Seascape Projects

Pete's poster

2010-03-17T15:00:16Z

Here's a look at the poster I put together for the Ocean Sciences conference:

I tried to keep it consise since so many posters look like the researcher barfed text all over them.
Also, the pictures allow a passerby to quickly get a sense of what's going on, and then, if they're interested, they can ask questions.

Feel free to post questions in the comments!

Below is a link to a pdf of the poster, but you should also be able to click on the image above to see a larger version.
stetson_OceanSciences2010_vFinal.pdf

The Eco-cast

2010-03-10T15:09:04Z

This post is a follow-up to my ecological forecasting post last month. The work that I presented at the Ocean Sciences meeting was built around the idea of producing forecasts of ecosystems. Much of what I presented was discussed in that post, so I'd just like to take a little space here to build on the vision.

I think about ecosystem forecasts as close analogues to weather forecasts. There are, of course, important differences, but I'll save that discussion for a later post. Here I'll focus on the analogy.

Early weather predictions were based on signs and signals from the immediate surroundings, and on past observations. These observations were interwoven with ecological ones as well. People have probably been making these sorts of predictions since the first homo sapiens scratched their heads and gazed upon a bold red sunset. Unfortunately, these first people left nothing to cite, so we'll have to do what we usually do, and refer to Aristotle on the matter.

Aristotle included many guidelines for weather prediction in Meteorologica, but actually my favorite work on the matter is Theophrastus' The Book of Signs. For centuries, predictions were made by following signs like this one, taken from The Book of Signs:

"It is a sign of rain when a tame duck gets under the eaves and flaps its wings."

In the 20th century, scientists began using computers to make forecasts. In the 1950s, the first computational forecasts were inferior to the more subjective, duck-watching methods. Computer models would suddenly predict an army of cyclones marching across the country. You would probably produce a better forecast by just saying, "Tomorrow's weather will be pretty much like today's."

Yet there was intense optimism regarding the potential of computational prediction. John von Neumann, one of the great scientists of the century, stated:

"All stable processes we shall predict. All unstable processes we shall control."

This was followed by decades of steady improvement. Computational weather forecasts are now the norm. While we do complain when meteorologists get it wrong, I think it's clear that today's forecasts have immense value and are a crucial part of how our society operates.

Von Neumann's vision, however, has not been realized. One contributing factor was the discovery of what is termed "chaos". Without going into too much detail, I'll just say that mathematicians discovered fundamental limits on the predictability of certain mathematical systems, including weather and ecosystems. As the founder, Edward Lorenz, phrased it:

"Predictability: Does the flap of a butterfly's wings in Brazil set off a tornado in Texas?"

Nevertheless, with better and better computers and techniques, scientists continue to improve weather prediction. A testament to their utility is the ubiquity of weather forecasts in our day to day lives. This history provides good context for the development of ecosystem forecasting. With steadily improving models and increasing monitoring, we are poised to transition from the more subjective and sign-based forecasts to more precise computational forecasts. Naturally, it will be some time before we're forecasting at the level of meteorologists, but one day in the future, you might be watching a broadcaster like this on your daily news:

OSM Day 7--Press Coverage (late update)

2010-03-01T13:27:47Z

The coolest part of the media experience has been watching the story spread around the world. This is a testament to the global reach of the BBC as well as the global interest in carbon issues, especially in Europe. As of 8:30 EST on 3/1, stories based on the BBC report have appeared in

Austria

Brazil

France

Hungary

Italy

Norway

Portugal

Slovenia

Spain

Sweden

Turkey

Vietnam

The Norwegian story is definitely my personal favorite. It includes a statement by Rasmus Hansson, the Secretary General of WWF Norway that the ideas are "Interessant og tankevekkende" (interesting and thought provoking). Like most online news, the NRK site has a comments section. They introduce the comments with "Synes du de høye CO2-utslippene er godt nok argument for å stanse hvalfangsten? Si din mening!" (Do you think high CO2 emissions are a good reason to stop the whale hunt? Say what you mean!).

OSM Day 7--Press Conference

2010-02-26T18:25:18Z

Note:l my ambition to blog each day fizzled like a Portland (OR) rainstorm. We'll try to add some additional reports from the meeting in the coming days.

For me, the most memorable part of the meeting was being invited to do a press conference--something I've never done before. I was invited to give a regular science talk in a session on the impact of climate change on marine ecosystems. I thought I would use this as an opportunity to talk about some calculations I've done characterizing the carbon footprint of whaling (see this earlier post). AGU, one of the societies that was running the meeting, thought the news media would be interested in this topic.

The hardest part was deciding to do it. Since I hadn't presented my calculations to many other scientists, I was worried that there was something I was overlooking. Visions of cold fusion were dancing in my head. In the end, I decided to go for it. To prepare, I organized a mock press conference at GMRI, with my colleagues acting as journalists. This was extremely helpful. At the conference, I spoke for about 15 min:

and then took questions. In addition to the reporters in the room, there were a couple joining on the phone. I then spoke with several reporters one-on-one, including the BBC:

The BBC story was online by 11PM (PST) last night, and by this morning, it had been translated into Hungarian, Slovenian, and Italian (I didn't know I was fluent in Italian). Here are some links to a few of the stories, if you're interested in reading more. All in all, a really fun experience.

BBC

Environmental Research Web

Discovery News

OSM Day 4--Fred's Talk

2010-02-23T06:01:55Z

The official meeting started at 8AM this morning. Meetings like this are the intellectual equivalent of drinking from a firehouse. At any given time, there are 15 different sessions in progress. Each session is organized around a particular theorem, and the themes at this meeting cover the full gamut of oceanography. About a year ago, groups of scientists submitted proposals for sessions. Once the sessions were selected, the oceanographic community was asked to submit abstracts. An abstract is a brief (~one paragraph) description of a study, and when you submit an abstract, you select which session you think is most appropriate for your work. Then, one of three things happens. 1. The session rejects your abstract, possibly passing to another session, 2. The session accepts your abstract and invites you to give a talk, or 3. The session accepts your abstract and asks to you prepare a poster.

Usually, breakfast is spent looking over the titles of the talks, and figuring out which ones you'll try to see. One talk was easy to add. Our very own Frederic Maps gave a talk at 8:30 in the morning on his work modeling copepods. Depending on the talk and the session, you can have anywhere from a few people to more than 50 (remember, you're up against 14 other talks). As you an see from the picture below, Fred's talk was quite popular:

OSM Day 2: Workshop

2010-02-21T16:16:45Z

The meeting officially starts tomorrow (Sunday) night, so why did I fly out on Friday? Science has always relied on communication and collaboration--hence, the need for conferences. Oceanography is an inherently interdisciplinary field, but it is very hard to get truly interdisciplinary projects funded. One way to get some interdisciplinary work done is to organize a workshop. The idea behind a workshop is to get a few very busy people to take a few days from their day-to-day work in order to work together on a common problem. So, this is why I'm spending this weekend in a conference room instead of hiking with my family.

The point of this weekend's workshop is to develop a better understanding of how changes in the Arctic affect the North Atlantic. I've stumbled into this line of research by uncovering a dramatic change in the Gulf of Maine plankton community that took place around 1990. Turns out, lots of other things changed right around that time: the waters became less salty and began flowing faster, herring became more abundant and right whale calves became rarer. Many of these changes were observed from New Jersey up to Newfoundland. The best explanation so far is that these changes originated when the winds over the Arctic pushed a slug of fresh water and ice into the North Atlantic. This created a pocket of fresher water that eventually moved down to the Gulf of Maine:

The conditions that created this slug persisted through much of the 1990s. The workshop, organized by my colleague (and former Ph. D. advisor) Chuck Greene, has brought together biologists like me, physical oceanographers, and Arctic climate specialists to try to get a better understanding of exactly what happened.

OSM Day 1: I'm on a plane!

2010-02-20T07:03:30Z

The Ecosystem Modeling Lab is headed to the other Portland for the biennial Ocean Sciences Meeting. OSM is the main oceanographic conference. Conferences like OSM are an important part of science. They provide an opportunity to learn about the latest developments in the field, catch up with colleagues, and find collaborators, employers, students, and post docs. This week, I'll try to give an inside view of a scientific conference.

First up, getting there. I'm writing you from a tiny little desk in the middle of O'Hare airport. I find that I can often get a lot of work done while traveling. Here's me working on my flight:

On the flight, I managed to update a time series of temperatures from the Gulf of Maine and do some analysis of the relationship between the number of right whale calves and the amount of their food. I walked off the plane feeling good, but with a battery at 50%. I've become pretty good at finding power outlets in airports, but O'Hare seems to be doing a good job hiding them. They do provide some very tiny desks with power outlets. Between the small desk, uncomfortable stools, and exposure in the middle of the concourse, I think I'll be moving on.

Ecological forecasting system -- the concept

2010-02-15T22:42:14Z

Early numerical/computational weather forecasts could not compete with traditional forecasting methods. Simple lore-based indicators--like "red sky at night, sailors delight"--could, in many cases, out-perform computational models. In fact, simply predicting that tomorrow's weather will be just like today's was much more reliable than the cyclone-ridden numerical predictions put forth by scientists.

Over the past 50 years, computational power, theory, and data collection have improved, leading to a revolution in weather forecasting. What began as an inferior practice quickly overtook other methods. Computational predictions have now replaced traditional subjective predictions, and detailed weather forecasts are integral parts of our everyday lives. The figure below (adapted from Shuman 1989) shows how computational weather forecasting models have progressed since the 1950s.

Figure adapted from Shuman 1989. Error in numerical weather forecasts has decreased substantially since the 1950s due to improvements in the models and the computers.

In contrast to weather forecasts, there are very few ecosystem forecasts available. Those that are available are at the cutting edge of science, and are often released only after the passage of the events they are trying to predict. Yet I would argue that we could be on the cusp of a similar revolution in ecological forecasting.

Many lessons from the past 50 years of weather prediction can be carried over to the development of systems designed for ecological forecasting. There are three key components to a good forecasting system, all of which we can begin to implement and/or take advantage of right away. These are: monitoring (steady streams of data), adaptive computational algorithms, and forecast availability. The common service provided by all of these components, and the crux, I would argue, to a revolution in ecosystem forecasting, is feedback.

I've sketched out below a concept map of an ecological forecasting system, highlighting the mechanisms of feedback. Briefly, data is input to the algorithm, which produces a forecast. This forecast then becomes available to a number of feedback avenues, including the designer, users, and the algorithm itself. (Side note: I used the "Concept Map Builder" designed by the Center for Ocean Sciences Education Excellence, which is a tool under development, worth checking out.)

Monitoring is, of course, a critical component as it provides input data. In this system, however, data plays another role. Suppose we are producing weekly forecasts. Then each week, we want to know how well we did with our forecast. At first, our forecasts might be little better than a random guess. That's okay. As long as we have the data coming in to tell us how well we're doing. When we're not doing well, we want to know so that we can adjust.

That brings us to the second component: adaptive computational algorithms. Using techniques borrowed from computer sciences, such as the genetic algorithm, a system can learn based on its past successes and failures. It's important for the system to be able to assimilate a steady stream of data and to adapt based on that. Ideally, a system should be flexible enough to incorporate new theoretical information as well.

The third component, forecast availability, is one that has been missing. In my opinion, this has been holding back progress in ecosystem forecasting. I suggest that, like numerical weather forecasters, we should be putting our forecasts out there, even while they are little better than subjective forecasts. It's okay if many forecasts fail. With an ensemble of forecasts available, and with a steady stream of observations coming in, our computational systems can begin to evaluate, learn, and adapt. Over time, the failures will presumably be fewer and fewer, and the forecasts will be more and more useful.

There are a number of subtleties that I've brushed over here, for the sake of simplicity and generality, as well as some practical considerations that are nontrivial. Nevertheless, these are the basic ingredients, as I see it, to a revolution in ecosystem forecasting. I'll be presenting these ideas at the Ocean Sciences meeting in Portland, Oregon next week. If this sort of thing is your bag, look me up.

What does a model look like?

2010-02-13T09:15:00Z

This intriguing question was posed to us by a high school student. My initial reaction to this question is to post a link to Heidi Klum. More productively, I'd like to try to present some ideas for thinking about models and what they look like.

To a scientist, a model is a way of representing an idea about how the world (or some part of it) works. In many ways, a model is just a way of expressing an hypothesis or a set of hypotheses. How a particular scientist thinks or the audience they're addressing to will dictate what the model looks like.

For me, I like to start with a conceptual model, usually represented as a drawing. For example, here's a diagram I use to explain how temperature and chlorophyll influence copepod growth and reproduction:

The circle at the left represents an egg. The red arrows show the path that the egg takes to become an adult copepod. The arrows are colored red to suggest that how long it takes to go through these stages depends on temperature. The long arrow at the top represents reproduction (adults making eggs). The number of eggs produced depends on the amount of food available. Since this particular copepod eats mostly phytoplankton, the arrow is colored green. These graphical models are very useful for helping think through a problem. My notebooks are filled with less attractive versions of these, and most days, there is some version written on my whiteboard with colored markers.

While conceptual models and diagrams are the most common models in science, when most scientists speak of models, they mean a mathematical models. The advantage of mathematical models is that they force the modeler to be very precise about how the components fit together. They also can be used to make predictions that can be compared to data. The disadvantage is that they require mathematical training to understand. Some mathematical models are relatively simple and can be written on a few sheets of paper. Other models are more complicated, and this is where computers come in. Here's a snapshot of some computer code that represents copepod growth and reproduction:

This code is written in a language called "C". The code is then given to a computer program called a compiler that turns the code into the language of 1's and 0's that the computer recognizes. We then push a button and wait while the program runs. The program produces a series of output files. To view the results, we have to load these files into Matlab and plot them in various ways.

This is probably my favorite step--part science, part engineering, part art. Pretty, in it's own way, but no Heidi Klum.

Red tide photogrammetry in Mexico

2010-02-05T02:55:08Z

Just a quick note on our sea surface monitoring project. We are working with a group in Ensenada, Mexico to apply our camera system (designed for oil spill mitigation) to a red tide monitoring project. The images below show a dry run, so there is no red tide present, but stay tuned. If this project gets off the ground, it would be a neat application of our system.

Original photo

Georectified photo

The ground control points (x's and o's) are just eyeballed in this rectification, so there is noticeable error, particularly with the middle point. This is something I hope to improve upon. Also, we hope to cover more area with multiple cameras.

Investing in global warming

2010-01-06T16:51:25Z

There is still substantial and vigorous debate within the scientific community about global climate change. --Not really on whether or not it's occurring, nor really about the general importance of carbon dioxide and other contributions by humans. Rather, the debate has more to do with the particulars. For example, why does the Arctic appear to be melting faster than forecasted? Which species will be the winners and losers as the climate changes? How will sea-level-rise impact beach barrier systems? These, and hundreds of other questions, comprise the scientific dialogue.

On the flip side is the public perception of climate change. This arena is a mix of scientific information, political agendas, sensationalism, ideology, fund raising, personal experience, and charismatic megafauna. Naturally, we can't expect the entire populace to always listen to and give due respect to scientific discipline. (That only happens when we're designing weapons.) There are so many sources of information that must be filtered--the scientific literature is only one such source.

For those looking for new lenses through which to view the topic of climate change, I'd like to draw attention to futures markets, or prediction markets. A futures market is one in which investors can buy or sell shares in the outcome of a certain event, such as an election. If your prediction turns out to be accurate, you get a return on your investment. The idea is to tap into what is known as the "wisdom of crowds," as well as people's desire to make money. These markets turn out to often be excellent tools for predicting future events.

Futures markets are now being used to try to predict the consequences of climate change. It's a way of getting people to put their money where there mouth is when it comes to global warming. A nice discussion of this idea can be found here. I'll just include this image, swiped from www.intrade.com, that shows one such market in action.

Here, the prediction is: "Average Global Temperatures for 2009 to be among five warmest years on record". The graph shows how the price of this prediction has increased from early 2009, eventually showing almost a consensus among investors. The intrade website also has markets attempting to predict 2010 - 2014.

There are certainly drawbacks to futures markets, which I will leave it to the reader to research. But to a first approximation, I think of this as capitalism weighing in on climate change.

By the way, here is the National Oceanic and Atmospheric Administration's take on 2009:

"NOAA scientists project 2009 will be one of the 10 warmest years of the global surface temperature record, and likely finish as the fourth, fifth or sixth warmest year on record."

(Note that if 2009 is merely the sixth warmest year on record, these investors will lose their bet.)

Year in Review

2009-12-30T15:09:54Z

Since top-10 lists are so last decade, I've decided to summarize SeascapeModeling's 2009 as a reverse top 7 list:

7 presentations (Ecological Society of America, Society for Marine Mammologists Biennial, Gulf of Maine Symposium, GLOBEC Open Science Meeting, Right Whale Consortium Meeting)

6 months of mostly operational copepod & right whale forecasts

5 invited lectures (Southern Marine Community College, Louisiana State University, Marine Environment Research Institute, National Science Teachers Association)

4 poster presentations (Gulf of Maine Symposium, Society for Marine Mammologists Biennial, SMS Grad Student Symposium)

3 graduate students (Pete, Dan, Nick)

2 post docs (Fred and Eli)

1 new Ph. D. **

**pending final edits to the dissertation

Kill time or be killed

2009-12-13T21:15:36Z

Andy's concerns about discriminating wisely between trend and noise, between low-frequency and high-frequency signals in time series of environmental variables (Air temperature in his example), apply as well to measurable quantities in ecosystems. Particularly relevant is the phenology of the species, which defines the timing of crucial recurrent events of their life cycle, like the date of first arrival to the nesting ground, the date of germination, the date of mating, the date of blooming etc. You may have noticed that I used examples related to birds and plants. Well, while phenology is a general concept, an empirical knowledge patiently accumulated by bird lovers and Sunday gardeners of the 19th and 20th centuries was first to be translated into systematic scientific surveys. And following rigorous statistical analyses of those long time series of observations, it has been firmly established that changes in the phenology of most species accompanied trends in temperature. The strength of the correlation is all the more important as the seasonality (latitude) of the ecosystem and the dependance of the species to their environment increase. The connection with climate change issues is straightforward. And it is not just about how one species or another will cope with changes of its environment, but rather about the interlocked interactions between all those species.

If you can easily think at a beautiful tulip as a species embedded in its environment, it is the same, in a more dynamic way, for planktonic marine species. Now oceanographers begin to benefit of the fruits of several long lasting monitoring programs. Unfortunately, the ever increasing pace of global climate change means that oceanographers are required to draw firm conclusions about the impact of environmental variability on ecosystems and develop predictive capabilities in the meantime ! And this will remain an elusive target as long as the mechanisms gearing those changes are not understood properly. Daunting task, as the changes in timing of such major event as diapause entrance and exit emerge form several layers of physiological and behavioral processes obeying their own dynamics while interacting with each others. But impossible is not known at the EML, so we decided to model the mechanisms behind the diapause of the dominant copepod Calanus finmarchicus. We already know that even if it can produce several generation a year, this critically important species thrives in its seasonal environment (Northern half of the North Atlantic) thanks to its diapause strategy, which means killing time at depth in order not to be killed by the detrimental conditions prevailing at the surface in winter. For this purpose, it makes a feast on large phytoplankton cells (mainly diatoms) during the short period they are available, and build up impressive amount of energy rich lipid reserves. Those swimming droplets of lipid are in turn the basis for the rest of the upper trophic levels.

And what about changes then ? Things are more sparse there... Records of physiological properties related to the diapausing strategy are about a decade old now. Not enough really to study trends on climatological scales, but enough to understand that interannual variability is high (see figure). But abundance data are enough to see changes, especially in areas localized at its biogeographical fringe. In the North Sea for example, the ecosystem shifted from a copepod population dominated by 80% of C. finmarchicus before the 60's to a present state dominated by 80% of its southern congener C. helgolandicus. What is the role of diapause in that ? Not known yet. One thing is certain though: changes occur at an ever accelerating pace, and the unforeseen consequences for the ecosystems are likely to appear before our eyes while we are still racing to improve our understanding. I strongly wish that Copenhagen "talks" will end up with agreements as legally constraining on our leaders than the climate changes will be actually constraining on us.

Superimposed to the climatological (2004-2008) relative abundance of the different copepodid stages are box plots of the estimated dates of initiation (late winter) and termination (summer) of diapause in Calanus finmarchicus in the Gulf of Maine. Data from UNH COOC WB-7 station. Figure from James J. Pierson.

Crazy Al--Part 2

2009-11-27T12:37:47Z

While I respect Al Gore (he did win the popular vote after all), he made a common error in his recent appearance on SNL. Near the end of his segment, Gore says:

"Have you been outside today? It's 60 degrees in late November. I mean there's a Christmas Tree in front of this building and guys are wearing flip flops. I mean, you can't say this isn't real."

Yes, I know that it's a comedy show and that I'm being dogmatic. However, Big Al is committing one of my climate pet-peeves, namely, confusing weather with climate. Climate science is all about probabilities. When scientists talk about climate change, they're talking about a shift in the odds towards a particular set of conditions (for example, an increased chance of warmer weather in November). A warmer than average day is not evidence for global warming any more than a colder than average day is evidence against warming. Rather, we need to show that the likelihood of experience a warmer than average day in November in New York has increased. This requires us to sample temperatures over many Novembers. Below is a graph of average temperatures in Central Park (blue) and in Portland, ME (green) for November 15-22.

The shaded areas are the standard deviations. Note: the means and standard deviations are over three years, to smooth out some of the variability. You'll notice considerable year-to-year variability as well as some longer warm/cool periods (for example, the 1950s were warm and the 60s were cool). First off, even during cool periods a 60 degree day in Central Park is still pretty likely. Since 1920, there is no significant trend in temperatures at either location during NBC's "Green Week." However, if you consider all weeks in November, there is a significant warming trend of 0.025 degrees/year in Central Park. The trend rises to 0.04 degrees/year if you use all the data back to 1876. In Portland, there is a slight cooling trend of 0.016 degrees per year. So, what's the point? The point is that climate change is complicated. There is a tremendous amount of variability, more commonly called "weather", in the data. Temperatures on any one day, at any one location, don't mean much. Real climate change signals can only be seen if you have enough data (in both time and space) to average over the variability.

Crazy Al Gore

2009-11-23T19:03:57Z

For those who missed it, here's the link to Al Gore's "Out-crazy the crazy" approach to dealing with climate change and other environmental problems:

The way we've ignored these dangers, and in some cases clung to denial, does border on crazy.