Light and Noise in the Anthropocene

There is a companion article exploring the issue from the perspective of environmental monitoring over at ArkFab.

Human influence on the environment has increased dramatically over the last 10,000 years, to the point that some geologists have argued that human reworking of the earth defines a new geologic age, The Anthropocene. (Zalasiewicz et al, 2008) Much of the focus has been on relatively robust, tangible changes in biogeochemistry. Examples include:

• the climate change and ocean acidification resulting from the addition of fossil fuel emissions to the carbon cycle.

• megafaunal extinction, accelerated erosion (Zalasiewicz et al, 2008) and nitrogen fixation resulting from the spread of intensive subsistence patterns
• the loss of stratospheric ozone resulting from the release of novel chlorofluorocarbons

However, fleeting and less tangible effects are also important. Two examples are:

• the light pollution resulting from urbanization and transportation infrastructure
• changes in the acoustic environment resulting from direct addition of sonic energy and memes, as well as indirect sources.

A year-long composite view of the earth at night, showing human light generation. White lights are cities; blue lights are fishing boats; green lights are natural gas flares, and red lights are ‘ephemeral light sources’, interpreted as fires. Image from  NOAA National Geophysical Data Center – click for source + discussion.

Light pollution, the scourge of urban astronomers, is a well-accepted phenomenon with serious consequences. A 2004 review begins:

In the past century, the extent and intensity of artificial night lighting has increased such that it has substantial effects on the biology and ecology of species in the wild. We distinguish “astronomical light pollution”, which obscures the view of the night sky, from “ecological light pollution”, which alters natural light regimes in terrestrial and aquatic ecosystems. Some of the catastrophic consequences of light for certain taxonomic groups are well known, such as the deaths of migratory birds around tall lighted structures, and those of hatchling sea turtles disoriented by lights on their natal beaches. The more subtle influences of artificial night lighting on the behavior and community ecology of species are less well recognized, and constitute a new focus for research in ecology and a pressing conservation challenge. (Longcore & Rich 2004)

The amount of sonic energy released by human activity is recognized as an urban nuisance as well as an occupational safety concern. It also has recognized ecological effects: urban European robins have begun singing at night, when they have less acoustic competition. (Fuller et al 2007) Frogs have begun changing the pitch of their croaks in order to talk over traffic noise (Paris et al 2009)  In addition to sonic energy, human activity has released sonic memes into the environment. A meme is a self-replicating information pattern; jokes and computer viruses are two examples of memes. A person or computer acquires a meme and then spreads it, through retelling or infected emails. Sonic memes, such as ambulance sirens and cellphone ringtones, have been picked and repeated by songbirds. (Stover 2009) This is very interesting: human memes, the basis of Richard Dawkins’ ‘extended phenotype’ concept, have organically extended into other animals’ extended phenotype. (Recent reports of dolphins mimicking human speech are also very interesting in this context. The reverse flow also occurs, as animal communications are repackaged as ringtones or ambient music.)

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

John Everett, part V: the good the bad and the elephant snot

A part of my John Everett series – read more: 0/I - II.0 - II.5 - II.75 -  III.0 - III.3 - IV.0 - IV.4 - IV.8 - V - VII - VIII - Full Report 

Part V of John Everett’s testimony (“Is this bad or good or just different?”) repeats several claims that we’ve already seen to be simply incorrect:

Little Rock Lake, site of a famous acid rain study. Image from Google Maps; sauceclick.

• That plants will be more successful in a high-CO2 world.
• That climatologists have incorrectly modelled the carbon cycle, leading to exaggerated projections of ocean acidification.
• That “the Earth has seen these conditions before, and that all the same types of animals and plants of the oceans successfully made it through far more extreme conditions.”
• That “there is nothing conclusive in the very recent scientific literature to indicate any reason for concern.”

The only new evidence he presents in this section regards a different pH problem: acid rain.

“During the acid rain issues in the 1980s, a lake basin in Wisconsin was deliberately acidified (with EPA and NSF funding) to a pH of 4.7 then allowed to recover. ‘Some species were decimated and others thrived, but the sum-total of life in the lake stayed the same.’ This is a level of acidification 1,000 X the worst-case scenario for the oceans. It provides a clue as to what a 2X change might be.”

His reference for this claim is this news item from ScienceDaily. To clarify, when Little Rock Lake was ‘allowed to recover’, acidification was halted and its pH was allowed to rise to its previous levels. The news item is reporting on the slow recovery, which only took place once the acidification ceased. Dr. Everett presents a quote from this news item, which would seem to suggest that things were just fine in the acidified lake. In fact, the quote in its entirety refers to the lake’s recovery, rather than its acidified state:

Why do people catch swine flu, but not tobacco mosaic virus?

I’m going to take a break from our regularly scheduled debunking of John Everett’s Senate testimony, to pose a question for creationists and cDesign Proponentsists: Why do people catch swine flu but not tobacco mosaic virus?

I’m not asking why people get sick. I’m not interested in a rehashing of the tired old arguments about the coexistence of god and human suffering. I want an explanation of the fact that, despite the myriad pathogens which infect other branches of the tree of life, it’s only pathogens from other animals (usually other vertebrates) which make humans sick. Poxes, tuberculosis, and anthrax infect cattle. HIV is a mutant variant of Simian Immunodeficiency Virus, which infects other primates. Why don’t we fall ill from Partitivirus, pathogen of fungi? Or T4 phage, parasite of bacteria?