Archive for January, 2012

Have A Different Car Everyday
The MOY Concept car was made for a generation used to using media to express themselves. The idea is everyone can design their own car on their own computer and then apply the design to their vehicle wirelessly or share it with others thru a website or even email. For those lacking creative skill, there’s a slew of templates to choose from. The car is always connected so imagine dynamically changing your vehicle’s skin in motion. Designed by Elvis Tomljenovic.

The Hum

From Wikipedia, the free encyclopedia

The Hum is a generic name for a series of phenomena involving a persistent and invasive low-frequency humming noise not audible to all people. Hums have been reported in various geographical locations. In some cases a source has been located. A Hum on the Big Island of Hawaii, typically related to volcanic action, is heard in locations dozens of miles apart. The Hum is most often described as sounding somewhat like a distant idling diesel engine. Typically, the Hum is difficult to detect with microphones, and its source and nature are hard to localize.

The Hum is sometimes prefixed with the name of a locality where the problem has been particularly publicized: e.g., the “Bristol Hum”, the “Taos Hum”, or the “Bondi Hum”.[1]




The essential element that defines the Hum is what is perceived as a persistent low-frequency sound, often described as being comparable to that of a distant diesel engine idling, or to some similar low-pitched sound for which obvious sources (e.g., household appliances, traffic noise, etc.) have been ruled out.

Other elements seem to be significantly associated with the Hum, being reported by an important proportion of hearers, but not by all of them. Many people hear the Hum only, or much more, inside buildings as compared with outdoors. Many also perceive vibrations that can be felt through the body. Earplugs are reported as not decreasing the Hum.[2] The Hum is often perceived more intensely during the night.

On 15 November 2006, Dr. Tom Moir of the Massey University in AucklandNew Zealand made a recording of the Auckland Hum and has published it on the university’s website.[3][4] The captured hum’s power spectral density peaks at a frequency of 56 hertz.[5] In 2009, the head of audiology at Addenbrooke’s Hospital in Cambridge, Dr David Baguley, said that he believed people’s problems with hum were based on the physical world about one-third of the time and the other two-thirds stemmed from people focusing too keenly on innocuous background sounds.[6]


In Britain, the most famous example was the Bristol hum that made headlines in the late 1970s.[6] It was during the 1990s that the Hum phenomenon began to be reported in North America and to be known to the American public, when a study by the University of New Mexico and the complaints from many citizens living near the town of Taos, New Mexico, caught the attention of the media. However, in the 1970s and 1980s, a similar phenomenon had been the object of complaints from citizens, of media reports and of studies. It is difficult to tell if the Hum reported in those earlier cases and the Hum that began to be increasingly reported in North America in the 1990s should be considered identical or of different natures.[citation needed]

On June 9th, 2011, it was reported that residents of the village of Woodland, England were experiencing a hum that had already lasted for over two months.[7]

This phenomenon has also been reported since 2010 throughout Windsor and Essex County in Ontario, Canada,[8] where some residents claim it to be correlated with the time of day, or week, while others seem unaffected or unable to hear it.[9]


In the case of Kokomo, Indiana, a city with heavy industries, the source of the hum was thought to have been traced to two sources. The first was a pair of fans in a cooling tower at the local DaimlerChrysler casting plant emitting a 36 Hz tone. The second was an air compressor intake at the Haynes International plant emitting a 10 Hz tone.[10][11]

[edit]Some possible explanations

Some explanations of hums for which no definitive source has been found have been put forth. These include:


Generated by the body, the auditory or the nervous system, with no external stimulus. However, the theory that the Hum is actually tinnitus fails to explain why the Hum can be heard only at certain geographical locations, to the degree those reports are accurate. There may exist individual differences as to the threshold of perception of acoustic or non-acoustic stimuli, or other normal individual variations that could contribute to the perception of the Hum by some people in the population and not by others.

While the Hum is hypothesized to be a form of low frequency tinnitus[12] such as the venous hum, some sufferers claim it is not internal, being worse inside their homes than outside. However, others insist that it is equally bad indoors and outdoors. More mystery is added as some notice the Hum only at home, while others hear it everywhere they go. Some reports indicate that it is made worse by attempted soundproofing (e.g., double glazing), which serves only to decrease other environmental noise, thus making the Hum more apparent. Tinnitus is also generally worse in places with less exterior sound.

People who both suffer from tinnitus and hear the Hum describe them as qualitatively different, and many hum sufferers can find locations where they do not hear the hum at all. An investigation by a team of scientists in Taos dismissed the possibility that the Hum was tinnitus as highly unlikely.[13][unreliable source?]

[edit]Spontaneous otoacoustic emissions

Human ears generate their own noises, called spontaneous otoacoustic emissions, which about 30% of people hear. The people that hear these sounds typically hear a faint buzzing or ringing, especially if they are otherwise in complete silence, but most people don’t notice them at all.[14]

[edit]Colliding ocean waves

Researchers from the USArray Earthscope have tracked down a series of infrasonic humming noises produced by waves crashing together and thence into the ocean floor, off the North-West coast of the USA. Potentially, sound from these collisions could travel to many parts of the globe.[15][16][17]

[edit]Media coverage

The Taos Hum was featured on the TV show Unsolved Mysteries.[18] It was also featured in LiveScience‘s “Top Ten Unexplained Phenomena”, where it took first place.[19]

[edit]In popular culture

In a 1998 episode of The X-Files titled “Drive“, Agent Mulder speculates that extremely low frequency (ELF) radio waves “may be behind the so-called Taos Hum”.[20]

[edit]See also


  1. ^ Melouney, Carmel (2009-05-24). “Bondi’s mystery noise maker”The Daily Telegraph (News Ltd). Retrieved 2009-05-25.
  2. ^ Wagner, Stephen (2008). “Unexplained Sounds”. Retrieved 2008-09-21.
  3. ^ Moir, Tom (2006-11-15). “Auckland North Shore Hum”T.J.Moir Personal pages. University of Massey. Retrieved 2006-11-24.
  4. ^ Hutcheon, Stephen (2006-11-17). “Mystery humming sound captured”Sydney Morning Herald (Fairfax). Retrieved 2006-11-24.
  5. ^ Hutcheon, Stephen (2006-10-26). “Mystery noise is a real humdinger”Sydney Morning Herald (Fairfax). Retrieved 2006-11-24.
  6. a b Alexander, James (2009-05-19). “Have you heard ‘the Hum’?”. BBC News. Retrieved 2011-06-13.
  7. ^ Alleyne, Richard (2011-06-09). “Tiny village is latest victim of the ‘The hum'”The Telegraph. Retrieved 2011-06-13.
  8. ^ Battagello, Dave (August 5, 2011). “Rumblings may prompt lawsuit”The Windsor Star.
  9. ^ Tweedie, Neil (June 18, 2011). “The Hum keeps people awake at night”The Daily Telegraph.
  10. ^ Cowan, James P. (October 2003) (PDF). The Kokomo Hum Investigation. City of Kokomo Board of Public Works and Safety. Retrieved 2006-11-27.
  11. ^ “Possible Source Found For Kokomo Hum: Hum Traced To Local Factory” (Internet Broadcasting Systems, Inc.). 2003-09-19. Retrieved 2006-11-27.
  12. ^ Sheppard, L.; Sheppard, C. (1993). “The Phenomenon of Low Frequency Hums”Tinnitus. Norfolk Tinnitus Society. Retrieved 2011-06-13.
  13. ^ The Taos Hum
  14. ^ Abrams, M. An Inescapable Buzz. Discover Magazine. October 1995.
  15. ^ Leggett, Hadley (August 7, 2009). “Scientists Track Down Source of Earth’s Hum”Wired. Retrieved 2011-06-13.
  16. ^ Dacey, James (July 10, 2009). “Coasts confirmed as main source of Earth’s ‘hum'” Retrieved 2011-06-13.
  17. ^ Walburn, Steve. “The Hearers” in Indianapolis Monthly, Dec. 2002.
  18. ^ “Taos hum”Unexplained events, Unsolved Mysteries.
  19. ^ “Top Ten Unexplained Phenomena”Strange news, Live Science.
  20. ^ DriveThe X Files. Ten Thirteen Productions. November 15, 1998. Event occurs at 40:00.

[edit]Further reading

[edit]External links

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This was my answer:

The above answer is absolutely correct, but in our universe which is the only universe we can observe, based on the assumption that santa is as he is described in popular media (sleigh, reindeer, overweight from all the cookies), here are the possible reasons:
1. Mathematics absolutely hates him. Every single aspect of what he does, what he is, and what he has, has a numeric value attached to it, which can be disproved.
a.Cookies. If the whole child population left out 3.5 cookies on average, santa would have 6.492 billion cookies to eat, if he ate one cookie every second, it would take him 205 years, 10 months, and 17 hours and 20 minutes to finish them all. Monday, November 11, 2217. Then the’d be another 1,337,352,000,000 cookies to finish off……42,407 years (or until Thursday, January 10, 44419 AD) then the cookies by then….will be finished off in 8.73 million years. Also, in a decade, he’d have enough to feed every child 2987 calories worth of food, or 21 pieces of toast.

So we can safely assume that santa is still finishing the cookies from WW1, the Black Death, and cave children (and if the dinosaurs were considerate, our prehistoric friends from 65 million years ago). My point is, considering santa is already overweight as depicted in cartoons, he would gain an extra 184,045 metric tonnes every single year. Since the average lifetime lasts 69.5 years, santa would have to consume a mountain of 451,200,000,000,000 cookies (23 times the number of blood cells in the human body) weighing twice the mass of the pyramid of giza.

So Santa is in need of major liposuction. Oh, and that reminds me. Each liposuction costs $7500 on average and removes 0.998 kilograms (2 pounds) of (cookie) fat, for santa to remove that, he’d need 14,100,000,000,000 trips to the cosmetic store and $105.8 quadrillion dollars (NZ). He must be two million times richer than Bill Gates, or 1 trillion, 58 billion times more than what the average US citizen gets to gob-smack over in a year.

He can’t possibly have worked for that, so it’s either he stole it all from the banks, landing him $1 quadrillion dollar fine, or that he found a huge goldmine in the North Pole, and transferred the 53.7 trillion troy ounces away.

Therefore, I can only conclude that Santa’s sleigh runs on collected cookies.

b.The milk. Lots of children growing up in wealthy countries, leave a glass of milk (187 grams) out for santa (which results in your parents drinking it). There are 1.887 billion children in the world, which gives you a figure of 359,900 metric tonnes of thick creamy milk. That’s the mass of a Ultra Heavy Large crude carrier oil supertanker. Or more than the weight of the Empire state building.

Lets figure out how much all of that would cost. There are 4.277 glasses of milk in a litre, so 438,859,013 litres of milk are given to santa, or a supermarket list worth $10 million. That’s significantly less than the cookies. Santa, all together has 12,827,027 tonnes of treats to consume. Or 222,220,000 calories. That’s the energy in a lightning bolt. If he decided to use the milk as thermonuclear power for his sleigh, he would earn himself a massive 32,346,100,050,713,600 joules. Yay! Nope, actually nothing to celebrate about because that’s equivalent to 7,731,000 tonnes of TNT. So, if you manage to survive a blast 515.4 times that of the Hiroshima bomb, you’ll know it’s all santa’s fault

c.The reindeer. No known species of reindeer can fly, although there are an estimated 300,000 species of living organisms left undiscovered. These compose mostly of sea sludge and glow in the dark fish. Although this does not rule out flying reindeer that only santa has seen, over 200 years, at least the Air Force should have discovered them. They’ll be pretty obvious. If each child gets a pack of presents weighing no more than 1.5 kg’s, santa’s reindeer will be sweating under a weight of 2.782 billion kilograms. If each reindeer can support no more than 50 kilograms, he’ll need a huge e-bay order of 55,640,000 reindeer.

If each reindeer gets a generous box of 20 carrots (as in the story books) , he’ll need a total of ONE BILLION CARROTS. And also, there’s a further problem. Naming them all.

d.Speed. Santa has a more than a night, 31 hours of present delivery time, thanks to our time zones. There are 91.8 million homes to visit, so there must be 0.78 miles per household. A total aerial tour of 75.5 million miles, and visit 822.6 houses a second. He’ll have to go at a breakneck speed of 2.435 million miles per hour. This means santa is travelling at 1080 kilometres a second, or 3173 times faster than the speed of sound.

Now we know that santa has to eat those cookies and milk far faster than once a second. He’ll gain 172.993 kilograms a second and 10,380 kilograms, or the weight of the average elephant in a minute.

A mass of 353,000 tons traveling at 650 miles per second creates enormous air resistance. This will heat the reindeer in the same manner as a spacecraft reentering the earth’s atmosphere. The lead pair of reindeer will absorb 14.3 QUINTILLION joules of energy. Per second. Each.
In short, they will burst into flame almost instantaneously, exposing the next pair of reindeer, and creating deafening sonic booms in their wake. The entire team will be vaporized within 4.26 thousands of a second.
Santa, meanwhile, will be subjected to centrifugal forces 17,500.06 times the force of gravity. A 300 pound Santa would be pinned to the back of his sleigh by 4,315,015 pounds of force.
And that concludes my entire answer. (I DID NOT MAKE THESE NUMBERS UP! CHECK THEM!)

Shutdown the internet?

I recently heard about the new (possibly stupid) copyright law that any website containing copyright material is liable to be shutdown. Yesterday, Wikipedia was shutdown. Next, Google, Facebook, Twitter, any website that contains anything that someone decides to claim a right for will be shutdown. Without any court trials. This law is ridiculous because the basis of communication is practically only copyright material. Something that someone else wrote. If it’s good, then eventually a lone comment or a post on a blog will eventually appear on dome random large, popular website, then that website will be liable to being shutdown if the person gives a no-no to that happening. I’d say that they should change it so internet giant, Google, dosen’t have a problem with it. Of course Google has copyright material, it’s a search engine, it’s meant to index web pages. Think about it. If the law applies to Google, 8.6 billion page views could be converted into nothing for a day. What’s so freaky about the law is that it applies to every website that appears on top 100 list of websites of popularity, this may be pretty depressing. But it dosen’t matter much, the websites will be shutdown not all at once (that would be a nightmare) but day by day. That’s significantly better. But if Google shuts down, where will we be? Google will lose a lot. It gets a net income of  US $28.5 million a day, and will lose around that much on average if no one sees it.

It is true, that while some laws have the ability to kick some of our favourite sites off the internet for 24 hours, it is also true that some governments can shutdown the internet for a period of 4 months, but fortunately not forever.

Meanwhile, don’t let this worry you, as it’s quite unlikely to happen. The bottom line is that powerful people and their internet laws can shut off parts of the internet, they probably can’t shut off the whole internet, unless we encounter a situation similar to WW2 where instead of the TV (which most people at that time loved just as much as we love the internet now) being cut off, the internet might be shut off. That’d be the end of the Information Age, and I’m hoping that won’t happen.

Students who logged onto Tumblr to update their blogs or turned to Wikipedia for a translation of their textbooks’ academic jargon Wednesday found themselves staring at a dark screen.

The two websites were among hundreds that went black for all or part of the day to protest two anti-piracy bills currently in Congress. Other sites included Reddit, WordPress and Mozilla.

If passed, the Stop Online Piracy Act proposed in the House and the Protect IP Act proposed in the Senate would increase anti-piracy control, limiting the spread and use of information online.

Weinberg senior Isabel Han said she had heard about the protests and planned blackout about a week ago but had not paid much attention until Wednesday.

“My first reaction was that sometimes a certain entity can be so powerful,” Han said. “I just suddenly realized that we rely on Wikipedia so much.”

By Wednesday evening, several senators who had co-sponsored the bills withdrew their support and suggested the legislation needed re-evaluation, according to the New York Times.

Medill Prof. Owen Youngman said the bills addressed the issue too broadly, and policy makers were unfamiliar with the consequences of the legislation.

“The goals of reducing the theft of intellectual property and reducing piracy of copyrighted material are laudable,” Youngman said. “But it seems unrealistic to think that punishing people who wittingly or unwittingly provide a link to places that might be hosting copyright material is going to get at the problem.”

Wikipedia eliminated access to articles published in English, although users could still access the mobile application through their smart phones. Google also showed support for the protests by “censoring” its logo and linking to articles detailing the anti-piracy legislation.

Several students said they had no prior knowledge the online protest was taking place, but supported the idea once they heard.

“I wasn’t aware of it, but I think if Google is doing it and big sites like that, that does raise awareness of what’s going on,” Weinberg juniorAja Ringenbach said.

Illinois Senator Mark Kirk also sent out a press statement Wednesday morning, stating that he is opposed to the bills.

Some students said they were unhappy with the idea of censorship of online media and were pleased to see Congress back down.

“Overall I support them, and I don’t think that Congress should have the power to censor any website that they deem has users that upload copyrighted content,” Weinberg senior Jared Cogan said. “I especially like that Wikipedia is doing it since they have no financial stake.”

Youngman said even though controlling the Internet is an ephemeral idea, the protests call attention to the need for balance in the way online content is controlled.

“The fears of the Internet community may be overblown, but they have a legitimate complaint about the chilling effect that (the acts’) adoption in their original form might have had,” he said.

Medill professor Rich Gordon, who teaches Journalism and the Networked World, said Congress has historically supported copyright holders, but protests by digital media companies have recently raised awareness of the need to balance the free flow of information and the protection of intellectual property rights.

“It’s a very challenging issue for most people to understand the impact of, and this is one good way to bring home what the potential impact of bad legislation could be,” he said.

Abstract. Realistic simulation of biological evolution by necessity re-
quires simpli cation and reduction in the dimensionality of the corre-
sponding dynamic system. Even when this is done, the dynamics remain
complex. We utilize a Stochastic Cellular Automata model to gain a
better understanding of the evolutionary dynamics involved in the ori-
gin of new species, speci cally focusing on rapid speciation in an island
metapopulation environment. The e ects of reproductive isolation, mu-
tation, migration, spatial structure, and extinction on the emergence of
new species are all studied numerically within this context.
1 Introduction
From the fossil records and radioactive dating we know that life has existed on
earth for more than 3 billion years [1]. Until the Cambrian explosion around 540
million years ago, life was restricted mainly to single-celled organisms. From the
Cambrian explosion onward however, there has been a steady increase in bio-
diversity, punctuated by a number of large extinction events. These extinction
events caused sharp but relatively brief dips in biodiversity and the fossil record
supports these claims. In our attempt to understand some of the dynamics in-
volved in this process, we decided to look at the speciation process and see if
we could model it in a way that would provide insight into some of the factors
which determine the dynamic behavior of what is an extremely complex process.
Speciation is the process by which new species are formed via evolutionary
dynamics. Speciation can be controlled (or driven) by a number of factors includ-
ing mutation, recombination and segregation, genetic drift, migration, natural
and sexual selection [1{3]. Throughout this paper we say that two populations
are of di erent species if they are reproductively isolated, i.e., no mating produc-
ing both viable and fertile o spring between the two populations occurs. That
is, we will use the biological species concept [1, 2]. In our model, we can identify
reproductively isolated populations by measuring the di erences in their genes;if their genes are suciently di erent, then there is a very low probability that
they can mate to produce viable and fertile o spring.
Speciation processes are dicult to verify via experiments or observations.
Primary of course is the fact that the time-scales involved in speciation typi-
cally are much longer than human life span. In addition, there does not exist a
continuous fossil record documenting new species, i.e., there are many gaps in
the fossil record. Moreover, existing data on genetic di erences between extant
species can be interpreted in a number of alternative ways.
We are thus led to di erent methods of investigating the speciation process by
using mathematical models. By necessity, models limit the number of parameters
associated with complex behavior. This implies that all factors may not be taken
into account in the simulation of complex processes. However, computer models
do provide a metaphor for the actual dynamics, assuming of course the model’s
algorithms accurately re
ect in some sense the actual dynamics being modeled,
i.e., the model is consistent.
Here, we describe a stochastic cellular automata explicit genetic model of
speciation in an island metapopulation. Typically, cellular automata used in
biological application are characterized by a rather small number of states: two
or, very rarely, three, usually focusing on whether a patch is occupied or not
[4{12]. However, even the simplest known biological organisms have hundreds
of genes and hundreds of thousands of DNA base pairs [1, 3]. This implies that
the number of possible genetic states for an organism is astronomically large.
For example, assuming that an organism has only 500 genes each coded by 1000
DNA base pairs, there can be potentially 4
500000  9:910
di erent genetic
states. This enormous dimensionality requires one to develop new methods of
modeling, analyzing, and visualizing the behavior of the corresponding cellular
automata. Below we describe some of the approaches that we have developed
within the context of studying speciation.
2 The CA Deme-Based Metapopulation Model
A common method for performing numerical studies of biological evolution and
speciation is to use an individual-based model in which a nite collection of
individuals are tracked through the birth-reproduction-death cycle as well as
the migration-mutation-survival cycle. Unfortunately, individual-based models
require an enormous amount of computational resources to obtain meaningful
results and are currently not practical for studying large-scale biological diversi -
cation. Here, instead of an individual-based model we build a deme-based model
[3, 13, 14] in which for each local population we explicitly describe only the ge-
netic state of its most common genotype. This simpli ed approach is justi ed if
mutation and migration are suciently rare and the local population size is suf-
ciently small so that only a negligible amount of genetic variation is maintained
within each local population most of the time. We will ignore the dynamics of lo-
cal population sizes. Following Hubbell [15], we disregard ecological di erencesbetween the species. Our main focus will be on genetic incompatibilities (i.e.
reproductive isolation) between di erent populations.
Reproductive isolation will be de ned by the threshold model [3, 13] in which
two genotypes are not reproductively isolated and, thus, belong to the same
species if they di er in less than Km genes. We will refer to parameter Km as
mating threshold. In some implementations of the model, we allow for multiple
populations per patch. A simple heuristic approach for doing this is to introduce
another threshold genetic distance, say Kc (> Km), reaching which will allow for
coexistence in a patch. We will refer to parameter Kc as coexistence threshold.
If the genetic divergence between two populations is below Kc, the competition
between them prevents their coexistence.
We consider here a large area divided into smaller connected areas called
patches. Each patch can be empty or occupied by one or more populations.
We model the habitat patches as nodes on a two dimensional grid. This is a
spatially explicit metapopulation model (which is often also called a lattice model
or stepping-stone model), in which migration is restricted to close or neighboring
Our metapopulation model simulates evolution of bit strings in a two dimen-
sional geometry. Each bit string can be considered to represent the DNA of a
population. The length L of this binary DNA string is speci ed as input. Note
that the number of possible genetic states is 2
. We then simulate metapopula-
tion dynamics within and between a given set of habitat niches (or patches).
What we are left with then after a time is a situation in which many geneti-
cally di erent populations exist in di erent habitat patches. Through a clustering
process, we can then determine which populations are close to each other ge-
netically by some measure. This process of grouping thus determines clusters of
similar populations, or species.
Our model dynamics occur on a time generation basis. For each generation we
determine stochastically whether each of the major events occurs in the following
1. Patch Extinction.
2. Single Population Extinction.
3. DNA Strand Mutation.
4. Population Migration.
Patch extinction is a situation where all populations in a speci c patch go
extinct. The exact details are not important, it could be due to depletion of a
viable food supply in the habitat patch or due to some catastrophic extinction
event which wipes out the populations such as a fatal disease epidemic.
Single population extinction can occur under similar circumstances, however
rather than the whole patch (which can include many populations) going extinct,
only a single population within the patch goes extinct.
Migration of individuals has two e ects. First, migrants can found a new pop-
ulation in a patch previously not occupied by a species. Second, migrants coming
into an occupied patch can bring genes that may spread in a local population
(see below).Bit strings change independently at each locus. The probability per genera-
tion that an allele at a locus changes to an alternative state is set to be
e =  + m N; (1)
where  is the probability of mutation per locus, m is the probability of migra-
tion, and N is the number of neighboring populations of the same species that
have the alternative allele xed at the locus under consideration. Expression (1)
utilizes the fact that the probability of xation of an allele that does not a ect
tness is equal to its frequency [16]. With migration, new alleles are brought in
the patch both by mutation (at rate ) and migration (at rate mN). In this ap-
proximation, the only role of migration is to bring in new alleles that are quickly
xed or lost by random genetic drift. For example, if initially both the focal
population and its four neighbors have allele 0 at the locus under consideration,
then the probability that an alternative allele 1 is xed in the focal population
per generation is e = . However, once this has happened, the probability of
focal population switching back to allele 0 is e =  + 4m. If the migration rate
m is much larger than the mutation rate per locus , switching back will hap-
pen much faster. As time increases, populations accumulate di erent mutations,
diverge genetically and become reproductively isolated species.
3 Model Implementation
There are two main computer programs utilized to implement our model of the
speciation process, Evolve and Cluster. As described above, Evolve simulates the
evolution of bit strings in a two dimensional grid based geometry undergoing
evolutionary dynamical processes. Cluster then determines which group of bit
strings or populations are within a speci ed Hamming distance of each other. The
clustering method is single linkage clustering [17] with an input parameter K.
In most cases, we set parameter K to the mating threshold Km. This procedure
produces clusters of mutually compatible populations (i.e. biological species).
Since the clustering process is hierarchical in nature, output from Cluster can
also be used to identify and group populations in a taxonomic manner, providing
insight into the hierarchical structure of the simulated populations. For example,
let us specify an increasing sequence of clustering thresholds K1 < K2 < K3 0 for all  > 0.
Essentially g()  1=.]
In addition, we can easily calculate how long it takes for speciation to occur,
how many species emerge, and what parameters a ect the rate of speciation and
species diversity.

df = 0

(a) t = 0


(b) t > 0
Fig. 1. The average pairwise distance d
and the average distance to the founder d
f at
two di erent time moments. The clades are represented by the spheres.0 5000 10000
Number of Species Time to Speciation (T)
Duration of Radiation ( )
Fig. 2. A typical speciation curve
Figure 2 illustrates a typical speciation curve (i.e., the number of species or
clusters vs. time). This gure also explains the meaning of two statistics: the
time to speciation T and the duration of radiation  . Note that the number of
species stays at 1 for a small amount of time, then rises relatively quickly to
reach a stochastic equilibrium level.
All data and results reported in this paper are based on multiple runs of the
same set of parameters, usually between 30 and 50 repeats.
Distance from the Founder, d
One quick check that our model is working well is based on analysis of how
certain dynamics match the theory. In [18, Eq. 4c], it was shown that the aver-
age Hamming distance from a single founding population changes according to

df (t) =
[1 exp(c()t)] (2)
where c() a function only of , the mutation rate. This is basically a solution
to a random walk problem on the binary hypercube. Our model showed that
the t to Equation 2 over hundreds of runs with varying parameter sets truly
is a function only of the mutation rate  and time. This perhaps is the single
best indication that our model is performing well with prediction and is inter-
nally consistent with the basic mathematical evolutionary theory concepts of
mutation, migration, and extinction.4 Parameter Studies
Since the Evolve-Cluster model seems to be modeling some aspects of the speci-
ation process well when compared with other models, it now remains to identify
other characteristics of our model. Speci cally we will be analyzing the e ect of
changing input parameters to rst see if the results make qualitative sense and
then use our model to uncover hidden trends and quantitative results.
Geometry Size, Mating Threshold, and Clustering Threshold
Figure 3 contains summary graphs of nine di erent parameter sets. The graphs
are ordered from top to bottom increasing in 2-D geometry size, 1010, 1414,
and 20  20. The graphs are ordered from left to right increasing in mating
threshold Km = 5; 10, and 15. Each graph is the summary of fty runs with
L = 256, m = 0:02 and  = 0:00004. On each graph there are ve curves. The
three speciation curves are for the di erent clustering thresholds K, while the
other two curves are the average pairwise distance (

d) between all populations
and the average distance from the founder (

df ) as a function of time.
In our model, extensive diversi cation occurs relatively fast. The graphs in
Figure 3 illustrate the fact that

d dominates initially, while

df eventually becomes
larger than 
d and stays that way. In addition, the asymptotics are consistent
with those discussed in the previous section. This trend can be understood by
considering the metaphor introduced above; the ball changes diameter quicker
than moving away from the origin initially, i.e., genetic changes go into producing
diversity at a rate quicker than moving the clade as a whole genetically away from
the founding population. After a short time, movement away from the founder
dominates while at the same time genetic diversity between the populations also
In our model, the probability of a genetic change e (see equation 1) depends
on the number of neighboring populations of the same species and, thus, on
mating threshold Km. With a higher Km, there are more neighbors of the same
species which e ectively reduces the rate of change and dampens

d expansion.
This is evidenced by the fact that the higher the mating threshold Km, the closer
the curves

d and 
df track each other. Since the number of loci L and mutation
probability  are the same in all of these cases, the d
f curve is the same in all
graphs as expected. It also appears that the larger the size of the system, the
greater the di erence between 
d and 
df , although the asymptotics still remain
the same as described above. This can be explained by the fact that with a larger

d increases unchecked by physical boundaries until boundary e ects
coupled with the nite number of loci L e ectively dampens

d expansion and
the asymptotics take over.
As the clustering threshold K increases, the number of species decreases.
This is as expected, since larger clusters (clusters containing more populations)
implies there are less clusters. It is also clear that the number of species increases
as geometry size increases. It appears here that boundary e ects do play a role
in speciation, e ectively suppressing the speciation process to some extent.0 5e+03 1e+04 1.5e+04 2e+04
0 5e+03 1e+04 1.5e+04 2e+04
0 5e+03 1e+04 1.5e+04 2e+04
@ K=5
@ K=10
@ K=15
0 5e+03 1e+04 1.5e+04 2e+04
0 5e+03 1e+04 1.5e+04 2e+04
0 5e+03 1e+04 1.5e+04 2e+04
@ K=10
@ K=20
@ K=30
0 5e+03 1e+04 1.5e+04 2e+04
0 5e+03 1e+04 1.5e+04 2e+04
0 5e+03 1e+04 1.5e+04 2e+04
@ K=15
@ K=30
@ K=45
Fig. 3. The e ects of geometry size, mating threshold Km, and clustering threshold K
on the number of species NS, the average pairwise distance

d, and the average distance
to the founder d
f as functions of time.
The time to speciation T increases as Km increases. This is due to the fact
that it takes longer to accumulate enough genetic di erences to separate popu-
lations into new species. The duration of radiation  increases as Km increases.
This is due to the observation that radiation still occurs, but is not as rapid as
at lower mating threshold values, more evidence of negative mutation pressure
applied by the higher mating threshold.
There are other observations which can be made from the graphs shown in
Figure 3, including
{ T increases as geometry size increases,
{  is approximately constant as geometry size increases,
{ The di erence between the number of species at di erent clustering levels
remains constant in time,{ The di erence between the number of species at di erent clustering levels is
approximately constant as mating threshold increases,
{ The di erence between the number of species at di erent clustering levels
increases as geometry size increases,
{  appears to be much less that T in all cases.
Migration and Patch Carrying Capacity
One of the parameter studies undertaken was to increase the carrying capacity
of each patch in the geometry so that multiple populations per patch could
exist at any time. With multiple populations allowed, the evolutionary dynamics
consist of a series of population splits followed by accumulation of additional
genetic di erences between emerging species which eventually allows for their
coexistence in the same patch (when genetic distance is > Kc), which in turn
leads to range expansions and increase in the number of populations per patch.
Figure 4 illustrates some results for a clustering threshold of K = 2 letting
migration rate m vary. Part (a) shows the number of species in the system which
we normalized by the patch carrying capacity (i.e., the number of populations per
patch). Note that increasing the patch carrying capacity increases the number
of species NS in the system disproportionately. NS is essentially constant with a
slight decreasing trend as m increases. Part (b) shows that the average pairwise
distance d
increases with the patch carrying capacity; d does not appear to de-
pend on the migration rate. Overall, allowing for multiple populations per patch
stimulates population expansion into multiple ecological habitat niches allowing
for rapid speciation to occur in parallel resulting in even more diversi cation, all
in approximately the same time frame.
1e-06 1e-05 0.0001 0.001 0.01
/1 pop @ K=2
/2 pops @ K=2
/4 pops @ K=2
/8 pops @ K=2
1e-06 1e-05 0.0001 0.001 0.01
1 pop
2 pop
4 pop
8 pops
Fig. 4. The e ects of migration rate m on the normalized number of species and on
the average pairwise distance

d in a model with 1, 2, 4 or 8 populations per patch.5 Conclusions
Our CA based metapopulation model allows us to investigate the dynamics of
genetic diversi cation in a large dimensional state space. The adaptive radiation
regime observed in the model is a rich source of data for helping one to better
understand the speciation process.
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