Recently in Computation Category

Apparently, the concept made its way into the mainstream scientific journals. The art resides in the numerous educated guesses and assumptions an ecologist faces when building and (as importantly) assessing the validity of his model. From the conceptualization of the scientific question addressed, to the choice of the numerical method(s), the amount and level of precision of the processes to represent, their mathematical formulation, and the determination of the parameters of the equations etc. At each step, some subjectivity, some instinct, some serendipitous success, maybe...

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...).

We live in a digital age.  Grocery stores use automatic scanning to read prices.  Factories use machines to build machines.  Robots can vacuum carpets and land on Mars.  Even our trusted local RoboCop struggles with his dual cyborg identity.

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:

A fun part of being a grad student, is making bonds beyond the regular "have-to-share-the-same-open-area-office" friendship. The challenges to overcome are so tough, the emotions shared are so strong than you can never break those bonds. So last week was emotional for me, as I assisted to the Ph.D. thesis defence of my last two buddies from my grad school modeling lab.

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.

Our lab has had a good month for publications.  Fred's paper on C. finmarchicus diapause, and the role of lipids, finally made it to press.  The paper appears in the Marine Ecology Progress Series, and can be found here.  The paper was submitted for review on the 4th of November, 2008--roughly 17 months ago.

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.

From Nick:

Here's our first LOPC cast, by the side of the dock.  Splice held.  Phoebe is here safely, after rowing this morning from Witch Island.  Plan is to test the weather tomorrow at 6 am, and hopefully begin sampling, though it could be rough.  If things go as planned, I won't be in internet contact after tomorrow morning.  Maybe next time we dock, I can get a signal and do another post.

The figure is the output from the LOPC test cast.  Each bar on the left indicates the number of particles passing through the unit that had a particular "equivalent spherical diameter".  As you can see, not a lot of big stuff. 

What is a modeler?  What do modelers do?  This is an elusive mystery, wrapped in an enigma, shrouded in ambiguity.  People have been calling me a modeler for years, and I don't even know the answer.

The term "model" is so broadly applied that when a scientist says that he or she has constructed a model, it is almost impossible, without context, to know what that means.  It could be a calculation of sound speed in a dynamic ocean, or of ecological drift in the rain forest.  It could represent the movement of plate tectonics, or of people on a subway.  It could be physical, biological, geological, statistical, etc.  All you really know is that it might involve some kind of calculation or equation--but not necessarily.

Sometimes models get a bad rap.  For example, when the markets crashed last fall, and the global depression loomed, Alan Greenspan explained it by saying,

"I found a ... flaw in the model that I perceived is the critical functioning structure that defines how the world works, so to speak."

When a lot of people rely on a model that turns out to have fatal flaws, modelers all over get a bad rap too, even if their models are nothing like the fatally flawed models.

We can't always help being pigeonholed.  Besides, sometimes it's nice being a modeler, since the term is broad enough to include almost anything.  Plus, we can make puns about "modeling" and "working with models all day long."  For what it's worth, here is a better explanation of what I do.

Most of my work in the EMLab falls under the growing field of computational ecology.  In a nutshell, this means using high powered computational tools and computer science techniques to answer ecological questions.  It involves synthesizing large data sets, working out theoretical ecological and bio-physical relationships, and setting up very large calculations that may take days to compute on our computing cluster.

While this might sound at first a little bit like counting eyelashes, it turns out to (usually) be fun and exciting.  We can apply this powerful field to tasks ranging from protecting endangered whales to understanding the ecological impacts of climate change.  It's a young field.  Universities are only just beginning to form computational ecology groups (e.g. Yale, Michigan State, UC Santa Barbara, et al.), and journals covering the topic are springing up (e.g. Ecological Informatics).  It's also a field with a lot of room for growth, since computing power is always increasing.

Here at the Seascape Modeling lab, the focus is the Gulf of Maine ecosystem.  There are a lot of open questions in this region--some of them fairly controversial.  Along with nets, hooks, buoys, and microscopes, one of the most fruitful tools is a massive series of high-powered computers.

...and, of course, a model: