Postdoctoral researcher Jesse Wilson has a new paper titled Using empirical dynamic modeling to assess relationships between atmospheric trace gases and eukaryotic phytoplankton populations in coastal Southern California in press in the journal Marine Chemistry. This paper is the culmination of a nearly two year effort to bring together two long-term datasets collected at the Ellen Browning Scripps Memorial Pier: the Southern California Coastal Ocean Observing System (SCCOOS) phytoplankton count and the Advanced Global Atmospheric trace Gas Experiment (AGAGE). Both of these programs encompass many more sites than the Scripps Pier, but that’s the happy point of overlap. The SCCOOS phytoplankton count (augmented by the McGowan chlorophyll time-series) is part of an effort to track potential HAB-forming phytoplankton in Southern California. Twice weekly microscope counts are made of key phytoplankton taxa, and weekly measurements are made for chlorophyll a and nutrients. AGAGE is, as the name suggests, a global effort to monitor changes in atmospheric trace gases. They do this using high frequency measurements of key gases with GC-MS and a cryo-concentration system known as Medusa.
Our study was motivated by the need to better understand the contribution of different phytoplankton taxa to atmospheric trace gases. Many phytoplankton (and macroalgae) produce volatile organic compounds (e.g., DMS and isoprene) and other trace gases (e.g., carbonyl sulfide). Some of these gases have interesting functions in the atmosphere, such as the formation of secondary aerosols. Although there are many laboratory studies looking at trace gas production by phytoplankton in culture, environmental studies on this topic are usually limited in space and time by the duration of a single cruise or field campaign.
The temporal and spatial limitation of field campaigns is what makes long-term time-series efforts so valuable. For this study we had 9 years of overlapping data between SCCOOS and AGAGE. To analyze these data Jesse designed an approach based on Empirical Dynamic Modeling (EDM) and Convergent Cross Mapping (CCM). For good measure he also aggregated the available meteorological data using a self-organizing map (SOM). EDM and CCM are emerging techniques that can identify causal relationships between variables. The basic idea behind EDM is that a time-series can be described by its own time-lagged components. Given two time-series (say a trace gas and phytoplankton taxa), if the time-lagged components of one describe the other this is evidence of a causal relationship between the two. For a more in-depth treatment of EDM and CCM see this excellent tutorial on Hao Ye’s website.
Not surprisingly our all-vs-all approach to these datasets was a bit messy. A lot of this is due to the complexity of the natural environment and the spatial and temporal disconnect between the measurements. The phytoplankton counts are hyper-local, and reflect the very patchy nature of the marine environment, while the trace gas measurements are regional at best, as the atmosphere moves and mixes over great distances in only a few hours. Nonetheless we made the assumption that ecological observations at the pier are some reflection of conditions across a wider area, and that trace gas measurements do reflect some local influence. So there should be observable links between the two even if those links are muted.
I’m particularly excited about what we can do with these data in the future, when we have several years of molecular data. As part of the Scripps Ecological Observatory initiative we’re sequencing 16S and 18S rRNA genes from the same samples as the SCCOOS microscopy counts. We have around 2.5 years of data so far. Just a few more years until we have a molecular dataset as extensive as the count data analyzed here! The key difference will be in the breadth of that data, which will allow us to identify an order of magnitude more phytoplankton taxa than are counted.
