Description: Planktonic primary consumers have been shown to strongly influence phytoplankton communities via top-down effects such as grazing and nutrient recycling. However, it remains unclear how changes in consumer richness may alter the stoichiometric constrains between producer and consumer assemblages. Here we test whether the stoichiometry of producer–consumer interactions is affected by the species richness of the consumer community (multispecies consumer assemblage vs single consumer species). Therefore, we fed a phytoplankton assemblage consisting of two flagellates and two diatom species reared under a 2 × 2 factorial combination of light and nitrogen supply to three planktonic consumer species in mono- and polycultures. As expected, phytoplankton biomass and C:nutrient ratios significantly increased with light intensity while nitrogen limitation resulted in reduced phytoplankton biomass and increasing phytoplankton C:N but lower N:P. Differences in phytoplankton stoichiometry were partly transferred to the consumer level, i.e., consumer C:N significantly increased with phytoplankton C:N. Consumer diversity significantly increased consumer biomass, resource use efficiency and nutrient uptake. In turn, consumer N:P ratios significantly decreased in consumer assemblages under high resource supply due to unequal changes in nutrient uptake. Consumer diversity further altered phytoplankton biomass, stoichiometry and species composition via increased consumption. Whether the effects of consumer diversity on phytoplankton and consumer performance were positive or negative strongly depended on the resource supply. In conclusion, the stoichiometric constraints of trophic interactions in multispecies assemblages cannot be predicted from monoculture traits alone, but consumer diversity effects are constrained by the resources supplied.

Description: Pronounced atmospheric and oceanic warming along the West Antarctic Peninsula (WAP) has resulted in abundance shifts in populations of Antarctic krill and Salpa thompsoni determined by changes in the timing of sea-ice advance, the duration of sea-ice cover and food availability. Krill and salps represent the most important macrozooplankton grazers at the WAP, but differ profoundly in their feeding biology, population dynamics and stoichiometry of excretion products with potential consequences for the relative availability of dissolved nitrogen and phosphorus. Alternation of the dissolved nutrient pool due to shifts in krill and salp densities have been hypothesized but never explicitly tested by using observational data. We therefore used the Palmer LTER dataset in order to investigate whether the dominance of either grazer is related with the observed dissolved nitrogen:phosphorus (N:P) ratios at the WAP. Across the whole sampling grid, the dominance of salps over krill was significantly correlated to higher concentrations of both N and P as well as a higher N:P ratios. Using actual long-term data, our study shows for the first time that changes in key grazer dominance may have consequences for the dynamics of dissolved nitrogen and phosphorus at the WAP.

Description: In their seminal paper, Goldman et al. suggested that phytoplankton close to maximum growth rate attains a restricted optimal N : P ratio close to the Redfield ratio of molar N : P = 16. Recently, the presence of such a global attractor for optimal phytoplankton stoichiometry has been questioned in models and empirical analyses. As the chemical composition of phytoplankton is of major importance for our understanding of global elemental cycles and biogeochemical transformations, we assembled 55 data sets of phytoplankton growth rate and biomass N : P ratios in a meta-analysis testing (1) whether phytoplankton N : P converges at high growth rates, (2) whether N : P ratios scale with growth rate, and (3) whether the optimal N : P ratios achieved at highest growth rates reflect organism traits or environmental conditions. Across systems and species, phytoplankton N : P decreased with increasing growth rate and at the same time showed decreasing variance, i.e., fast-growing phytoplankton is more P rich and has a more confined elemental composition. Optimal N : P increased with increasing N : P of available nutrients, i.e., with increasing P limitation. Other differences were rare, except cyanobacteria showed higher optimal N : P than diatoms. Understanding the role of phytoplankton in biogeochemical transformation requires modeling approaches that are stoichiometrically flexible to reflect the dynamics of growth and nutrient supply in primary producers.

Description: Zooplankton community structure is often characterized by using traits as a function of environmental conditions. However, trait-based knowledge on Southern Ocean mesozooplankton is limited, particularly regarding size and elemental composition. Nine stations around the northern Antarctic Peninsula were sampled during austral autumn to investigate the spatial variability in mesozooplankton taxonomic composition, size structure and stoichiometry in relation to environmental predictors, but also to the abundance of Antarctic krill and salps. The mesozooplankton communities around the South Shetland Islands were dominated by small copepods, mainly Oithonidae and Oncaeidae, while stations along the frontal zones and the Weddell Sea revealed a higher proportion of larger organisms. Spatial differences in taxonomic composition and size structure were significantly altered by salp abundance, with stronger impact on small-sized copepods. Furthermore, taxonomic composition was significantly related to temperature and total carbon but not chlorophyll a, indicating reduced relevance of phytoplankton derived food during autumn. Bulk mesozooplankton stoichiometry, however, showed no significant relation to environmental conditions, mesozooplankton size structure or dominant taxa. Our results indicate that aside from bottom-up related drivers, top-down effects of salps may lead to mesozooplankton communities that are more dominated by larger size classes with potential consequences for trophic interactions and nutrient fluxes.

Description: Zooplankton samples were collected between 03/26/2018 and 04/27/2018 around the northern tip of the Antarctic Peninsula (63° 0' 1.843'' S, 60° 0' 16.901''W) onboard the RV Polarstern during the PS112 campaign in order to identify spatial distribution in response to environmental variables (CTD raw data files from POLARSTERN cruise PS112, https://doi.org/10.1594/PANGAEA.895969) and the abundance of krill (Euphausia superba) and salps (Salpa thompsoni). Samples were taken using a Bongo net with a mesh size of 150 µm. The net was equipped with a flowmeter (HydroBios) to measure the filtered volume. On board, the net sample was sieved over a 2000 µm mesh in order to separate organisms 〉2000 µm. The smaller fraction (150 – 2000 µm) was homogenized in 200 mL 0.2 µm filtered seawater and equally split into 4 x 50 mL by using a Hensen-Stempel pipette. The mesozooplankton size range of 150 – 2000 µm was defined according to Atkinson et al. (2012). Two parts were then filtered on 47 mm GF/C Whatman filters (precombusted, acidified and weighed) for analysis of dry weight (DW), bulk carbon (C), nitrogen (N) and phosphorus (P) content, while the third part was preserved in 4 % formalin for abundance, biovolume and size structure analysis. The C/N filters were sealed in tin capsules and analyzed using a CHN analyzer (Thermo, Flash EA 1112). Prior to the analysis, filters for particulate phosphorus were combusted at 450 °C for 5 hours. Particulate organic phosphorus (POP) was measured photometrically as orthophosphate (PO4) by molybdate reaction after sulfuric acid and heat digestion at 90 °C, modified after (Grasshoff et al., 2009). Another filter containing the 4th part served as a back-up. Mesozooplankton bulk stoichiometry data are shown in dataset one. The zooplankton subsamples for taxonomic analysis were scanned using the ZooScan digital imaging system (Model Biotom, Hydroptic Inc., France), a water-proof scanner with a resolution of 2400 dpi (Gorsky et al., 2010; doi:10.1093/plankt/fbp124). Prior to scanning, the formalin preserved samples were rinsed and five samples were further subdivided with a Motoda splitter to reduce the number of organisms per scan and avoid overlapping in the scanning frame. The splits were then placed on the scanner and overlapping organisms were separated manually. Subsequently, the obtained scanning image was processed with ZooProcess, a macro of the image processing software ImageJ (Rasband, 2012) to allow automated processing and measurement of images. These single object images and their metadata were uploaded to the web-based application EcoTaxa (https://ecotaxa.obs-vlfr.fr/prj/2529). Manual validation of the results was required to ensure correct classification. The images were identified to the lowest taxonomic level possible. Prior to quantitative analysis of the obtained data, the image categories containing no zooplankton organisms such as “detritus”, “fiber”, “bubbles” etc. were removed. Abundance of zooplankton taxa was calculated based on the number of images per taxonomic category. Zooplankton organisms were identified to the lowest possible taxonomical level. Whenever identification to species level was not possible, the sample was identified to the next identifiable taxonomical category and assigned a putative species name. The abundance and biovolume data are shown in dataset two and three. The metadata of each image also contain the estimates for body size (body length: major axis of the best fitting ellipse; body width: minor axis) that were used to calculate the biovolume of each object. For the biovolume per size class, the biovolume (mm³/m³) was sorted in octave-scale size class intervals given as individual biovolume (mm3). The lowest limit of the first size class corresponded to the smallest detected ellipsoidal biovolume of 0.00025 mm³. Each size class was then doubled with respect to the previous one. Consequently, the resulting intervals were narrow for small body sizes and became progressively wider with increasing body size. The largest size class was determined by the largest individuals in each sample. As a result, the lower boundary of each size class equaled the interval width. The biovolume (mm³/m³) was then summed for each size class interval. The size distribution (mm³) with total biovolume (mm³/m³) per size bin is given in dataset four.