In the 1930s, evolutionary geneticist Sewall Wright pulled together research strands in the biology of inbreeding, the genetics of coat color in guinea pigs, statistical methods (including path analysis), and mathematics that codified the changes in gene frequencies in populations as a result of natural selection, mutation, and migration.
His resulting description of these threads set the stage for qualitatively different perspective on the evolutionary process. Wright described his perspective as a “shifting balance” model of evolutionary change, and it highlighted the role of small populations in the transitions between periods of high and low fitness. This pattern, which followed from his use of the term “drift”, describes the fluctuations of gene frequencies that result from the random sampling of small populations. This random sampling comes from mating in small populations that, because of chance, produces small deviations from the numbers of genes originally represented in the population.
Wright’s Shifting Balance perspective coincided with his introduction of the adaptive landscape as a term to describe the space in which random fluctuations of gene frequencies in small populations could push the populations away from adaptive peaks or periods in which they were reproductively successful, and which would in turn allow natural selection to push them towards new adaptive peaks – areas of differential reproductive success.
Though Wright’s perspective on evolution is controversial (in a generative way), the perspectives and tools that emerged from his ideas have endured. For example, Wright’s work preceded algorithmic approaches to optimization problems in mathematics, networks (traveling salesman), metallurgy (simulated annealing), and artificial intelligence – to name a few
The process of Shifting Balance is described as a series of three dynamic phases:
Phase 1, the exploratory phase, the action of small groups explores new combinations. Most stay on the suboptimal fitness peak (reasonably successful), but some get caught in adaptive valleys (unsuccessful).
In Phase 2, selection causes the groups that are in the adaptive valleys to move toward new, higher-fitness peaks.
Finally, in phase 3, groups at higher fitness peaks send off migrants helping other groups move to higher fitness peaks.
Phase 1: The Exploratory Phase
Phase 2: The Selection Phase
Phase 3: The Migration Phase
While Wright’s process was intended for population genetic systems, an increasing convergence between social processes, cognitive psychology, technology, ecology, and creative practice suggests that the concepts apply well to the exploratory, form-finding processes that precede the design and production of materials and services. The implementation of the Shifting Balance process as a analog for social and creative strategy is useful for the production of highly original and robust creative solutions – or, at least it’s a testable hypothesis.
For some, analogies between biological and social processes are difficult to comprehend. However, the design of services and interactions is dependent on the ordering and reordering of processes, materials, people, and ideas. Combinations and recombinations of these things, when developed thoroughly and communicated, can impact the delivery and relational aspects of individuals working in cooperation or separately.
We could envision this process as a sort of charette (period of intense design in collaborative groups) activity where:
The exploratory phase initiates adaptive schema (creative combinations) which are driven by the interactions, specializations, and diverse perspectives of small groups;
Intergroup selection resulting from evaluation, the inherent heterogeneity among groups, and intended service platforms begins the iterative process of amplification of good combinations;
Export and translation of valuable forms/schema to other groups in order to test them against different problems, social contexts for cooperation, and consumptive patterns.
The immediate benefit of this strategy is the demonstration of expertise in practice, the role of discourse, and the chance events that can drive innovation. Participants from different disciplines will have to opportunity to observe and engage in creative problem solving within highly diverse communities. Here the focus is on collaborative ideation followed by problem-solving across disciplinary and expertise-based boundaries and ultimately an exercise in cooperative translation, storytelling, and communication.
There is enough social scientific research to at least point to the benefit of diverse groups, although it would be worthwhile to have a better handle on an ideal number – i.e. what counts as a small population. Plus, how do we go about choosing? What is the process of selection…or should we instead be saying, “What is the process of attachment?” And finally, are there specific patterns of translation or dissemination that we should aim for? For if migrants endowed with the most successful schema do disperse and link up with others, they have an opportunity to cooperate and raise the capacity the other groups elsewhere. But through which mechanisms to we initiate and implement these processes?
There are a few other ideas that seem uniquely coupled to the Phases of Shifting Balance. An example is the goal of participation as a unique form of empowerment in community planning exercises. One particular model of participatory engagement provided by Conde et al. (2004) is used in the context of climate change planning (below).
The Landscape of Participation
This example shows transitional categories in participation. When viewed through a model of culture which emphasizes process over characteristics, these are skills acquisition categories that indicate differences with an impact on fitness – i.e. reproductive success.
Each category represents a different level of engagement, a level that itself suggests a tighter relationship between participants and the tools of participation or cooperation.
Informative participation is an exchange of information, which may or may not be meaningful.
Consultation requires that participants begin asking questions as well as providing information.
Functional engagement means that different participants identify and agree to share goals, thus ordering their actions in accordance with each other.
Interaction means the initiation of feedback, where signals and shifts in the participation is met with responsiveness and dialog with the others.
Self-motivated participation is demonstrated by the points at which processes are acquired and reorganized by the participants themselves.
Migration ultimately expands the instances of participation which have been successful, sharing them with other communities, and finding cooperative allies elsewhere.
References:
Conde, C., Lonsdale, K., Nyong, A., & Aguilar, I. (2004). Engaging stakeholders in the adaptation process. Adaptation policy frameworks for climate change: Developing strategies, policies and measures, 47–66.
Wright, S. (1977) Evolution and the Genetics of Populations. Vol. 3: Experimental Results and Evolutionary Deductions. University of Chicago Press, Chicago.
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.
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
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.
A comparison of interaction records in two group of hens. This figure illustrates the comparison feature of the music notation program showing the interaction records in two groups of hens interleaved in two-hour blocks.
Ivan Chase demonstrates a compelling use of musical notation for visualizing social interactions and (conceivably) networks using musical notation. Chase suggests that:
music notation graphs can be of particular help in a variety of fields interested in social interaction in humans, animals, and machines such as behavioural ecology, behavioural economics, social organization in animals, development of social networks in humans, human conversational analysis, and the coordination of actions in social robots.
Here is a sketch I made showing the locations and extent of intellectual property claims on 22 chromosomes and the X and Y. These data are from 2005. The extent is larger today.
I’m reading a book entitled, When Species Meet, by Donna Haraway. She’s one of my favorite authors, not only because of her subject matter, the relationships between ourselves and other organisms, science, and the stories we use to create meaning for how we act in the world, but because her literary style mixes the meanings of words and maintains her constantly questioning presence in the text.
Potamopyrgus antipodarum under the dissecting scope
In the third chapter of the book, she handles suffering, particularly of organisms in highly-constructed laboratory settings, with great care. By pointing out that we are always linked to killing in one form or another, the questions she raises is not if we do it at all, but rather how we approach, encounter, and leave those organisms that we are inextricably bound to.
My favorite passage from that third chapter is the one in which she asks some of her colleagues in the biological sciences how they demonstrate concern for the organisms in the lab as part of their practice. This is a question very close to home for me because it describes so much about my own motivations for doing science in the lab, how ‘reliable’ data are produced, and what kinds of practices can result.
I’m reminded of that famous quote from Barbara McClintock, also the title of Evelyn Fox Keller’s book, that emphasizes how “Getting a Feeling for the Organism” inserts itself so profoundly into daily scientific practice. This is empathy, yes, but the question Haraway asks is how we learn to recognize and therefore intervene in existing situations to show concern and enact strategies for care.
I think back to my own experiences in the lab, or rather, a temperature-controlled cool room. Others had brought snails back from a mountainous lake region in the southern hemisphere, and I was responsible for their care. These snails happened to be an invasive species in the U.S., requiring an extra level of containment to keep them, their offspring, and the parasites out of the regional ecosystem. My relationship with them meant creating the best possible environment for their growth and reproduction. They were, in effect, prisoners (although escape did have a potentially huge payoff). My role in their care meant feeding, finding and installing balanced spectrum lighting to mimic the ambient wavelengths, bringing in local plants to help filter the water in a huge freshwater ecosystem, making sure the water kept moving, installing irrigation systems to distribute a constant flow across many individual containers, adding sterilized rocks to the containers to allow for micronutrients, bacteria and other microorganisms, and even keeping fish and crayfish in the main tank to help condition and scavenge the water. For me, all of these technologies were about care. For one thing we couldn’t maintain the relationship these snails had with their parasites in the lab because we thought they just weren’t being taken care of well enough. There was this very important relationship, then, between how we cared for these snails and how and what kind of data we could collect about their own tight relationship with the parasites they came with.
For design, I’m thinking of how we script care. How can it be made obligatory as part of the function of a service, object, or process? How is it that we find connections and feel compelled to spend our time and energies attempting to make an environment or artifact more comfortable for another? How are we able to recognize what matters in this equation, especially when there are so many possibilities to misinterpret or just plain get it wrong. I suppose we look for signs of health, reproduction, and activity as indicators that we are on the right track. In doing so we create synergies between ourselves and others. By designing for their comfort, we link our vigor and theirs.
This is actually a really old post from when I was doing my master’s work in host-parasite biology. Nonetheless, it turns out that I’m revisiting it in preparation for an upcoming project.
Behavioral differences between the sexes may explain sexually dimorphic patterns of infection. The risk of infection may be one such factor that an analysis of movement paths can predict. For example, if males spent more time than females foraging for food and, as a result, passively ingest more parasites while doing so, then their risk for infection would generally be greater than females. The tortuosity (or crookedness) of movement paths between the sexes were compared to see if any differences in movement (e.g. foraging) could suggest an explanation for male-biased infection. These differences may suggest that males and females experience their environment at different scales.
Image Analysis
The first thing that needs to be done is to plot the movement of the snails. This can be done by hand, but time-lapse digital photography can help to automate the process. The easiest way to do this was to set up a tripod with the camera pointed down. A white container was used to hold the snails and create the highest contrast background for the photography. Pay attention to the reflection of your light source on the surface between the subject and camera (in this case, water and plastic container). A picture was taken approximately every minute, and to make things simple for the analysis program, I used only two snails per trial- one female and one male. Once I had a stack of pictures (over the course of an hour or two), I loaded them into the image analysis program.
ImageJ is the java implementation of an image analysis program developed by the National Institutes of Health. ImageJ allows you to track the movements of individuals on the screen and outputs a list of XY coordinates for each subject. The first thing that had to be done though was to import the images as a greyscale stack. Once that was done, I cropped out the uninteresting parts of the frame to show only the subject of interest. Further processing was needed to create a binary (black/white) image source for the analysis. Using Process>Subtract Background, I created more contrast with the subject and background. Finally, using the Process>Binary>Threshold, I was able to make the stack be completely composed of black and white images with no greytones inbetween. This is crucial if the analysis algorithm is going to separate the subject from the background. Some parameters may need adjusting for optimal results, but it usually works without too much toying. The final step in ImageJ is to apply the Plugin “Tracker”. This plugin tracks the subject(s) on the screen and outputs a datafile with the coordinates of the movement path. These can then be saved into a text file for later use. I used only two individuals per trial because Tracker is limited to only two subjects. A plugin called MultiTracker is available, but I found it difficult to keep it focused on both individuals. When individuals overlap in space MultiTracker assigns both sets of coordinates to a single individual.
Movie 1. Male and female movement played back after image processing and before tracking analysis.
Measuring the Fractal Dimension of the Paths
I found a great program for measuring the fractal dimension (D) of the snail movement paths. This measurement is thought to measure the scale at which an organism percieves its landscape. Differences in D for different populations would suggest that the populations utilize their landscape differently- perhaps as a result of their perception. The program for measuring D is called Fractal (Nams 2003), and it allows you to import the XY coordinates (after you pare them down to the basic data in excel or something like it). It also allows you to do this as a batch process, making large datasets more manageable. Fractal will give you D for your sample along with confidence intervals. I used a paired-sample t-test in my final analysis. It turned out to be important that I paired similar individuals in the trials; the results did indicate a positive relationship between D and body length. Luckily, I put males and females of the same size in each trial. You’ll have to look into the guidelines for using Fractal yourself if you are going to take a stab at it, but the descriptions are pretty easy to follow. With a bit of doing, it shouldn’t pose a problem to measure these types of behaviors yourself.
A comparison of movement paths for a male and female in maps generated by Fractal.
Selected Bibliography
Bascompte, J., C. Vila. 1997. Fractals and search paths in mammals. Landscape Ecology 12:213-221.
Dicke, M., P. A. Burrough. 1988. Using fractal dimensions for characterizing tortuosity of animal trails. Physiological Entomology 13:393-398.
Escos, J. M., C. L. Alados, J. M. Emlen. 1995. Fractal structures and fractal functions as disease indicators. Oikos 74:310-314.
Nams, V. O. 1996. The VFractal: a new estimator for fractal dimension of animal movement paths. Landscape Ecology 11:289-297.
Nams, V. O. 2001. Using animal movement paths to measure response to spatial scale. submitted.
Turchin, P. 1996. Fractal analyses of animal movement: A critique. Ecology 77:2086-2090.
With, K. A. 1994. Using fractal analysis to assess how species percieve landscape structure. Landscape Ecology 9:25-36.
This afternoon we concluded a week-long workshop in the so-called bioarts (go here for a nuanced discussion of the term) at the National Center for Biological Sciences (NCBS) in Bangalore, India. The workshop, conducted by Symbiotica and organized through a collaboration between NCBS, The Arts Catalyst and the Center for Experimental Media Art at the Srishti School of Art Design and Technology, brought together both Indian and international artists to engage with the tools of biotechnology as a way of investigating opportunities for research at the intersections of biology and art practice.
The capstone to the week was a community discussion among the participants and graduate students and faculty from NCBS. The conversation wa quite lively as it had been all week beginning with a opening keynote from Oron Catts about bioart and its role in cultural and scientific discourse.
Mukund Thattai moderated the discussion and acted as a provocateur by highlighting the potential for artists to become long-term interlocutors within the NCBS community. He particularly asked for skeptics of this art/biology engagement to share their concerns. Some of questions and concerns raised were:
How does the arts research percolate ‘down’ into culture given that these are two “ivory towers” largely speaking to each other?
Why is it that artists seem to be so vague in their proposals, seemingly lacking the precision of language to communicate ideas?
That the sciences practiced in institutions like NCBS are not intended for the average person and that possibly they shouldn’t be involved in it’s production because of the responsibility involved.
That the artworks produced appear to be superficial.
That the ideas or concepts presented through the work are already known and aren’t progressive enough to indicate value.
That biological research is drawing on ancient and traditional ways of knowing that largely obviate the need for any questioning of its categories and ways of understanding life.
That biologists just need to become better communicators and all of the problems associated with, e.g. acceptance of evolution, will disappear (this was actually raised during our first day’s interaction with the NCBS community).
These questions were sincere and engaging, and I was happy that there was such a good turnout to discuss these issues. People shared many different perspectives that varied widely in their desire for further such engagements, different models of engagement, and skepticism for the value of the kinds of activities that we were engaged in.
I was somewhat restrained from entering the fray directly because one of my main goals is to elicit the widest possible display of concerns from a community like this. Sometimes I feel it is better to just listen and use the issues raised as areas for getting tactically involved.
This brings me to a rationale for art/science engagement that I think deals with many of the concerns raised. Art, when engaged with biology, performs a social function of ‘witnessing.’ Steven Shapin and Simon Schaffer (Leviathan and the Air Pump, 1985) highlights this process in their analysis of Robert Boyle’s experiments with pneumatics and Thomas Hobbes’s critiques of his experimental program. They describe three processes that effectively multiply witnesses to experimentation and the resulting production of scientific knowledge: 1) facilitating replication–so that users can perform experiments themselves, 2) performance of experiments in a social space–i.e. sharing in the embodied experience, and (perhaps most importantly) 3)virtual witnessing–i.e. production in a user’s mind an image of the experimental scene such that it obviate the need for direct witness.
In the context of the workshop, I think this process of witnessing is increasingly relevant for the production of the biological program and its social contract with society. On the one hand, by teaching artists to use the tools of biology, Symbiotica creates an expectation that non-specialists could theoretically repeat experiments for themselves and verify their validity. Indeed, simple hypothesis testing was performed using environmental sampling of microorganisms and transformation of E. coli with a green fluorescent protein marker. Another example where replication of an experimental program is facilitated comes from the Critical Art Ensemble’s Marching Plague in which US military experiments in biowarfare were replicated with a critical eye for how the results did or did not support defense practice and the politicization of biotechnology. Each of these examples demonstrate how the practices of biology can be effectively replicated to allow for a wider social engagement of science and it’s relationship to other social groups and cultural concerns.
The second aspect of witnessing in shared spaces is perhaps the easiest to show. There were twenty residents at NCBS during the week, engaging in shared processes, visiting labs, and discussing the methods and implications of biological research in India. There’s a worldwide trend of artists working in labs with organizations. Kevin Kelly has a nice list of these residencies here.
What follows from these forms of replication and shared space is the dissemination of a virtual reality of the experimental program. I think that what comes out of artists’ engagement is a type of circumstantial evidence for scientifically-produced knowledge. It relies not on fact or even certainty but solely the residue of artistic engagement. Shapin and Schaffer point to these as circumstantial, stylized, accounts that do not exist as pure forms but instead as publicly acknowledged moves towards or away from “the reporting of contingencies.” Contingencies here means events or things that might jeopardize the validity of the experiment. By allowing the full spectrum of the experimental ‘scene’–perhaps through the inclusion of additional perspectives, political persuasions, or ideas–a better picture of experimentation and its context can be understood. The CEMA blog this week documented the workshop in detail. How often do you see that level of detail in the daily working of, say, a genetics lab? Consider also how art exports knowledge into other spaces and disciplines, either though its images or simply through the engagement itself.
Reflecting on all of this (and I’m tired now), I think one of the interesting questions to pursue is to ask what difference artistic engagement makes along each of these three axes. Does it differ from other methods of communication, and if so what are the behaviors and practices that make it so?
Behavioral differences between the sexes may explain sexually dimorphic patterns of infection. The risk of infection may be one such factor that an analysis of movement paths can predict. For example, if males spent more time than females foraging for food and, as a result, passively ingest more parasites while doing so, then their risk for infection would generally be greater than females. The tortuosity (or crookedness) of movement paths between the sexes were compared to see if any differences in movement (e.g. foraging) could suggest an explanation for male-biased infection. These differences may suggest that males and females experience their environment at different scales.
