We recently received the instrument back from Seabird and I was stumped on what to do with it. I knew it was capable of RS232 communication, which might free us from the UCI GUI and solve at least one of our problems, but I have limited experience with serial device communication. With few other options and only cryptic guidance from the SUNAV2 manual I grabbed a power supply and USB-serial converter and gave it a try.

PuTTY was the logical starting place as I use it for SSH connections on my Windows laptop. After the normal guessing of COM ports I made contact at the specified baud rate of 57600 and otherwise default connection parameters. It took me a few tries to realize that I needed to send some (any) character to the instrument to “wake it up” and induce a response and command prompt. Protip, you gotta be fast on that command prompt else it the instrument goes to sleep. But you can’t prompt with a command!

PuTTY worked great for the initial connection but provides no solution for continuous, real-time monitoring. You can get PuTTY to create a log file (containing your data and anything else sent over the serial connection), but the file isn’t created until the session closes which makes it not very useful for real-time monitoring. PuTTY includes the plink utility that is supposed to provide an automation solution. While it can do some cool things I couldn’t crack it for this task. Because the instrument takes some time to respond to the initial “wake up” keystroke, a programmatic solution needs a delay, and I couldn’t find a way to implement one with plink.

Almost in desperation I turned to Python, which is probably where I should have started. A previous day’s frustration turned into a pleasant hour of tinkering over a cup of coffee, and voilà! Here’s the solution, may it help others. The major unsolved issue is that some encoding issue (?) prevents proper parsing of the data passed from the instrument. I tinkered with io.TextIOWrapper and various encoding options to no avail, and ultimately settled on Python string operations to set things right.

Avishek selected 48 historic samples collected by our group in collaboration with the Palmer Long Term Ecological Research study for shotgun metagenomic analysis. Although this is not a particularly large sample set by today’s standards, using Avishek’s iMAGine pipeline it was nonetheless sufficient to yield 2,940 bins (collections of contigs that might be from similar genomes) and 137 dereplicated, high quality bins that we considered metagenome assembled genomes (MAGs). MAGs are not perfect constructs; they’re incomplete and likely composites of different, highly similar genomes. Nonetheless, standard genomic tools and analyses can be applied to them to try and infer likely metabolisms and other characteristics.

You really have to stare at the above figure for a while to start to wrap your head around the details, but the main points are: 1) There’s a lot of potential for carbohydrate degradation potential, as we might expect for bacteria and archaea that respond to phytoplankton blooms. 2) There’s a lot of dark carbon fixation potential, and this is often combined with different heterotrophic metabolisms to produce a prokaryotic mixotrophic functional type (i.e. an organism that can switch between autotrophic and heterotrophic metabolisms). This is one of those things that we assume must be fairly common without actually observing it. Right now there is a lot of discussion in various communities about the role of mixotrophic protists in marine foodwebs and the carbon cycle, but mixotrophic bacteria and archaea get surprisingly little attention.

DESCRIPTION

Under supervision, independently perform a variety of standard laboratory and data analysis procedures (and some non-standard procedures) related to the function of coastal ocean environments. Coordinates and conducts instrument calibrations and data collection for long-term time-series of microbial community structure, microbial abundance, and dissolved gases. Responsible for the operation and maintenance of a membrane inlet mass spectrometer, flow cytometer, and in situ imaging flow cytometer (IFCB), DNA extraction, data entry, and light programming in Python and R. Travel to field stations as needed, which may involve driving University vehicles and operating small boats for diving and coastal field work. Scuba dive to clean and service underwater instrumentation. Coordinate and communicate with lab members about supplies, data and sampling techniques. Oversee and work-direct undergraduate research assistants. Process, analyze, and interpret results from data sets, evaluate quality of data, generate and update design and method documentation, and update web pages. Perform general office duties including but not limited to filing, photocopying, faxing and library searches for research articles. Manage laboratory space, computers, and equipment.

• Must be able to lift 50 lbs.

QUALIFICATIONS

• B.S. in Chemistry, Marine Science, Oceanography, or equivalent combination of education and experience with a strong background in data analysis and computer operations.
• Demonstrated experience with diving and ability to acquire or maintain AAUS and SIO scientific diving certification.
• Demonstrated knowledge of mathematics, scientific, and programming principles.
• Demonstrated experience with R, Matlab, or Python programming languages for data analysis and visualization.
• Demonstrated laboratory experience. Demonstrated knowledge and experience with laboratory techniques and instrumentation, specifically flow cytometry and DNA extractions. Demonstrated experience with laboratory safety procedures and calibration techniques.
• Proven ability to work effectively on multiple tasks in parallel, with each requiring a different focus and level of detail and attention. Proven ability to prioritize tasks and solve problems.
• Demonstrated data entry and data analysis experience. Demonstrated experience with spreadsheets and/or databases for data entry, archival and basic data analysis using standard software (e.g., MS Excel, MS Access, Matlab, or other statistical software packages).
• Experience communicating and interacting with a variety of people from the public to governmental agencies, students and volunteers. Ability to effectively communicate instructions and interact using tact and diplomacy with diverse personalities including academic, staff, student and volunteer employees and institutions/organizations.
• Proven ability and experience using PCs, email, internet, general office tools and software.
• Tolerance of repetitive tasks such as data entry and checking, or extended periods in laboratory filtering samples or analyzing seawater samples via flow cytometry.
• Demonstrated ability to find and follow written and oral procedures from standard laboratory resources.
• Must be organized and a self-motivator with the ability to work efficiently while unsupervised.
• Proven ability to document significant results of data analysis in technical notes. Good writing skills. Ability to integrate data products and methodologies from laboratory and field instrumentation into research results for publication purposes.
• Proven ability to communicate with technical and scientific personnel. Ability to instruct and aid research associates and students on the use of software packages and data procedures/protocols.
• Ability to travel for days to weeks for field work and work extended hours as needed.
• Ability to drive University vehicles to field stations. Valid driver’s license.
• Proven ability to work with others under demanding conditions, sometimes for extended periods of time.

SPECIAL CONDITIONS

• Ability to work at sea. Must have demonstrated experience with SCUBA diving and ability to acquire and maintain AAUS and SIO scientific diving certification.
• Must have valid driver’s license and ability to drive University vehicles to field stations.
• Ability to travel for days to weeks for field work and work extended hours as needed.
• This position is subject to a DMV check for driving record. Fluency in Spanish is preferred.

It is valuable to start out with a basic definition: a model is a simple representation of a complex phenomenon. Models are useful because they explicitly describe important mechanisms, which then can be tested against observations. This testing will ultimately demonstrate if your concept of a natural phenomenon was valid or that it needs to be refined.  With very little modeling experience myself, I started with an existing model from the excellent textbook “A Practical Guide to Ecological Modeling” by Karline Soetaert and Peter Herman from Springer. If you use R as a coding language, it is a great book to start modeling, as they have many conceptional explanations paired with highly understandable code. All the examples from the book are in the R package ecolMod:

install.packages("ecolMod”)

library(ecolMod)

demo("chap2")

Once you have the package loaded, you can click through the examples to see how to build a simple ecological model, where a forcing function causes flow between state variables. It is easier to understand with the below visual (Fig 2.1 of Soetart and Herman).

In oceanography, a common real-world application of this conceptual type of model is the NPZD, which stands for Nutrient, Phytoplankton, Zooplankton and Detritus. It is important for us to understand the flow of carbon and nitrogen (among other elements!) through both the macroscopic (zooplankton) and microscopic (detritus that is re-mineralized by bacteria) food web. This is one of the simplest ways to mathematically model it.

Along with figures, the authors are kind enough to include the code for the model. In their code, each of the state variables of NPZ or D (the boxes) are mathematically equal to the flows in minus the flows out. Based on the figure above for instance, PHYTO = f1 – f2. In turn, each of the flows are their own mathematical equations with parameters (constants that are experimentally determined). The equation provided for f1 for instance is:

 f1 = Nuptake  <- maxUptake * PAR/(PAR+ksPAR) * din/(din+ksDIN)

This is because Nuptake is dependent on solar radiation (PAR) and the amount of nutrients that are available (din), as well as the parameters maxUptake, ksPAR and ksDIN which are set as equal to 1/day, 140 muEinst/m2/s and 0.5 mmolN/m3 respectively when we define our parameters later in the model. I encourage you to download the model code and follow how each of the state variable definitions, flows and parameters are connected. Even in a model as simple as this it gets complicated!

Even more exciting are the model solutions, which show a sensible story over two years. As you know from above, the forcing function for the model is PAR (solar radiation), which varies over the season (the sine wave in panel A of the following figure). As PAR increases in the spring, there is a modeled increase in Chlorophyll and Zooplankton (what oceanographers call a “spring bloom”!) and a decrease in DIN.

As I was teaching a class called Polar Microbes, I wanted to change some parts of the model to better reflect a polar environment. Since the model’s forcing function is the seasonal light cycle, I knew it was the first thing that needed to change. The tilt of our rotation axis ensures that our poles have a much more extreme seasonal light cycles, with time in both full darkness and full light.

When you change the model to reflect this planetary fact (just change the PAR function to have a steeper slope and a period of darkness), the output variables change drastically (the Polar Model is in blue below):

Our class had long discussions about this model output. Is it sensible? What can you infer about the polar regions from this? How could it be improved? In our class, we ended up even adding another state variable, Bacteria, and altering the flows from it (viral lysis) to see what happens.

I encourage you to download the ecolMod package and see for yourself! If you are a high school student, consider joining us next summer at Sally Ride Science for my summer class on Polar Microbes as well.