The Distribution of Intellectual Property Claims on the Human Genome. Source Data: Jensen and Murray (2005) Intellectual Property Landscape of the Human Genome. Science 310:239.
Click on the image for a Processing animation of patent locations.
Approximately one quarter of human genes are protected by intellectual property regulations. Little information about the number and distribution of gene patents is available in a manner empowering to members of the public. Existing gene patent resources rely almost exclusively on verbal search strategies for access in contrast to visual interfaces that promote exploration and discovery. This can be traced to the relative immateriality of genes which cannot be seen and whose effects are experienced through a web of medical, environmental, and social constructors.
One solution to this problem is to create a visual map of patent claims in the human genome. By representing the location, number, functional, and patent characteristics of genes, such a map could provide immediate visual access and cues for further investigation. Maps are created through the contributions of multiple constituencies and exist as objects for discussion, reflection, and mediation. Using patent data from the human genome developed by Jensen and Murray (Science 310: (2005) p239-240), we have started this project as a series of creative sketches. CAMBIA continues to update these data in accordance with current information.
Genes involved in human health, disease, and drug discovery tend to be heavily patented. A map would provide reasonably accessible information to non-specialists and help to scaffold conversations surrounding these issues. It is helps to document regions of positive selection, where specific genes are being disproportionately valued, by social and technological actors operating on human and non-human life processes.
This is short presentation I gave to the Melton Foundation’s Symposium on Innovation which was held in Bangalore in August, 2009. I spoke on Innovation in Education, coming from the perspective of someone with the aim of bridging disciplines and interpretations.
This post consists of some notes that looking at the analogy of natural & artificial selection to design and its consequences. A worthwhile paper on a related but different topic is Christina Cogdell’s Products or Bodies? Streamline Design and Eugenics as Applied Biology (2003) Design Issues, 19(1), 36-53. doi:10.1162/074793603762667683
Types of Selection
The purpose of this page is to describe how natural selection can be used as a framing tool for recognizing how artifacts, services, and interventions can affect individuals and natural populations of humans and other species. The point is not to draw a direct analogy, but to try to link the effects of the things we make to the behaviors, growth, and flourishing of living things. These are not so much set rues as they are a set of guides that can help us reconsider the expected effects of changing our environment in order to evaluate the risk and alternative future possibilities involved in the production of technology from the most precursory to the most complex.
I was intrigued after a reading group discussion we had about anthropometrics. Wikipedia defines anthropometrics as the measurement of human to gather statistical data about the distribution of body dimensions in the population are used to optimize products. I would alter this definition slightly to say design products rather than optimize. Humans change and so do products.
We were a little unsettled by the focus only on human needs and the intent that anthropometry be entirely in support of comfort and ease of use. Taking a more critical approach, we started to brainstorm all of the different ways that design structures human and non-human behavior. We started to keep an eye open for ways that design and evolution can begin to interact. We hit some dead ends so I reached out.
I asked a group of colleagues if they knew of any comprehensive taxonomy of selection, and here is what one of them (Joel) contributed:
There are so many different ways to split selection up that it can be mind-boggling. To make it worse, those who study molecular evolution use different terms (positive, balancing) than those of us who study phenotypic selection. I don’t think there’s a way to taxonomize the terms satisfactorily, at least in a tree. It would probably look more like a convoluted Venn diagram.
That said, Joel laid out four areas that can be used to focus our attention. I’ve modified them from his interpretation, but they are basically agents, episodes, modes, and scales.
Here is how he originally wrote about it in his response to me:
In the phenotypic selection realm, I tend to split selection up in four different ways, based on agents, levels, fitness components, and mode.
The agent of selection, that is, the factor that causes fitness differences to arise, can be either ecological (phyisical or heterospecific) or conspecific. I would call the former ecological selection and the latter social selection (sensu West-Eberhard). The latter would tend to subsume sexual selection, which tends to be caused by male-male or male-female interactions. Includes frequency-dependent and density-dependent or other x-dependent.
The level of selection describes the units that exhibit fitness differences (which, annoyingly, Gould call “agents” of selection). This can be individual selection, and at higher levels, family selection, group selection, kin selection, social selection (sensu Wolf, Moore, and Brodie), etc. Hard and soft selection can fall under this category as well–Wade and Goodnight have good papers discussing this.
Third, selection can be split into different “episodes” by splitting total fitness into multiple components. This is usually done because it is empirically convenient, or to examine evolutionary trade-offs. This gives rise to terms like survival selection, fecundity selection, and sexual selection.
Finally, you can describe selection based on the shape of the fitness surface, i.e. the “mode.” This includes directional (linear), stabilizing, disruptive, and correlational (all three quadratic). Of course, the shape of the fitness surface is often complex, and you can have elements of all of these going on at once when you’re considering multiple traits.
Reframing Selection
We might think about what Joel said differently and transform it as the grammatical structure of a sentence. Where:
AGENT = SUBJECT
SELECTION =VERB
EPISODE = DIRECT OBJECT
MODE might be akin to diagramming the entire sentence. while SCALE is more like the context that the sentence takes place within (e.g. the paragraph or passage).
Agents
Agent refer to the most causal explanation for the response to selection. Agents provide the mechanistic explanation and frequently are the antagonists to the entity/entities experiencing the effects of selection.
From a designer’s perspective, these agents should be the artifacts or services we create either with the intent to exert some selective force or ameliorate it.
We can understand these as ecological agents that affect anything from the climate of our surroundings, our food supply, the structure or our living and working spaces, interactions with outer species (as in pets, disease, or domesticated laborers), and even perhaps to our conventional definitions of time that enable further articulations of the environment.
Similarly social agents work along the lines of our own perception, learned, and innate behaviors to enumerate male-male, male-female, family, and cooperative interactions. Sensation and display are extremely important because they distinguish among individuals to allow decisions about how to interact. Social agents range from clothing, jewelery and other status symbols to weapons, traditions, and business plans as agents of cooperation or competition.
There is a nice hybrid space too where ecological and social meet in the production of artifacts favoring or disfavoring reproduction–in vitro fertilization on one hand…and condoms on the other, for example.
Often, agent-based selection is described as selecting for trait ‘x’ and can even be more complicated when traits x, y, and z covary as a result of this selection. As a consequence we find that selection can be multi-facited and not reducible to a single interaction. Hence, we need to reconsider the cumulative effects of each agent’s contribution.
Episodic
Moving along the causal chain (if we can indeed identify it), we would then want to understand the factors or physical attributes that are on the receiving end of the agents’ work. From an empirical perspective, this is often where conflict begins and fluctuates in an ever-present set of trade-offs. We can split the effects into many different components looking at reproduction, lifespan, health, outlook, social status, niche, range, communicativeness, and, perhaps most importantly, agency (as the ability of an individual to act as its own agent).
This is the main point of interest in design–i.e. what, where, when do the effects of the design work manifest in nature?
Mode and Variance
In order to understand what patterns are present, evolutionary biologists look at variation and the response of a particular trait or episode to selection from agents. Here attention is focused on the values of the entire population in contrast to just the trait itself. We can certainly use these visualizations and modes to describe the distributions of episodic traits, but here there is explicit quantitative emphasis on the response to selection over one or more generations.
We can think about it in different ways: populational and interactional or hard and soft.
Populational
Populational patterns include the ideal types of directional (linear), stabilizing, disruptive, and correlational (all three quadratic), and null (no selection). The shape of the fitness surface is often complex, and you can have elements of all of these going on at once when you’re considering multiple traits.
This graph depicts four abstract types of natural selection. The colors are used only to differentiate between types. The axes show the proportion of individuals in the population as a function of their trait values through time.
The graph below is composed of four of these types where the axes show the proportion of the population as a function of trait values through time. The shaded areas represent the part of those populations that is being selected for. The color simply differentiates between types. Correlational selection is not shown because it consists of the interaction of multiple traits in response to selection, and we would need a 3-dimensional graph to show just two of those traits changing.
From the graph you can see the response of a population to null selection. Because mutation-based variation is not selected out of the population, the shape of the distribution randomly changes and will not fit a “standard” distribution.
The blue, directionally-selected distributions move (you guessed it) directionally because of pressure against one end of the distribution.
Likewise, stabilizing selection in green is like directional, but instead of one end the pressures are exerted to stabilize the mean.
For diversifying (aka disruptive) selection in red, the mean is selected against–leaving greater proportions near the previous tails of the distribution.
Interactional
The second way is what I call interactional, meaning it depends on the interactions among agents, often in space. Here, ecological agents and social agents exert their effects. The goal of this description is to capture the meaning behind the mechanism (thus, interaction) rather than the change over time. When coupled with population visualization techniques, one begins to get a dynamic picture of evolutionary change.
We may be able to consider correlational selection as a special case of interactional (and not populational) because the internal constraints within a population’s gene pool and genomic regulation are effectively a suite of internal genetic interactors at a different scale.
Normally however, we can think of interactional patterns in terms of frequency-dependence, density-dependence, or some other x-dependent factor related to ecological or social agents. So frequency-dependence, for example, is just a way of describing the total effect of agents…or of saying that the trait in question responds in a way that is frequency-dependent.
The main difference is that interactional describes the mechanism of selection itself (within a generation), while populational describes the response to selection (change between generations).
Putting the two together means we could graph dynamic change.
Hard
Both hard selection and soft selection are relative to the population as a whole. Hard selection is like a bat chasing an insect. The insect has some maximum speed that it can flee and the bat has some speed that it can chase. Assuming it is only the bat and the insect, then there is hard selection for the speed at which the insect can flee.
Soft
Of course insects are probably not alone since they tend to aggregate in large populations. Soft selection takes this into account and considers the effect of more than one insect fleeing. Here the insect needs not be faster than the bat, just faster than the other insects that the bat is following!
The major difference is one of absolute value or of percentage. Hard selection works on the absolute value of a trait while soft selection works on a percentage of the distribution of trait values.
Scale
The scope of impact of a particular service of artifact is also important, especially when we ask the question, “for whom?” Is it working on a emergent trait or even creating one? Examples might include political systems or policies that increase or decrease emmigration, the locating of a hazard that increases mutation rates, or one child policy.
Whereas before we were only considering a single ideal population, what happens when we include multiple populations? Does the work of the designer or design team affect traits that span across individuals and include qualities that can only be formed from collective-action?
Some levels of scale might include: individual, family, kin, group, social, community, or ecological.
If you are willing to accept some non-human based research in pursuit of the human, you might find these helpful. They have a pretty heavy biological and philosophical bent to them. Some like Gablik, Miller, Darwin, and Dissanyake provide pretty sweeping theories for the evolution of art and design (with varying levels of detail and different forms of evidence). Others deal mainly with theory and
research derived from observation of non-human animals. Still others use social science and humanities based approaches to the question (Loos/Danto (with Gablik replying in ‘progress in art’), Bergson, Luhmann). Also see Bobbi S. Low’s cite for what may be the only scientifically testable prediction in the bunch.
Donath, J.S. Signals, Truth and Design. MIT Press, Cambridge, MA, forthcoming.
Burke, E. 1757. A philosophical enquiry into the origin of our ideas of the sublime and beautiful. R. and J. Dodsley, London.
Endler, J. A. 1992. Signals, signal conditions and the direction of evolution. American Naturalist 139:S125-S153.
Gablik, S. 1976. Progress in Art. Rizzoli International Publications, Inc., New York.
Kirkpatrick, M. 1982. Sexual selection and the evolution of female preference. Evolution 36:1-12.
Miller, G. F. 2001. Aesthetic fitness: How sexual selection shaped artistic virtuosity as a fitness indicator and aesthetic preferences as mate choice criteria. Bulletin of Psychology and the Arts 2:20-25.
Ryan, M. J. 1990. Sensory systems, sexual selection, and sensory exploitation. Oxford Surveys of Evolutionary Biology 7:157-195.
Scheib, J. E., S. W. Gangestad, and R. Thornhill. 1999. Facial attractiveness, symmetry, and cues of good genes. Proceedings of the Royal Society of London B 226:1318-1321.
West-Eberhard, M. J. 1979. Sexual selection, social competition and evolution. Proceedings of the American Philosophical Society 123:222-234.
Loos, A., & Opel, A. (1997). Ornament and Crime: Selected Essays. Ariadne Press (CA).
Ellen Dissanayake, Art and Intimacy: How the Arts Began (Seattle: University of Washington Press, 2000),
Christy, J. H., and P. R. Y. Backwell. 1995. The Sensory Exploitation Hypothesis. Trends in Ecology & Evolution 10:417-417.
Laland, K. N. 1992. A Theoretical Investigation of the Role of Social Transmission in Evolution. Ethology and Sociobiology 13:87-113.
Miller, G. 2000. The Mating Mind: How Sexual Choice Shaped the Evolution of Human Nature. Doubleday, New York.
Nettle, D. and H. Clegg. Schizotypy, creativity and mating success in humans. Proc. R. Soc. B (2006) 273, 611–615
Kavolis, V. Community Dynamics and Artistic Creativity. American Sociological Review, Vol. 31, No. 2. (Apr., 1966), pp. 208-217.
Luhmann, N. Art as a social system. Stanford University Press. Stanford, Calif. 2000.
Network Theory—the Emergence of the Creative Enterprise. Albert-László Barabási. Science 29 April 2005:Vol. 308. no. 5722, pp. 639 – 641
Low, Bobbi S. 1979. Sexual selection and human ornamentation. In Chagnon, Napoleon A., and William Irons, eds., 462-87. – describes a test of sexual selection for art as the comparison of stable versus unstable symbolic systems
Danto, A. C. 1986. The Philosophical Disenfranchisement of Art. Columbia University Press, New York.
Darwin, C. 1871. The descent of man, and selection in relation to sex. John Murray, London.
Endler, J. A., and A. L. Basolo. 1998. Sensory Ecology, Receiver Biases, and Sexual Selection. Trends in Ecology & Evolution 13:415-420.
Lenski, R. 1999. A Distinction Between the Origin and Maintenance of Sex. Journal of Evolutionary Biology 12:1034-1036. -distinguishes between the orgin and maintenance of sexual reproduction
Turney J. (2004). THE ABSTRACT SUBLIME: Life as information waiting to be rewritten. Science as Culture, 13, 89-103. Retrieved July 17, 2008, from
Dissanayake, E.: What Is Art For? Seattle, University of Washington
Press (1988)
Dissanayake, E.: Homo Aestheticus : Where Art Comes from and Why. 1st University of Washington Press ed. Seattle, University of Washington Press (1995)
Healy, S., & Braithwaite, V. (2000). Cognitive ecology: a field of substance? Trends in Ecology & Evolution, 15(1), 22-26.
Bergson, H. (2005). Creative Evolution. Cosimo Classics.
Ryan, M. J., Phelps, S. M., & R, A. S. (2001). How evolutionary history shapes recognition mechanisms. Trends in Cognitive Sciences, 5(4), 143-148. Retrieved July 17, 2008, from http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VH9-42PC695-G&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=7e622bb148b27c4cbf3d290d0a790563
Arak, A., & Enquist, M. (1995). Conflict, Receiver Bias and the Evolution of Signal Form. Philosophical Transactions: Biological Sciences, 349(1330), 337-344.
Endler, J. A., & Basolo, A. L. (1998). Sensory ecology, receiver biases and sexual selection. Trends in Ecology & Evolution, 13(10), 415-420.
Jansson, L., & Enquist, M. (2003). Receiver bias for colourful signals. Animal Behaviour, 66(5), 965-971. Retrieved July 17, 2008, from http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6W9W-49J8TBN-J&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=d7fd52368874c927aac68023a8029efd
Scourfield, J., N. Martin, G. Lewis, and P. McGuffin. 1999. Heritability of social cognitive skills in children and adolescents. Br J Psychiatry 175:559-564.
The word mitochondrion comes from the Greek μίτος or mitos, meaning thread and χονδρίον or chondrion, meaning granule (thanks! wikipedia). But this isn’t about the mitochondrion itself. Rather, this is a story about how the genetic information that helps mitochondria reproduce and silk threads are rewoven together.
What is a mitochondrion? It’s an organelle (kind of like an organ in your body) for a cell. They generate much of the chemical energy used by a cell to carry out its different processes.
I have been working on a project for the last few months that extends work on what I call Silking Systems. By calling it Silking Systems, I’m trying to emphasize the patterning of silk and textile production as a set of relationships, things and interactions to accomplish varieties of silk/non-silk relationships, rather than as modes of behavior or production which are static – or should I say pre-threaded?
In 2008, some of my students researched How Silk is Made (after How Stuff is Made) for my class on Design for Sustainability. Their work documents the collection and processing of the silk fiber from cocoons to the thread you find in finished textiles.
Steps to a square cocoon.
About a year later, I worked with students at CEMA to develop square cocoon. Yes, a square cocoon. However, we also succeeded in learning a lot about sericulture – the raising of silk moths and worms – for silk cocoons which are then turned into thread. You can see some of process for making a square cocoon – as well as a lot of other aspects of silk production – in this flickr set documenting some of our work on Silking Systems.
In attempting to learn about sericulture from scratch, I visited some local producers in Karnataka, India and pulled in some textual research and advice – including Joseph Needham’s classic series on Science and Technology in China (1998 ed).
The most recent concept that I want to document here is pretty simple. Human mitochondrial genome sequences are woven in sequence using silk to produce a pattern that matches the mitochondrial nucleotide patterns.
Ashwathnarayann
Before I go further, I should acknowledge the assistance of Ashwathnarayan who aided me tremendously is becoming knowledgeable about silk production and weaving. He also did all of the weaving by hand with some help from me in reading the sequence. Nonetheless it was a true collaboration throughout. David Matthew was also instrumental in helping to build some of the loom pieces as well as providing emergency translation from Kannada to English when my conversations with Ashwathnarayan became difficult or too complex. At the beginning too was Millie who accompanied us to a silk production house in Vijayapura, Karnataka – just north of Bangalore. Millie did some great translation acrobatics using her English and knowledge of Tamil to translate for me and to speak with Ashwathnarayan – who in turn was speaking with the silk producers in Kannada.
Checking the loom's warp.
I have a few implicit goals and a few explicit ones as well. An implicit one is that I am attempting to push the relationship between craft, production, economic agency, and hybridity. I am drawing to some extent from the idea that economic value is generated through recombination – that goods and/or services emerge and create value when they are mixtures of other (especially unrelated) things.
Eric Beinhocker details this concept of value through hybrids along with an evolutionary algorithmic perspective on economics in his book The Origin of Wealth (2006). The book was recommended to me by Cesar Hildago, a Research Fellow at Harvard University’s Center for International Development. Cesar’s work on complex networks has also influenced this project, starting with his article on the Product Space of Nations (2007) and continuing with images like figures 1 and 2 which came out of his research. The network graphs make it easy to see how different economies differ in the products they export.
Fig 1. This image maps the products produced by the United States in 2000. The squares are things they are good at – in the US's case vehicles, chemicals, forest products, for example.
Fig 2. This image maps the products produced by India in 2000. The squares are things they are good at – in India's case textiles, chemicals, and diamonds, for example.
My thinking is that by challenging some aspects of the status quo in silk and textile production, new value propositions might be found. This comes, perhaps, by demonstrating that square cocoons are possible or by remixing molecular genetics and weaving to create a series of silk stoles based on a mitochondrial haplotype found frequently in southern India.
Another goal is to simply visualize the mitochondrial genome – and to make it as accessible for teaching and learning as possible. Making it tactile and making it in silk allows people to touch, feel, and to see individual sequence variation. Silk thread is a good scale for this sort of thing – not too small and not too big either. So in viewing these stoles (which measure about 5 meters each in length) one is challenged to look for patterns and they are rewarded with the same.
The mitochondrial sequence used to produce the pattern next to shuttles that carry the silk thread through the warp.
The process is pretty simple. I started with the stored Genbank sequence of the M2 haplotype which is traceable to early settlers of India. I took the nucleotide sequence information (atctcgctagatagacat, etc) and printed it out in BIG type so that we could follow the pattern easily. By assigning a color to each base type, patterns will reveal themselves. For our first prototype, I chose yellow, blue, green, and red. These are used commonly in genomic sequencing and prediction software (at the University of Michigan, for example) and I wanted to start with something that would resonate with biologists and would also suggest a playfulness associated with childhood and formative development.
Checking and threading the warp. You can see the silk fibers and how thin a single one is. It takes years to master silk weaving because it is a very delicate and dexterity-rich process.
Weaving the pattern is excruciatingly slow. In fact, this kind of work goes against a lot of how silk waving is organized from a production standpoint. There are no repeated patterns and each thread is individually sequenced – that’s the point! We accepted that we might introduce our own errors into the fabric, but then that fits well with the concept; as we try to speed up we might lose fidelity with the original sequence. There are a handful of good correspondences between the weaving process and DNA replication, and they are themselves teachable moments for students that encounter the project. It also gets them thinking critically about what correspondences do or do not exist, as a way of developing their own comprehension.
Finished pattern stretched on the loom.
I’ll expand this article as the project develops further, but I’ll end now with one nagging curiosity. The pattern that is being produced is engaging and pleasing. It makes me wonder if it in some ways exploits a bias we humans may have towards certain arrangements. Specifically I’m thinking about pink noise patterns…but I need to search more.
References
Needham, J., & Kuhn, D. (1988). Science and civilisation in China: spinning and reeling. Vol. 5. Chemistry and chemical technology. Pt. 9. Textile technology. Cambridge University Press.
Beinhocker, E. D. (2006). The origin of wealth: evolution, complexity, and the radical remaking of economics. Harvard Business Press.
Hidalgo, C. A., Klinger, B., Barabasi, A., & Hausmann, R. (2007). The Product Space Conditions the Development of Nations. Science, 317(5837), 482-487. doi:10.1126/science.1144581
Philip Zimbardo conveys how our individual perspectives of time affect our work, health and well-being. Time influences who we are as a person, how we view relationships and how we act in the world. Via RSA
Disaggregation among natural and social scientific communities can lead to misunderstandings about the different components of disaster management and socio-ecological systems. Terms like resilient, adaptive, robust are often used to describe systems and their processes and come up in the literature, policy, and the media very frequently. They have catch my attention because they have different use patterns in the field I know a little about: biology.
Adaptation, coping, resilience, and robustness have similar definitions, but they sometimes have different technical definitions across disciplines. Their different meanings contribute to their value, and they highlight the differences in perspectives that each scientific community contributes. However, the details matter for distinguishing important components of systems and what aspects might be suggestive for new insights or that might be responsive to intervention or assessment. It’s also important to establish common ground meanings when communities get together and work towards common goals.
The following represents some of my notes and thinking as I try to sort out the definitions on my own. For me, it means asking how different perspectives contribute to the ways in which we interact in socio-ecological systems.
Adaptation
The Intergovernmental Panel on Climate Change (IPCC) 4th Assessment Report defines adaptation as:
Initiatives and measures to reduce the vulnerability of natural and human systems against actual or expected climate change effects. Various types of adaptation exist, e.g. anticipatory and reactive, private and public, and autonomous and planned. Examples are raising river or coastal dikes, the substitution of more temperature-shock resistant plants for sensitive ones, etc.
This definition takes its function from the ability of humans to manipulate their environment, making it better suited to human-identified goals and interests, even if acting on behalf of other organisms. Some synonyms include alteration, modification, redesign, remodeling, revamping, reworking, reconstruction, conversion, adjustment, acclimatization, acclimation, accommodations, habituation, acculturation, assimilation, and integration.
Adaptation is also used to describe genetically-accumulated evolutionary change over time in organisms as a response to natural selection. This is different from the case where manipulating the environment substitutes in the short-term replaces the pressure of genetic adaptation over the long term.
So I suppose this is why it calls to mind a version of evolution based on characters acquired in its lifetime (commonly known as Lamarckian inheritance)–if only for the appropriation of the term adaptation to refer to intra (within) generational processes and not inter (between) generational processes.
Adaptation for evolutionary biologists typically means processes through which a population becomes better suited to its environment over the course of many generations, often through natural selection. A great deal of debate and research has been directed at how we recognize adaptation in hindsight. This is because it can be difficult to state the causes for the evolution of a trait when we do not have direct observation and only historical signatures to learn from. Most notably this was discussed in “The Spandrels of San Marco”, a paper by Stephen Gould and Richard Lewontin (1979) that uses an analogy from architecture for the evolution of organismal form and function.
I agree that changing the environment in the ways mentioned in the IPCC definition will likely limit vulnerabilities for humans and other populations. However, there is an implicit assumption here that the goal should be for humans NOT to have to adapt over a course of generations–despite the inevitability of genetic change over time. It presupposes an assumption of stasis – and a very western one when compared to eastern notions of change and mutability. Richard Nisbett catalogues how some of these assumptions about change and stasis in his book The Geography of Thought. For me, it depends on what time scale one is looking to understand if stasis or change is more relevant. Still, I think its difficult to argue anymore that stasis is more relevant than change.
The necessary question should not be IF we should adapt (genetically or by manipulating the environment). Instead we should ask, “What are we adapting to and how are we getting there?” Will humans and other populations be adapting to artificially-supported ‘vulnerability balloons’ as we are almost surely doing now through our uses of technology and fossil fuels?
This question of adaptive goal is important because the IPCC definitions include definitions of costs and benefits with its description of adaptation. To what goal are these costs and benefits applied? Within the frame of a generation or an organism’s lifetime, explicating goals may make sense, but ascribing goals to a ecosystem – much less whole populations – gets very very slippery. You start to need some way to implicate who or what is writing that mission statement.
Similarly the IPCC includes adaptive capacity in its glossary as the ability, institutions, and resources that can be used to implement adaptation measures.
I think this is all a bit confusing, and I feel it makes more sense to reserve the definition of adaptation for genetic, phenotypic, and behavioral attenuation of organisms or systems to their environment across generations. To describe the processes that organisms and systems use during their lifetimes I think we need a term that encompasses more variability, one that is less blatantly anthropocentric and functionalist in its approach to socio-ecological coevolution. We also need a long view on systems not ones that are limited to single generations only – something that the biological definition of adaptation retains but that the socio-technical one does not.
Borrowing from the literature of evolutionary biology, behavior, and developmental biology, plasticity seems far better suited to the processes of environmental manipulation being described by the IPCC. This is because it references a material (plastic) that maintains its basic molecular structure while having variable capacity to take on any number of manipulations or forms.
Coping and Plasticity
The terms coping and adaptation are sometimes used interchangeably leading to confusion. Here I think there is some opportunity to disentangle the two. A compilation of brainstorming sessions by groups of development practitioners in Ghana, Niger and Nepal described some differences which were then documented in the Climate Vulnerability and Capacity Analysis Handbook. The results of the group’s sessions were pointing to what I think was a difference between 1) consistent and conscious actions to reduce vulnerability (adaptation) versus 2) ad hoc solutions (coping).
It’s worthwhile to differentiate coping and adaptation as within and between generation processes, respectively. Biologists use plasticity to describe the ability of an organism or group to adjust within its lifetime via behavioral or developmental responses to the environment. This may indeed include manipulation of the environment to decrease vulnerability. Phenotypic plasticity is a description that could easily encompass artifacts, behaviors, institutions, and aggregations of resources as extensions of an organism’s phenotype. It invokes important concepts from evolutionary biology including the role of cooperation in building and maintaining extended phenotypes (such as aggregations of useful materials like insurance, band-aids, and water) or how phenotypic reaction norms can change in response to different environments–shedding light on why a strategy in one environment may not be as successful in another. There is further correspondence here with plasticity and the concept of developmental canalization (that organismal systems can get locked in to specific trajectories) and with the concept of path dependence in the development of economic and institutional systems.
So a better definition of plasticity might re-appropriate the IPCC’s definition of adaptation and rework it as:
An adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects, which moderates harm or exploits beneficial opportunities. Plasticity operates through cognitive (sensing), social (interactional), physiological, and other mechanisms that can adjust to a wide range of variability. Plasticity is the ability to respond to variability and a range of realized and possible futures continuously and in a sustained approach. Plasticity or coping strategies attenuate the use of resources to local needs and involve planning that hybridizes old and new knowledge and strategies in an exploratory process.
Here I think this definition makes it much easier to bridge what may be happening at a physiological level (cellular temperature variation, sweating) with responses at an artifact level (clothing, ventilation) and an institutional (e.g. policies towards what it means to be cool).
This is because the term plasticity explicitly invokes a connotation of variability, while adaptation feels more like a description of how well two things (in this case organism or population and environment) fit together. Clearly, if the environment is highly variable we need variability in our systems, not assumptions and values of how well we already fit and work within it.
Coping, on the other hand, seems pretty straightforward. Survive. It makes sense to leave a lot of variability open for this one, because when it comes time for coping strategies, any and all tactics may be appropriate. But then again, there can be ways to cope that are more responsive than others. But I think this starts to dig into a definition of resilience or robustness, where the system properties begin to matter more than than how they manifest themselves in practice. What I mean by this is that as people, organisms, and ecosystems attempt to cope with change, their ability to draw on networks or strategies for coping is itself embedded in the system. Some systems, as a function of their structure, cope better than others. Consequently the adapt better than other too.
Resilience
The Climate Vulnerability and Capacity Analysis Handbook adapts its definition from UNISDR (2009) defining resilience as “the ability of a system to resist, absorb, and recover from the effects of hazards in a timely and efficient manner, preserving or restoring its essential basic structures, functions, and identity.”
The IPCC defines resilience as “the ability of a social or ecological system to absorb disturbances while retaining the same basic structure and ways of functioning, the capacity for self-organisation, and the capacity to adapt to stress and change.”
While Walker et al (2004) define resilience as “the capacity of a system to absorb disturbance and reorganize while undergoing change so as to still retain essentially the same function, structure, identity, and feedbacks.”
In these cases resilience emphasizes a system’s ability to maintain or return to specific structural or functional features–i.e. to maintain its identity, its durability, its persistence. But as noted by Erica Jen in her article “Stable or Robust? What’s the Difference?” (2005), the choices of features or structural elements that we attend to are important for assessing both the capacity and quality of that responsiveness to change.
So what is the function, what is functional, and for whom? Definitions matter.
One way to think about resilience is to imagine a couple of different water balloons. One balloon is filled halfway full. Another is filled so that the latex rubber that composes its surface and membrane is stretched tightly to hold the water in. Now you can throw both balloons back and forth between each other, and neither may pop. But what do you think will happen when the balloons are stretched, twisted, or allowed to drop on the ground where a twig might be a hazard to the already tense surface of the overfilled balloon? It will probably pop and spill the water out.
A system’s resilience is a lot like a water balloon, and the degree of resilience is determined by how much water is forced into the balloon, the size of the balloon, and how much it is pushed to its limits. We might think of the balloons shape, its ‘throwability’ or the thickness of its membrane as examples of functional or structural elements. In most cases, we are looking at how well the balloon is able to maintain it shape and its continuity despite being stressed – i.e. it is functionally a ‘water balloon’, it has a round shape, and responds to the exterior and interior pressures of air and water.
Rarely do we think that a water balloon might reconfigure itself, rearranging the organization of its functions, structural elements, or features to be able to accomplish the same task differently. What would happen if the water and the balloon separated or if the water balloon system was able to draw on other systems (e.g. refrigeration) to change the relationships between its functional elements? What if we no longer simply considered only the water inside of the balloon as the system responding to the task of throwing? What if the throwing and catching movements were also included? Would we still think of a resilient system, or would we start to walk a path of robustness–of being able to adjust the definitions and constraints of the systems themselves in pursuit of coevolutionary relationships between them?
Robustness
Robustness is a different beast altogether – literally. While resilience is focused on maintaining a system, we can describe robustness as the ability of a system to change and in doing so to respond to environment and to develop entirely new functions as a result.
Some argue that robustness describes the ability of a system to withstand mutations and maintain its phenotype or “shape” as a result (Wagner, 2005). Instead I think there is a greater correspondence of robustness with transformation as used by Walker et al (2004). Transformability is “the capacity to create a fundamentally new system when ecological, economic, or social (including political) conditions make the existing system untenable.” I’m less sure about the “untenable” part of Walker et al’s definition.
Robustness is the ability of a system to evolve system functions, not simply maintain those that already exist. In this way, an analogy can be drawn between adaptation/robustness and plasticity/resilience. Similarly, I think robustness has a quality of being parametric. Parametric architecture has the quality of being built from common construction principles, but by varying the parameter values of those rules of construction, endless forms become possible.
References
Walker, B., C. S. Holling, S. R. Carpenter, and A. Kinzig. 2004. Resilience, adaptability and transformability in social–ecological systems. Ecology and Society 9(2): 5. [online] URL: http://www.ecologyandsociety.org/vol9/iss2/art5
UNISDR, 2009. Terminology: Basic terms of disaster risk reduction and IISD et al, 2007. Community-based Risk Screening – Adaptation and Livelihoods (CRiSTAL) User’s Manual, Version 3.0.
Climate Vulnerability and Capacity Analysis Handbook
IPCC, 2007: Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Annex I., M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge, UK, 976pp.
Stephen Jay Gould and Richard C. Lewontin. “The Spandrels of San Marco and the Panglossian Paradigm: A Critique of the Adaptationist Programme” Proc. Roy. Soc. London B 205 (1979) pp. 581-598
Wagner, Andreas. 2005. Robustness and Evolvability in Living Systems (Princeton Studies in Complexity). Princeton University Press.
Nisbett, R. E. (2004). The Geography of Thought: How Asians and Westerners Think Differently…and Why. Simon and Schuster.
