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.

Below is a list of five things I really learned – and relearned – as a first quarter grad student. I am only one quarter done with a potentially six-year degree (which if we do some quick GRE math, means I am one quarter of one sixth done, which is a little over 4 percent), so this is by no means an exhaustive list. I’m actually really interested to see which of the five becomes more important as the years go on, and please comment if as a graduate student you think I missed something important.

Five lessons from the first quarter of graduate school:

Find help.  This I think was the most often repeated piece of advice I saw going into my first year, and it really holds up. Finding people who I could be myself around – to whom I could ask stupid questions and lament about failed experiments with— was essential to my first quarter survival. My best memories from a packed schedule of lab and class are from study groups and group lab coding sessions. I also got involved on campus once I felt settled, which was a great way to meet and work alongside older students and mentors.

Planning ahead saves money. I worked in a lab as an undergrad and as a tech once I graduated, but I was never in charge of purchasing lab equipment or supplies. This quarter marked the transition for me between being an ignorant consumer of lab supplies to a conscientious one, now that I know exactly how expensive it all is and how much money you can waste by doing a poorly designed experiment (extra hint: include controls!). More generally, the application process for PhDs includes applying for grants, which is just the beginning in learning to apply for and manage money as a researcher. I’ve realized I have a lot to learn about budgeting and management in my journey to become a successful scientist.

Grades aren’t everything anymore.  It was a hard habit to break, but all of your time shouldn’t be spent studying for your general first year classes. I learned to diversify how I obtained knowledge. Reading scientific papers and attending seminars from visiting professors were places where I learned the most this quarter. An afternoon spent reading a paper closely related to your research or an hour attending an interesting seminar often meant more to me than studying for a midterm in my more general oceanography classes.

Say yes. I am writing this from a ship off of the coast of Antarctica, where I am conducting field work, all because my advisor asked if I wanted to go and I said yes. Saying yes to collecting samples in the field is one example, but even to something simpler but still scary – like a surfing class or going to a social event where you don’t know anyone – just say yes.  

You will make mistakes. I think the biggest lesson I learned from the first few months of grad school was how often you make mistakes. It is a daily (sometimes hourly) part of life, in both lab and in class. I am still working on how to learn from and move past mistakes, both large and small.

I think since I am still in my first year I have yet to really experience burnout or writer’s block, which I know happen often to older PhD students. I feel so fortunate to be able to study and do science as a Bowman lab member at Scripps, and I hope my insight from a great first quarter help put any prospective students that are reading this at ease.