Succession of protistan functional traits is influenced by bloom timing

Abstract

Surface ocean eukaryotic phytoplankton biogeography can be determined as chlorophyll-a using remote sensing techniques yet evaluating its community composition remains limited. Given our ability to track site-specific chlorophyll-a concentration, we tested which factors influenced protistan functional trait distribution, and whether the distributions can be inferred from bloom succession. Here we surveyed the Labrador Sea during spring over three consecutive years, sequenced 18S data over 15 stations and collected satellite-derived chlorophyll-a concentration from March to July for each year. We evaluated changes in distribution of taxonomic composition as well as the functional traits of protistan size, trophic strategy (defined as phototrophy, phagotrophy, and mixotrophy as capable of both), motility and dimethylsulfoxide or dimethylsulfoniopropionate production by building a functional trait database after an extensive literature review. More variability in the biogeography of protistan functional traits was explained across water masses, and among years than taxonomic composition and patterns in trait variability were more apparent when site-specific timing of peak chlorophyll-a was considered. We found that reconstructing bloom phenology using days before peak (DBP) chlorophyll explained a significant amount of variability in functional trait community structure that was previously attributed to water masses or years, suggesting that spatial and interannual variations can be explained by the sampling moment during succession. Approximately 30 days prior to peak, mixotrophy as a trophic strategy was replaced by phototrophic protists of typically larger size classes. Our work suggests DBP influences protistan community trait succession that could inform biogeochemical models, and likely acts a proxy for the onset of stratification.

Prokaryotic communities across oceanic depths produce stable dissolved organic nitrogen

“…Meso-and bathypelagic treatments are referred to as "deep" when discussed together. Incubations were carried out in 11 L acid and base washed glass bottles with silicone stoppers for 18 months (LaBrie et al 2022). Abiotic conditions (no light and temperature at 4°C) were constant during the entire experiment.…”

“…At each time point, water was poured into an acid washed, pre-conditioned glass bottle to avoid contamination. Subsamples were collected for various chemical analyses (DOC, DON, FDOM, AA, FT-ICR-MS) described in LaBrie et al (2022).…”

“…Broad DON characterization using optical properties FDOM concentration was monitored over time to assess the cycling of labile and recalcitrant DOM using a bulk approach using excitation-emission matrices coupled with parallel factor (PARAFAC) analysis (see LaBrie et al 2022). The PARAFAC model included four different peaks (Fig.…”

“…Based on δ 15 N-AA profiles, it has been recently hypothesized that RDON production may be mostly constrained to the surface ocean and mixed into the deep ocean (Ianiri and McCarthy 2023). However, deep prokaryotic communities were comparatively more efficient at producing stable DOC in the MCP (LaBrie et al 2022). If similar communities also produce RDON, then the MNP could be most effective in the deep ocean.…”

“…Given the range of physical and biological factors that influence the microbial production and cycling of DON, characterizing these processes in natural environments remains a challenge (Sipler and Bronk 2015). Experimental bottle incubations enable this type of investigation, and we recently used this approach to characterize how prokaryotes from different ocean depths, from the deep winter convection region of the Labrador Sea (Yashayaev and Loder 2017), transform surface DOC (LaBrie et al 2022). The production of RDON and RDOC are likely intertwined, as C and N are fundamental elements of DOM and their cycles are closely linked (Sipler and Bronk 2015).…”