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Indeed, these are the questions that plague me in the wee hours of night. I think to myself, "if only I could pare down those ecosystems to their fundamental properties, and tinker with them." But alas, the biosphere is far too complex.
Instead, I build simplified ecosystems like the one shown below. If you have Java 5 or higher enabled in your browser settings, you can play with this system of "simpupods". These simpupods bounce around randomly within this artificial ecosystem. Different species are denoted by different colors. Each species has an assigned egg size, and an adult size. When two individuals encounter each other, after an implied struggle for survival, the larger one dispassionately consumes the smaller one, and grows accordingly. Once an individual reaches its adult size, it divides its mass into new individuals. "Adult size" and "egg size" are traits that are passed on to offspring.
You'll notice from the histograms below that some species (i.e. egg size / adult size combo) go extinct quickly, while others persist. You can add species by clicking the "add species" button. You can also adjust the speed of the simulation, making it easier to watch.
Nick Record, signing off.
CLICK HERE FOR MODEL
Some hardy marine scientists are still out there sampling, but for this ecosystem modeling lab, the darker months are a time when we turn our efforts toward knitting scarves and coding models. A few of us even enjoy the view of the ocean from our lab in the winter.
View of the ocean from the Seascape Modeling Lab.
Our library of ecosystem models continues to grow. One of the capabilities we're adding this winter is integration with the Regional Ocean Modeling System. To be hip with the jargon, you should call it "ROMS". The model basically a computation of the equations that govern the motion of the ocean.
I'm just learning this particular model now myself. Becoming familiar with a new model is often an emotional affair. Generally, after warming my frigid hands with a cup of coffee in the morning, I spend the subsequent hours alternately tugging my hair out and then crying out in exultation.
Below I've included a link to an animation of the first model computation that most ROMS learners start off with. There are a few things missing from this animation, so don't worry too much if it's not clear what's happening---I've only just started using this model, after all. What you should be seeing is a model of wind-driven upwelling. This is a well-documented process in the ocean. Wind effectively pushes surface water in one direction, and the deeper waters rise up to replace it. The color scale shows temperature (C).
Yes, I acknowledge that this is a crude plot, and much is wrong with it. But it's important through these cold, coding months to celebrate the little things.
The highest level of certainty an ecological modeler knows is that there are some apparently unavoidable pitfalls. One is mortality (any biological modeler reading this will nod in spite of himself). Sooner or later, in a meeting like the one I am this week, you'll hear something spirited like "...but your mortality function is not based on any mechanism, so what the ... are we (non-modelers) supposed to do with your results..."
Mortality rates of the small plankton are notoriously difficult to measure in the field, and thus, this term is one of the most difficult to constrain. Most single species copepod models have developed empirical relationships with temperature and/or food (for seasonality purpose) and many include some form of density dependence (for numerical purpose). Those choices arise from the trade-offs between the availability of data and the necessity to move forward and do actual modeling.
The case of temperature-dependent functions illustrates this situation: the Gulf of Maine time series suggest that herring predation may limit Calanus finmarchicus abundance. Predation by herring is the highest in the summer and the seasonal changes could then be approximated as a function of temperature. If spring conditions were warmer, we might expect that herring would begin feeding earlier, and thus, the temperature dependent mortality would adequately reflect interannual changes in a mechanistic way. However, it seems unlikely that herring predation would respond to a temperature anomaly of a few days, and it is unclear whether a warming throughout the year would correspond to higher mortality.
A novel approach of mortality in copepod models requires a mortality function that reflects some aspects of the dynamical response of predator populations to copepod abundance. This requirement becomes essential to enable realistic projections under climate variability and change. Our knowledge of copepod predators remains limited, and attempting to model the populations of all of the major predators of the life stages of our copepod would just be unfeasible. Following the "middle-out" framework, in future iteration of our models we want to use a compromise mortality function. This new function will make use of several populations of predators, each representing predation by progressively larger animals preying on progressively larger copepods. We will use classical size-dependent feeding behavior for the predators, namely a type II ingestion function (rapid increase at low food concentrations) for small predators and type III function (depressed feeding at low concentrations) for large predators. The result will be that on one hand, the predation rate on smaller copepods (early life stages) will increase largely through changes in the abundance of the predators, while on the other hand predation on larger copepods (later life stages) will respond to changes in their own abundance through the variable ingestion rate of the larger predators. That, is a bold move!
This picture has nothing to do with what I just talked about... I just feel that the pictures on the blog are discriminatory toward the earthly mammals ! And raccoons are cute (at 3pm, not 3am, though...).
But what does this have to do with plankton?
Digital instruments are changing the way we view the ocean as well. While nets are still the most common plankton sampling device, other instruments are starting to catch on. In our lab, we use the laser optical plankton counter, or LOPC, which I've written about before. Instead of hauling up a net and counting every critter by eye, we lower this instrument into the ocean, it scans the nearby water with a laser, and records what it sees. Very futuristic.
The advantage to this technology is that we can now collect large amounts of detailed data at a much faster rate, and sometimes in rougher weather conditions. Also, we don't have to mess with chemicals and look through a microscope for long hours to identify each critter one at a time.
Still, as we march relentlessly toward a dystopian future ruled by hyper-intelligent robots, it's important to bear in mind the value of a human--in this case, a taxonomist human. To illustrate the point, I've invented a game called "Where's Sheldon? The plankton-or-detritus game." When we lower the LOPC into the water, it records every particle that is sees. Some of those particles are planktonic, and others are not. It can often be difficult to distinguish the two.
Can you tell the difference? I did a lab test, and passed these items through the LOPC:
Can you identify Sheldon the copepod? Click on the figure for the answer.
Some of the items are easy to identify, like the coin and the paper clip. Others are trickier. Also, these items are roughly 10 times larger (at least) than the plankton that we're interested in. Now imagine not just trying to pick out the plankton, but trying to identify the species. That means that the plankton-or-detritus game that we play in the lab is much more difficult than the version that you just played.
To me, this is an important reminder of the value of expert humans. It's also a reminder of the value of collecting samples of actual animals that can be identified by eye. Digital technology, so far at least, is at best a good compliment to conventional methods.
On the other hand, in order to get around this problem, scientists are now using machine-learning algorithms. Essentially, this means that we program computers to be able to think, and they are definitely getting smarter and smarter all the time. Still, I think it'll be quite some time before we have robot oceanographers.
Both worked on the Hudson Bay system, a very exciting environment to work on. It's the southernmost Arctic sea, a transition zone between the Arctic Ocean and the Atlantic Ocean at the forefront of the impacts of the current global warming.
Pierre St-Laurent defended brilliantly the 17th of May his thesis entitled "Variabilité saisonnière et interannuelle des eaux douces dans les mers Arctiques : Le cas de la baie d'Hudson".
Pierre showed the audience how the fresh-water budget is regulated in the Hudson Bay. He tackled both liquid and solid (seasonal sea-ice) aspects of it. As an example of how great a tool is modeling in a well formed scientific mind, he first studied this issue with a realistic high resolution sea-ice / ocean 3-D circulation model of the Hudson Bay, developed in the numerical modeling lab of ISMER in Rimouski.
He then constructed an idealized system to sort out the relative importance of the various hydrological, atmospheric and oceanic forcing.
This allowed him to demonstrate for the first time
the role of changing wind regimes in the periodic retention/expulsion
of fresh water from the Hudson Bay towards the North-West Atlantic.
Pierre will soon lend his brain as a post-doc to the Old Dominion University in Norfolk, VA
(He's too modest to agree for me to tell you that there is a tenure track position attached at the end of his 3 years as post-doc).
Virginie Sibert defended not less brilliantly the 20th of May her thesis entitled "Modélisation de la variabilité saisonnière et de la sensibilité au climat des productions glacielle et pélagique de la baie d'Hudson".
Virginie managed to build a model of primary and secondary production within the sea-ice in Hudson Bay.
She coupled this to the ice compartment of the same high-resolution 3-D circulation model than Pierre. After characterizing the spatio-temporal patterns of this system, she coupled it further with an NPZD pelagic production model to have a complete picture of the primary production in the system.View image
After a rigorous validation process which guaranteed a good confidence in the model results, she finally tested one of the IPCC scenario of climate change (A2) for the Hudson Bay system.
A nice outcome of her work is
that the Hudson Bay system would not, for its most part, pass a
tipping point yet. Primary production of both ice algea and
phytoplankton would increase, even if their respective blooms would
occur sooner in the season.
Virginie has already brought her talent and charm as a post-doc in the IFREMER lab of Brest, France.
Not all review experiences are as lengthy or arduous. Our lab had three other papers accepted for publication this month. Two of them were submitted earlier this year. We will post an update when they make it to press. Meanwhile, a list of our publications can be found on our welcome page, here.