaps, the environmental variability favoring the growth of some clones over others is a mechanism that might be involved in the drastic change in allelic frequency within a bloom of A. catenella in the Chilean fjords. It is similar to other populations in the same geographic region, as has been suggested by Gao et al. (Gao et al., 2019) and Paredes et al. (Paredes et al., 2019). Therefore, diversity in reaction norms of growth response, and likely in other traits associated with environmental responses (Richards et al., 2006), are important attributes that could explain the temporal persistence and geographical extension of a bloom event. Indeed, the bloom that occurred in Southern Chile in 2016 showed that an event can spread over a wide area (560 km) and last for almost 4 months and that vegetative cells can proliferate in both protected (inner sea) and exposed waters of the Pacific Ocean (Paredes et al., 2019). In Southern Chile, both the phenotypic plasticity and the diversity are relevant attributes that favor the survival of the A. catenella vegetative cells and bloom persistence, which would also increase the cells potential for dispersal. Different reaction norms detected among A. catenella clones imply a significant potential to evolve by selection (Richards et al., 2006). If the reaction norms are positively correlated with fitness, the plasticity can evolve differently in distinct populations, depending on the selection pressure that each one faces (Schlichting and Pigliucci, 1998). Across the A. catenella distribution in the Chilean fjords, the environmental conditions vary widely, resulting in bioclimatic, ecoregion or biogeographic subdivisions (Camus, 2001; Spalding et al., 2007; Niklitschek et al., 2013). In this context, we might expect that selective pressures upon genotypes isolated from different geographical localities would have favored local adaptation, although the sample size per region was too small to draw any general conclusions about population growth adaptation in each region. However, the different reaction norms among genotypes isolated from different localities do not support our conjecture. Some strains did not show different reaction norms (i.e. not significant G × E) for some environmental drivers (light and N form experiments), while in those where the differences were observed (i.e. significant G × E), the clones belonged to the same locality. These results, especially the former, might be affected by the strains’ adaptation to the laboratory conditions, something which could homogenize the responses. The strains isolated in 1994 (ACC07) and 2004 (Q6) seem to have been in culture for enough time to accumulate mutations that might allow them to adapt to laboratory conditions. As observed in Alexandrium minutum, after 2 years in culture exposed to selective laboratory conditions, an invariant mean of growth rate was observed, higher than that that could be attributed mainly to genetic adaptation (Flores-Moya et al., 2012). However, strains with 1 year in culture might not have had enough time to adapt to laboratory conditions. Thus, the results where these strains were used, i.e. temperature, light and N:P ratio experiments, could be more representative of the native conditions. On the other hand, in the experiments where the strains had different ages in culture, the response of the strains was disparate and inconclusive regarding the effects of the age of the cultures. In the N form experiment, the preference shown by the strains for ammonium and urea was not consistent with the form of nitrogen used to maintain the cultures in the laboratory, i.e. nitrate. In the salinity experiment, the only strain (Q12) that showed a different reaction norm had 1 year in culture, but those strains that showed the same reaction norm were maintained 1, 6 and 16 years in cultivation. However, the responses observed for salinity, temperature and N:P ratio, with a significant G × E component, could be affected by selection, and thus different strains might be favored across spatial and/or temporal environmental gradients. Optimal growth responses Temperature and salinity are important environmental drivers for structuring the phytoplankton community at geographic scales (e.g. González et al., 2011; Paredes et al., 2014; Tobin et al., 2019), especially in the Chilean fjords where they have shown both latitudinal and longitudinal variability (Iriarte et al., 2014). According to the present study, A. catenella can grow over a wide range of environmental conditions, but optimal growth, namely, the highest growth rate and maximum cell density, occurs under a narrower range of temperatures (between 12 and 15°C) and wider for salinities (between 20 and 30 PSU). These ranges are consistent with the results observed in previous studies. For example, strains of A. catenella also isolated from Chilean fjords had shown higher growth rates at 12 and 15°C than at 10°C or 16°C (Navarro et al., 2006; Aguilera-Belmonte et al., 2011). In the case of salinity, the high growth rates were observed between 20 and 35 PSU with the lowest at 15 PSU (Uribe et al., 2010; Aguilera-Belmonte et al., 2013). Distinct laboratory experiments have shown that the optimal temperature and salinity for growth differs within A. catenella species, especially among strains belonging to different geographic regions. For instance, strains from a temperate region had an optimal growth at 15°C (A. fundyense in Etheridge and Roesler, 2005), whereas a strain from subtropical region (i.e. Hong Kong) reached highest growth at a range of 20–25°C (Siu et al., 1997) and a strain from Mediterranean regions at 27°C (Laabir et al., 2011). On the other hand, unlike Chilean strains, other strains of A. catenella have shown a narrower range of salinities. For instance, in strains from the Gulf of Maine, the highest growth rate was reported between 25 and 30 PSU (Etheridge and Roesler, 2005), while in strains from Hong Kong this growth was achieved between 30 and 35 PSU (Siu et al., 1997), and in Thau Lagoon the highest growth was at between 35 and 40 PSU (Laabir et al., 2011). Probably the ranges and optimum salinities for growth might be associated with the presence or absence of freshwater discharges that the species encounter in the coastal zones where they are found. The comparison of field cell densities between the regions, and their relationship with the laboratory growth responses, could contribute to a better understanding of the relevance of temperature and salinity as field drivers of bloom dynamics. The range of salinity and temperature where the highest densities of Alexandrium cells were observed in the Aysén Region seems to be related to the high environmental variability described in the region (Pantoja et al., 2011; Iriarte et al., 2014; Saldías et al., 2019). Although part of these ranges, especially at the extremes, might be explained by processes like advection that moves the cells away from their optimal growth conditions carried by moving bodies of water (Molinet et al., 2003). Also, active swimming, i.e. vertical migration, would likely help the cells avoid rough conditions, and it might seem to proliferate beyond areas optimal for growth. By contrast, the extremely narrow thermal and salinity niches observed in the Los Lagos Region could be attributed to the colonization process in progress and/or to the narrower range of salinity and temperature observed in this region (Pantoja et al., 2011; Saldías et al., 2019). In spite of A. catenella having been observed at different localities in this region (Mardones et al., 2010), the highest densities have mainly been observed around Chiloé Island (Buschmann et al., 2016), where the environmental conditions are oceanic rather than estuarine (Silva and Vargas, 2014). The high density observed in this region has been attributed to advection from the northern area of the Aysén Region (Buschmann et al., 2016), perhaps because normally when the high cell densities have occurred in Los Lagos Region, they are initially observed to the south of the island. The clear correspondence between the high(est) cell densities observed in the Aysén Region and the laboratory growth response seems to show the relevance of temperature and salinity for A. catenella bloom occurrence in this region. Thus, the high(est) cell densities tend to occur in localities (or at times) where these conditions might match 12–14°C and intermediate to high salinities, i.e. 20–35 PSU. The discrepancy in the thermal range from high(est) cell densities shown between the Magallanes Region and the northern regions (Los Lagos and Aysén) could be attributed to local adaptation, as has been suggested in other studies. (c.f. Thomas et al., 2012; Boyd et al., 2013). As indicated by these studies, the optimal temperature for growth seems to be strongly related to the mean annual temperature of a particular locality (Thomas et al., 2012). Additionally, in an analysis of phytoplankton communities and their relationships with local environmental conditions in the Magallanes Region, Paredes et al. (Paredes et al., 2014) have asserted that the temperature and nutrients are relevant to the geographic distribution of A. catenella. Thus, in this region, A. catenella seems to show local temperature adaptation, such that the highest cell densities observed in the field are reached at lower temperatures than in the northern regions. In this context, the strains grown in the laboratory seem not be representative of the Magallanes populations probably due to sampling bias. The light available for phytoplankton growth is highly dependent on water transparency, which varies seasonally and spatially not only in terms of solar variation year-around but also with respect to the impact of organic matter in the discharge from rivers (Huovinen and Gomez, 2011; Iriarte et al., 2014). For example, as indicated by Huovinen and Gómez (Huovinen and Gómez, 2011), if light intensity reaches 1500 μmol m−2 s−1 on the surface of the water in the fjords, the intensity could decrease to 160 and 50 μmol m−2 s−1 at 9 and 15 m depth, respectively. This may explain the results obtained in our study, where A. catenella was able to grow under a wide range of light intensities (20–120 μmol m−2 s−1), representing the light variability to which the cells are usually exposed. This plastic response might be accomplished by photosynthetic pigment variation as has been observed in strains of A. catenella collected from the north part of the fjords where the chlorophyll concentration did vary at different light intensities (Carignan et al., 2001; Montecino et al., 2001). Probably, during summer and autumn, close to the surface, A. catenella must deal with high light intensity, reducing its growth rate (as observed at 250 μmol m−2 s−1). In this context, A. catenella can avoid light stress (i.e. high light intensity and/or UV radiation) through vertical migration (Montecino et al., 2001). This strategy could explain the vertical stratification of cell density in the Chilean fjords where the highest abundance has been recorded mainly at 5–10 m deep (Molinet et al., 2003). The availability of the nutrients (e.g. N, P or N:P ratio) and the forms of a specific nutrient (e.g. NH4+ vs NO3−, organic vs inorganic) could significantly affect the phytoplankton community composition and species dominance (Glibert, 2016). As it has been generalized for dinoflagellates, in Alexandrium species, the reduced forms of nutrients tend to be favored over oxidized N (Glibert, 2016). Consistently, our results have shown that the optimal growth responses of A. catenella were better using NH4+ and urea than NO3−. Following the same pattern, A. catenella from Thau Lagoon, France (Collos et al., 2007; Jauzein et al., 2008), and A. tamarense and A. catenella from Hong Kong (Xu et al., 2012) showed the prominent role of the reduced forms in nitrogen nutrition. Even though the preeminence of NH4+ and urea varies among these species, in this work the growth rates do not show significant differences. Even the generalization of favored N forms is far from universal. For instance, a strain of A. tamarense isolated from Hiroshima Bay, Japan, did not show differences in growth rate between urea, ammonium and nitrate (Leong Yew et al., 2004), whereas the A. tamarense strain from Kure Bay, Japan, showed higher growth rate with nitrate than in ammonium and urea (Leong Yew et al., 2004). On the other hand, our results show that A. catenella growth was better at N:P ratios of 18:1 and 30:1, which are over the Redfield proportion, suggesting that growth is favored with low P rather than high concentrations. It has been argued that high N:P ratio is associated with slow growth rates such as those exhibited by dinoflagellates compared to diatoms. This is probably due to the high P requirement of ribosomes, which are essential for cell division and high growth rates (Glibert et al., 2016). The range of environmental conditions where A. catenella achieved, in this study, high growth performance is similar to some of the environmental characteristics established in Margalef’s mandala for dinoflagellates, i.e. environmental traits that could predict a species’ fitness (Glibert, 2016). For dinoflagellates, the conditions that favored its growth are related to the preference for reduced nitrogen forms rather than oxidized, such as NH4+ and urea; imbalanced and often high N:P ratio; and preference for low turbulence, high temperature and adaption to low light intensity, among others (Glibert, 2016). These characteristics are also associated with low growth rates, and with a K strategy, conversely, high growth rates and r strategy are associated with diatoms (Glibert, 2016; Glibert et al., 2016). These similarities between Mandala’s dinoflagellates characteristics and our results support the proposal that the conditions would favor A. catenella blooms. Thus, the N-nutrient acquisition strategy may explain the dominance of the A. catenella in certain geographic areas or periods of the year. For instance, the consistent N injection from the deep to surface waters, as occurs in upwelling areas, is considered the primary source of the oxidized N form (NO3−) available mainly to diatoms, while chemically reduced N forms (NH4+ and urea) are abundant later in the summer, resulting in communities often dominated by mixotrophic dinoflagellates and (pico)cyanobacteria (Glibert, 2016). In the surface water of the Chilean fjords, the nutrient concentrations are too low to promote the growth of A. catenella. Nitrate and phosphate, even ammonium, are generally highest in the deeper waters of most of the channels and fjords (Silva, 2008), as a result of organic matter decomposition, which releases nutrients in association with the advection of external high nutrient marine water. Considering A. catenella as a K-strategy species, this species could be a bad competitor when nutrient loads are high (especially when the nutrients are rich in oxidized N form) but grows better under oligotrophic conditions. However, to achieve the biomass of a bloom, the cells require access to higher nutrient concentrations either by cell migration to deeper layers water or by the vertical mixing of water, forced by the wind or the tide (Silva, 2008), in some shallow areas (50–100 m). Its preference for a reduced form of N seems to be consistent with the recurrent summer–autumn blooms when the dominance of regenerated nutrient occurs. Furthermore, mixotrophic nutrition might also be important in this context, as this nutritional strategy has also been recognized in Alexandrium species (Anderson et al., 2012). The optimal growing conditions described in this study might help in predicting the eventual northward extension of the geographic range of A. catenella. The exceptional bloom of this species, which occurred in 2016 (Buschmann et al., 2016), surprised many due to its geographical extension and the expansion into new areas, i.e. beyond Patagonia fjords (Buschmann et al., 2016; Paredes et al., 2019). This bloom is a clear example of how the vegetative cells spread and bloom along the open coast of the Pacific Ocean. Further north, along the Chilean coast, the superficial sea temperature and salinity seem to be favorable to a further expansion of cells. From the last bloom record of A. catenella further north, i.e. 39°42′S, up to the 30–35° S the superficial sea temperature does not exceed the 15°C, and the salinity does not go beyond the 35 PSU (Karstensen and Ulloa, 2009; Iriarte et al., 2014). Furthermore, near the coast, currents are identified through a minimum in sea surface temperature, because they carry colder water from the south northward but more important by the entrainment of the cold upwelling waters, which are particularly rich in nutrients due to the remineralization of organic matter (Karstensen and Ulloa, 2009). However, a clearer idea of this possible northward dispersion requires identifying other aspects of this species that are currently poorly understood. For instance, the nutrient-rich upwelled waters near Chilean coasts are dominated by large diatoms and copepods (Karstensen and Ulloa, 2009), which might act as significant competitors and predators, respectively. On the other hand, we should also improve our knowledge concerning the benthic stage in the life cycle of this species, i.e. the cysts. The role of this stage as inoculate of the blooms and the benefits of mapping cyst beds in surface sediments are widely recognized (Figueroa et al., 2018). However, information concerning the physiological responses of cysts under environmental drivers or their abundance and distribution is limited or absent. So far we know that the excystment success seems to be improved by high salinities and low temperatures (Mardones et al., 2016), which might support the northward spread, but the knowledge about cyst populations in the sediment is limited in the Patagonia fjords (Mardones et al., 2016) and nonexistent along the Chilean exposed coast. CONCLUSION In this study, we have shown that the growth response of A. catenella, estimated under different environmental drivers, describes a plastic response across an environmental gradient and that there are differences in the reaction norms among clones (probably genetic variability for plasticity) in some of the responses. These attributes are relevant in the face of environmental heterogeneity where this species occurs and might increase its survival and potential dispersal across a variable environment, favoring its range expansion. Despite their plasticity for growth across a wide range of environmental factors, A. catenella showed well-defined ranges of conditions where it achieves optimal growth. These ranges were narrow for temperature (12–15°C) but wider for salinity (20–30 PSU) and light irradiance (20–120 μmol m−2 s−1). These results are partially consistent with highest cell densities recorded in the field in Chilean fjords, especially in the north area. Regarding nutrients, the reduced form of nitrogen (urea and NH4+) rather than oxidized form (NO3−), and under high N:P ratio (18:1 and 30:1), has a strong effect on the optimal growth responses. These ranges resemble several trait characteristics of dinoflagellates established in the Margalef’s mandala and explain, in part, bloom dynamics. ACKNOWLEDGEMENTS We thank Andrea Zuñiga, Karen Correa, Bianca Olivares, Adrian Villarroel and Jaen Mayorga for taking the samples, the strain isolation and culture maintenance. Special thanks to Dr Matthiew Lee for the manuscript translation and to Dr Federico Winkler and anonymous reviewers for significant comments to manuscript improvement. FUNDING Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT No. 1080548 and N° 1130954); Chilean scholar fellowship Doctorado Nacional 2012 from the Comisión Nacional de Investigación Científica y Tecnológica (CONICYT) and by Proyecto Interno (FNI02/16 ULA CR8520). References Aguilera-Belmonte , A. , Inostroza , I., Carrillo , K. S., Franco , J. M., Riobó , P. and Gómez , P. I. ( 2013 ) The combined effect of salinity and temperature on the growth and toxin content of four Chilean strains of Alexandrium catenella (Whedon and Kofoid) Balech 1985 (Dinophyceae) isolated from an outbreak occurring in Southern Chile in 2009 . 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TI - Growth performance of Alexandrium catenella from the Chilean fjords under different environmental drivers: plasticity as a response to a highly variable environment
JF - Journal of Plankton Research
DO - 10.1093/plankt/fbaa011
DA - 2020-04-29
UR - https://www.deepdyve.com/lp/oxford-university-press/growth-performance-of-alexandrium-catenella-from-the-chilean-fjords-Ren4uK7Lu5
SP - 119
VL - 42
IS - 2
DP - DeepDyve
ER -