Carbon Footprints of Conservation Biologists: A missing link?

About a month ago, I wrote a post on “One big reason climate skeptics don’t believe in [anthropogenic] climate change“. In it, I used my own personal experiences to point out the hypocrisy inherent in the life styles of some ecologists. Although I was trying specifically not to single out individuals and places, some people did, inevitably, get offended. I apologize profusely if you were in that category. The whole point of the article was to highlight a much larger picture based on my experiences and observations, not to pass judgement.

Well, a friend of mine (code name: ‘Bearded Bear‘. No doubt he will appear in future posts, usually as a badger) read my earlier post and had a few, friendly words of disagreement with me about some particulars. However, I think he largely agreed with me (correct me if I’m wrong, BB). In a follow up, he sent me a recent opinion letter (by recent, I mean 3 years old) from Frontiers in Ecology and the Environment written by a bevy of ecological bigwigs, including Helen Fox (marine conservation scientist for the World Wildlife Fund), Peter Kareiva (chief scientist for The Nature Conservancy), and eleven others (sorry, I couldn’t list you all, as much as I wanted to).

This letter, titled “Why do we fly? Ecologists’ sins of emission” points out that conservation biologists tend to leave a larger carbon footprint than the average American. Despite having a 16% lower carbon footprint than average Americans in every day life (due to driving hybrids, bicycling, etc.), the high amount of travelling to and from conferences, research sites, etc., mean that conservation biologists have, on average, more than double the carbon footprint of average Americans. (Also, a side note, even though conservation biologists have a 16% lower ‘everyday’ carbon footprint than most Americans, they still use 3x more carbon than the global average reported in this letter).

The authors provide a nice table describing well-justified and poorly justified reasons to fly, as well as providing some solutions for the problem. The authors posit that the crux of the issue is that there are too many meetings and ecology, as a field, has done a poor job of integrating technology into their meetings (i.e. videoconferencing, etc) that would reduce the need to attend in person. If ecologists collectively reduce travel by ~30%, we can save 42,000 tons of carbon per year (about 7,300 cars on the road). That’s a lot of carbon saved in a very simple way.

Granted, the authors only surveyed 13 conservation biologists (themselves, actually) and the estimate of carbon use by the average American was not rigorously calculated (it was an Op-Ed piece, after all), but the point is well made. Whereas the average American uses more carbon (slightly) in their daily lives, frequent travelling more than wipes out any carbon-conscious decisions made at home by environmentally friendly scientists.

Needless to say, I liked this letter. If you have access to it, read it or find someone who can send you a copy. It’s short (1.5 pages) and to the point. I would like to say, emphatically, I am not calling conservation biologists hypocrites. As stated, when living at home we tend to be fairly good about having a lower carbon footprint than the average American (assuming, of course, that all ecologists have carbon footprints similar to the 13 authors of this letter). However, Fox et al. make a very good case for ecologists to be a little more ‘carbon savvy’ when it comes to travel plans.

UPDATE: As I think about this more, I’m not entirely sure that the estimate of 7,300 cars off the road is reasonable. The authors equate not flying with not driving, but there is a fundamental difference: if conservation biologists choose not to fly, those planes fly anyway (and still burn fuel). If a conservation biologist chooses not drive, then the car doesn’t run (thereby saving fuel). So, will reducing travel by conservation biologists really help reduce carbon emissions? Probably not at the population level, but it certainly reduces an individual’s carbon footprint. However, reducing an individual carbon footprint doesn’t necessarily equate to lower overall emissions if the fuel gets burnt anyway. I think that this letter was in a similar vein as my earlier post: Individual actions like riding bikes or reducing travel might not make much of an overall effect, but it sends a messages and sets an example. As always, correct me if I’m wrong.

Fundamentals of the Scientific Method Applied to Climate Change

A friend of mine recently sent me an article from the New Yorker: “The Truth Wears Off: Is there something wrong with the scientific method?” by Jonah Lehrer. In it, the author tracks various scientific hypotheses, from the effectiveness of pharmaceutical drugs to symmetry as a driver of sexual selection (i.e. beauty = good genes). Each of these hypotheses had something in common: the hypothesis was tested, a positive result was found, widespread acceptance followed, but retesting of the hypothesis by others (or in some cases the same author) could not reproduce positive results! Thus, Lehrer concludes that there might be something wrong with the scientific method, that so many studies can produce positive results of phenomena that appear not to be real.

I liked this article a lot. It brought up some good points about publication bias, bias in experimental design, and entrenchment of scientists in their ideas despite evidence to the contrary. These are indeed problems with the field of science as a whole, but it does not indicate that there are issues with the scientific method. In fact, this article demonstrates that the scientific method is working like a charm!

Consider this: Suppose a psychologist is interested in studying ‘verbal overshadowing’, where forcing a subject to verbally describe an object reduces his or her ability to accurately recall the object later (this example is from the article). Even if subjects are chosen at random from a general population, as they should be, there is still a chance that the psychologist might detect a positive effect (i.e. reject the null hypothesis) just by random sampling.

Now, suppose the psychologist happens to detect a positive effect. Scientists are skeptical by nature (people forget this). So the experiment will be replicated by others, under identical or differing conditions, in an attempt to validate the first psychologist’s results. The new studies find no effect. Is there something wrong with the scientific method?

Not at all. It’s working just fine! Example: A new study comes out, proposes a new hypothesis or theory. The new theory undergoes rigorous testing from multiple experimenters in multiple scenarios. If the new theory fails to hold up, it is tossed aside, improved, or reconsidered. If the new theory holds up, it is continuously tested until it is generally accepted. Note that general acceptance only occurs after many independent tests of the theory. This is the scientific method at its best, and this is the process Lehrer describes. Finding a false positive is not an issue in the scientific method, because those claims will be checked, those experiments will be validated, and that theory will be refined.

So can anything be considered to have passed the test? I mean, it seems fairly easy to take a theory, try to replicate it, and fail. But the big scientific ideas, the concepts that make it in to textbooks, the hypotheses and theories accepted as canon by scientists, are the very ones that have withstood this testing: Evolution, Competition, Exponential Growth (under ideal conditions), Sexual Selection, Phenotypic Plasticity, the Structure of DNA, Mitosis/Meiosis and the Mechanisms of Sexual Reproduction, Climate Change (you had to know this was coming), etc. Yes, these are broad concepts, and likely we need very specific information to apply this to very specific systems, but they work.

Climate Change? Yes. Climate change is a fantastic example of the scientific method verifying a process, the opposite of ‘verbal overshadowing’. A new phenomenon was proposed, it has been tested thousands of times by tens of thousands of scientists around the world using hundreds of metrics (i.e. CO2, ice cores, tree rings, etc), hundreds of statistical tests, and has been peer-reviewed thousands of times. In almost every one of these thousands of experiments and analyses, the answer is the same: Climates are changing, the earth is warming up, these changes correlate well with atmospheric CO2 concentrations, and it all started, oddly, shortly after the industrial revolution. Scientists have tested this hypothesis so many times that, if it wasn’t real, we would have found out by now.

Yes, there are issues with the implementation of the scientific method: Publication bias towards positive results, unfriendly reviewers (reviewers are anonymous, so they can be as nasty as they like. If you are providing negative results of a pet hypothesis of one of the reviewers, don’t expect constructive criticism or even to have your article accepted. Of course, there’s no way to know this beforehand), and experimental bias. For example, pharmaceutical companies have a big monetary incentive to get positive results, so they may not sample as randomly as they ought to and certainly have no incentive to double check their positive results with follow-up experiments (leading to high rates of follow-up tests failing to find an effect of the drug). Or suppose an ecologists really wants to make a statement about certain processes, let’s say they’re studying whether marine protected areas are effective and they really want to show that these protected areas work. They might choose unprotected sites they know to be in poor condition to compare with protected sites that they know to be incredibly healthy, even if these sites are not representative of the choices as a whole (i.e. the ecologist is picking and choosing only the few protected sites that work from an array of protected sites that, on average, don’t). They are biasing their site selection, rather than randomly choosing multiple sites of each type. It works exactly the same for people wanting to show that protected areas don’t work.

There’s nothing wrong with proposing a hypothesis that turns out to be mostly wrong. It’s the testing and retesting of this hypothesis that stimulates new research, generates new ideas, and gets people thinking about critical questions. For example, the Metabolic Theory of Ecology (MTE) was a vast, sweeping hypothesis that almost all biological and ecological interactions can be predicted on the basis of metabolism, which varies with temperature (if you’re an ectotherm) and body size. The original article has been cited over 1,000 times in 8 years, and a good number of these citations show that MTE is pretty flawed (it was too big not to be). Does that mean it’s worthless? No! Even if it is wrong, MTE has generated hundreds of new papers, spawned a huge number of hypotheses, and forced ecologists to reconsider the role of temperature in ecological interactions (like plant-herbivore interactions, because those are the most important, clearly). Or, if you’re me, you started out as huge fan of MTE, thought it could be used to predict absolutely everything (I still think there’s a theory out there for that), based an entire dissertation on MTE because you thought it was awesome, wound up disproving it whenever you were actually trying to validate it (oops! I totally did not see that coming….), and now have a much more nuanced view of temperature effects on ecological interactions. From the standpoint of the scientific method, MTE was a huge success. I mean, my whole scientific career to this point is predicated on it and the general concepts it outlines, even if I’m not sure it can do all the things it was initially proposed to do.

So are there problems, yes, but they’re not the ones listed in the New Yorker article. If anything, that article, to me, is a great exposé on the scientific method doing exactly what it’s supposed to do: test, propose, retest, retest, retest again, and reduce/refine/recycle.

The Politicization of Climate Models

Climate models are computer programs that solve the complex mathematical formulas that describe Earth’s climate. These models work in time-steps, where the output at time 2, for example, is used as the starting information at time 3. These models are integral to our understanding of all aspects of climate change: severity, climactic effects, biological effects, etc. These models are widely misunderstood, given that 42% of Americans think climate models are in the same category as daily weather models. In fact, the study linked in the last sentence pretty clearly demonstrates the severe lack of information in the general American public with regards to climate change and climate science. So, how are these models being discussed in the public realm, such that so many Americans misunderstand them?

A new paper by Akerlof et al. in Nature Climate Change suggests that these models are increasingly discussed not by news shows but on political opinion shows. This is problematic when the majority of Americans are already unsure as to whether these models can be trusted.

First, very little reporting of climate science appears to be ‘explanatory’, much of it is informative or opinionated but still assumes a basic understanding of the subject matter. According to the Yale study (first link), this is a bold assumption to make. Perhaps more explanatory material needs to be made available to the public through standard media outlets.

Second, climate models have seen an extraordinarily low amount of attention in news outlets (especially in comparison to stories mentioning global warming or climate change). In fact, The New York Times published far more stories on climate models than any other paper. Over the past decade, climate models have received less and less attention. Not only to climate models receive little attention, many (25%) of the ‘stories’ on climate models were op-ed pieces or letters to the editor, not written by journalists. Most of the mentions of climate models were negative, as well.

So what does all this mean? Well, it means that climate science isn’t being discussed in the public sphere by people qualified to evaluate those models. Most discussion of climate models is done by political pundits who have no more information available than their audience. What can we do? One possibility is that scientists, especially climate scientists, could take a more proactive role. More letters to the editor or journalists. Spend a significant part of the letter explaining climate science and climate models, much like teaching a class. Don’t assume too deep of an understanding of climate science. I would like to see a wave of op-ed pieces, written by scientists for everyone, on global change. Given that there are relatively few (10 or fewer) journalists who report on climate models, perhaps it is time that we cut the middle man and do it ourselves.