Primary production and plankton carbon biomass in a river-influenced upwelling area off Concepción, Chile

Progress in Oceanography 92–95 (2012) 97–109

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Primary production and plankton carbon biomass in a river-influenced upwelling area off Concepción, Chile Jose Luis Iriarte a,d,e,⇑, Cristian A. Vargas b,d, Fabián J. Tapia c, Rafael Bermúdez b,1, Roberto E. Urrutia b a

Instituto de Acuicultura, Universidad Austral Chile, Sede Puerto Montt, PO Box 1327, Puerto Montt, Chile Aquatic System Unit, Environmental Sciences Center EULA Chile, Universidad de Concepción, Concepción, Chile c Centre for Oceanographic Research in the Eastern South Pacific (COPAS), Universidad de Concepción, Concepción, Chile d Centro de Investigación de Ecosistemas de la Patagonia (CIEP), Bilbao 466, Coyhaique, Chile e Centro COPAS-Sur Austral, Universidad de Concepción, Concepción, Chile b

a r t i c l e

i n f o

Article history: Available online 18 July 2011

a b s t r a c t The combined influence of freshwater inputs and wind-driven upwelling may generate contrasting environmental conditions over small spatial scales in the coastal ocean. Over two consecutive years (mid-2006 through to mid-2008), we compared the springtime and wintertime composition, biomass, and primary production of the main phytoplankton groups at two coastal stations (RV and UW) near the Itata River mouth in the upwelling area off central Chile. Hydrographic and nutrient profiles showed distinct seasonal features: a relatively weak surface thermocline and weak river influence at both stations in spring, and in winter a stronger surface halocline at station RV, located closer to the river mouth. At both stations, primary production (24–8000 mg C m2 d1) and chlorophyll-a concentrations (5–20 mg Chl-a m3) were highest in the spring, with a dominance of microphytoplankton (Chaetoceros spp., Thalassiosira spp.). Total primary production and chlorophyll-a in the winter corresponded mostly to smaller size fractions (pico- and nanoplankton), which dominated the phytoplankton community (>50%) in terms of carbon biomass at station RV. At this river-influenced station, small autotrophic and heterotrophic groups (<20 lm), including picophytoeukaryotes, photo- and heterotrophic nanoflagellates, and ciliates, were two to four times more abundant than at station UW. We conclude that most of the integrated carbon biomass and production rates during winter months are accounted for by small cells in the microbial food web. This component of the phytoplankton community may be enhanced in response to the additional surface input of nutrients by river discharges into the nearshore environment. Ó 2011 Elsevier Ltd. All rights reserved.

1. Introduction Fluctuations in coastal phytoplankton communities associated with changes in freshwater inputs and water column stratification/mixing may greatly influence biogeochemical fluxes and the annual trophic status of coastal ecosystems. In association with freshwater inputs to the surface ocean, increased rates of primary production (PP) and autotrophic biomass, along with the development of algal blooms, have been documented in major productive coastal areas such as the Gulf of Trieste (Malej et al., 1995), the Mississippi River (Liu et al., 2004), the NW Peninsula Iberica (Varela et al., 2005), the Washington/Oregon coast (Frame and Lessard, 2009), and Antofagasta, Chile (Iriarte and González, 2004).

⇑ Corresponding author at: Instituto de Acuicultura, Universidad Austral Chile, Sede Puerto Montt, PO Box 1327, Puerto Montt, Chile. Tel.: +56 65 27 7124; fax: +56 65 23 3385. E-mail address: [email protected] (J.L. Iriarte). 1 Present address: M.S. Program in Biological Oceanography, Leibniz-institut für Meereswissenschafter an der Universität Kiel, Kiel, Germany. 0079-6611/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.pocean.2011.07.009

The seasonal upwelling of cold, nutrient-rich water along the central and northern coast of Chile–Peru (Thomas et al., 2001) produces a highly productive phytoplankton assemblage, which supports a large commercial fishery. The continental shelf off Concepción, central Chile (36°S) corresponds to one of the widest sections of the continental shelf along the Chilean coast, and it is influenced by freshwater discharges from the Itata and Biobío rivers, with mean outflows of 286 and 1699 m3 s1, respectively (Sobarzo et al., 2007a). These fresh water river inputs supply substantial amounts of silicic acid, nitrate, and orthophosphate to the adjacent coastal ocean (Sánchez et al., 2008). The area is also characterized by the occurrence of wind-driven upwelling in the austral spring–summer months (Cáceres and Arcos, 1991). In association with these events, PP rates in the area increase dramatically and reach some of the highest values in the world ocean (4–20 g C m2 d1; Montero et al., 2007). Phytoplankton blooms that are typically dominated by long-chain-forming diatoms have been observed near Coliumo Bay (Vargas et al., 2007). Along the river-influenced continental shelf of central Chile, the coastal upwelling area off Concepción is one of the most productive in

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the Humboldt Current System (Daneri et al., 2000; Montero et al., 2007). This area exhibits a clear seasonal regime in PP, with spring–summer and autumn–winter rates of 5–9 g C m2 d1 and <2 g C m2 d1, respectively (Daneri et al., 2000; Farías et al., 2004; Montecino et al., 2004; Montero et al., 2007). Spring months are characterized by a high biomass of autotrophic phytoplankton that corresponds mostly to the microphytoplankton fraction (González et al., 1989; Ahumada et al., 1991; Vargas et al., 2006, 2007; González et al., 2007) and, more specifically, to chain-forming diatoms such as Skeletonema sp., Chaetoceros spp., Thalassiosira spp., and Detonula sp. In contrast, in winter, autotrophic biomass is low (<2 mg Chl-a m3) and dominated by small-sized components (<20 lm), such as phytonanoflagellates and cyanobacteria (González et al., 1989; Anabalón et al., 2007; Böttjer and Morales, 2007). Studies conducted in this highly productive area suggest that nanoplanktonic groups may account for a substantial fraction of the autotrophic biomass in winter months. Studying phytoplankton productivity in a size-structured fashion is, thus, essential for assessing the contribution and fate of photosynthetically-fixed carbon by small algal cells to the food web dynamics in this region. Coastal systems such as upwelling and estuarine areas display drastic changes in hydrographic regimes in a range of temporal scales that goes from daily (e.g., tides, summer sea breeze) to intra-seasonal (e.g., upwelling events, storms, river discharge pulses) to seasonal (e.g., coastal wind patterns, radiation). All of these variations in physical forcing and coastal hydrography may elicit changes in the distribution and composition of phytoplankton. Off Concepción, the wind-forced upwelling of deep, nutrient-rich waters into the euphotic zone is the main physical factor driving spring pulses of high PP and autotrophic biomass. Furthermore, the annual cycle of inorganic nutrients in Concepción Bay has shown low nitrate and orthophosphate concentrations during winter, whereas higher nutrients values are observed during spring, when frequent coastal upwelling events take place (Ahumada et al., 1991). Nevertheless, near river outflows, the input of bottom-water nutrients driven by upwelling may interact with the nutrient load associated with freshwater inputs, thereby establishing spatial gradients in the concentrations and ratios of inorganic nutrients that may, in turn, determine shifts in the composition of the phytoplankton community. We suggested that changes in biomass composition and the production rates of the phytoplankton community could be a consequence of spatial variability observed during non-upwelling (winter) and upwelling (spring) periods, as a result of the balance of upwelling-driven fertilization of the coastal ocean versus the input of river-borne nutrients into surface layers. The aim of this study was to assess the winter–spring changes in biomass composition and productivity of phytoplankton at two coastal stations in the upwelling area off Concepción, one of which was strongly influenced by freshwater inputs from the Itata River. We used a combination of approaches that included estimates of size-fractionated autotrophic biomass and PP, as well as the carbon biomass of the main pico-, nano-, and microplanktonic groups. We showed that changes in the biomass composition and PP of coastal phytoplankton do occur at these stations and that the changes are mostly detected at the station that is closer to, and more strongly affected by, freshwater inputs near the Itata River mouth.

2. Materials and methods 2.1. Physical and hydrography We used daily fields of Level 3 gridded QuikSCAT data provided by NASA’s Jet Propulsion Laboratory (ftp://podaac.jpl.nasa.gov/ pub/ocean_wind/quikscat/L3/) to characterize patterns of wind

stress variability over the study area. A daily series of meridional wind stress was extracted from images collected between July 2002 and September 2009 for a pixel centered at 36°37.50 S, 73°37.50 W (ca. 60 km SW of station UW). For the same period, a weekly time series of surface PAR radiation over the study area was gathered from 8-day composite SeaWIFS images with a spatial resolution of 9 km, whereas weekly Sea Surface Temperatures (SST) and chlorophyll-a concentrations in the vicinity of stations RV and UW (5 km offshore) were obtained from 8-day composite MODIS-Aqua images with a 4 km spatial resolution. Additionally, we used a time series of in situ SST measurements conducted daily (9 AM) at the Dichato Marine Biology Station (University of Concepción), ca. 6 km SE of station UW, to assess the timescales over which surface conditions in the area respond to wind forcing. Hourly data on river discharges of freshwater were obtained from the Direccion General de Aguas (www.dga.cl), and corresponded to stations located near the mouth of the rivers Itata and BioBio (see Fig. 1). During each cruise, surface distributions of water temperature and chlorophyll-a (Chl-a) concentrations were monitored from satellite imagery collected by MODIS-Aqua and made available by the ANTARES network (http://www.antares.ws). At each station, temperature, salinity, oxygen, and fluorescence profiles were recorded from the surface to near-bottom depths using a SeaBird SBE-19 plus CTD equipped with a YSI-calibrated Beckman oxygen sensor and a Wetstar fluorometer. 2.2. Chemistry Additional samples for phytoplankton analyses and dissolved inorganic nutrient determinations were collected from the surface, fluorescence maximum, and at a depth of 1% surface PAR (photosynthetically active radiation). Samples for nutrient analyses,  3 including nitrate (NO 3 ), nitrite (NO2 ), orthophosphate (PO4 ), and silicic acid (Si(OH)4), were filtered onboard through GF/F glass-fiber filters and frozen at 20 °C until analysis in the laboratory. Nitrate and orthophosphate concentrations were determined via spectrophotometry following Parsons et al. (1984) and Murphy and Riley (1962). Nitrite concentrations were measured using an automated nutrient analyzer (ALPKEM, Flow Solution IV) and following the US Environmental Protection Agency protocol (Method 353.2). As part of an environmental monitoring program, additional samples for similar dissolved inorganic nutrient analyses were collected monthly at the Itata River mouth (St. IR, Fig. 1). Nutrients in these samples were determined following Standard Methods 20th Edition (WEF, 1998) at the Environmental Chemistry Laboratory of the EULA Center (Universidad de Concepción). Daily records of river outflow were obtained from the National Water Directorate (http:// www.dga.cl), whereas PAR time series were obtained from an HOBO weather station (Onset Computer Corp., USA) installed by the COPAS Center (Universidad de Concepción) at 36°31.6870 S and 72°57.9550 W. 2.3. Plankton Water samples (1 L) for analyses of nanoplankton and microplankton abundance and biomass were collected from discrete depths (1, 5, 10, 25, 35 m) with a rosette system equipped with 12 Niskin bottles. Nanoplankton samples were preserved in glutaraldehyde (6.0% W/V in 0.2-lm prefiltered seawater). Microplankton samples were preserved in alkaline Lugol’s solution 1% (Levinsen and Nielsen, 2002). Nanoflagellates were measured and biovolumes were estimated from a minimum of 80 cells per group. Biomass was estimated using a size-dependent carbon:volume ratio as suggested by Verity et al. (1992). Large cells were counted under the same inverted microscope. Subsamples of 50 mL were

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(a)

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(c) Fig. 1. (a) Study area with locations of the sampling stations in the Itata River plume area (Stn RV), the upwelling site off Coliumo Bay (Stn UW), and the Itata River mouth station (Stn IR). Side panels show representative images of surface chlorophyll concentration (mg m–3) in (b) austral spring (December 26th, 2006) and (c) austral winter (June 19th, 2007).

allowed to settle for 24 h in sedimentation chambers (Utermöhl, 1958) prior to the identification, enumeration, and measurement of diatoms, dinoflagellates, and ciliates. Plasma volumes were calculated (Edler, 1979) and averaged for a minimum of 50 cells per species. Biovolumes of ciliates were calculated assuming conical shapes with length:diameter ratios of 1.25 for ciliates <50 lm and 2.0 for ciliates >50 lm (Tiselius, 1989). We assumed carbon:plasma volume ratios of 0.11 pg C lm3 for diatoms (Edler, 1979), 0.3 and 0.19 pg C lm3 for heavily thecate and athecate dinoflagellates forms (E.J. Lessard unpubl. data fide Gifford and Caron, 2000), and 0.148 pg C lm3 for ciliates (Ohman and Snyder, 1991). The abundance of bacteria, cyanobacteria, and picophytoeukaryotes was estimated by flow cytometry. Subsamples of 150 mL were processed on a FACSCalibur flow cytometer equipped with an ion-argon laser of 488 nm of 15 mW (Becton Dickinson). Identification of coccoid cyanobacteria (Synechococcus) and photosynthetic eukaryotes was based on differences in side light scatter and fluorescence in orange (cyanobacteria) and red (eukaryotes) wavelengths. Abundance of heterotrophic bacteria was estimated from samples previously stained with Sybr green I (Molecular Probes) (Marie et al., 1997). Small nanoflagellates were quantified with the proflavine technique (Haas, 1982). For the enumeration of nanoflagellates, subsamples were filtered with a 0.8-lm polycarbonate membrane filter, stained with Proflavine (0.033% w/v in distilled water) following Haas (1982), and fixed with glutaraldehyde (as above) for subsequent analysis. Nanoflagellates were counted with an inverted microscope OLYMPUS IX-51 equipped with UV model UMWU2 (width band pass 330–385 nm) and FITC model U-MWB2 (width band pass 450–480 nm) filter sets.

incubated in 125-mL polycarbonate bottles (two clear + one dark bottle) and placed in a natural-light incubator for ca. 4 h (roughly between 10:00 AM and 14:00 PM). Ambient temperature was regulated by running surface seawater over the incubation bottles. Sodium bicarbonate (30–40 l Ci–NaH14CO3) was added to each bottle. Primary production was measured using the method described by Gächter et al. (1984). Samples were manipulated under subdued light conditions during pre- and post-incubation periods. Filters (0.7 lm) were placed in 20-mL plastic scintillation vials and kept at 15 °C until reading (15 days later). To remove excess inorganic carbon, filters were treated with HCl fumes for 24 h. A cocktail (8 mL, Ecolite) was added to the vials and radioactivity was determined in a Beckmann scintillation counter. Differential size fractionation of phytoplankton was carried out in three consecutive steps for PP and autotrophic biomass determinations. To obtain the nanoplankton fraction (5.0–20 lm), seawater was prefiltered using a 20-lm Nitex mesh and the filtrate was collected on a 2.0-lm Nuclepore. Further filtration with the 2.0-lm Nuclepore and collection of the residue on a 0.7-lm MFS filter (fiber-glass filter, Micro Filtration System) were conducted to extract the picoplankton fraction (0.7–2.0 lm). The microphytoplankton fraction (>20 lm) was obtained by subtracting the value estimated for picoplankton and nanoplankton from the total PP or total Chl-a values. Water samples for Chl-a measurements were taken at the same depths as the samples collected for PP analysis. Seawater samples (200 mL) were filtered (GF/F), extracted in 90% v/v acetone, and analyzed using a digital PS-700 Turner fluorometer (Parsons et al., 1984). Depth-integrated PP and Chl-a values in the euphotic layer were estimated by the trapezoidal integration method.

2.4. Primary productivity

2.5. Statistical analyses

Water samples for PP estimates were collected at four depths (0 m, the subsurface maximum fluorescence, 15 m, and 20 m) using a 5.0-L PVC Go-Flo bottle (General Oceanics, USA). Samples were

Environmental variability was characterized through a principal component analysis (PCA) of the set of physical–chemical measurements (e.g., temperature, salinity, oxygen, nutrient

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stations and SD is the standard deviation of the n differences computed within each season and taxonomic group. Significance of the test statistic, given the null hypothesis that depth-integrated biomass (or PP) at station RV is equal to, or lower than, station UW, was determined as N0 /(N + 1), where N0 is the number of iterations with trnd > t. A Bonferroni correction for multiple comparisons was used to assess the significance of each comparison (Manly, 1997). Finally, to test for correlations between phytoplankton biomass/productivity and wind forcing, cumulative alongshore wind stress was computed over periods that ranged between 2 and 30 days prior to each cruise. Depth-integrated biomass for each taxonomic group and PP estimates for each size fraction were used in the correlation analyses.

concentrations) taken at both stations during each cruise, together with indices derived from such measurements (e.g., seawater density, Si:N and N:P ratios). The data matrix used in our multivariate analysis consisted of 10 variables and 65 cases (5 depths  6 cruises at St. RV plus 5 depths  7 cruises at St. UW). Although riverinduced variability in physical–chemical conditions faced by the phytoplankton community is a central component of this study, the daily outflow data available for the Itata River were not included in the PCA dataset given their lack of spatial resolution (horizontal and vertical). The riverine influence was better captured by CTD data, as shown by the temperature-salinity diagrams for each station and season (Fig. 2b). We used the first two principal components to represent environmental variability in the study region. Subsequently, Spearman correlations between these ‘new’ environmental variables and our biological measurements were calculated for each depth sampled during our cruises. Potential differences in community structure between stations RV and UW were tested for using a randomized paired t-test that compared depth-integrated biomass estimates for each taxonomic group within each season (see Manly, 1997). The same approach was used to perform among-site comparisons of depth-integrated PP for <2 lm, 2–20 lm, and >20 lm size fractions, and to compare biomass and primary production across spring cruises (see Table 1) at station UW. For each comparison, N = 5000 iterations of a randomization procedure were used to determine the empirical prob ability distribution of the test statistic t ¼ s xpD ffiffin, where D PN 1  xD ¼ n i¼1 RV i  UW i is the mean of paired differences between

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3.1. Physical forcing, surface conditions and hydrography Wind stress in the study region exhibits a distinct seasonal cycle, with prevailing equatorward winds from early spring through to early autumn (September–April), and events of poleward winds during late autumn–late winter (Fig. 3a and e). During this period, Photosynthetically Available Radiation reaches minimum values (June–July, Fig. 3b and f) and precedes the minimum SST values recorded typically in July–August at both sampling stations (Fig. 3c and g). Spring–summer temperatures were almost identical

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Fig. 2. (a) Temperature and salinity profiles for Stn RV and Stn UW during each sampling campaign. (b) Temperature – salinity diagram for the spring and winter cruises. Density contours correspond to sigma-t.

Table 1 Integrated primary productivity (mg C m2 d1) in the upper 20 m during all field campaigns at the Itata River plume and off Coliumo Bay. Numerically dominant size fractions are marked in bold. St. RV (River Plume)

St. UW (off Coliumo Bay)

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<2

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>20

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>20

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80 337 90 17 – 189 38

132 186 45 2 – 199 29

3534 4690 27 5 – 5258 6

158 72 12 14 104 212 16

112 56 5 5 137 318 8

5381 3056 19 8 4532 7566 1

I II III IV V VI VII

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Fig. 3. Time series (A–D) and 2002–2009 climatologies (E–H) for satellite-derived data on atmospheric forcing and surface conditions at the study area. Data on daily wind stress (A and E) and 8-day average PAR radiation (B and F) near the study area were obtained from Level-3 QuikSCAT and SeaWIFS images, respectively. Data on 8-day average Sea Surface Temperature (C and G) and surface chlorophyll-a concentration (D and H) at the study sites were obtained from Level-3 MODIS-Aqua images. Error bars on climatologies correspond to standard deviations. Vertical dashed lines indicate dates on which cruises took place.

between stations, although surface waters at station RV appeared to be slightly cooler than at station UW in autumn and winter months (Fig. 3c and g). Satellite-derived chlorophyll concentrations exhibited large temporal fluctuations at both stations (Fig. 3d), a mean annual cycle with minimum values in May– August and maxima in October–March, with slightly higher concentrations at station UW during late spring and summer months (Fig. 3h). Daily fluctuations in meridional wind stress during 2006–2008 (Fig. 3a) were significantly auto-correlated (a = 0.05) over scales of 3–4 d, whereas in situ SST anomalies near station UW (not shown) showed scales of de-correlation of 12–13 d, and significant (a = 0.05) negative cross-correlations with changes in wind stress (i.e., drops in SST when winds blew equatorward and vice versa) over lags of 0–2 d. Satellite-derived SST and Chl-a images indicated dramatic differences in the spatial distribution of temperature and pigments between spring and winter cruises (Fig. 1b and c). During the spring cruises, patches of cold water (ca. 8 °C) and high concentrations of Chl-a (7 mg Chl-a m3) spanned a wide area over the continental shelf off Concepción (Fig. 1b), whereas winter cruises tended to find higher SST (11 °C) with more homogeneous distributions, together with low chlorophyll concentrations (0.3 mg Chl-a m3) except for, a few inner-shelf areas (Fig. 1c). Dramatic differences between cruises were also found when comparing CTD profiles of temperature and salinity (Fig. 2a). Winter cruises were characterized by a strong river (i.e., low salinity) signal at the surface and

the absence of thermal stratification. In spring, strong thermal stratification occasionally co-occurred with a freshwater signal from the Itata River (Fig. 2a). Temperature-salinity diagrams plotted with CTD data for the depths from which phytoplankton and nutrient samples were taken showed a distinct river signal that was apparent at both stations during wintertime cruises, though more strongly at station RV (Fig. 2b). A strong riverine influence was also detected at station RV during the first two spring cruises (Fig. 2b, grey circles). Although freshwater discharges from the Itata River can reach values >1000 m3 s1 in winter (Figs. 4 and 5a; DGA, 2000–2007), our winter cruises took place on days when average discharges fluctuated between 100 and 300 m3 s1. At the scale of the entire autumn–winter season (1 May–31 August), however, a substantial drop in mean river discharge was observed from 2006 (mean ± SD = 690.4 ± 620.7 m3 s1) to 2007 (297.3 ± 221.0 m3 s1). During the spring cruises, river discharge ranged from 50 to 150 m3 s1 (Fig. 5c). 3.2. Nutrients Surface concentrations of inorganic nutrients measured at the Itata River mouth showed increased nitrate + nitrite in winter months (May–August), with annual values ranging between 4 and 14 lM, whereas orthophosphate values remained constant between 0.5 and 1.5 lM (Fig. 5b). The seasonal pattern of silicate showed the

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Itata BioBio 6

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Fig. 4. Daily series of freshwater discharge from the two main rivers in the study region (see Fig. 1a). Dashed vertical bars indicate the dates on which spring (red) and winter cruises (black) were carried out. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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highest concentrations in fall and winter, with high annual values fluctuating between 100 and 235 lM (Fig. 5b), as have been detected by Karrasch et al. (2006). During the spring cruises, the river discharges ranged between 50 and 150 m3 s1 (Fig. 5c). The record of solar radiation for the region from 2006 to 2008 showed seasonal fluctuations with lower values in winter (June–August:<500 lmol

s1 m2) and higher values in summer (December–February: 1500–2000 lmol s1 m2) (Fig. 5c). Except in spring 2006, high orthophosphate and nitrate concentrations were found throughout the water column, with values ranging from 0.5 to 2.0 and between 10 and 20 lM, respectively, within the first 30 m depth in winter 2007, 2008 and spring 2007

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winter (ca. 100 cells L1) (Table 2). Finally, dinoflagellates and ciliates were found to be relatively important in terms of abundance at both stations in winter (Table 2).

at both stations (Fig. 6a). Silicic acid profiles showed the highest surface concentrations at station RV (10–30 lM) with a subsurface minimum (5–10 lM) at 10 m in winter 2007, 2008 and spring 2007. Silicic acid profiles at station UW, on the other hand, showed seasonal variations: concentrations in winter were higher at the surface (10–20 lM) and homogeneous down to 35 m (Fig. 6b) and, in spring, were lower at the surface (<6 lM) with a tendency to increase with depth (5–25 lM at 25 and 35 m).

3.4. Primary productivity Depth-integrated estimates of PP varied from 24 to 8000 mg C m2 d1 (Table 1) and were two orders of magnitude higher in the spring. A three-way Analysis of Variance performed on the data shown in Table 1 indicated that phytoplankton size (F2,31 = 11.97, p = 0.0001) and season (F1,31 = 14.45, p = 0.0006), but not station (F1,31 = 0.07, p = 0.795), have a significant effect on depthintegrated primary production. At both stations, photosynthetic rates and Chl-a biomass were highest at depths below the pycnocline (10–15 m; Figs. 7 and 9). In terms of carbon uptake, microphytoplankton (>20 lm) dominated and accounted for a substantial proportion (50–90%) during cruises with higher total PP level (spring 2006, 2007; Table 1); whereas nanoplankton and picoplankton fractions dominated (>50%) during the low-PP winter months (Fig. 9). A comparison of depth-integrated biomass and PP rates among stations indicated that the wintertime biomass of bacteria, cyanobacteria, and PNF were significantly greater at station RV (Fig. 8 and Table 3). Depth-integrated PP corresponding to the <2 lm size fraction was also significantly greater at station RV in winter months (Tables 1 and 3). When biomass and PP estimates for station UW were compared across spring seasons (i.e., 2006 and 2007), biomass estimates for cyanobacteria were significantly greater (p < 0.0001) during the spring of 2006, whereas PNF and ciliate biomass, as well as pico-phytoplankton production, were significantly greater (p < 0.0001) during the spring of 2007.

3.3. Size-fractioned chlorophyll and plankton community structure Primary production and autotrophic biomass showed seasonal variability that is typical of the upwelling area off Concepción. Chlorophyll-a measurements at stations RV and UW showed high levels of autotrophic biomass (5–20 mg Chl-a m3) in spring, whereas winter months were characterized by low values (0.2–5 mg Chl-a m3). On average, the contribution of three phytoplankton size classes to total Chl-a revealed the dominance of microphytoplankton (>70%) during the spring (Fig. 7), and a greater importance of nano- and picoplankton (>60%) in the winter. Wintertime estimates of depth-integrated biomass at station RV showed a dominance of heterotrophic nanoflagellates (5–20 lm) and bacteria (Fig. 8a and c) and a rather constant or ‘background’ signal of picophytoeukaryotes and small heterotrophic photoautotroph nanoflagellates. In contrast, at station UW, heterotrophic nanoflagellates (HNF) were less important in both seasons and diatoms dominated the spring biomass (Fig. 8b, d, and f). The highest abundance of chain-forming diatoms such as Chaetoceros spp. and Thalassiosira spp. was found at both stations in spring (>10,000 cells L1), whereas the lowest values were found in

(a)

NO3 - PO4 Concentration 0

0

10

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0

10

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Depth

-10

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Winter 2007

Spring 2007

Winter 2008

-40

NO3

PO4

(b)

Si(OH)2

Si:N

Si(OH)4 Concentration 0

10

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30

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Depth

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Winter 2007

Spring 2007

Winter 2008

-40 0

2

4

6

8 10 12 0

2

4

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8 10 12 0

2

4

6

8 10 12 0

2

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8 10 12

Si:N ratio Fig. 6. Vertical profiles of (a) dissolved nitrate (NO3) and orthophosphate (PO4), and (b) silicic acid (Si(OH)2) in lM units and Si:N ratio averaged for spring 2006, winter and spring 2007, and winter 2008. Black and grey symbols correspond to St. RV and UW, respectively.

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2 - 20 µ m

< 2 µm

Total

-3

Size-fractioned chlorophyll a (mgChl m ) 0

5 10 15 20 25

0

5 10 15 20 25

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0.5

1.0

0.0

0.5

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0

5 10 15 20 25

0.0

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5 10 15 20 25 0.0 0.2 0.4 0.6 0.8 1.0

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Ommix 3 11 Jun

Ommix 2 16 Dec

Ommix 1 12 Dec

0.5

SPRING 2006

1.0

0

5 10 15 20 25

Ommix 6 13 Dec

Ommix 5 11 Dec

Ommix 4 13 Jun

WINTER 2007

Ommix 7 9 Aug

SPRING 2007

WINTER 2008

3

Fig. 7. Vertical profiles of size-fractioned chlorophyll (mg Chl m ) during each sampling campaign at both Stn RV (upper panel) and UW (lower panel). Size fractions correspond to picoplankton (<2 lm), nanophytoplankton (2–20 lm), and microphytoplankton (>20 lm).

3000 2500

Winter 2007

Spring 2006

Spring 2007

(a)

Winter 2007

Spring 2006

Winter 2008

Spring 2007

Winter 2008

(b)

Bacteria Cyanobacteria Picophytoeuk

2000 1500 1000 500

-2

Biomass (mgC m )

0 1000 800

(c)

(d)

PNF MNF HNF

600 400 200 0 16000

(e)

(f)

Dinoflagellates Ciliates Diatoms

12000 8000 4000

VI I

VI

V

M O

M

M

M IX

IX

IX M O

M O

M IX M O

O

M

M IX

IV

II I

II

I M O

O

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M

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IX

M O

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VI

V IX

IV

O

M

M IX

M IX M O

O

II

I

M IX

IX

M O

M M O

II I

0

Campaign/Date Fig. 8. Upper 20 m depth-integrated biomass (mg C m2) of major taxonomic groups in the picoplankton (a and b), nanoplankton (c and d), and microplankton size fraction (e and f) averaged for spring 2006, winter and spring 2007, and winter 2008 at the Stn RV (a, c, and e) and UW (b, d, and f).

3.5. Physical forcing, hydrography and plankton community properties The PCA performed on our environmental dataset yielded two principal components that jointly explained 56% of the total

variance (Table 4). The first principal component (PC1) explained 41.4% of the total environmental variability and accounted mostly for variability induced by cold, nutrient-rich, bottom waters that are brought closer to the surface during the upwelling season.

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Table 2 Mean abundance of numerically dominant (>200 cells L1) microphytoplankton and microzooplankton groups during all field campaigns at the maximum fluorescence depth. St. RV

St. UW

Mean fluorescence max.

Ommix I–II Dec. 2006 10

Diatom chains Asterionellpsis glcialis Chaetoceros compressum Chaetoceros radicans Chaetoceros socialis Chaetoceros sp. 1 Chaetoceros sp. 2 Eucampia cornuta Lauderia borealis Odontella sp. Odontella longicruris Skeletonema costatum Thalassiosira sp. 1 Thalassiosira sp. 2 Thalassiosira sp. 3 Thalassiosira sp. 4 Thalassiosira sp. 5 Thalassiosira sp. 6 Thalassiosira sp. 7

0 0 0 0 0 0 0 8589 3304 0 0 102,545 11,475 141,437 58,944 13,875 27,750 7929

0 0 0 0 250 0 0 0 0 0 0 62 0 0 0 0 0 0

0 0 85,661 0 1503 886,663 0 0 10,520 4508 0 10,520 0 0 171,321 0 0 3006

Pennate diatoms Cilindroteca closterium Frustulia vulgaris Navicula gregaria Navicula vitata Navicula dicephala Navicula sp. Nitzchia sigma Pseudonitzchia sp. Unidentified pennate cell

0 0 0 0 0 0 0 19,822 24,447

499 0 0 74 324 0 0 0 1004

0 9604 0 482 0 0 1928 1446 4338

0 128 0 0 0 0 0 128 255

0 0 581 0 0 0 0 4306 4306

675 0 0 379 126 0 0 0 845

0 5475 0 0 0 3792 6843 572 5822

0 152 0 0 0 304 0 760 706

0 3079 0

1454 0 0

0 0 0

0 0 0

0 5032 2226

95 0 0

0 0 0

0 0 0

661 0 0 0 13 0 42

0 851 0 3821 357 0 0

0 0 25 4338 482 0 0

0 766 639 1150 255 861 0

0 0 0 0 67,934 0 13

126 128 28 4559 635 22 43

0 0 0 745 572 0 0

0 304 0 456 0 304 0

5 0 0 0 29

0 0 567 8 1474

23 12 40 0 24

23 287 1435 0 1722

12 0 0 0 16

0 0 888 11 12

0 0 0 0 30,658

0 0 1179 2063 1474

Centric diatoms Corethron criophylum Cocsinodiscus sp. 1 Cocsinodiscus sp. 2 Dinoflagellates Protoperidinium leonis Protoperidinium spp. 25–50 lm Dinophysis acuminata Gymnodinium spp. 8–20 lm Gyrodinium spp. 10–28 lm Katodinium spp. 18–20 lm Not identified athecate Ciliates Helicostomella spp. Udella spp. Strombidium capitatum Strombidium compressum Strombidium spp.

Ommix III–IV Jun. 2007 8

Ommix V–VI Dec. 2007 10

Ommix VII Aug. 2008 8

Ommix I–II Dec. 2006 10

0 0 0 0 0 0 0 0 0 0 1660.6 0 0 0 255.5 0 0 0

0 0 0 0 593 0 0 0 581 0 7548 124,062 20,419 106,788 83,805 24,919 8710 2129

0 0 0 0 0 0 0 0 0 0 678 0 0 762 0 0 0 0

1828 2860 32,487 16,015 1369 598,657 0 0 7528 1144 0 64,418 0 0 410,295 0 0 7425

0 0 0 0 912 152 0 0 0 0 456 0 0 0 0 0 0 0

PC1 was characterized by large, positive coefficients assigned to nitrate and orthophosphate concentrations and water density, and a similarly large but negative coefficient assigned to temperature (Table 4). The second principal component accounted for 15% of total variability, and corresponded to a balance between river and marine influences, with a large, positive coefficient assigned to the silicic acid concentration, and large negative coefficients assigned to salinity and density (Table 4). PCA scores corresponding to PC1 and PC2 produced an ordination of environmental data that separated winter from springtime observations along the PC2 axis (Fig. 10, filled versus empty symbols) corresponding to the winter–spring shift in the balance between river and marine influences. Since there was no apparent separation between stations within seasons (Fig. 10, squares versus circles), correlation analyses for phytoplankton abundance versus water column conditions were conducted separately for spring and winter cruises. Significant positive correlations were found

Ommix III–IV Jun. 2007 5

Ommix V–VI Dec. 2007 10

Ommix VII Aug. 2008 5

between PC1 and the springtime abundance of diatom chains and naked dinoflagellates near the surface (1 and 5 m, Table 5). In winter, there was a strong negative correlation between PNF abundance near the surface and PC1 (Table 5), whereas centric diatoms at 1 m were positively correlated with PC2. All significant correlations between PC2 (i.e., balance between river and upwelling influences) and the abundance of phytoplankton taxa were positive in winter months (Table 5). Correlations between depth-integrated biomass and cumulative alongshore wind stress were contrasting among taxonomic groups and similar across stations. Diatom biomass was positively correlated with cumulative wind stress at both stations (i.e., biomass increased when alongshore winds were upwelling-favorable) and reached maximum correlation when wind stress was integrated over periods of 10–20 days prior to each cruise. A similarly delayed and strong response to wind, but with the opposite sign, was found for the biomass of cyanobacteria, ciliates, and picophytoeukaryotes

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Total

2 - 20 µ m

> 20 µ m -3

-1

Size-fractioned primary production (mgC m h ) 0

40

80 120 160

0

40

80 120 160

0

1

2

3

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5

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1

2

3

4

5

0

40

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0

40

80 120 160

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1

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Ommix 2 16 Dec

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1

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SPRING 2007

Ommix 7 9 Aug

WINTER 2008

Fig. 9. Vertical profiles of size-fractioned primary production (mg C m3 h1) during each cruise at St. RV (upper panel) and UW (lower panel). Size fractions correspond to picoplankton (<2 lm), nanophytoplankton (2–20 lm), and microphytoplankton (>20 lm).

Table 3 Results of randomized paired t-tests that compared depth-integrated biomass and size-fractioned primary production among stations within each season. Significant differences at a = 0.05 are shown in boldface. Bonferroni-corrected significance levels for multiple biomass and primary production comparisons were ab = 0.0056 and ap = 0.0167, respectively. Spring

Winter

t

p

t

p

Biomass (mg C m2) Bacteria Cyanobacteria Picophytoeukaryotes PNF MNF HNF Dinoflagellates Ciliates Diatoms

1.13 1.27 1.65 0.97 1.11 0.71 0.43 1.00 0.93

0.757 0.738 0.755 0.246 0.121 0.243 0.379 0.516 0.749

3.53 2.01 1.43 13.96 0.02 1.39 1.50 0.71 1.17

0.000 0.000 0.120 0.000 0.373 0.115 0.119 0.382 0.881

Primary production (mg C m2 d1) <2 lm 2–20 lm >20 lm

0.51 0.14 0.68

0.382 0.249 0.748

1.53 1.55 1.02

0.000 0.130 0.127

(i.e., biomass of these groups increased after downwelling events, which are typically associated with winter storms in this region). Although this negative correlation was found at both stations, results were significant only at station UW, where the biomass of PNF and mixotrophic nanoflagellates were negatively and significantly correlated with wind integrated over 25–30-day periods. 4. Discussion The coupling between riverine inputs of nutrients and biological responses of the coastal ecosystem in the outflow region of continental shelves has been a major issue in coastal ecology,

Table 4 Principal component analysis (PCA) performed on environmental data collected at both sampling locations. Columns show the weights assigned to originally measured variables. Weights > 0.3 are shown in boldface. Physical–chemical variable

PC1

PC2

Temperature (°C) Salinity (psu) Density Oxygen (mL L1) NO3 (lM) NO2 (lM) PO4 (lM) Si(OH)4 (lM) Si:N N:P

0.392 0.275 0.352 0.241 0.419 0.119 0.337 0.256 0.253 0.199

0.090 0.494 0.436 0.202 0.212 0.256 0.051 0.519 0.286 0.201

% Total variance

41.4

15.0

biogeochemistry, and oceanography, especially given the future scenarios of climatic-driven hydrological regime changes (Lohrenz et al., 2008). Over two consecutive winter–spring transitions in the coastal upwelling area off Concepción, we have found substantial changes in phytoplankton primary productivity (carbon uptake), autotrophic biomass (Chl-a), and the abundance and relative cell biomass (cell carbon) of planktonic groups. Primary production estimates ranged between 0.025 and 5.6 g C m2 day1, and were comparable to those previously recorded in the same region (0.1–9 g C m2 d1; Daneri et al., 2000; Montero et al., 2007; Vargas et al., 2007), as well as in other upwelling systems such as Perú (1–5 g C m2 d1: Barber and Smith, 1981), Benguela (0.5–4.0 g C m2 d1; Brown and Field, 1986), Antofagasta, Chile (1.1–8.1 g C m2 d1; Iriarte and González, 2004), NW Peninsula Iberica, Spain (0.1–2.5 g C m2 d1; Varela et al., 2005). All these regions showed similar patterns of strong seasonal variability, with highest PP estimates during spring compared to winter.

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PC2

2

0

-2

St RV - Spring St RV - Winter St UW - Spring St UW - Winter

-4 -8

-6

-4

-2

0

2

4

6

8

PC1 Fig. 10. PCA-based ordination of environmental data from stations RV (circles) and UW (squares). Axes correspond to the first two principal components, which together explained 56% of total variance (see Table 4).

A low-productivity winter season dominated by nanoplanktonic components is followed by a spring period characterized by high rates of microphytoplankton PP and biomass, sustained mainly by the input of cool, nutrient-rich waters forced by the prevailing equatorward winds. Previous studies conducted in the same area have documented similar changes in the pattern of biological response to physical forcing (e.g., Sobarzo et al., 2007b) and point to seasonal cycles in meteorological forcing (i.e., wind patterns and solar radiation) and resulting changes in the chemical environment (i.e., inorganic nutrient concentrations) as being key factors that modulate the dramatic increases in algal biomass observed for this region (González et al., 1989; Daneri et al., 2000). Our results also highlight the substantial contribution of the smallest fractions of pico- and nanoplankton to total primary productivity (carbon uptake) and biomass (cell carbon), as well as the contribution of heterotrophic nanoflagellates to total carbon

biomass during winter months. Although we were unable to detect springtime differences between stations RV and UW in sizefractionated PP and autotrophic biomass, our PCA results suggest that greater PP and Chl-a concentrations at both sites responded to upwelling-forced physical and chemical variability rather than to the influence of freshwater inputs from the Itata River. This contrasts with results reported for other river-influenced continental shelf environments. For instance, Lohrenz et al. (2008) reported that primary production has been shown to be positively correlated with riverine NO3 fluxes in the Mississippi River plume outflow region. Lee Chen et al. (2004) showed that summer phytoplankton production in the riverine coastal water of the East China Sea is modulated not only by nutrients from coastal upwelling events, but also by nutrients from the Changjiang River discharge. Similarly, in the coastal upwelling region of Lisboa Bay, the interannual differences observed in the phytoplankton community, varied according to both the duration and strength of the upwelling events but also associated with the precipitation regime and Tagus River flow regime (Silva et al., 2009). Nevertheless, observations in the California Current System, have also shown that during the productive season (July–September period), primary production and chlorophyll concentrations associated with upwelled waters may mask the effect of the Columbia River plume (Thomas and Strub, 2001). We also demonstrate that the influence of freshwater inputs from the Itata River in this coastal upwelling site was greater in winter and more clearly detected at station RV, where photosynthetic picoplankton and picophytoeukaryotes dominated in terms of PP and biomass, respectively. In general, the carbon biomass of picophytoeukaryotes, heterotrophic nanoflagellates, and ciliates was considerably higher in winter (max. 1000 mg C m2) than in spring. This dominance of small pico- and nanoplankton has also been observed for other stratified seas (e.g., Marañon et al., 2000) as well as in other upwelling areas during periods of intense stratification, such as the shelf of the NW Iberian upwelling system (Estrada, 1984; Varela et al., 1991; Tilstone et al., 2003).

Table 5 Spearman correlation analysis (SPC) between reported principal components and the phytoplankton community for each season. Correlations that were significant at a = 0.05 and a = 0.01 are italicized and in boldface, respectively. PC 1

PC 2

1m

5m

10 m

Spring cruises Cyanobacteria

25 m

0.64

0.72

0.69

0.21

Nanoflagellates PNF HNF

0.64 0.07

0.46 0.29

0.82 0.14

Dinoflagellates Armored Naked

0.07 0.96

0.43 0.61

Diatoms Pennate Centric Chains

0.58 0.25 0.82

Winter cruises Picophytoeukaryotes Nanoflagellates PNF HNF

35 m

1m

5m

0.75

0.21

0.71

0.04 0.14

0.31 0.75

0.21 0.18

0.69 0.14

0.21 0.39

0.31 0.28

0.71 0.71 0.86

0.50 0.68 0.32

0.50 0.00 0.21

0.14

0.09

0.71

0.89 0.37

0.94 0.37

Dinoflagellates Armored

0.61

Diatoms Pennate Centric Chains

0.37 0.76 0.03

10 m

25 m

35 m

0.22

0.93

0.57

0.25 0.14

0.04 0.50

0.14 0.79

0.56 0.79

0.39 0.75

0.20 0.36

0.06 0.54

0.79 0.30

0.56 0.38

0.78 0.61 0.50

0.08 0.00 0.54

0.93 0.86 0.71

0.21 0.64 0.79

0.11 0.11 0.54

0.46 0.36 0.71

0.94

0.71

0.09

0.66

0.43

0.37

0.89

0.60 0.54

0.39 0.87

0.31 1.00

0.71 0.26

0.09 0.60

0.83 0.60

0.67 0.59

0.14 0.83

0.64

0.81

0.83

0.66

0.79

0.07

0.47

0.09

0.49

0.37 0.80 0.31

0.41 0.11 0.09

0.71 0.43 0.83

0.77 0.66 0.66

0.26 0.93 0.43

0.20 0.51 0.60

0.81 0.26 0.57

0.71 0.31 0.66

0.71 0.43 0.66

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The influence of freshwater inputs was also apparent in Si(OH)4 profiles, which showed maxima near the surface and minima in the subsurface (5–10 m depth). Based on our observations, we suggest that, although cold and nutrient-rich upwelling waters fueled PP and microphytoplankton biomass in spring months, the predominantly river-induced conditions of winter months led to sustained high heterotrophic biomass, and pico- and nanoplankton PP. This alternate state of the phytoplankton community in winter may play an important role in the carbon budget and trophic state of the coastal upwelling area off Concepción. Whereas small-sized phytoplankton (<20 lm) have been suggested as potentially important contributors to the total PP in coastal upwelling areas (Hall and Vincent, 1990; Iriarte and González, 2004), high abundances of picophytoplankton and picoeukaryotes have been observed at the front between river and oceanic waters off Mississippi (Liu et al., 2004), whereas enhanced bacterial production associated with increased river flows has been reported for the Bay of Biscay (Iriarte et al., 2003). There seemed to be an association between wintertime bacterial biomass at station RV and changes in river outflow in 2007. Furthermore, the biomass of bacteria, nanoplanktonic heterotrophs, ciliates, and heterotrophic dinoflagellates appeared to be more tightly coupled during the less productive winter months than in the more productive spring season. The observed changes in the microbial community structure in wintertime, associated with a greater influence of freshwater inputs, could be summarized in the following hypothesis: an increase in river outflow in winter provides more nutrients and dissolved organic matter (DOM) to the nearshore water column off Concepción and promotes an increase in bacterial and picophytoplankton productivity that, in turn, promotes an increase in grazing by heterotrophic nanoflagellates and ciliates. It is not clear to what extent this relationship is a direct response to changes in the quality and quantity of nutrient and DOM inputs, regardless of seasonal changes in water column stability. Humic substances supplied by freshwater runoff from the heavily forested Itata watershed may play a role in the wintertime increase of microbial abundance; as such substances have been shown to have a positive effect on the growth of several protozoan species (e.g., Carlsson et al., 1995; Hallegraeff et al., 1995; Weise et al., 2002). Furthermore, protozoans have been shown to play an important role as grazers of phytoplankton in river plume areas (Fahnenstiel et al., 1995; Dagg et al., 2004). Indeed, during these same sampling campaigns, Vargas and Martínez (2009) showed a relatively high grazing impact of ciliates on bacterivorous nanoplankton and autotrophic nanoflagellates, which accounted for around 99% of the PP per day, in winter within the river plume. Our results clearly show the extent to which the composition and biomass of the phytoplankton community may vary spatially and temporally in the upwelling area off Concepción. They also illustrate the potential for seasonal and inter-annual changes in the river plume-upwelling balance to generate small-scale spatial differences in phytoplankton biomass and community composition during certain months. The existence (absence) of significant differences in phytoplankton biomass and composition in winter (spring) months could be partly explained by seasonal changes in the interaction of river outflow and prevailing coastal winds. Wintertime coastal winds often blow polewards (Sobarzo et al., 2007a) and, at the same time, river plumes tend to move cum sole due to the Coriolis effect (e.g., Piñones et al., 2005). The end result of this resonance between prevailing winds and the alongshore displacement of river discharges is a north–south gradient in river influence on the physical–chemical conditions for phytoplankton growth at both stations. In the springtime, river discharge is minimal and strong coastal winds blow consistently towards the Equator (Sobarzo et al., 2007a). Persistent coastal upwelling in this

region is expected to generate a more homogeneous set of physical–chemical conditions for phytoplankton growth. Differences among taxa in the delay with which biomass responded to variability in alongshore wind forcing were consistent with expectations based on food web dynamics. For instance, whereas the diatom biomass appeared to respond to upwellingfavorable winds within 5 days, the biomass of bacteria and dinoflagellates were positively correlated with wind forcing over periods P15 days. Wind-integration periods for which correlations with PP reached maximum (and significant) values could provide an indication of characteristic response times for shelf phytoplankton in the region. Among-station differences in biomass composition, as well as the response of small-cell PP to wind forcing, highlight the potential for divergence in phytoplankton dynamics over small spatial scales in this region. We propose that such differences are linked to a shift in the relative importance of upwelling versus river influence as a driver of physical–chemical variability in the nearshore water column. In summary, our results support the hypothesis that the spatial–temporal variability of the river plume influence and its importance relative to wind-induced coastal upwelling in central Chile may drive changes in the biomass and size composition of coastal phytoplankton. These changes may sustain and/or enhance a wintertime ‘‘microbial food web’’ in this highly productive upwelling region. These results highlight the ecological and biogeochemical implications of current and future changes in the volume and/or composition of river inputs to the coastal ocean. Acknowledgements We thank the captain and crew of the research vessel L/C Kay Kay and the OMMIX team who participated in our cruises, especially Paulina Contreras, Cynthia Valenzuela, and David Opazo. We are also indebted to Rubén Escribano (COPAS Center) for providing the PAR data, as well as all the logistical facilities at the Marine Research Station of Dichato and onboard the RV Kay-Kay, Universidad de Concepción (e.g., CTDO, Tucker trawl nets, Niskin bottles). Financial support for this study was fully provided by FONDECYT Grant No. 1060709 to CAV and JLI, and FONDECYT Grant No. 1095069 to CAV. References Ahumada, R., Matrai, P., Silva, N., 1991. Phytoplankton biomass distribution and relationship to nutrient enrichment during an upwelling event off Concepción bay, Chile. Boletín de la Sociedad Biológica de Concepción, Chile 62, 7–19. Anabalón, V., Morales, C.E., Escribano, H.R., Varas, M.A., 2007. The contribution of nano- and micro-planktonic assemblages in the surface layer (0–30 m) under different hydrographic conditions in the upwelling area off Concepción, Central Chile. Progress in Oceanography 75, 396–414. Barber, R.T., Smith, R.L., 1981. Coastal upwelling ecosystems. In: Longhurst, A.R. (Ed.), Analysis of Marine Ecosystems. Academic Press, New York, pp. 31–68. Brown, P.C., Field, J.C., 1986. Factors limiting phytoplankton production in a nearshore upwelling area. Journal of Plankton Research 8, 55–68. Böttjer, D., Morales, C.E., 2007. Nanoplanktonic assemblages in the upwelling area off Concepción (36 S), central Chile: abundance, biomass, and grazing potential during the annual cycle. Progress in Oceanography 75, 415–434. Cáceres, M., Arcos, D.F., 1991. Variabilidad en la estructura espacio-temporal de un area de surgencia frente a la costa de Concepción, Chile. Investigación Pesquera 37, 55–66. Carlsson, P., Graneli, E., Tester, P., Boni, L., 1995. Influences of riverine humic substances on bacteria, protozoa, phyto-plankton, and copepods in a coastal plankton community. Marine Ecology Progress Series 127, 213–221. Dagg, M., Benner, R., Lohrenz, S., Lawrence, D., 2004. Transformation of dissolved and particulate materials on continental shelves by large rivers: plume processes. Continental Shelf Research 24, 833–858. Daneri, G., Dellarossa, V., Quiñones, R., Jacob, B., Montero, P., Ulloa, O., 2000. Primary production and community respiration in the Humboldt Current System off Chile and associated oceanic areas. Marine Ecology Progress Series 197, 41–49. Edler, L., 1979. Recommendations for marine biological studies in the Baltic Sea. The Baltic Marine Biologists Publication 5, 1–38. Estrada, M., 1984. Phytoplankton distribution and composition off the coast of Galicia (northwest of Spain). Journal of Plankton Research 6, 417–434.

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