Functional group-specific traits drive phytoplankton dynamics in the oligotrophic ocean

Abstract

A diverse microbial assemblage in the ocean is responsible for nearly half of global primary production. It has been hypothesized and experimentally demonstrated that nutrient loading can stimulate blooms of large eukaryotic phytoplankton in oligotrophic systems. Although central to balancing biogeochemical models, knowledge of the metabolic traits that govern the dynamics of these bloom-forming phytoplankton is limited. We used eukaryotic metatranscriptomic techniques to identify the metabolic basis of functional group-specific traits that may drive the shift between net heterotrophy and autotrophy in the oligotrophic ocean. Replicated blooms were simulated by deep seawater (DSW) addition to mimic nutrient loading in the North Pacific Subtropical Gyre, and the transcriptional responses of phytoplankton functional groups were assayed. Responses of the diatom, haptophyte, and dinoflagellate functional groups in simulated blooms were unique, with diatoms and haptophytes significantly (95% confidence) shifting their quantitative metabolic fingerprint from the in situ condition, whereas dinoflagellates showed little response. Significantly differentially abundant genes identified the importance of colimitation by nutrients, metals, and vitamins in eukaryotic phytoplankton metabolism and bloom formation in this system. The variable transcript allocation ratio, used to quantify transcript reallocation following DSW amendment, differed for diatoms and haptophytes, reflecting the longstanding paradigm of phytoplankton r-and K-type growth strategies. Although the underlying metabolic potential of the large eukaryotic phytoplankton was consistently present, the lack of a bloom during the study period suggests a crucial dependence on physical and biogeochemical forcing, which are susceptible to alteration with changing climate. metatranscriptomics | phytoplankton | ecological traits |

Phytoplankton response to a plume front in the northern South China Sea

“…A large shift in phytoplankton assemblage from small to large cells could arise following the addition of nutrients from deep seawater in the North Pacific Subtropical Gyre (McAndrew et al, 2007;Mahaffey et al, 2012). The success of large phytoplankton in the oligotrophic ocean would largely depend on external environmental dynamics, although it has the metabolic potential of enhancing production (Alexander et al, 2015). It is thus important to understand not only the mechanisms for nutrient variations but also the response of the size-fractionated phytoplankton community to the diverse nutrient supplies, particularly at the frontal zone where the patchiness of phytoplankton can be affected by complex physical dynamics (Li et al, 2012).…”

Defining the ecological and physiological traits of phytoplankton across marine ecosystems

“…Interestingly, these data appear mirror observations of the abundance of unique gene sets Figure 5-4, as the unique gene sets of CCMP379 and CCMP374 were observed to increase in abundance following N-addition ( Figure 5-4). These data suggest that the metabolic profiles of strains based solely on shared genes are similar, at least in the context of this very oligotrophic environment, where haptophytes are thought to be limited (Alexander et al, 2015b). Strikingly, the addition of N appears to change the metabolic profile of all strains in a similar way, e.g.…”

“…Incubations were modeled after a simulated 10% deep seawater (DSW) upwelling as described in Alexander et al (2015b). The concentration of iron was based on Marchetti et al (2012) and vitamin B 12 was modeled after Bertrand et al (2007).…”

“…Transcriptome sequences and annotations generated through the Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP) (Keeling et al, 2014) that were made public as of 17 March 2014 were collected and treated as per Alexander et al (2015b) to track species composition of the metatranscriptomes. Due to the large size of the resulting MMETSP database, trimmed reads from the metatranscriptome were mapped to the MMETSP using the Burrows-Wheeler Aligner (Li and Durbin, 2010) (BWA-mem, parameters: -k 10 -aM) and then counted using the HTSeq 0.6.1 package (Anders et al, 2014).…”

“…carbon fixation). Focusing on this relatively well-annotated fraction, RSEM (Li and Dewey, 2011) was used to following DSW addition (Alexander et al, 2015b). No difference was seen between strains in the first three components in the treatments to which no N was added ( Figure 5-5A,B).…”