Cows are magnets.
Hot off the press at the Proceedings of the National Academy of Science is an article about magnetoreception in cows and deer [1]. Begall and colleagues, perhaps having spent far too much time playing with Google Earth, noticed that grazing cows and deer [2] seem to slightly prefer aligning themselves N-S, like magnets in the Earth's magnetic field. From the abstract
To test the hypothesis that cattle orient their body axes along the field lines of the Earth's magnetic field, we analyzed the body orientation of cattle from localities with high magnetic declination. Here, magnetic north was a better predictor than geographic north. [3]
Time to rewrite the physics textbooks, I guess: Cows are spheres, yes, but magnetic spheres!
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[1] S. Begall, J. Cerveny, J. Neef, O. Vojtech and H. Burda, "Magnetic alignment in grazing and resting cattle and deer." PNAS 105, 13451-13455 (2008).
[2] I was very disappointed, when I read through the article itself, to find no satellite pictures of cows aligned N-S. Indeed, there was not a single picture of a cow (or deer)!
[3] This is a good control to test the possibility that the E-W orientation of the sun drove the animals to prefer N-S in order to maximize the absorbed solar radiation. So, it seems that there really is some kind of weak magnetoreception going on. Now it's a matter of figuring out what biological compounds convey this magnetic sensitivity, and how its response to the Earth's field percolates up to behavioral tendencies.
Posted on August 26, 2008 in Things that go squish | permalink | Comments (3)
Welcome to the club, Magpie (Pica pica)
The past 20 years have already been a humbling experience for people sympathetic to the idea that humans are unique among animals. For many years, it was thought that tool use was an exclusive trait of humans, but then the evidence rolled in that other primates routinely use tools. The real shocker, of course, is that some birds also use tools [1]. And so it also went for cultural knowledge , long-term planning behavior, creativity, and dreaming. All of these behaviors are seen not just in other mammals, but in some species of birds. All this evidence makes it increasingly difficult to argue that there is much fundamentally special about Homo sapiens, except perhaps the coincidence of all of these behaviors in a single species.
To this list of human-like behaviors that birds exhibit, it seems we can now add self-recognition, at least for the European Magpie (Pica pica) [2]. This idea is usually illustrated by the so-called mirror test [3]: some kind of mark is placed on an animal in a spot they cannot normally see (on their chin, forehead, etc.), and a mirror is then placed so that they can see the mark on themselves. The usual interpretation of the resulting behavior is that if the animal recognizes themselves in the mirror, they will scratch directly at the foreign mark that they cannot normally see [4]. This kind of mirror use is seen in all the great apes (humans older than 18 months, bonobos, chimpanzees, organutans and gorillas), dolphins, killer whales and elephants.
Of course, we still have no explanation of what allows such mirror-recognition to happen. Is it the size of the brain alone? (Probably not, but size is likely important in some ways.) Is there some special structure in the brain that mediates it? (Maybe. It would be interesting to stick some of these animals into a CT scanner while they're doing this test to see if there are homologous regions of the brain that light up.) Etc. One thing that's interesting about this new discovery of self-recognition in Magpies is that these birds are in the corvid family, which includes other clever birds like crows. This group of birds are fairly large, and thus have relatively large brains, so it may be that whatever neurological structures facilitate self-recognition, they require relatively large masses of neurons. From a more general perspective though, it's exciting that all of these "complex" behaviors appear in birds. Because their brains are structured quite differently from mammal brains, it suggests that there's not one single way to create "intelligent" brains. If it turns out that there are many many ways to create complex behavior, then perhaps one day we'll figure out what the essential structures and dynamics are, and be able to build one from scratch.
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[1] This is pretty clearly a case of convergent evolution, since surely not all of the interceding species between birds and mammals were also tool users.
[2] H. Prior, A. Schwarz and O. Güntürkün, "Mirror-Induced Behavior in the Magpie (Pica pica): Evidence of Self Recognition." PLoS Biology 6, e202 (2008).
[3] The mirror test has, in fact, been heavily criticized for its indirectness and its bias toward human-style self-recognition. Of course, as anyone who's encountered the mind-numbing stupidity of corporate customer service, it can be extremely difficult to judge from behavior alone whether an animal is actually a thinking being. Examples abound of humans fooling ourselves and each other on this issue. If you ever want to experience this difficulty first-hand, try discussing with someone else whether your subjective experience of the world is similar or different from theirs.
[4] The control for this kind of experiment is to use two different color marks, one that is flesh- (or feather-)colored and one that is not.
Posted on August 20, 2008 in Obsession with birds | permalink | Comments (0)
Announcement: BC Net Workshop
Tis the season for networks workshops, it seems. Here's another announcement I recently received, this time for a workshop in Barcelona. Although the keynote-speaker lineup looks pretty good, and the organizers have done a lot of interesting work over the years, I will probably have to skip this event as I'm running a workshop on Networks and Inference at SFI only a few days before. If any of my dear readers go, I'd love to get a summary afterward.
BC Net Workshop: Trends and perspectives in complex networks
December 10-12, 2008 at the Physics Department, University of Barcelona, Barcelona, Spain.
Organizers: Marian Boguñà (U. Barcelona), Albert Díaz-Guilera (U. Barcelona) Romualdo Pastor-Satorras (U. Politècnica de Catalunya), and M. Àngels Serrano (IFISC).
Description: Ten years have elapsed since the publication of the celebrated paper by Watts and Strogatz on small-world networks. During this decade, the development of foundational aspects and methodologies set the grounds of complex network science, an interdisciplinary research area connecting Statistical Physics, Biology, Information Technology, Sociology, Economy, and others. Time has come to ask what have been the major contributions of this emerging field to prospect its future in perspective. We believe network science is now mature enough to start developing problem-solving ability and engineering and predictive power.
The spirit of this workshop is to stimulate researchers in complex networks and related areas to find new perspectives, trends, and applications that guarantee this headway. To this end, internationally recognized specialists will be invited to explain their current investigations and to discuss the expected progress of their research within the context of the field. The workshop will present as well selected contributions compliant with its purpose. An open colloquium session will also be organized where keynote speakers, participants, and committee members will have the opportunity to debate all together on the present situation of complex networks science and its outlook.
Posted on July 27, 2008 in Conferences and Workshops | permalink | Comments (2)
Dancing
Where the Hell is Matt? (2008) from Matthew Harding on Vimeo.
It's hard to top the explanation that NASA's Astro Photo of the Day page gave to this video, so I'll just quote it here:
What are these humans doing? Dancing. Many humans on Earth exhibit periods of happiness, and one method of displaying happiness is dancing. Happiness and dancing transcend political boundaries and occur in practically every human society. Above, Matt Harding traveled through many nations on Earth, started dancing, and filmed the result. The video is perhaps a dramatic example that humans from all over planet Earth feel a common bond as part of a single species. Happiness is frequently contagious -- few people are able to watch the above video without smiling.
Posted on July 27, 2008 in Humor | permalink | Comments (1)
Evolution and Distribution of Species Body Size
One of the most conspicuous and most important characteristics of any organism is its size [1]: the size basically determines the type of physics it faces, i.e., what kind of world it has to live in. For instance, bacteria live in a very different world from insects, and insects live in a very different world from most mammals. In a bacterium's world, nanometers and micrometers are typical scales and some quantum effects are significant enough to drive some behaviors, but larger-scale effects like surface tension and gravity have a much more indirect effect. For most insects, typical scales are millimeter and centimeters, where quantum effects are negligible, but the surface tension of water matters tremendously. Similarly, for most mammals [2], a typical scale is more like a meter, and surface tension isn't as important as gravity and supporting your own body weight.
And yet despite these vast differences in the basic physical world that different types of species encounter, the distribution of body sizes within a taxonomic group, that is, the relative number of small, medium and large species, seems basically the same regardless of whether we're talking about insects, fish, birds or mammals: a few species in a given group are very small (about 2 grams for mammals), most species are slightly larger (between 20 and 80 grams for mammals), but some species are much (much!) larger (like elephants, which weigh over 1,000,000 times more than the smallest mammal). The ubiquity of this distribution has intrigued biologists since they first began to assemble large data sets in the second-half of the 20th century.
Many ideas have been suggested about what might cause this particular, highly asymmetric distribution, and they basically group into two kinds of theories: optimal body-size and diffusion. My interest in answering this question began last summer, partly as a result of some conversations with Alison Boyer in another context. Happily, the results of this project were published in Science last week [3] and basically show that the diffusion explanation is, when fossil data is taken in account, really quite good. (I won't go into the optimal body-size theories here; suffice to say that it's not as popular a theory as the diffusion explanation.) At its most basic, the paper shows that, while there are many factors that influence whether a species gets bigger or smaller as it evolves over long periods of time, their combined influence can be modeled as a simple random walk [4]. For mammals, the diffusion process is, surprisingly I think, not completely agnostic about the current size of a species. That is, although a species experiences many different pressures to get bigger or smaller, the combined pressure typically favors getting a little bigger (but not always). The result of this slight bias toward larger sizes is that descendent species are, on average, 4% larger than their ancestors.
But, the diffusion itself is not completely free [5], and its limitations turn out to be what cause the relative frequencies of large and small species to be so asymmetric. On the low end of the scale, there are unique problems that small species face that make it hard to be small. For instance, in 1948, O. P. Pearson published a one-page paper in Science reporting work where he, basically, stuck a bunch of small mammals in an incubator and measured their oxygen (O2) consumption. What he discovered is that O2 consumption (a proxy for metabolic rate) goes through the roof near 2 grams, suggesting that (adult) mammals smaller than this size might not be able to find enough high-energy food to survive, and that, effectively, 2 grams is the lower limit on mammalian size [6]. On the upper end, there is an increasingly dire long-term risk of become extinct the bigger a species is. Empirical evidence, both from modern species experiencing stress (mainly from human-related sources) as well as fossil data, suggests that extinction seems to kill off larger species more quickly than smaller species, with the net result being that it's hard to be big, too.
Together, this hard lower-limit and soft upper-limit on the diffusion of species sizes shape distribution of species in an asymmetric way and create the distribution of species sizes we see today [7]. To test this hypothesis in a strong way, we first estimated the details of the diffusion model (such as the location of the lower limit and the strength of the diffusion process) from fossil data on about 1100 extinct mammals from North America that ranged from 100 million years ago to about 50,000 years ago. We then simulated about 60 million years of mammalian evolution (since dinosaurs died out), and discovered that the model produced almost exactly the size distribution of currently living mammals. Also, when we removed any piece of the model, the agreement with the data became significantly worse, suggesting that we really do need all three pieces: the lower limit, the size-dependent extinction risk, and the diffusion process. The only thing that wasn't necessary was, surprisingly, the bias toward slightly larger species in the diffusion itself [8], which I think most people thought was necessary to produce really big species like elephants.
Although this paper answers several questions about why the distribution of species body size is the way it is, there are several questions left unanswered, which I might try to work on a little in the future. In general, one exciting thing is that this model offers some possibilities for connecting macroevolutionary patterns, such as the distribution of species body sizes over evolutionary time, with ecological processes, such as the ones that make larger species become extinct more quickly than small species, in a relatively compact way. That gives me some comfort, since I'm sympathetic to the idea that there are reasons we see such distinct patterns in the aggregate behavior of biology, and that it's possible to understand something about them without having to understand the specific details of every species and every environment.
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[1] An organism's size is closely related, but not exactly the same as its mass. For mammals, their density is very close to that of water, but plants and insects, for instance, can be less or more dense than water, depending on the extent of specialized structures.
[2] The typical mammal species weights about 40 grams, which is the size of the Pacific rat. The smallest known mammal species are the Etruscan shrew and the bumblebee bat, both of whom weight about 2 grams. Surprisingly, there are several insect species that are larger, such as the titan beetle which is known to weigh roughly 35 grams as an adult. Amazingly, there are some other species that are larger still. Some evidence suggests that it is the oxygen concentration in the atmosphere that mainly limits the maximum size of insects. So, about 300 million years ago, when the atmospheric oxygen concentrations were much higher, it should be no surprise that the largest insects were also much larger.
[3] A. Clauset and D. H. Erwin, "The evolution and distribution of species body size." Science 321, 399 - 401 (2008).
[4] Actually, in the case of body size variation, the random walk is multiplicative meaning that changes to species size are more like the way your bank balance changes, in which size increases or decreases by some percentage, and less like the way a drunkard wanders, in which size changes by increasing or decreasing by roughly constant amounts (e.g., the length of the drunkard's stride).
[5] If it were a completely free process, with no limits on the upper or lower ends, then the distribution would be a lot more symmetric than it is, with just as many tiny species as enormous species. For instance, with mammals, an elephant weights about 10 million grams, and there are a couple of species in this range of size. A completely free process would thus also generate a species that weighed about 0.000001 grams. So, the fact that the real distribution is asymmetric implies that some constraints much exist.
[6] The point about adult size is actually an important one, because all mammals (indeed, all species) begin life much smaller. My understanding is that we don't really understand very well the differences between adult and juvenile metabolism, how juveniles get away with having a much higher metabolism than their adult counterparts, or what really changes metabolically as a juvenile becomes an adult. If we did, then I suspect we would have a better theoretical explanation for why adult metabolic rate seems to diverge at the lower end of the size spectrum.
[7] Actually, we see fewer large species today than we might have 10,000 - 50,000 years ago, because an increasing number of them have died out. The most recent population collapses are certainly due to human activities such as hunting, habitat destruction, pollution, etc., but even 10,000 years ago, there's some evidence that the disappearnace of the largest species was due to human activities. To control for this anthropic influence, we actually used data on mammal species from about 50,000 years ago as our proxy for the "natural" state.
[8] This bias is what's more popularly known as Cope's rule, the modern reformulation of Edward Drinker Cope's suggesting that species tend to get bigger over evolutionary time.
Posted on July 21, 2008 in Evolution | permalink | Comments (0)
Announcement: ICDM Workshop on Analysis of Dynamic Networks
Since I'm pretty sure that part of the future of research on complex networks lays in understanding how networks evolve over time, this workshop seems quite relevant. Judging by the associated conference and the organizers, this workshop will probably focus on algorithmic techniques for analyzing large amounts of network data.
IEEE Conference on Data Mining (ICDM) Workshop on Analysis of Dynamic Networks (ADN)
December 19, 2008 at the IEEE Conference on Data Mining (ICDM) in Pisa, Italy
Organizers: Tanya Berger-Wolf (UIC), Malik Magdon-Ismail (RPI) and Jared Saia (UNM).
Description: The goal of the Analysis of Dynamic Networks (ADN) workshop is to bring together research that addresses explicitly the dynamic nature of networks in the context of analysis of social, electronic, biological and other networks. We aim to further the development of a computational framework in which one can model, discover and analyze complex interaction systems as they form and evolve.
We invite contributions presenting new computational methods for analysis of dynamic interaction networks, new models of dynamic behavior of networks, or applications of dynamic network analysis in various contexts. Papers presenting new methods should provide experimental or empirical evidence of the performance of the new methods.
In this context, submission topics can include, but are not limited to:
- Modeling dynamic behavior of networks
- Network structure prediction
- Analysis of spreading processes in networks
- Community structure inference
- Search and routing in dynamic networks
- Identification of critical nodes
- Comparison of dynamic networks
- Visualization of dynamic networks
Other topics within the subject area are welcome. Note, that all submitted papers should demonstrate the relevance to the topic of dynamic networks. If unsure whether your paper fits the session theme, please contact one of the co-chairs.
Posted on July 15, 2008 in Conferences and Workshops | permalink | Comments (0)
Announcement: DIMACS Workshop on Network Models of Biological and Social Contagion
This workshop looks pretty interesting, and that's not because it's being organized by my friends. Comfortably, the topics align with several of what I think are the "future" of network science (tip to Jake).
Update 18 July 2008: Having just received an invitation to speak from the organizers, I think it's likely that I'll be attending. In addition to learning about new science at DIMACS, it'll be a great opportunity to also visit some friends and colleagues in New York City.
DIMACS / DyDAn Workshop on Network Models of Biological and Social Contagion
November 3 - 4, 2008 at DIMACS, Rutgers
Organizers: Lauren Ancel Meyers (UT Austin) and Michelle Girvan (UMD).
Description: The spread of infectious diseases and the flow of ideas and information through populations fundamentally depend on the complex structure of the underlying network of interactions between individuals. Disease ecologists and sociologists have historically studied the dynamics of contagion using models that assume very simple population structures. Recently, however, network modeling has revolutionized both fields by enabling the rigorous exploration of the relationship between complex individual-level behavior and the higher-level emergence of outbreaks. The field draws on advanced statistical tools for inferring network structure from often limited data, data-driven algorithms for generating realistic network structures, and mathematical approximations for predicting transmission dynamics that draw from the methods of percolation theory and other fields within statistical physics.
While network models are more complex than their mass-action predecessors, they are remarkably tractable, often reducing to low-dimensional descriptions and allowing straightforward calculations of the dynamics of contagion. The fields of infectious disease epidemiology and sociology are simultaneously experiencing an explosion of computationally-intensive agent-based simulation models, that allow much higher-resolution representations of populations but often preclude comprehensive analysis. Selecting among the diversity of modeling approaches is non-trivial, and may be highly dependent on the system and the questions.
This workshop will focus on network models for biological and social contagion, and how they compare to alternative approaches. It will address the challenges of inferring network structure from sociological and/or epidemiological data, understanding the emergence of such network structure from simple individual-level behavior, and predicting the dynamics of contagion from simple characterizations of the underlying network.
Topics:
- Inferring network structure from data
- Generative models of social and epidemiological networks
- Modeling the dynamics of biological and social contagion on networks
- Modeling feedback from contagion dynamics to network structure
- Model selection -- choosing the right level of complexity
Posted on July 09, 2008 in Conferences and Workshops | permalink | Comments (0)
