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Article

A Strong Link Between Oceanographic Conditions and Zooplankton δ13C and δ15N Values in the San Jorge Gulf, Argentina

by
David Edgardo Galván
1,*,
Manuela Funes
2,
Flavio Emiliano Paparazzo
1,3,
Virginia Alonso Roldán
4,5,
Carla Derisio
6,
Juan Pablo Pisoni
1,
Brenda Temperoni
2,6,
Daniela Alejandra del Valle
6,7,
Valeria Segura
6 and
Seth D. Newsome
8
1
Centro para el Estudio de Sistemas Marinos (CESIMAR-CONICET), Boulevard Brown 2915, Puerto Madryn U9120ACD, Argentina
2
Instituto de Investigaciones Marinas y Costeras (IIMyC, UNMdP-CONICET), Juan B. Justo 2550, Mar del Plata B7608FBY, Argentina
3
Instituto Patagónico del Mar (IPAM-UNPSJB), Boulevard Brown 3051, Puerto Madryn U9120ACD, Argentina
4
Grupo de Investigación en Gestión Desarrollo Territorial y Ambiente (GesDTA-UTNFRCH), Facultad Regional Chubut, Universidad Tecnológica Nacional, Av. del Trabajo 1536, Puerto Madryn U9120QGQ, Argentina
5
Instituto Patagónico para el Estudio de los Ecosistemas Continentales (IPEEC-CONICET), Boulevard Brown 2915, Puerto Madryn U9120ACD, Argentina
6
Instituto Nacional de Investigación y Desarrollo Pesquero, Paseo Victoria Ocampo Nº1, Mar del Plata B7602HSA, Argentina
7
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires C1425FQB, Argentina
8
Biology Department, University of New Mexico, Albuquerque, NM 87131-0001, USA
*
Author to whom correspondence should be addressed.
Biology 2024, 13(12), 990; https://doi.org/10.3390/biology13120990
Submission received: 31 October 2024 / Revised: 22 November 2024 / Accepted: 27 November 2024 / Published: 29 November 2024
(This article belongs to the Special Issue Applications of Stable Isotope Analysis in Ecology)
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Simple Summary

Stable isotope analysis provides valuable data for marine ecology research. For proper interpretation of results, baseline data from organisms at or near the base of the food web is critical. This study analyzes how stable isotope ratios in small zooplankton (copepods) vary across San Jorge Gulf and relates the observed variation to physical and chemical oceanographic conditions. San Jorge Gulf is a basin of 40,000 km2 located in the Argentine Sea (southwest Atlantic Ocean). The area is of great importance, supporting several natural contributions to people, such as large fisheries and ecotourism based on the marine mammal and seabird populations. We sampled copepods, as they feed on phytoplankton and are robust tracers of processes occurring at the base of the food web. The results revealed significant variation in both carbon and nitrogen stable isotope ratios. This variation was related to nutrient concentration, water column depth, and oceanographic processes that mix waters, all factors impacting phytoplankton growth rates. Our findings provide baseline data to address questions related to the feeding and movements of other animals inhabiting the region.

Abstract

Maps of (baseline) δ13C and δ15N values of primary producers or consumers near the base of food webs provide crucial information for interpreting patterns in the isotopic composition of consumers that occupy higher trophic levels. In marine systems, understanding how oceanographic variables influence these values enables the creation of dynamic isoscapes across time and space, providing insights into how ecosystems function. The San Jorge Gulf (SJG) in the southwest Atlantic Ocean (45° S–47° S) is an area of particular importance, as it is located on one of the most productive continental shelves in the world, supporting large fisheries and marine mammal and seabird populations. We reconstructed spatial variation in zooplankton δ13C and δ15N values across SJG and investigated their relationship with physical and chemical oceanographic conditions. During cruises in the austral spring of 2016 and 2017, we collected medium-sized copepods whose isotopic composition integrate short-term (days to weeks) variation in oceanographic conditions recorded by phytoplankton at the base of the food web. We also collected data on water column depth, surface and bottom temperatures, water column stability, and macronutrient (nitrate, phosphate, and silicic acid) concentrations. The results revealed significant variation in both δ13C and δ15N values of up to 7-8‰ over a relatively small spatial scale (200–300 km). Copepod δ13C values were lower at the center of the SJG, showing an inverse correlation with water column stability, surface nitrate concentration, and water column depth. δ15N values showed a strong and negative relationship with surface nitrate concentration and water column stability, increasing from south to north in the SJG. δ15N values also showed a positive relationship with surface silicic acid concentration. These spatial patterns in nutrient dynamics and copepod carbon and nitrogen isotope values are interpreted in the context of the dominant northward current and temporal development of the frontal systems in the SJG.

1. Introduction

Carbon (δ13C) and nitrogen (δ15N) isotope analysis is a powerful and routinely used tool in marine trophic ecology [1,2]. Baseline δ13C and δ15N values of organisms at or near the base of the marine food webs are critical for accurate interpretation of the trophic dynamics of organisms [3], the food web structure [4], and the relative reliance on benthic versus pelagic sources of energy [5]. Baseline data can be constructed from the δ13C and δ15N values of primary producers [6] or primary consumers [7], but their isotopic composition varies over time and space due to physicochemical and biological processes. This spatial variation is represented by isoscapes, which are maps of interpolated isotope values collected from a limited set of locations, spanning areas that vary in extent from tens [8] to thousands [9] of square kilometers. Understanding how δ13C and δ15N values are predictably influenced by oceanographic variables provides insights into ecosystem functioning [7], crucial for interpreting consumer-derived isoscapes [10], enabling the creation of dynamic isoscapes [11] that represent isotopic variation in both time and space.
In marine ecosystems, baseline isoscapes are usually derived from sessile or low-motility primary consumers, such as bivalves [5,12] or zooplankton [5,7], under the assumption that they effectively reflect and integrate the isotopic variation of local primary producers. In marine ecosystems, various physicochemical and biological factors influence the δ13C and δ15N values of phytoplankton. The extent of δ13C discrimination during photosynthesis is primarily influenced by: (1) the concentration and isotopic composition of dissolved inorganic carbon in seawater, (2) phytoplankton growth rates [13], and (3) cell size [14,15], which is often related to assemblage composition [6]. Larger phytoplankton cells are 13C-enriched compared to smaller cells due to passive CO2 diffusion into the cell [16]. Furthermore, there is a negative relationship between the external CO2 concentration and phytoplankton growth rate, such that a decrease in CO2 concentration reduces 13C discrimination [6]. In temperate systems, this relationship is observed during seasonal blooms when high growth rates result in increased δ13C values of phytoplankton due to reduced discrimination between CO2 and fixed organic carbon [13].
Phytoplankton growth rate and community composition are primarily controlled by the concentrations of three macronutrients: nitrogen (N), phosphorous (P), and silica (Si) [17]. Measuring the availability of N is crucial for interpreting isoscapes, and there are four forms of inorganic nitrogen in ocean systems: NO3−, NO2−, NH4+, and N2. NO3− (nitrate) has the fastest assimilation rate, and as such is the primary form fueling primary production in most areas of oceans [18]. In most regions, N is limiting, so the average isotopic composition of phytoplankton mirrors that of nitrate [19]. The concentration of the other macronutrients, Si and P, could also influence δ13C and δ15N values at the base of pelagic marine food webs. Under certain conditions, primary producers can deplete available nutrients, reducing their concentrations to levels that limit growth rates and influence δ13C and δ15N discrimination. For example, in the Argentine outer shelf, the Si:N ratio positively correlates with diatom abundance, which influences their δ13C values [20].
There is scarce spatially explicit baseline information on δ13C and δ15N values on the Argentine shelf in the western South Atlantic Ocean (~35° S–56° S). In the middle and outer shelf (100 m–200 m deep), δ13C values of surface particulate organic matter (POM) showed a strong correlation with sea surface temperature (SSTs) [20], which likely reflects temperature-related variations in aqueous CO2 concentrations and their effect on carbon isotopic discrimination by marine phytoplankton [20]. These patterns appear to be passed up regional food webs, as whole-blood δ13C values of Magellanic penguins (Pheniscus magellanicus) decrease with increasing latitude of collection [21]. Regarding δ15N values, SST and nutrient pools including unutilized nitrate, Si:N ratios, and phosphate concentration were identified as key drivers of POM δ15N values on the middle and outer Argentine shelf [20]. In coastal waters between 41° S and 53° S, δ15N isoscapes of penguin whole blood revealed two distinct zones: lower δ15N values south of ~47° S and higher values to the north. The transition between these zones occurs within the San Jorge Gulf (SJG), a region of particular importance.
The SJG is a semicircular basin (~40,000 km2) located on the Argentine shelf (Figure 1), one of the most productive continental shelves in the world supporting large and lucrative invertebrate and vertebrate industrial fisheries [22,23]. Due to its unique oceanographic and ecological features, as well as its susceptibility to human impacts, the SJG has been designated as one of the five priority research areas by the Argentine government under the Pampa Azul initiative (http://www.pampazul.gob.ar accessed on 31 October 2024). The SJG’s waters are sourced from the south, primarily via the Patagonian Current, which is influenced by cold and low-salinity waters from the Magellan Strait [24,25]. Circulation within the SJG varies seasonally and is regulated by atmospheric heat fluxes, which generate a cyclonic gyre for most of the year [26,27]. The SJG has no significant freshwater inputs, as it is located in a semi-desert region that receives ~250 mm rain year−1 and lacks river discharges.
The bioavailability of macronutrients (especially nitrate) for primary producers in the SJG is primarily driven by thermal stratification of the water column [28] and its disruption in frontal areas. Frontal areas within the gulf are generated by the interaction of tidal currents with bottom topography [29,30], islands, and headlands [31] or driven directly by winds [32]. Two frontal systems, referred to as the Northern Patagonian Frontal System (NPFS) and the Southern Patagonian Frontal System (SPFS), are the areas of highest productivity (Figure 1) [33,34]. The NPFS develops during austral spring and summer along the inner shelf and extends into the northern half of the SJG (Supplement Materials Figure S1). This frontal system is driven by seasonal thermal stratification and high dissipation of tidal energy [33,35]. In contrast, the SPFS is a permanent thermohaline front characterized by the transition between the tidally mixed, nutrient-rich, and seasonally stratified waters of the Patagonian Current and the relatively saline and seasonally stratified inner waters [25,33].
Biology 13 00990 g001
Figure 1. Map showing grid sampling design; dark blue and light blue circles denote zooplankton and water sampling locations and green circles denote stations where only oceanographic conditions were measured via CTD. The average position of the Northern Patagonia Frontal System (NPFS) and the Southern Patagonia Frontal System (SPFS) were sourced from [33] and are shown as red dashed lines. The main current direction was sourced from [27] is shown as blue arrows.
Figure 1. Map showing grid sampling design; dark blue and light blue circles denote zooplankton and water sampling locations and green circles denote stations where only oceanographic conditions were measured via CTD. The average position of the Northern Patagonia Frontal System (NPFS) and the Southern Patagonia Frontal System (SPFS) were sourced from [33] and are shown as red dashed lines. The main current direction was sourced from [27] is shown as blue arrows.
Biology 13 00990 g001
The diverse physical and chemical conditions of the SJG promote high biological production [36,37,38], with spatio-temporal variation in phytoplankton [39] and zooplankton [40,41] abundance. Spring phytoplankton blooms strongly associated with the two frontal systems are dominated by diatoms and dinoflagellates [36,37,38]. The magnitude of spring primary production is comparable to that reported for the rich Argentinian shelf break [38] and supports a productive local zooplankton community dominated by copepods that account for >80% of the biomass in the zooplankton community. Medium and large calanoid copepods (>1 mm total length) are primarily herbivorous/omnivorous [41], whereas relatively small (<1 mm) cyclopoid copepods exhibit more omnivorous and/or detritivorous foraging behavior [42,43]. In addition to these trophic preferences that likely influence the isotopic composition of copepods, the δ13C and δ15N values of zooplankton increase within the SJG from north to south during the austral summer in response to environmental physico-chemical variation [40], specifically SST and inorganic N availability [44]. A similar latitudinal pattern has been reported for the outer Argentine shelf [20]. These spatial patterns also agree with the δ13C and δ15N isoscapes constructed from penguin tissues [21]. Additionally, dust inputs derived from westerly windstorms [45] may play an important role in regulating baseline isotope values in this region.
To enhance our understanding of how physical and chemical oceanographic conditions influence stable isotope values at the base of the food web, we analyzed spatial and interannual variation in δ13C and δ15N values of zooplankton collected in the SJG for comparison to local physical and chemical oceanographic data, including water depth, surface/bottom water temperature, and macronutrient (N, P, and Si) concentrations. This study was conducted during the austral spring, when primary production is expected to be at its highest [34]. We chose to target zooplankton sized between 200 and 300 μm (prosome width), because this assemblage primarily consists of immature copepods, assumed to be primary consumers [5,40], and as such are robust tracers of short-term spatial variation in δ13C and δ15N values [7,10].

2. Material and Methods

Samples and data were collected during the austral spring, from 12 to 26 November 2016 and from 28 October to 8 November 2017, aboard the R/V Puerto Deseado (CONICET, Buenos Aires, Argentina). The sampling comprised a regular grid (Figure 1 and Supplement Materials Table S1). At each sampling station, profiles of temperature, conductivity (salinity), and pressure were measured using a CTD SBE 911 Plus©, Sea-Bird Scientific, Washington, EEUU. Water column stability was estimated using the Brunt–Väisälä frequency [46] calculated from potential density values integrated in the water column up to the maximum depth reached at each station (<100 m). Seawater samples were collected using a rosette system equipped with Niskin bottles near the surface (5 m depth, hereafter referred to as ‘surface’) and bottom (5 m above the seafloor, hereafter referred to as ‘bottom’). Samples were collected to measure nitrate, phosphate, and silicic acid concentrations (Figure 1). These were preserved at −20 °C in polyethylene bottles previously washed with distilled water and 5% HCl and then rinsed with distilled water. Once on land, macronutrient concentrations were determined using a Skalar San Plus autoanalyzer (V.B. 2005a,b,c), Skalar Analytical®, Breda, The Netherlands.
Zooplankton samples were collected at 21 stations in 2016 (Figure 1) and 14 stations in 2017 by oblique tows with a Minibongo net (200 µm mesh). Fewer stations were sampled in 2017 due to the time constraints of the cruise. Both the 2016 and 2017 samplings were conducted on a regular grid covering similar areas (Figure 1 and Supplement Materials Table S1), with 14 stations shared between the two years. The primary difference between the samplings is that, in 2016, two additional stations were located outside the SJG—one to the north and one to the south—while five were interspersed within the gulf, maintaining the same boundary at the gulf’s mouth. After each tow, we separated the fraction of zooplankton sized between 200 and 300 μm of prosome width, which mainly included immature copepods between 1 and 1.5 mm in size (total length), by sieving the samples through a 300 µm mesh. Then, samples were stored at −20 °C until processing for stable isotope analysis. This size fraction has been previously used to estimate pelagic isotopic baselines by [5] in the SJG. Zooplankton samples were dried at 60 °C for 72 h and homogenized into a fine powder, and δ13C and δ15N values were measured via a Costech 4010 elemental analyzer coupled to a Thermo Scientific Delta V Plus isotope ratio mass spectrometer at the University of New Mexico Center for Stable Isotopes (Albuquerque, NM, USA). Internal standards used were soy, tuna, whey protein, casein, IAEA N1, IAEA N2, USGS 42, and USGS 43. Mean within-run standard deviation (SD) of a suite of in-house (soy, tuna, casein) and internationally accepted (IAEA N1, IAEA N2, USGS 42 and USGS 43) reference materials were 0.1‰ for both δ13C and δ15N values. Given that samples with C/N ratios greater than 3.5 were observed, and that lipid extraction was not performed prior to stable isotope analysis, δ13C values were lipid-normalized using the general equation proposed for aquatic organisms [47]. All subsequent data analyses and results were conducted using lipid-normalized δ13C values.
To determine the natural abundance of 13C (δ13C) of particulate organic carbon (POC) at eight selected stations (four in the south and four in the north of the SJG each year), surface seawater samples were collected in polycarbonate bottles and immediately filtered onto pre-combusted Whatman GF/F glass-fiber filters. The filters were stored dry, and once on land they were fumed with HCl and encapsulated. The δ13C of POC in the samples was analyzed using an Elementar Vario Micro Cube elemental analyzer (Elementar Analysensysteme GmbH, Hanau, Germany) interfaced with a PDZ Europa 20-20 isotope ratio mass spectrometer (Sercon Ltd., Cheshire, UK) at the Stable Isotope Facility of the University of California in Davis (USA).
A Bongo net (300 µm mesh) equipped with flowmeters was used to estimate zooplankton community composition at a subset of stations. Bongo net samples were fixed immediately after collection with a 5% formalin–seawater solution. They were inspected in the laboratory under a Wild M5 stereoscopic microscope to estimate both the abundance and the taxonomic composition of the 1 mm–2 mm zooplankton fraction, classifying the species according to their trophic function sensu [41,48,49].
We analyzed the relationship among physical and chemical oceanographic variables and zooplankton δ13C and δ15N values using generalized linear mixed models (GLMMs), assuming a Gaussian error distribution. The GLMM aimed to investigate the temporal and spatial variation of these variables. We included water column depth, sea surface temperature (5 m depth), near-bottom temperature (5 m above the bottom), and water column stability. Nitrate, phosphate, and silicic acid concentrations at the surface and bottom were included as predictors of nutrient availability. Station ID and year were treated as random variables. Before conducting the GLMM, we performed a data exploration process following a standard procedure [50]. This process included outlier detection, assessment of variance heterogeneity, checking for collinearity among predictors, and selection of predictors using a variance inflation factor (VIF) analysis. Initially, complete models were fitted, incorporating all non-collinear predictors, and alternative structures for the random factor were compared. Subsequently, based on the best random structure, a backward elimination procedure was employed to select the most parsimonious model and identify the best predictors. The criteria for the backward elimination procedure were the lowest Akaike’s Information Criterion (AIC) values and a delta AIC > 2 [51]. To examine spatial autocorrelation among residuals, we applied Moran’s I coefficient, as described in [52]. Furthermore, we evaluated goodness of fit and deviations from model assumptions by the Shapiro–Wilk test of normality, fitting linear models using the residuals and absolute values of the residuals against the predicted values and visually inspecting a QQ plot, which compares the quantiles of the theoretical residuals under the assumption of normality to the quantiles of the observed residuals [51]. To facilitate interpretation of the results, we identified the stations where macronutrients might have limited primary producers according to the thresholds proposed by [53]: nitrate < 0.7 μM and silicic acid < 1.8 μM. The statistical software program R [54] was used for data analysis, with the nlme package [55] for model fitting and the ape [56], gstat [57], and sp [58] packages for assessing spatial autocorrelation. In addition, the correlation between zooplankton and phytoplankton δ13C values was examined using linear regression and compared to an assumed slope of 1 and an intercept of 1‰ (a 1:1 relationship with a discrimination of 1‰). The regression model was fitted using mean δ13C values of zooplankton for the 12—six stations per year—samples of phytoplankton. Model assumptions were tested using the same procedure previously described.

3. Results

Oceanographic conditions differed between the two years. The stability of the water column suggests a larger area of stable waters with low vertical mixing and well-established frontal systems in 2016, particularly the SPFS, compared to 2017 (Figure 2 and Table 1). Additionally, the range between the extreme values of surface and bottom temperatures was broader in 2016 compared to 2017 (Table 1).
Zooplankton δ13C and δ15N values varied among sampling stations, with 2016 showing greater variability compared to 2017 (Table 1 and Figure 3). δ15N values generally increased from south to north and from offshore to inshore waters in both years (Figure 3), a pattern that was more pronounced in 2016 than in 2017. Lower δ13C values were observed in the central region of the SJG, and likewise this pattern was more pronounced in 2016 than in 2017 (Figure 3).
The exploratory data analysis revealed correlations between macronutrient concentrations and water column structure (Figure 4), except for surface silicic acid. Macronutrient concentrations near the bottom exhibited correlations among each other and increased with depth (Figure 4). Nitrate and phosphate at the surface were also positively correlated. However, surface silicic acid did not show a strong correlation with any of the studied variables. In agreement with the results of the correlation matrix, the variables with the most information (VIF < 2) selected as predictors for the GLMMs were: water column depth, water column stability, and surface nitrate and silicic acid concentrations.
Data analysis by GLMM showed that year and sampling station had intrinsic differences, as the best structure for the random components included a variable intercept for the stations nested in the years (Supplement Materials Table S2). In the final simplified model, δ15N values were strongly and negatively related to surface nitrate (Table 2). This relationship was evident in the spatial patterns in 2016 (Figure 3 and Figure 5) and was fully captured in the model. Surface silicic acid and water column stability were also significant variables in the model (Table 2). Higher surface silicic acid concentrations, and more stable water columns were related to 15N-rich zooplankton (Figure 2 and Figure 6). The model had a good fit, as residuals did not show spatial autocorrelation (I = 1.1 × 10−4 p = 0.73) or departure from the assumptions of normal distribution of errors (W = 0.99, p = 0.85) and homogeneity of variance (Supplement Materials Table S3 and Figure S2). For δ13C values, the selected model showed a negative correlation with water column stability and surface nitrate concentrations (Table 2). A relationship between stable waters with low vertical mixing at the center of the SJG and low zooplankton δ13C values was particularly evident in 2016 (Figure 2 and Figure 3). Additionally, water column depth significantly contributed to the model, with zooplankton in coastal waters exhibiting lighter isotopic values than those in central and outer waters (Table 2 and Figure 3). The residuals from the model did not show signs of spatial autocorrelation (I = −2.5 × 10−3, p = 0.84) or departure from the assumptions of hnormal distribution (W = 0.99, p = 0.51) and homogeneity of variance (Supplement Materials Table S3 and Figure S3).
The surface nitrate concentration was highest in the southern Gulf, with values close to 11 µM (Table 2, Figure 5). As circulation progressed in the SJG northward, it decreased, likely due to stratification and phytoplankton consumption. Consequently, in 2016, numerous northern stations were observed with values below the minimum concentration required for the normal development of primary producers (<0.7 µM) (Figure 3). The other macronutrient with a significant effect, silicic acid, followed a similar trend to nitrate (Figure 6). Additionally, the selective consumption of this macronutrient led to limiting concentrations in certain areas for the organisms that require it for their development (<1.8 µM) (Figure 3). For both macronutrients, this pattern was more pronounced in 2016 than in 2017.
Zooplankton δ13C values correlated well with phytoplankton δ13C values (r2 0.60, Figure 7). The regression model exhibited a slope of 0.75 (sd = 0.21, p = 0.005), which included the theoretical 1:1 relationship with a discrimination of 1‰ within its 95% confidence interval. Although there are two extreme points of high leverage, the result adds support to the assumption of a predominance of zooplankton feeding on phytoplankton.
The taxonomic description of zooplankton samples showed that the fraction of zooplankton smaller than 2 mm comprised immature copepods (copepodites) and some adult copepods (Table 3). Copepod abundance was higher in 2016 than 2017 (Figure 8). In 2016, copepod abundances varied between 42 and 2571 ind m−3 (mean = 554; SD = 641), with the highest abundances found in the northern sector of the SJG. In 2017, abundances varied between 63 and 501 ind m−3 (mean = 222; SD = 120), but the highest abundances occurred towards the southern sector and inner waters of the SJG. Copepodites presented similar abundances in both years, but 2017 had less variability among stations than 2016 (Figure 8). Copepod community composition was similar in both years. The most abundant species, in decreasing order, were Ctenocalanus vanus, Drepanopus forcipatus, and Acartia tonsa, while Oithona aff. helgolandica and O. atlantica were less abundant. Copepod assemblages showed similar spatial patterns in specific and functional composition in both years (Figure 8). The herbivore D. forcipatus was only present in the southern sector, while the central and northern sectors were dominated by herbivore-omnivores (C. vanus) and omnivore-detritivores (A. tonsa, O. aff. helgolandica, and O. atlantica).

4. Discussion

Our results show that copepod δ13C and δ15N values varied by up to 7–8‰ over a relatively small spatial scale (200–300 km) across the SJG. Analysis of zooplankton assemblage composition indicated that herbivorous copepods dominated both years. In addition, the observed relationship between POM and zooplankton δ13C values suggests that zooplankton isotopic composition was a good proxy for that of phytoplankton at the base of the food web. We identified distinct spatial patterns for δ13C and δ15N values, including lower δ13C values in the center of the SJG that were inversely related with water column stability, surface nitrate concentration, and water column depth. In contrast, δ15N values generally increased from south to north, showing an inverse relationship with surface nitrate bioavailability and water column stability and a positive relationship with surface silicic acid concentration. Below we explore the potential abiotic and biotic factors that influence the observed spatial patterns in copepod isotopic variation, with special attention to the bioavailability of nitrate in the photic zone as influenced by the dominant northward current and seasonal development of frontal systems in the SJG.
The oceanographic conditions in SJG are likely to exhibit substantial interannual variability [34], with even sporadic upwelling events having a great effect on primary productivity [44]. Our results show that spring water column stability was similar in both years, although it exhibited a broader range in 2016. Reduced stability was observed in the south–southwest region during 2016, whereas stability in the northeast was lower in 2017. Similar findings were found using the Simpson stability parameter for the 2016 and 2017 cruises [59], which were lower than the values estimated for the spring of 2008 [38] and the spring of 2019 [59]. These observed differences among years may also depend on the timing of data collection and the temporal progression of frontal development. Spring sampling started ~15 days earlier in 2017 than in 2016, which may partially explain observed differences between years. Furthermore, the spring 2008 surveys reported in [38,59] were conducted in late November and early December, when the water column is expected to be more stable, whereas the published data for spring 2019 [59] were collected at a similar time as the spring 2016 and 2017 data reported here. These differences in water column stability between years, together with the temporal process of frontal development, are likely influencing the isotopic composition of primary producers and consumers at the base of the food web.
δ13C values in marine ecosystems generally decrease with increasing latitude [60,61] due to the temperature-related effects of CO2 solubility on carbon isotope discrimination between aqueous CO2 and fixed organic carbon [20]. In highly productive areas, however, dissolved CO2 concentrations are also controlled by phytoplankton growth rates [6]. We did not observe a positive correlation between latitude and copepod δ13C values, likely because spatial patterns in productivity and water column structure do not follow a latitudinal pattern across the SJG. The SJG is located at temperate latitudes (between 45° S–47° S) where the surface heat flux tends to stratify the water column during the spring, which dampens the supply of nutrients from bottom waters. However, the complex topography near the mouth of the southern sector of the SJG, in conjunction with tidal currents, homogenizes the water column [29,62], bringing cold, nutrient-rich waters from the Magellan Plume to the surface. Moreover, at the mouth of the SJG in the southern frontal zone, tides facilitate the influx of low-salinity, nitrate-rich waters, which provides a lateral fertilization mechanism to maintain the surface chlorophyll layer [63]. In addition to these mechanisms, intense winds in coastal areas of the southwestern sector of the SJG can lead to upwelling that mixes the water column [26,32,44]. This local process was evident in 2016, when a strong windstorm caused coastal upwelling at 46.5° S, destabilizing water column stratification and locally increasing surface nitrate concentrations [32,44]. In the northern sector near the coast, tidal currents and topography interact to vertically mix the water column [64,65], generating a highly turbulent region [31] that in the warm (productive) season is fed by nutrient-rich waters originating from the center of the SJG [30]. These conditions combine to create areas with cold bottom waters upwelled to the surface that enhance nutrient availability in the southern and northern sectors of the SJG, which by extension increases local phytoplankton growth rates [28,44]. Such factors affect carbon isotope discrimination, leading to an increase in phytoplankton δ13C values.
The spatial pattern in δ13C values described above was more evident in 2016 than in 2017 (Figure 2). This difference is likely because of differences in the time of sampling between years. In 2017, sampling occurred just after the formation of a weak seasonal frontal system [59], which resulted in lower primary production (phytoplankton biomass) in comparison to the previous year [59]. As zooplankton quickly integrate changes in the isotopic composition at the base of the food web through grazing and assimilation of phytoplankton, environmental conditions along with phytoplankton composition and growth rates in the days prior to sampling are crucial for accurate data interpretation. In 2016, chlorophyll a concentrations estimated from satellite-derived color imagery that serves as a proxy of phytoplankton biomass were higher in the northern and southern regions of the SJG compared to the central area during the week leading up to sampling (Supplement Materials Figure S1). However, there was no equivalent increase in chlorophyll a concentrations in the frontal areas before the cruise in 2017, which was conducted two weeks earlier than in 2016 (Supplement Materials Figure S1). This short delay meant that, in 2017, phytoplankton had less time to capitalize on favorable spring conditions, such as increased light availability and abundance of nutrients. Overall, this resulted in higher zooplankton δ13C values in 2016 relative to 2017 (Figure 3), which is a product of the observed interannual differences in primary productivity in the frontal area at the time of sampling between these two years (Figure 6).
A previous study [40] sampled the zooplankton community of the SJG during the summer of 2014 and reported a range of mean δ13C values (−25‰ to −18‰) similar to what we observed, but with a general increase from north to south. During the summer of 2014, the northern SJG was stratified, while the southern sector exhibited a homogeneous water column [40]. Thus, prior results are consistent with the patterns reported here, showing that zooplankton δ13C values are inversely correlated with the vertical structure of the water column, being lower in the region where conditions favor nutrient input from deep waters.
Another factor explaining the observed δ13C values was water column depth, as the SJG deepens toward its center. Specifically, δ13C values were inversely correlated with water column depth, being lower in the center of the SJG and showing the expected trend from coastal to deeper zones [7]. However, the relationship between water column depth and δ13C values is likely indirect and mediated by water column stability. Both water column depth and stability had a weak positive correlation (Figure 4), as deeper waters tend to have more stable vertical profiles.
On the outer Argentine shelf, POM δ15N values increase from ~2.5‰ at 44° S to 5.6‰ at 54° S [20]. Our zooplankton results show a similar latitudinal pattern, which was more pronounced in 2016 than in 2017 (Figure 3). Nitrate and silicic acid might have limited phytoplankton growth in much of the study area during 2016–2017 (Figure 3). A negative correlation between POM δ15N values and nitrate and phosphate concentrations was reported for the outer Argentine shelf, suggesting that nitrate availability was likely the main driver of latitudinal variation in δ15N [20]. Our results for the SJG agree with this mechanism, as zooplankton δ15N values and surface nitrate concentrations were negatively related. In addition, we found that surface nitrate and phosphate concentrations were highly correlated (Table 2). In agreement, there is a previous description of a northward increase in zooplankton δ15N values for the zooplankton community within a range comparable with our results (6–15‰) [40]. Following the same line of evidence and reasoning previously offered to describe patterns in δ13C values, phytoplankton may uptake 15N-depleted nitrate from the Magellan Plume in the southern sector of the SJG, which is available at the surface along frontal zones [66]. Consequently, the nitrate concentration is likely to be reduced by biological uptake, leading to low concentrations at the center of the SJG (Figure 5) and concentrations below the limiting concentration in the northern sector of the gulf [44]. The bottom waters in the center of the SJG are rich in organic matter that supports a community of microbial decomposers and benthic scavengers (e.g., Pseudechinus magellanicus and Grimothea gregaria; [67]) that generate 15N-enriched inorganic nutrients [28] that are upwelled to the surface by the northward circulation pattern [30] during the warm season in the northern frontal zone. Combined, 15N-depleted nitrate in the south and 15N-enriched ammonium in the north [44] support primary production in the SJG to create the observed latitudinal pattern in zooplankton δ15N.
In addition to the process described above, copepod δ15N values may have been influenced by a spatial shift in trophic level related to resource availability. In this scenario, δ15N values would be expected to increase if omnivory becomes a more prevalent foraging strategy. Estimates of copepod trophic position were generated from adults (up to 2 mm in size) collected with a 300 µm mesh net, which is larger than the fraction used for stable isotope analysis that consisted of slightly smaller and immature copepods in the CIV–CV copepodite stages (1–1.5 mm). These slightly smaller copepods likely had similar dietary preferences to the adults, as previously described for calanoid copepods [68,69]. Herbivorous species dominated the southern region of the gulf, while the northern region consisted of a combination of herbivore-omnivores and omnivore-detritivores. This distribution in the functionality of the copepod community aligns with expectations for spring and summer, when the most abundant species in the size fraction used for stable isotope analysis were C. vanus, followed by D. forcipatus and A. tonsa [40,41]. Both C. vanus and A. tonsa are typically herbivores when phytoplankton is abundant; however, they may exhibit omnivorous behavior and also utilize heterotrophic microorganisms [48] when phytoplankton is less available. Contrary to the expected trend of a trophic level increase, our findings indicate that the highest δ15N values observed coincided with high phytoplankton availability; e.g., in the northern SJG in 2016 [42]. While the influence of omnivory cannot be completely ruled out, we suggest that the primary drivers of zooplankton δ15N are spatial gradients in the isotopic composition of inorganic nitrogen sources available for primary production.

5. Conclusions

Our results represent the first reconstruction of the spatial variation in zooplankton δ13C and δ15N values across the SJG, with interpretations based on a combination of physical and chemical oceanographic data. This is a crucial step in developing dynamic isoscapes for this productive region that supports high biodiversity; however, it is important to acknowledge the limitations of our study. Our inferences are based on two sampling periods conducted during the austral spring when frontal zones were established, especially in 2016. Since the SJG is in a temperate region, it will be essential to conduct year-round sampling to gain a better understanding of the geographic patterns and the influence of various oceanographic variables on shaping the δ13C and δ15N values of primary producers and consumers in this region. Additionally, to develop baseline data representative of the different energy pathways that fuel this productive and diverse continental shelf food web, it would be beneficial to sample other basal pelagic (e.g., euphausiids) and benthic (e.g., filter feeders) consumers in future research efforts.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biology13120990/s1, Figure S1: Maps showing the horizontal distribution of satellite-derived chlorophyll concentration; Figure S2: Plots to evaluate goodness of fit and deviations from the model assumptions of the model for δ15N values; Figure S3: Plots to evaluate goodness of fit and deviations from the model assumptions of the model for δ13C values. Table S1: Table showing the location of the stations in the grid sampling design; Table S2: AIC values of the rounds of model fitting to select the best random structure; Table S3: Linear models of the residuals and absolute values of the model residuals against the predicted values to explore departures of homogeneity of variance.

Author Contributions

Conceptualization: D.E.G., M.F., and S.D.N.; sampling: D.E.G., M.F., F.E.P., B.T. and J.P.P.; data acquisition and curation: M.F., F.E.P., C.D., B.T., V.S., D.A.d.V. and J.P.P.; statistical analysis: D.E.G., V.A.R. and M.F.; writing—original MS: D.E.G., M.F., S.D.N., V.A.R., F.E.P., C.D. and B.T; writing and editing––final MS: all authors; funding acquisition: D.E.G., S.D.N., D.A.d.V. and V.S. All authors have read and agreed to the published version of the manuscript.

Funding

The project “Iniciativa Pampa Azul” of the Argentine government funded the cruises. The projects PICT 2017-2403 (D.E.G.), PROYECTOS DE INVESTIGACIÓN Y DESARROLLO TECNOLÓGICO DE LA INICIATIVA PAMPA C2 (Ana M. Parma), and C24 (D.A.d.V.) funded the specific costs.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. Data disclosure is at the discretion of the author of the communication.

Acknowledgments

The authors would like to acknowledge the crew of the oceanographic vessel R/V Puerto Deseado for their invaluable help during the cruises. We thank Lucrecia Allega from the Remote Sensing Program of INIDEP for the processing of the maps of satellite-derived chlorophyll concentrations.

Conflicts of Interest

The authors declare that their research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 2. Water column stability during 2016 (left) and 2017 (right). Low values correspond to strong vertical mixing, while high values correspond to stable waters with low vertical mixing.
Figure 2. Water column stability during 2016 (left) and 2017 (right). Low values correspond to strong vertical mixing, while high values correspond to stable waters with low vertical mixing.
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Biology 13 00990 g003
Figure 3. Maps of zooplankton δ15N (top) and δ13C (bottom) values for 2016 (left) and 2017 (right). Circles show the station locations, with colors indicating sites where macronutrients with significant effects in the GLMMs may have limited primary production. White: no limitant; purple: surface nitrate and silicic acid limitant; light blue: surface silicic acid limitant.
Figure 3. Maps of zooplankton δ15N (top) and δ13C (bottom) values for 2016 (left) and 2017 (right). Circles show the station locations, with colors indicating sites where macronutrients with significant effects in the GLMMs may have limited primary production. White: no limitant; purple: surface nitrate and silicic acid limitant; light blue: surface silicic acid limitant.
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Biology 13 00990 g004
Figure 4. Pearson correlation among oceanographic variables. Water column depth (De in m), temperature at the surface (TS in °C), temperature near the bottom (TB in °C), water column stability (St in m−1), and nitrate, phosphate, and silicic acid concentrations at the surface (NS, FS, and SS in μM) and near the bottom (NB, FB, and SB in μM). Correlation coefficients are expressed numerically in the lower left quadrant and represented graphically as colored bubbles in the upper right quadrant.
Figure 4. Pearson correlation among oceanographic variables. Water column depth (De in m), temperature at the surface (TS in °C), temperature near the bottom (TB in °C), water column stability (St in m−1), and nitrate, phosphate, and silicic acid concentrations at the surface (NS, FS, and SS in μM) and near the bottom (NB, FB, and SB in μM). Correlation coefficients are expressed numerically in the lower left quadrant and represented graphically as colored bubbles in the upper right quadrant.
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Biology 13 00990 g005
Figure 5. Maps of surface water nitrate (NO3−) concentration in 2016 (left) and 2017 (right).
Figure 5. Maps of surface water nitrate (NO3−) concentration in 2016 (left) and 2017 (right).
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Biology 13 00990 g006
Figure 6. Maps of surface water silicic acid concentration in 2016 (left) and 2017 (right).
Figure 6. Maps of surface water silicic acid concentration in 2016 (left) and 2017 (right).
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Biology 13 00990 g007
Figure 7. Linear regression between zooplankton and POM δ13C values (r2 = 0.60). The blue line is the observed relationship (b = 0.75, a = −4.8, p = 0.005), the shaded area indicates the 95% confidence interval, and the red line represents a theoretical 1:1 relationship with a discrimination of 1‰, assuming herbivory.
Figure 7. Linear regression between zooplankton and POM δ13C values (r2 = 0.60). The blue line is the observed relationship (b = 0.75, a = −4.8, p = 0.005), the shaded area indicates the 95% confidence interval, and the red line represents a theoretical 1:1 relationship with a discrimination of 1‰, assuming herbivory.
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Biology 13 00990 g008
Figure 8. Abundance maps (ind m−3) of immature (top) and adult (bottom) copepods in 2016 (left) and 2017 (right). Adult copepods are grouped by trophic function: herbivores (green), herbivore-omnivores (blue), and omnivore-detritivores (red).
Figure 8. Abundance maps (ind m−3) of immature (top) and adult (bottom) copepods in 2016 (left) and 2017 (right). Adult copepods are grouped by trophic function: herbivores (green), herbivore-omnivores (blue), and omnivore-detritivores (red).
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Table 1. Summary of measured oceanographic variables and zooplankton isotopic values for the years 2016 and 2017.
Table 1. Summary of measured oceanographic variables and zooplankton isotopic values for the years 2016 and 2017.
20162017
MeanSDRangeMeanSDRange
Surface Temperature (°C)11.40.99.4 to 12.511.00.69.9 to 11.9
Bottom Temperature (°C)9.90.98.4 to 11.69.10.48.5 to 10.2
Stability (10−7 m−1)32.823.1−0.2 to 74.737.520.70.7 to 65.9
Surface Nitrate (μM)2.93.9BDL * to 11.22.94.1BDL * to 11.4
Bottom Nitrate (μM)9.45.40.4 to 15.912.23.25.8 to 15.7
Surface Phosphate (μM)0.90.20.5 to 1.61.00.20.5 to 1.5
Bottom Phosphate (μM)1.60.51.0 to 2.41.70.60.8 to 3.3
Surface Silicic Acid (μM)2.11.21.1 to 6.51.31.30.2 to 4.8
Bottom Silicic Acid (μM)4.42.90.6 to 9.35.82.50.3 to 9.3
Water column depth (m)80.215.839 to 10183.115.937 to 107
δ15N (‰)11.61.85.5 to 13.69.11.56.7 to 11.4
δ13C (‰)−20.21.7−24.8 to −17.3−21.41.3−24.0 to −18.7
* BDL Below detection limit.
Table 2. Summaries of the selected parsimonious GLMM models with the best predictors.
Table 2. Summaries of the selected parsimonious GLMM models with the best predictors.
ParameterSDDFp-Value
δ15N
Intercept11.570.87126<0.0001
Surface nitrate−0.500.0830<0.0001
Surface silicate0.610.21300.0085
Stability−0.240.11300.0481
δ13C
Intercept−14.921.33126<0.0001
Surface nitrate−0.190.07300.0107
Stability−0.360.14300.0178
Depth−0.030.01300.0384
Table 3. Summary of observed copepod species and developmental stages, including their mean abundances and standard deviations.
Table 3. Summary of observed copepod species and developmental stages, including their mean abundances and standard deviations.
CopepodsMean Abundance ± S.D.
2016		Mean Abundance ± S.D.
2017
Total copepods554 ± 641222 ± 120
Copepodites IV–V 109 ± 10793 ± 62
Adults445 ± 556129 ± 71
Calanoides carinatus6 ± 54 ± 2
Ctenocalanus vanus409 ± 538120 ± 73
Acartia tonsa33 ± 4512 ± 18
Drepanopus forcipatus39 ± 475 ± 4
Oithona aff. helgolandica11 ± 123 ± 1
Oithona atlantica5 ± 33 ± 2
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MDPI and ACS Style

Galván, D.E.; Funes, M.; Paparazzo, F.E.; Alonso Roldán, V.; Derisio, C.; Pisoni, J.P.; Temperoni, B.; del Valle, D.A.; Segura, V.; Newsome, S.D. A Strong Link Between Oceanographic Conditions and Zooplankton δ13C and δ15N Values in the San Jorge Gulf, Argentina. Biology 2024, 13, 990. https://doi.org/10.3390/biology13120990

AMA Style

Galván DE, Funes M, Paparazzo FE, Alonso Roldán V, Derisio C, Pisoni JP, Temperoni B, del Valle DA, Segura V, Newsome SD. A Strong Link Between Oceanographic Conditions and Zooplankton δ13C and δ15N Values in the San Jorge Gulf, Argentina. Biology. 2024; 13(12):990. https://doi.org/10.3390/biology13120990

Chicago/Turabian Style

Galván, David Edgardo, Manuela Funes, Flavio Emiliano Paparazzo, Virginia Alonso Roldán, Carla Derisio, Juan Pablo Pisoni, Brenda Temperoni, Daniela Alejandra del Valle, Valeria Segura, and Seth D. Newsome. 2024. "A Strong Link Between Oceanographic Conditions and Zooplankton δ13C and δ15N Values in the San Jorge Gulf, Argentina" Biology 13, no. 12: 990. https://doi.org/10.3390/biology13120990

APA Style

Galván, D. E., Funes, M., Paparazzo, F. E., Alonso Roldán, V., Derisio, C., Pisoni, J. P., Temperoni, B., del Valle, D. A., Segura, V., & Newsome, S. D. (2024). A Strong Link Between Oceanographic Conditions and Zooplankton δ13C and δ15N Values in the San Jorge Gulf, Argentina. Biology, 13(12), 990. https://doi.org/10.3390/biology13120990

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