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In 1990 (*), on one of his now famous works, Christopher Langton (link) decided to ask an important question. In order for computation to emerge spontaneously and become an important factor in the dynamics of a system, the material substrate must support the primitive functions required for computation: the transmission, storage, and modification of information. He then asked: Under what conditions might we expect physical systems to support such computational primitives?
Naturally, the question is difficult to address directly. Instead, he decided to reformulate the question in the context of a class of formal abstractions of physical systems: cellular automata (CAs). First, he introduce cellular automata and a simple scheme for parametrising (lambda parameter, λ) the space of all possible CA rules. Then he applied this parametrisation scheme to the space of possible one-dimensional CAs in a qualitative survey of the different dynamical regimes existing in CA rule space and their relationship to one another.
By presenting a quantitative picture of these structural relationships, using data from an extensive survey of two-dimensional CAs, he finally review the observed relationships among dynamical regimes, discussing their implications for the more general question raised above. Langton found out that for a 2-state, 1-r neighbourhood, 1D cellular automata the optimal λ value is close to 0.5. For a 2-state, Moore neighbourhood, 2D cellular automata, like Conway’s Life, the λ value is then 0.273.
We then find that by selecting an appropriate parametrisation of the space of CAs, one observes a phase transition between highly ordered and highly disordered dynamics, analogous to the phase transition between the solid and fluid states of matter. Furthermore, Langton observed that CAs exhibiting the most complex behaviour – both qualitatively and quantitatively- are found generically in the vicinity of this phase transition. Most importantly, he observed that CAs in the transition region have the greatest potential for the support of information storage, transmission, and modification, and therefore for the emergence of computation. He concludes:
(…) These observations suggest that there is a fundamental connection between phase transitions and computation, leading to the following hypothesis concerning the emergence of computation in physical systems: Computation may emerge spontaneously and come to dominate the dynamics of physical systems when those systems are at or near a transition between their solid and fluid phases, especially in the vicinity of a second-order or “critical” transition. (…)
Moreover, we observe surprising similarities between the behaviours of computations and systems near phase transitions, finding analogs of computational complexity classes and the halting problem (Turing) within the phenomenology of phase transitions.
Langton, concludes that there is a fundamental connection between computation and phase transitions, especially second-order or “critical” transitions, discussing some of the implications for our understanding of nature if such a connection is borne out.
The full paper (*), Christopher G. Langton. “Computation at the edge of chaos”. Physica D, 42, 1990, is available online, here [PDF].
“I don’t do drugs. I am drugs” ~ Salvador Dalí.
The photo, which dates from 1969, depicts the 65-year-old Catalan surrealist Salvador Dalí emerging from a Paris subway station led by his trusty giant anteater. Surrealism‘s aim was to “resolve the previously contradictory conditions of dream and reality.” Artists painted unnerving, illogical scenes with photographic precision, created strange creatures from everyday objects and developed painting techniques that allowed the unconscious to express itself. [from Wikipedia, link above].
Nocturnal moth trails – Fluttering wings leave lacy trails as moths beat their way to a floodlight on a rural Ontario lawn. The midsummer night’s exposure, held for 20 seconds, captured some of the hundreds of insects engaged in a nocturnal swarm. [Photo: Steve Irvine, National Geographic, 2013, link]
Photo – Venation network of young Populus tremuloides (quaking aspen) leaf (4X). By Benjamin Blonder, David Elliott (2011), University of Arizona, Department of Ecology & Evolutionary Biology, Tucson, Arizona, USA (link).
(…) Pando (Latin for “I spread“), also known as The Trembling Giant, is a clonal colony of a single male Quaking Aspen (Populus tremuloides) determined to be a single living organism by identical genetic markers and one massive underground root system. The plant is estimated to weigh collectively 6,000,000 kg (6,600 short tons), making it the heaviest known organism. The root system of Pando, at an estimated 80,000 years old, is among the oldest known living organisms. (…) in, Pando (tree), Wikipedia [link].
“I met Death today. We are playing chess. (…) My life has been a futile pursuit, a wandering, a great deal of talk without meaning. I feel no bitterness or self-reproach because the lives of most people [plague] are very much like this. But I will use my reprieve for one meaningful deed.”, Antonius Block.
The Seventh Seal (Det sjunde inseglet). Written and directed by Ingmar Bergman, Sweden 1957 – (…) Disillusioned knight Antonius Block and his squire Jöns return after fighting in the Crusades and find Sweden being ravaged by the plague. On the beach immediately after their arrival, Block encounters Death, personified as a pale, black-cowled figure resembling a monk. Block, in the middle of a chess game he has been playing alone, challenges Death to a chess match, believing that he can forestall his demise as long as the game continues. Death agrees, and they start a new game. The other characters in the story do not see Death, and when the chess board comes out at various times in the story, they believe Block is continuing his habit of playing alone. (…)
(…) In the confessional, the knight says “I use a combination of the bishop and the knight which he hasn’t yet discovered. In the next move I’ll shatter one of his flanks.” Death (in disguise as the priest) replies “I’ll remember that.” When they play by the beach, the knight says: “Because I revealed my tactics to you I’m in retreat. It’s your move.” Death captures his opponent’s knight. “You did the right thing“, states the knight, “you fell right in the trap. Check! Don’t worry about my laughter, save your king instead.” Death‘s response is to lean over the chess board and make a psychological move. “Are you going to escort the juggler and his wife through the forest? Those whose names are Jof and Mia and who have a small son.” “Why do you ask?” says the knight. “Oh, no reason”, replies Death“. (…) from Wikipedia [link] (Nota bene – bolds and underlines are mine).
[…] In conclusion, much elegant work has been done starting from activated mono-nucleotides. However, the prebiotic synthesis of a specific macromolecular sequence does not seem to be at hand, giving us the same problem we have with polypeptide sequences. Since there is no ascertained prebiotic pathway to their synthesis, it may be useful to try to conceive some working hypothesis. In order to do that, I would first like to consider a preliminary question about the proteins we have on our Earth: “Why these proteins … and not other ones?”. Discussing this question can in fact give us some clue as to how orderly sequences might have originated. […] A grain of sand in the Sahara – This is indeed a central question in our world of proteins. How have they been selected out? There is a well-known arithmetic at the basis of this question, (see for example De Duve, 2002) which says that for a polypeptide chain with 100 residues, 20^100 different chains are in principle possible: a number so large that it does not convey any physical meaning. In order to grasp it somewhat, consider that the proteins existing on our planet are of the order of a few thousand billions, let us say around 10^13 (and with all isomers and mutations we may arrive at a few orders of magnitude more). This sounds like a large number. However, the ratio between the possible (say 20^100) and the actual chains (say 10^15) corresponds approximately to the ratio between the radius of the universe and the radius of a hydrogen atom! Or, to use another analogy, nearer to our experience, a ratio many orders of magnitude greater than the ratio between all the grains of sand in the vast Sahara and a single grain. The space outside “our atom”, or our grain of sand, is the space of the “never-born proteins”, the proteins that are not with us – either because they didn’t have the chance to be formed, or because they “came” and were then obliterated. This arithmetic, although trivial, bears an important message: in order to reproduce our proteins we would have to hit the target of that particular grain of sand in the whole Sahara. Christian De Duve, in order to avoid this “sequence paradox” (De Duve, 2002), assumes that all started with short polypeptides – and this is in fact reasonable. However, the theoretically possible total number of long chains does not change if you start with short peptides instead of amino acids. The only way to limit the final number of possible chains would be to assume, for example, that peptide synthesis started only under a particular set of conditions of composition and concentration, thus bringing contingency into the picture. As a corollary, then, this set of proteins born as a product of contingency would have been the one that happened to start life. Probably there is no way of eliminating contingency from the aetiology of our set of proteins. […]
Figure – The ratio between the theoretical number of possible proteins and their actual number is many orders of magnitude greater than the ratio between all sand of the vast Sahara and a single grain of sand (caption on page 69).
[…] The other objection to the numerical meaning suggested by Figure (above) is that the maximum number of proteins is much smaller because a great number of chain configurations are prohibited for energetic reasons. This is reasonable. Let us then assume that 99.9999% of theoretically possible protein chains cannot exist because of energy reasons. This would leave only one protein out of one million, reducing the number of never-born proteins from, say, 10^60 to 10^54. Not a big deal. Of course one could also assume that the total number of energetically allowed proteins is extremely small, no larger than, say, 10^10. This cannot be excluded a priori, but is tantamount to saying that there is something very special about “our” proteins, namely that they are energetically special. Whether or not this is so can be checked experimentally as will be seen later in a research project aimed at this target. The assumption that “our” proteins have something special from the energetic point of view, would correspond to a strict deterministic view that claims that the pathway leading to our proteins was determined, that there was no other possible route. Someone adhering strictly to a biochemical anthropic principle might even say that these proteins are the way they are in order to allow life and the development of mankind on Earth. The contingency view would recite instead the following: if our proteins or nucleic acids have no special properties from the point of view of thermodynamics, then run the tape again and a different “grain of sand” might be produced – one that perhaps would not have supported life. Some may say at this point that proteins derive in any case from nucleic-acid templates – perhaps through a primitive genetic code. However, this is really no argument – it merely shifts the problem of the etiology of peptide chains to etiology of oligonucleotide chains, all arithmetic problems remaining more or less the same. […] pp. 68-70, in Pier Luigi Luisi, “The Emergence of Life: From Chemical Origins to Synthetic Biology“, Cambridge University Press, US, 2006.
Darwin by Peter Greenaway (1993) – Although British director Peter Greenaway is best known for feature films like The Cook, the Thief, His Wife and Her Lover, Prospero’s Books, and The Pillow Book, he has also completed several highly respected projects for television, including this 53-minute exploration (now free) of the life and work of Charles Darwin. Darwin is structured around 18 separate tableaux, each focusing on another chapter in the naturalist’s life, and each consisting of just one long uninterrupted shot. Other than the narrator’s voice-over, there is no dialogue.
Figure – Poker final hand rankings. Poker is a typical example of bounded rationality in our daily lives. Without having all the information available, you still have to make a decision. In one of his works, Herbert Simon states: “boundedly rational agents experience limits in formulating and solving complex problems and in processing (receiving, storing, retrieving, transmitting) information“.
[…] Bounded rationality is the idea that in decision making, rationality of individuals is limited by the information they have, the cognitive limitations of their minds, and the finite amount of time they have to make decisions. It was proposed by Herbert Simon as an alternative basis for the mathematical modelling of decision making, as used in economics and related disciplines; it complements rationality as optimization, which views decision making as a fully rational process of finding an optimal choice given the information available. Another way to look at bounded rationality is that, because decision-makers lack the ability and resources to arrive at the optimal solution, they instead apply their rationality only after having greatly simplified the choices available. Thus the decision-maker is a satisfier, one seeking a satisfactory solution rather than the optimal one. Simon used the analogy of a pair of scissors, where one blade is the “cognitive limitations” of actual humans and the other the “structures of the environment”; minds with limited cognitive resources can thus be successful by exploiting pre-existing structure and regularity in the environment. Some models of human behaviour in the social sciences assume that humans can be reasonably approximated or described as “rational” entities (see for example rational choice theory). Many economics models assume that people are on average rational, and can in large enough quantities be approximated to act according to their preferences. The concept of bounded rationality revises this assumption to account for the fact that perfectly rational decisions are often not feasible in practice due to the finite computational resources available for making them. […] In Wikipedia, (link).
Book cover – Herbert A. Simon. Models of Bounded Rationality, Volume 1, Economic Analysis and Public Policy, MIT Press 1984. The Nobel Prize in Economics was awarded to Herbert Simon in 1978. At Carnegie-Mellon University he holds the title of Professor of Computer Science and Psychology. These two facts together delineate the range and uniqueness of his contributions in creating meaningful interactions among fields that developed in isolation but that are all concerned with human decision-making and problem-solving processes. In particular, Simon has brought the insights of decision theory, organization theory (especially as it applies to the business firm), behavior modeling, cognitive psychology, and the study of artificial intelligence to bear on economic questions. This has led not only to new conceptual dimensions for theoretical constructions, but also to a new humanizing realism in economics, a way of taking into account and dealing with human behavior and interactions that lie at the root of all economic activity. The sixty papers and essays contained in these two volumes are grouped under eight sections, each with a brief introductory essay. These are: Some Questions of Public Policy, Dynamic Programming Under Uncertainty; Technological Change; The Structure of Economic Systems; The Business Firm as an Organization; The Economics of Information Processing; Economics and Psychology; and Substantive and Procedural Reality. Most of Simon’s papers on classical and neoclassical economic theory are contained in volume one. The second volume collects his papers on behavioral theory, with some overlap between the two volumes. (from MIT).
Drawing (Pedigree of Man, 1879) – Ernst Haeckel‘s “tree of life”, Darwin‘s metaphorical description of the pattern of universal common descent made literal by his greatest popularizer in the German scientific world. This is the English version of Ernst Haeckel‘s tree from the The Evolution of Man (published 1879), one of several depictions of a tree of life by Haeckel. “Man” is at the crown of the tree; for Haeckel, as for many early evolutionists, humans were considered the pinnacle of evolution.
“…living matter, while not eluding the “laws of physics” as established up to date, is likely to involve “other laws of physics” hitherto unknown, which however, once they have been revealed, will form just as integral a part of science as the former.“, Erwin Schrödinger (1944), Chapter VI, .
[…] The structure of DNA and the genetic code may have alluded us for some time more if Crick had not read Erwin Schrödinger‘s What Is Life? [1,2]. The research lead that Crick got by doing so was how a small set of repeating elements could give rise to a large number of combinatorial products, a mathematical relationship that Schrödinger illustrated using the Morse Code, based on an idea that he had actually got from the visionary work of Max Delbrück. Delbrück, Schrödinger and Crick were physicists with an enthusiasm for tackling the unknown for the natural world. Crick‘s own motivation came directly from reading What Is Life? . It seemed reasonable to make the cross-over as the infant field of biochemistry was bound to be governed by the same chemical and physical laws revealed in other, non-biological, disciplines. This was especially true given the progressive focus of biology on the increasingly small, until an effective convergence of scales in the studies of the biologically relevant on the biologically irrelevant. Hence the justification for Schrödinger‘s unspecific book title. Although some of the notions in the book have been superseded by modern science, this remains a classic, written with great insight and modesty (Schrödinger downplays his potential as a biologist), and is worth the read if only as a portal in to the minds of those luminary workers. By the time Watson and Crick were piecing together the jigsaw that would lead to their grand discovery, the far-reaching potential of Schrödinger‘s code script had been aligned with Chargaff‘s finding of a variable sequence of nucleotide bases, and the stage was set for that immortal terminal sentence, “It has not escaped our notice that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material.” […], Derry, J. F. (2004). Review of What Is Life? By Erwin Schrödinger. Human Nature Review. 4: 124-125.
“This is the first time any synthetic DNA has been in complete control of a cell“, Craig Venter, May 2010 (video below).
“In 1953, when the structure of DNA was determined, there were 53 kilobytes of high-speed electronic storage on planet earth. Two entirely separate forms of code were set on a collision course. Primitive as it may be, we now have one of the long-awaited results.”, George Dyson, May 2010.
On April 9, 2010, 41 days ago, Science Journal receives a manuscript for revision signed by no least than 24 scientists. Then, 7 days ago it was accepted for publication. It was released today, May 20, 2010. And what we are now assisting today, is no less than a pivotal moment in Human history, in fact, a turning-point for the entire planet and it’s life. Entitled “Creation of a Bacterial Cell controlled by a Chemical Synthesized Genome” , the paper describes how these 24 scientists have succeeded in developing the first synthetic living cell. Being the ability to design and create new forms of life so extraordinary, that a truly scientific landmark was indeed today realized. That’s -indeed- one small step for synthetic biology, one giant leap for mankind.
The new cell, is in some-ways a code within a code. As science historian George Dyson points out, “from the point of view of technology, a code generated within a digital computer is now self-replicating as the genome of a line of living cells. From the point of view of biology, a code generated by a living organism has been translated into a digital representation for replication, editing, and transmission to other cells.”
First step was to previously made a synthetic bacterial genome, and transplanted the genome of one bacterium into another. Then, both methods were put together in order to create the present synthetic cell, even if only its genome is truly synthetic. By sequencing its genetic code and then using synthesis machines to chemically construct a copy, a different organism could then be form, taking the synthetic chromosome, and transplant it into a recipient cell. As Venter and his team point out, “As soon as this new software goes into the cell, that cell reads that software and converts the new cell into the species specified in that genetic code.”
We code it, and the new cell reads it. It’s anyhow of full interest to follow with caution their final words on the paper  (the entire work could be accessed here from where both pictures were depicted):
[…] If the methods described here can be generalized, design, synthesis, assembly, and transplantation of synthetic chromosomes will no longer be a barrier to the progress of synthetic biology. We expect that the cost of DNA synthesis will follow what has happened with DNA sequencing and continue to exponentially decrease. Lower synthesis costs combined with automation will enable broad applications for synthetic genomics. We have been driving the ethical discussion concerning synthetic life from the earliest stages of this work. Assynthetic genomic applications expand, we anticipate that this work will continue to raise philosophical issues that have broad societal and ethical implications. We encourage the continued discourse . […]
Watermarked on the new synthetic cell DNA (embedded) there is a quote from Richard Feynman: “What I can not build I can not understand“. No matter what, from this point on, we should really re-question what Life is?
TED in the field video – Craig Venter unveils synthetic life, May 2010.
Ref. notes:  Erwin Schrödinger (1944), “What Is Life?” Cambridge: Cambridge University Press, (novel edition 2002). |  Francis Crick (1989) What Mad Pursuit. Penguin. |  James Watson (1981) The Double Helix. Weidenfeld and Nicholson. |  Daniel G. Gibson, John I. Glass, … Craig Venter et al., (2010), “Creation of a Bacterial Cell Controlled by a Chemically Synthesized Genome“, Science Journal, released and visited on-line on May 20, 2010.
“There’s Plenty of Room at the Bottom“, ~ Richard Feynman (referring to NanoTechnology).
There are huge life scales in our world with which we are not acquainted to. While some prefer to wonder about “alien life” on movie theaters simultaneously eating popcorn, right here at Planet Earth, some lakes and rivers are full of them. What you see above is a tiny water flea ‘Crown Thorns‘ photographed by zoologist Jan Michels (Christian Albrecht University in Kiel, Germany). It was nominated as the best microscopic life image of 2009, last week, at BioScapes (short for Biological landscapes – a competition sponsored by Olympus in order to recognize microscope photos of plants, animals, and other life-forms that capture the “fascinating minutia of life”).
The snaking ridge at top left took top honors in the 2009 BioScapes microscope imaging contest. If water flea parents sense that their habitat is shared by their main predators, tadpole shrimp, the flea offspring sport these pointy crowns – which are unappetizing to the shrimp. Jan Michels, added a dye to reveal the tiny animal’s exoskeleton (green) and cellular nuclei (blue smudges). The blue-and-red dots are one of the animal’s compound eyes, like those of a fly.
This image, kind of remembers me of another one I used in the past for a series of Artificial Intelligence conferences I have held in the past, during 2004 (Budapest, Hungary), 2005 (Muroran, Japan) and 2006 (Jinan, China) (SIP workshop series – Swarm Intelligence and Patterns). This image below was used as the conference symbol; a termite head scanned trough SEM (Scanning Electron Microscope) taken by University of Toronto, Canada.
But probably one of the images I most love at this nano-scale is one of a red ant grabbing a tiny electronic circuit board (microchip) on his mouth (Science Museum, UK). Reason is simple. This image (below) could have several readings. By using SEM, image is formed by focusing an electron beam onto the sample surface. As the beam scans across the surface the sample emits secondary electrons which are then detected and used to modulate the image signal much like a television. More electrons is translated into a brighter image. As the beam scans the surface each point is mapped out just as the electron beam in a television maps the image onto the screen. Here we are able to see all the details of one of natures smallest denizens holding one of mankind’s smallest creations, a silicon microchip (the building blocks of digital electronics).
What’s funny is that ant colonies are known (among many other interesting features) for their remarkable cemetery organization capabilities, that is, their sequential clustering task of corpses and objects (as this microchip below). Ant colonies do show that the coordination and regulation of building activities do not depend on the workers themselves but are mainly achieved by the nest structure: a stimulating object configuration triggers the response of a termite worker, transforming the configuration into another configuration that may trigger in turn another (possibly different) action performed by the same termite or any other worker in the colony.
Ants do all this by simple manipulating objects using stigmergic capabilities. Ants form piles of items such as dead bodies (corpses), larvae, or grains of sand. Initially, they deposit items at random locations. When other ants perceive deposited items, they are stimulated to deposit items next to them, being this type of cemetery clustering action, organization, and brood sorting a type of self-organization and adaptive behavior. Some bio-inspired branches of computer science use this kind of behaviors to solve highly complex problems, such as Data Mining, Data analysis and classification, Data clustering, Image retrieval, among many others.
Indeed, life on its own is the ultimate science-fiction. And, as Richard Feynman mentioned once, there is plenty of room below!
Fig. – Knight, Death and the Devil (1513). This is one of three metal engravings by Albrecht Dürer in a series called Meisterstiche (since I have started this blog, I have also chosen a woodcut engraving done by Dürer, – his Rhinoceros – for several reasons, one being that it appeared in Europe for the fisrt time trough Lisbon in 1515). The others are Melancholia I and Saint Jerome in His Study. The engraving is dated 1513, two hundred years after the dissolution of the Knights Templar in 1313. We see a skull in the bottom left corner; the night in full armour (shining armor?) carries a lance; behing him is a pig-snouted horned devil and he is passing Death on his pale horse, who is carrying an hourglass. Under the knight’s horse runs a long-haired retriever, a hunting dog. Dürer called this picture Reuter, which is, Rider. (source).
“Every evil leaves a sorrow in the memory, until the supreme evil, death,
wipes out all memories together with all life“. Leonardo da Vinci.
Carlos Gershenson (Complexes blog), some days ago just uploaded a short (5 pp.) philosophical essay about life, death and artificial life (*) (aLife), which I vividly recommend. He starts his “What Does Artificial Life Tell Us About Death?” with this precise Leonardo’s quote (above). Among other passages it’s interesting to see how different notions of death are deduced from a limited set of different notions of life (in many situations, opposing terms could be used to define each other). Carlos points us out to six currents, or lines of thought:
• If we consider life as self-production (Varela et al., 1974; Maturana and Varela, 1980, 1987; Luisi, 1998), then death will the the loss of that self-production ability.
• If we consider life as what is common to all living beings (De Duve, 2003, p. 8), then death implies the termination of that commonality, distinguishing it from other living beings.
• If we consider life as computation (Hopfield, 1994), then death will be the end (halting?) of that computing process.
• If we consider life as supple adaptation (Bedau, 1998), death implies the loss of that adaptation.
• If we consider life as a self-reproducing system capable of at least one thermodynamic work cycle (Kauffman, 2000, p. 4), death will occur when the system will be unable to perform thermodynamic work.
• If we consider life as information (a system) that produces more of its own information than that produced by its environment (Gershenson, 2007), then death will occur when the environment will produce more information than that produced by the system.
I was aware of Kauffman’s “blender thought experiment”, however Gershenson adds much more into it. A variation. He goes on like this. Nice reading:
[…] Focussing on our understanding of death, this will depend necessarily on our understanding of life, and vice versa. Throughout history there have been several explanations to both life and death, and it seems unfeasible that a consensus will be reached. Thus, we are faced with multiple notions of life, which imply different notions of death. However, generally speaking, if we describe life as a process, death can be understood as the irreversible termination of that process. The general notion of life as a process or organization (Langton, 1989; Sterelny and Griffiths, 1999; Korzeniewski, 2001) has expelled vitalism from scientific worldviews. Moreover, there are advantages in describing living systems from a functional perspective, e.g. it makes the notion of life independent of its implementation. This is crucial for artificial life. Also, we know that there is a constant flow of matter and energy in living systems, i.e. their physical components can change while the identity of the organism is preserved. In this respect, one can make a variation of Kauffman’s “blender thought experiment” (Kauffman, 2000): if you put a macroscopic living system in a blender and press “on”, after some seconds you will have the same molecules that the living system had. However, the organization of the living system is destroyed in the blending. Thus, life is an organizational aspect of living systems, not so much a physical aspect. Death occurs when this organization is lost. […]
(*) even if, I do not recommend this Wikipedia entry. Extremely poor.
… meanwhile, … at the bottom of our food chains everything seems to be blooming. Here recently over the Atlantic within Spain (south) and France (east) at the bay of Biscay. Plankton (phyto-plankton and zoo-plankton) are some of the most important living things on planet Earth. Phyto-plankton absorb carbon dioxide and release oxygen and are at the foundation of the food chain, followed by zoo-plankton. Plankton blooms are so massive that they are visible from space (image from NASA’s Visible Earth).