Graphing Out Loud: ups and downs

A while back, we started looking at a poorly thought-out article from the website C3Headlines. C3 is starting to make a name for itself as a goldmine of climate comedy- their claims have recently been addressed at Tamino and SkepticalScience.

We’re going to keep digging into C3‘s claim that carbon dioxide concentrations have been increasing linearly over the 20th century. They seemed to draw this claim by eyeballing the graph of CO2 concentrations and qualitatively describing them as linear, apparently using the inset in their first figure to compare linear, quadratic, and exponential trends. This is a faulty method: it’s an elementary fact of calculus that ANY smooth curve, when viewed appropriately, will appear linear. The point has already been made but it’s worthwhile to keep looking because there are some interesting graphical follies at play; examining them further might help us understand how and why graphs are misunderstood.

Figure 1: From C3Headlines’ article on “The Left/Liberal Bizarro Anti-Science Hyperbole”, which claims that CO2 concentrations are increasing linearly. Click to read it, if you dare…

C3‘s second graph in this article measures the change in atmospheric CO2 by calculating a month-to-month percentage change. It’s not entirely clear why they are using a percent change, rather than the standard practice of expressing rate of change as concentration change per year (like the source of their data uses). Whereas ppm/year is an absolute measure, each datum generated by the percentage-change method depends strongly upon the value of the previous month. As a measure of long-term rate of change, it is a bit questionable.

My primary concern, though, is with their use of monthly data in the first place. In my last article, we noted that, without explanation, C3 confined their focus to January CO2 concentrations. Were they consistent, they’d also look at January rates of change – of course, doing so might lead to unacceptable conclusions.

 Figure 2. Rates of CO2 accumulation have been calculated for the month of January, consistent with earlier investigation of January CO2 concentration. Over the period of observation, rates have increased at a significant (P~0.0005) acceleration of 0.11 ppm/year^2. Monthly rates throughout this article have been calculated by considering the change in CO2 between adjacent months, and assuming that a month is 1/12 of a year. Interpolated values of CO2 were used to avoid annoying data holes early in the record.

Instead, they look at the rate of change for every single month on record. Why do I find that problematic? Well, let’s look at the full record, with monthly resolution:

Mycology Symposium, Poster Session

It is a lovely spring day and I am absorbing some sunlight, hanging out in the tail end of the Carrboro Really Free Market while I type up my notes on the Duke Mycology Symposium. [CLICK HERE FOR DAYS ONE AND TWO]

There were a couple of posters which really caught my eye. One thing that I think is very interesting about fungi is their symbiotic relationships with plants. So I was excited when I saw two posters, both put together by Ryoko Oono and colleauges: “Populations structure in Lophodermium spp., a common fungal endophyte of loblolly pine” and “Effcts of foliar fungal endophyte diversity on plant protection against pathogens”. The first presents some preliminary information about the distribution of Lophodermium amongst pine trees in North Carolina. They found that there are three distinct subgroups of the of the fungus, despite not being geographically isolated. This suggests that there is limited gene flow between the subgroups. The second poster discusses the ecological role of fungal symbiotes: both single and multiple fungal colonizations can increase pathogen resistance, and since individual fungi types antagonize specific pathogens, you might expect a diverse group of colonizers to repel the most pathogens. However, there may also be a sort of tragedy of the commons effect, in which the individual members of diverse group of symbiotes have no particular dedication to protecting the host plant. Clarifying these issues will require more research, and the poster outlines a plan for further study.

The biochemistry of metals was a recurring theme in this symposium. We’ve already looked at iron, nickel, and cobalt; so let’s wrap up our tour of the transition metals with “Copper homeostasis as a virulence factor in systemic infection by the human fungal pathogen Cryptococcus neoformans,” by Chen Ding and colleauges at Duke. They describe the susceptibility of Cryptococcus to copper toxicity in the host, and the role of a class of biomolecules called metallothionens in protecting Cryptococcus from the metal. Interestingly, they also present data showing that copper levels are elevated in the serum of Cryptococcus patients – evidence, perhaps, for the immune system incorporating copper into its chemical weaponry! This would be the exact opposite reaction that it has when it comes to iron, which it withholds in an attempt to starve pathogens of nutrients (Nesse and Williams 1994; p. 29-30)

Yeast colony macrostructure - photo from the Magwene Lab - click to visit them

Finally, there was “Genetics, genomics, and variation in yeast colony morphology”, presented by Josh Granek and colleagues at Duke. They studied the yeast saccharomyces cerevisiae under a variety of different growing conditions. They found that, under conditions of abundant nitrogen but scarce fermentable carbon, the yeast colonies developed complex, organized structures large enough to see with the naked eye. This sort of emergent behavior is very interesting; it shows the bottom-up organization of biology by which relatively simple units can have complex system-level behavior … and understanding how cells communicate and cooperate in a colony can provide insights to the transition from unicellularity to multicelluarity.

That’s all there is to say about the symposium. One thing that I have been thinking about is the involvement of mycology communities in doing environmental monitoring. Simple citizen science monitoring programs already exist for animals and plants (Cohn 2008). Why not monitor the third domain of eukaryotes? Mycological enthusiasts already have local clubs, and the data gathered could provide insights into fungal biogreography and ecological change.

Further Reading
Cohn, J. (2008). Citizen Science: Can Volunteers Do Real Research? BioScience, 58 (3) DOI: 10.1641/B580303

Randolph Nesse, & George Williams (1994). Why We Get Sick: The New Science of Darwinian Medicine. Vintage Books: New York

Mycology Symposium, Day 2

Day 2 of the Duke Mycology Symposium has wound to a close, [DAY 1 HERE] and I am sitting on my porch contemplating the afternoon’s lectures:

“Pathogen recombination during the amphibian Chytridiomycosis pandemic: Why change what’s working?”

A genetics perspective on Bd, a fungus responsible for widespread amphibian mortality. Apparently one of the factors in its spread is the abundance and transport of bullfrogs (raised for food) and xenopus frogs (used in medical research), which can carry the disease without being killed by it. The recent spread is caused by a single Bd strain which reproduces by cloning itself – it should therefor be genetically uniform. Yet, in practice Bd has a ‘dynamic genome’. This led to discussion led to mechanisms for genetic change without sex, such as mitotic crossover and gene conversion.

“Pathogenicity factors in the chytrid fungus and amphibian pathogen B. dendrobatidis”

Further discussion of Bd, this time from a molecular / genomic perspective. Perhaps the most interesting part was evidence that chytrids contain rhodopsin, a light-sensitive pigment. [] I was also alerted to the existence of the 1000 fungal genomes project.

“Pleiotropic roles of the UPR pathway in Cryptococcus”

UPR is the unfolded protein response – when there are bits of proteins floating around inside a cell, it’s a bad sign. Maybe those proteins were torn apart by heat, or a toxin. This talk looked at the responses of Cryptococcus to the presence of the UPRs. In some cases, they release ‘chaperones’, proteins which help other molecules assemble correctly. Or, they might release dedegredation enzymes to clean up the mess. In extreme cases, they may even trigger apoptosis, a sort of cellular suicide.

“The adaptive value of Flo11‐dependent flocculation and adhesion in yeast”

Epigenetics: Not just for woo-meisters! Click for sauce.

Proteins on the surface of certain yeast cells act  to let the cells stick together and form clusters, which then fall out of their liquid medium. The gene for this surface protein is under considerable epigenetic control – there was a really beautiful picture the speaker presented, in which genetically identical yeast cells nonetheless have different levels of gene expression. Additionally, this phenomenon is an example of the green beard effect.

“Fear the Titans: When bad yeast get worse”

Titan cells are variants of cryptococcus. as much as 20 times as large as typical cells.

Mycology Symposium, Day 1

When I’m not too busy raging at skuptaloids online, I enjoy molecular biology and mycology, the study of fungi. Towards those ends, I’m visiting the Duke Symposium in Celebration of Mycology and Mycologists. I was only able to attend a few afternoon lectures on the first day of this conference, but am enjoying it greatly! Some of the lectures I attended:

“Glycoengineered yeast: from platform to product”

A completely qualitative assesment of the information storage in various biochemical media. You can see why I have a huge crush on glycans. Souce is "Emerging Glycomics Technologies" by Turnbull and Feild 2007; click for lynkz

Discussed the engineering considerations is convincing yeasts to produce biochemicals – for example, drugs. A major challenge is in glycosylation, the addition of complex sugars to proteins. Glycochemistry is very interesting to me; it is still very much a biochemical frontier.

“Membrane lipids and fungal virulence”

Glucosylceramides in fungi and humans are different, with fungal compounds featuring an unsaturated site and a methyl side group. Humans and fungi also have slightly different enzyme active sites to deal with these differences, suggesting that drugs can be developed to target the active sites in fungal pathogens without disrupting human biochemistry. The drug candidates discussed actually have analogs in commercial fungicides.

Graphing Out Loud: curves and lines

I love graphs – my eyes quickly glaze over at a table of numeric data, but a graph, used correctly, can quickly and easily tell the whole story.

‘Used correctly’ is the key phrase – for all their power, graphs are infamously easy to bungle, and when used incorrectly they can misinform – or lie outright.

I’m going to look at an example that touches on a few graphical and statistical concepts near and dear to my heart, as well as carbon geochemistry.

Fig. 1: An image from C3Headlines; the 3 C's are "Climate, Conservative, Consumer". Oh, and the article is titled "The Left/Liberal Bizarro, Anti-Science Hyperbole Continues". It sure would be tragic if they made obvious n00b mistakes after using such language. Click for link!

Coming from an article on the website C3Headlines, this image claims that carbon dioxide concentrations have ‘Linear, Not Exponential Growth’. thereby ‘expos[ing] the lunacy of typical left/liberal/progressive/Democrat anti-science’, The author has reached this conclusion by graphing January CO2 levels* and fitting a linear trendline to them.

Already this is a warning sign – the comparisons the author makes are entirely qualitative, apparently  based up on eyeballing the graph. However, trend lines are created by a statistical process called a linear regression, which comes with a caveat: it will fit a trend line to ANY data given to it, linear or nonlinear. Fortunately, there are also ways of evaluating how good a trend line is.