Can We Reduce the Carbon Cost of Scientific Mega-Meetings?

ResearchBlogging.orgI admit it. I love big scientific meetings. There’s something about the intense intellectual hubbub of thousands of my fields greatest minds gathered in one place for a few days of showing off the latest, greatest, flashiest work that just fills me with joy. Also a need to sleep for a week afterwards due to my brain going at a Matrix-like pace to keep up with all of the new and interesting information while spouting off ideas, critiques, beginning collaborations, and constantly questing to understand the growing shape of the research fields that interest me. It’s quite simply an intellectual smörgåsbord. But like all such dining experiences, there is a cost. A cost I’ve been wrestling with in this new piece in Enthobiology Letters with my collaborator Alexandra Ponette-González.

It’s a carbon cost. A cost for climate change.

Simply, there a lot of people at these Mega-Meetings. A LOT. And they are rarely local. Most of us fly in, from across the country, from another country, or even another continent. Those flights put out CO2 emissions – a lot of it. Heck, even driving the full distance to some of these meetings would have a high emissions profile given the distances. And it makes you stop and wonder – we ecologists who are so environmentally conscious, what is the carbon cost of our engagement in big Mega-Meetings? Could we be doing better? How?

A map of the location of the last several ESA meetings and the 2010 AGU meeting (triangles with Carbon Cost next to them) as compared ti the distribution of attendees (circles proportional to number of attendees from that area over all meetings). Costs are in per capita metric tons.

A few years ago, this issue came up at the DISCCRS conference – an annual interdisciplinary gathering of early career climate researchers that is truly amazing. During the coffee afterwards I got to talking with a fellow attendee, and we began brainstorming. How could the big scientific societies of the world – the ESAs, AGUs, or, heck, maybe even the AAASs – still conduct their vital business of intellection discourse while reducing their carbon footprint from meeting travel?

Travel is the key – if attendees, even the same number of attendees, didn’t have to travel to far and use air travel, it’s possible that we could dramatically lower carbon costs. Merely limiting the number of meetings or restricting the number of possible attendees seemed draconian and not possible. Carbon offsets have proven to be unreliable. Telecommuniting to meetings limits the real value of live social interaction (so far). It seemed like there wasn’t a good solution. But then we began to think about a second kind of meeting that some, but not all, of us attend.

I’m talking about meetings that are smaller, cozier, with researchers rarely from more than a few states away. Grad students have piled into cars, trucks, vans, llama-powered motor-scooters, and more to make the pilgrimage for the meeting’s weekend of showing their stuff and finding new colleagues, collaborators, and mentors. These are the meetings where you form deep relationships that you come back to year after year – relationships that slowly bear great intellectual fruit. Meetings like The Western Society of Naturalists, for example.

True, Mega-Meetings are quite different from these smaller more local meetings – like the big flash of molecular gastronomy to the simple elegant nourishment of slow food – elBulli to Chez Panisse. Therein, however, lies their intrinsic value – a value that attendees of only Mega-meetings may actually be missing.

So we began to ponder – what if societies alternated between Mega-meetings and a large number of smaller more regional meetings? Could this be a possible solution? Intellectually, sure, I’m sure some would still argue against it, but that would be moot if the carbon savings were trivial. So we sat down over the next few months and did the computational equivalent of some back-of-the-envelope calculations of carbon as currently emitted versus carbon emitted based on several different scenarios of meeting distributions.

And then we sat back, pretty surprised.

Assuming that pretty much everyone drives, but that no one carpools (or uses llamas), carbon savings under our most pessimistic set of assumptions were around 50%. That’s right, halving the carbon emissions.

Granted, this is back-of-the-envelope, but, the idea is pretty compelling. And yes, there are other costs – administrative, logistic, etc. But thinking from a carbon perspective alone, this result is pretty stunning. Not only are there large carbon benefits, but local meetings confer other benefits – contribution to regional economies, better ties to regional organizations and NGOs, and quite likely a higher degree of participation from graduate students (and lower attendance barriers to undergraduates and the community).

We also considered other alternatives – lowering carbon costs by taking the distribution of members into account, reducing international participation, etc. But the drawbacks in these seemed to be ones that most people would not, at least currently, accept, when we floated ideas to others.

So, this local-regional alternation seems to be something worth thinking about. Would you be willing to participate in an alternative society structure – one where meetings alternated between being large and international and then small and regional? What would be lost for you? What would be gained? Would it be too much of an additional burden on organizers? Would that burden be justified by carbon savings?

Also of note, we had a hard time getting this published. We had a lot of wonderful comments from editors and reviewers who were very positive about this work, but then would say, “Oh, but, you know, we just don’t have a venue for this.” (sometimes followed weeks later by editorials stating “THIS IS A PROBLEM! WHERE ARE THE SOLUTIONS?” which we thought curious) We tried multiple generalist and specialist journals, journals for societies and by regular publishers. I’d like to thank Ethnobiology Letters for going out on a limb and publishing this, as conversations like this need to be had in the peer reviewed literature.

Ponette-González, Alexandra G, & Jarrett E Byrnes (2011). Sustainable Science? Reducing the Carbon Impact of Scientific Mega-Meetings Ethnobiology Letters, 2, 65-71

Seeing Through the Measurement Error

I am part of an incredibly exciting project – the #SciFund Challenge. #SciFund is an attempt to have scientists link their work to the general public through crowdfunding. As I’m one of the organizers, I thought I should have some skin in the game. But what skin?

Well, people are pitching some incredibly sexy projects – tracking puffin migrations, coral reefs conservation, snake-squirrel interactions (WITH ROBOSQUIRRELS!), mathematical modeling of movements like Occupy Wall Street, and many many more. It’s some super sexy science stuff.

So what is my project going to address? Measurement error.


But wait, before you roll your eyes at me, this is REALLY IMPORTANT. Seriously!

It can change everything we know about a system!

I’m working with a 30 year data set from the Channel Islands National Park. 30 years of divers going out and counting everything in those forests to see what’s there. They’ve witnessed some amazing change – El Niños, changes in fishing pressure, changes in fishing pressure, changes in urbanization on the coast, and more. It’s perhaps the best long-term large-scale full community subtidal data set in existence (and if there are better, um, send ’em my way because I want to work with them!)

But 30 years – that’s a lot of different divers working on this data set under a ton of different environmental conditions. Doing basic sampling on SCUBA is arduous, and given the sometimes crazy environmental conditions, there is probably some small amount of variation in the data due to processes other than biology. To demonstrate this to a general non-statistical audience, I created the following video. Enjoy watching me in my science film debut…oh dear.

OK, my little scientific audience. You might look at this and think, meh, 30 years of data, won’t any measurement error due to those kinds of conditions or differences in the crew going out to do these counts just average out? With so much data, it shouldn’t be important! Jarrett just wanted an excuse to make a silly science video!

And that’s where you may well be wrong (well, about the data part, anyway). I’ve been working with this data for a long time, and one of my foci has been to try and tease out the signals of community processes, like the relative importance of predation and grazing versus nutrients and habitat provision. Your basic top-down bottom-up kind of thing. While early models showed, yep, they’re both important, and here’s how and why, some rather strident reviewer comments came back and forced me to rethink the models, adding in a great deal more complexity even to the simplest one.

And this is where measurement error became important. Measurement error can obscure the signal of important processes in complex models. A process may be there, may be important in your data, but if you’re not properly controlling for measurement error it can hide real biological patterns.

For example, below is a slice of one model done with two different analyses. I’m looking at whether there are any relationships between predators, grazers, and kelp. On the left hand side, we have the results from the fit model without using calibration data to quantify measurement error. While it appears that there is a negative relationship between grazers and kelp, there is no detectable relationship between predators and grazers (hence the dashed line – it ain’t different from 0).

This is because there is so much extra variation in records of grazer abundances due to measurement error that we cannot see the predator -> grazer relationship.

Now let’s consider the model on the right. Here, I’ve assumed that 10% of the variation in the data is due to measurement error (i.e., an R2 of 0.9 between observed and actual grazer abundances). So, I have “calibration” data. This error rate is made up, just to show the consequences of folding the error in to our analysis.

Just folding in this very small amount of measurement error, we get a change in the results of the model. We can now see a negative relationship between predators and grazers.

I need this calibration data to ensure that the results I’m seeing in my analyses of this incredible 30 year kelp forest data set are real, and not due to spurious measurement error. So I’m hoping wonderful folk like you (or people you know – seriously, forward around to everyone you know! RIGHT NOW!) will see the video, read the project description, and pitch in to help support kelp forest research.

If we’re going to use a 30 year data set to understand kelp forests and environmental change, we want to do it right, so, let’s figure out how much variation in the data is real, and how much is mere measurement error. It’s not hard, and the benefits to marine research are huge.