BEACON | An NSF Center for the Study of Evolution in Action

BEACON Researchers at Work: How the cricket lost its song

Posted on January 3, 2012 by Danielle Whittaker

This week’s BEACON Researchers at Work post is by MSU postdoc Robin Tinghitella.

Last April I had the pleasure of writing the very first BEACON Researchers at Work blog post. I shared a story about how a tropical field cricket, Teleogryllus oceanicus, hitchhiked from Australia (where they’re native) through the Pacific, colonizing island after island, until they finally reached the Hawaiian Islands. It’s a great story about how humans can drive biological evolution because the crickets probably traveled on canoes with Polynesian settlers as they themselves island-hopped through the Pacific during the Polynesian expansion. For an evolutionary biologist like myself, the really intriguing part of the story is what happened once the crickets arrived in Hawaii. As a family friend announced during my wedding ceremony, the Hawaiian crickets lost their “chirpedness.” In other words, a rare mutation wiped out their ability to sing to attract mates. But how could that possibly work?

Graphic representation of founder's effect

Figure 1

When organisms colonize new environments, rapid evolutionary change frequently follows. This is primarily because of two things: genetic bottlenecks and novel selection pressures. Genetic bottlenecks happen when the size of a population is drastically reduced by chance. Large populations are typically made up of lots of different types of individuals with different characteristics. In other words, they have high genetic diversity. But when a chance event happens, the individuals who live on to reproduce may not represent all of the types that were found in the original population, many genes can be lost in the process, and the genetic diversity ends up being much, much lower. When organisms colonize new locations, the same type of thing happens. Founding populations are typically made up of very few individuals who don’t necessarily represent all of the genetic diversity present in their source populations (Figure 1). This makes a lot of sense when you think about something as drastic as colonizing islands like Hawaii. Hawaii is 2000 miles from the nearest Pacific island – how many individuals would you expect to arrive there by floating on flotsam, being carried on wind gusts or hitching a ride on a canoe? My guess is not very many. In the case of the tropical field crickets, the trip across the Pacific serves as the “bottleneck” through which only a few individuals make it. Once founders arrive in a new location, they face a whole host of challenges in their new environments – novel selection pressures – things that may not have existed in their source ranges and that may favor very different characteristics than those that were advantageous in their original habitats.

Back to the tropical field crickets, then. Crickets are a classic study system for biologists who are interested in sexual signals, things like bird plumage and frog calls that are typically used by males to attract females. Even Darwin was puzzled by these sorts of showy, exaggerated traits precisely because they are so conspicuous and seem to defy natural selection. Crickets are a great model system for this sort of work because males produce two songs that are used in mating contexts – first, they sing a calling song (a long distance, high intensity song) to attract females from afar. Females are the locomotory sex, so they move through the environment (grassy fields) in search of singing males. Once they are in close proximity, males switch to producing a quiet courtship song. Both songs are thought to be required before a female will mate with a male and mating is entirely under the control of the female – it’s a classic “female choice” system in which males cannot coerce females into mating. Females even have more subtle preferences for certain aspects of the song (like the length of certain parts of the song or what proportion of singing time is filled with sound (the duty cycle)) that they use to decide which males they will mate with. In any event, the songs are really conspicuous and like many sexual signals, they often attract the attention of unintended receivers, things like predators and parasites that instead use these mating signals to locate potential hosts and prey. In Hawaii, T. oceanicus encounters a novel natural enemy that exists nowhere else in its range – it’s a parasitoid fly called Ormia ochracea that uses the crickets’ mating song to locate hosts for their maggots to live off of.

Figure 2

In an alien-like plot, this fly has drastically changed the way the crickets locate mates on two islands in Hawaii. Pregnant female flies locate signaling males and spray larvae on and around them, some of which will burrow into the body cavity of the cricket where they’ll spend the next 7-10 days literally eating the cricket from the inside out (Figure 2 – photo by J.T. Rotenberry). Hawaiian T. oceanicus, then, face a conflict between natural and sexual selection. They should sing to locate females, but singing is very risky. What happened next in Hawaii was a surprise to those of us who study this system – the crickets just stopped singing.

Figure 3

After several months of scratching our heads, we put the pieces of the puzzle together. The crickets had a rare mutation that we called “flatwing”. The mutation changes their wing morphology in a way that eliminates their ability to sing to attract mates. Male crickets typically sing using specialized ridged structures on the wings, but flatwing males are missing all of those structures (Figure 3), so they’re physically incapable of producing the songs that crickets are so well known for. The flatwing mutation probably originally appeared in just one or very few individuals, but it spread through the population to 95% of males on the island of Kauai in fewer than 20 generations. That’s one of the fastest recorded evolutionary shifts in a wild population! This discovery led to so many questions: Why did the mutation spread so quickly? What’s the advantage to being a flatwing male over a calling male? If you already guessed that flatwing crickets avoid being parasitized by the fly, you’re right. We dissected over 120 flatwing males and only one of them was parasitized. That’s a far cry from the >35% parasitization rates that were found on Kauai before the mutation appeared.

But even if flatwing crickets are safe from the fly, the rest of the story still doesn’t make sense. Silent crickets should be really unsuccessful when it comes to mating (males use song to locate females and t

o “convince” them to mate), so a mutation like flatwing shouldn’t be passed on to future generations. Yet this mutation spread like wildfire and there’s a thriving population of almost all silent crickets on Kauai. How do males and females find each other without the use of song? And, why do females mate with them? These questions made up the bulk of my dissertation research, and what I discovered has inspired my subsequent work to answer a basic question about female mating decisions: “why that guy?”

It turns out that male crickets sometimes use an alternative mating behavior called satellite behavior to locate mates. Instead of calling themselves, satellite males hang out near singing males and attempt to intercept females who have been attracted to the other guys. Lots of organisms like frogs, toads, and insects that signal acoustically sometimes use satellite behavior. The advantage is that satellites can avoid the energetic costs of producing songs or calls as well as the risk (of predation or parasitization) associated with singing or calling themselves. Check out this cartoon that explains how satellite behavior works. In Hawaii, flatwings behave as satellites to the remaining 5% or so of males who can call. The flies can’t find the flatwings, but flatwing males can still manage to find females! What’s more, we found out that satellite behavior existed as a behavioral option for males before the change in wing morphology. Regardless of their wing morphology, males seem to use satellite behavior most when they haven’t mated recently. We can imagine that might happen anytime the population density is low or the competition for mates is stiff. What’s really cool about this is that is suggests pre-existing behavior facilitated the loss of song, so maybe behavior plays an important role in rapid evolution. Without satellite behavior silent males couldn’t find mates and flatwing males wouldn’t have passed their flatwing genes on to future generations.

Robin "surfing" on Hawaii

Satellite behavior gets us as far as males and females finding each other, but it still doesn’t explain why females are willing to mate with a male who can’t produce the sexual signal. Past research tells us that females clearly have strong preferences, even requirements, for hearing the courtship song before mounting males for mating. Let’s think back to the very first females who colonized the Hawaiian populations of T. oceanicus to see if they can help us reason this out. Recall that the vast open ocean between Hawaii and the nearest land serves as a bottleneck that only a few crickets made it through to colonize the Hawaiian Islands. The small initial population size may also have contributed to the spread of the flatwing trait. Imagine you’re a female cricket and you land on an island somewhere in the Pacific with not very many mates to choose from. If you’re extremely choosy you may never find an acceptable mate, which means the end of your genetic line. In other words, small founding populations may favor (select for) females who are lax in their mating decisions. In mating trials with females from across the crickets’ range (Australia, Oceania, and Hawaii) we found that females from Hawaii, and Kauai in particular, were the least choosy – they mate with silent flatwing males 50% of the time whereas females from Western Australia accept them only <10% of the time. So, the process of island colonization seems to have primed the Hawaiian populations for this rapid evolutionary change. The unfussy females allowed the mutation to take hold and spread on Kauai. In another location, like Australia, even if the mutation occurred, it may not have spread because picky females would not have accepted silent flatwing males as mates. So, again, it seems we found a special role for behavior in rapid evolution. And now I have a bit more information with which to answer the question, “why that guy?”

The papers I’ve covered in this post are:

Tinghitella, R.M. & Zuk, M. 2009. Asymmetric mating preferences accommodated the rapid evolutionary loss of a sexual signal. Evolution. 63: 2087-2098.

Tinghitella, R.M., Wang, J.M.* & Zuk, M. 2009. Pre-existing behavior renders a mutation adaptive: flexibility in male phonotaxis and the loss of singing ability in the cricket Teleogryllus oceanicus. Behavioral Ecology. 20: 722-728.

Zuk, M., Rotenberry, J.T. & Tinghitella, R.M. 2006. Silent Night: Adaptive disappearance of a sexual signal in a parasitized population of field crickets. Biology Letters. 2: 521-524.

For more information about Robin’s work, you can contact her at hibbsr at msu dot edu.

Posted in BEACON Researchers at Work | Tagged BEACON Researchers at Work, Biological Evolution, crickets, Field Biology, mate choice, mating behavior, sexual signaling | Leave a comment

BEACON Researchers at Work: Deciphering Avian Aromas

Posted on December 26, 2011 by Danielle Whittaker

This week’s BEACON Researchers at Work post is by BEACON Managing Director Danielle Whittaker.

The sense of smell is one we often take for granted in our own lives. However, even though we may not be conscious of it, odor can tell us a lot about a person, like how closely related they are to us, or how healthy they are – or how attractive we find them. Many mammals rely heavily on smell to communicate and have the anatomy to prove it: a wet nose, a large olfactory bulb in the brain, a vomeronasal organ, and lots of different scent glands. Birds, on the other hand, have a very small olfactory bulb, no vomeronasal organ, and no dedicated scent glands. For a long time people have thought that birds had little to no sense of smell. However, a growing body of research is demonstrating that birds can detect many different kinds of odors, that they produce odors themselves, and that those odors may affect their behavior.

Dark-eyed junco, pink-sided subspecies. Photo by Marine Drouilly.

I study the dark-eyed junco (Junco hyemalis), a small North American sparrow that is commonly found in backyards, especially in the winter. Juncos breed at high elevations and high latitudes, which means I get to do my fieldwork in beautiful mountain locations like Grand Teton National Park. I started working with juncos when I joined Dr. Ellen Ketterson’s lab at Indiana University as a postdoc in 2006, and still work with them today.

A junco preen gland

Like most birds, juncos have only one large sebaceous gland – the uropygial or preen gland, located just above the base of the tail. This gland produces an oil that birds spread over their feathers while preening, which helps protect the feathers from exposure to the environment and parasites, and also helps waterproof the feathers and keep the bird warm. Preen oil contains volatile compounds (like perfume!) that give the bird an odor. In collaboration with Dr. Milos Novotny and Dr. Helena Soini at the Institute for Pheromone Research at IU, I’ve been decoding the information contained in these volatile compounds. Different bird species have different compounds. For example, juncos typically have about 19 different volatile compounds in their preen oil, including several linear alcohols, methyl ketones, and carboxylic acids. Within a species, individuals vary in the relative proportion of each of these compounds. In juncos, males have higher proportions of the methyl ketones tridecanone and pentadecanone, while females have higher proportions of the linear alcohol undecanol.

Like most temperate bird species, juncos are seasonal breeders. In the winter, they produce very small amounts of these compounds, and the amounts go up dramatically during the summer breeding season. Does this change have something to do with mating behavior?

In my newest paper, I was interested in the hormonal mechanisms responsible for this seasonal change. At Dr. Ketterson’s long-term study site at Mountain Lake Biological Station, I captured male and female juncos during the first four weeks of the breeding season – a time when the birds are undergoing many physiological changes as they shift into breeding condition. As I expected, male juncos showed a steady increase in volatile compound concentration over the first four weeks of the breeding season. Females, however, showed a huge increase over the first three weeks, followed by a drop in the fourth week. What caused the drop? I looked closely at breeding data for the population that year, and found that the peak in female volatile compounds corresponded with a peak in egg-laying. I think that females may be using a strong odor to tell males that it’s time to mate.

What does bird odor have to do with evolution in action? Animals use a variety of signals to attract mates: visual, acoustic, and olfactory. When populations diverge, we often see a corresponding shift in these signals that help reinforce the isolation of the two populations by preventing mating with each other, and over time these populations can become different species. In southern California, a small population of juncos colonized the University of California San Diego college campus beginning around 1980, and over just 30 years has rapidly changed compared to their parent population in the nearby Laguna Mountains. The UCSD birds are now smaller and less aggressive, they no longer migrate, and they invest more effort in caring for offspring than the Laguna Mountain population. They look different, too – males have smaller amounts of white in their tail feathers, a trait that is attractive to female juncos. In collaboration with IU postdoc Jonathan Atwell, I found that they also smell different – male UCSD juncos have a more “female-like” odor than male Laguna Mountain juncos. I’m interested in how these different modes of signaling work together to communicate individual quality and identity to potential mates, and how they may be involved in population divergence and speciation.

Venn Diagram showing overlap in junco and white-throated sparrow volatile compounds

Overlap in junco and white-throated sparrow volatile compounds (presence/absence data)

Sometimes, even though two species are completely different, hybridization might occur. The white-throated sparrow looks nothing like the junco, and the two species produce completely different songs – and yet, they have been known to occasionally mate and produce hybrid offspring. How could they possibly make a mistake like that? We analyzed white-throated sparrow preen oil and found that white-throated sparrows and dark-eyed juncos share many of the same volatile compounds. Perhaps their odor is the reason for the inter-species allure.

For more information about Danielle’s research, you can contact her at djwhitta at msu dot edu. For more about juncos, check out the Junco Media Project!

Posted in BEACON Researchers at Work | Tagged BEACON Researchers at Work, Biological Evolution, birds, chemical communication, communication, Field Biology | Leave a comment

BEACON Researchers at Work: Evolving Robotic Fish

Posted on December 19, 2011 by Danielle Whittaker

This week’s BEACON Researchers at Work post is by MSU graduate student Jianxun Wang.

Have you ever imagined that you could swim with robotic fish someday? This may happen in the near future. More surprisingly, these robots would be capable of monitoring the aquatic quality around you and may even save you when you are drowning. These scenes have happened a couple of times in my dreams. I am Jianxun Wang, a Ph.D student of Prof. Xiaobo Tan in the Smart Microsystems Lab at Michigan State University, and I have been working on robotic fish for more than two years.

Back when I was a senior in college in China three years ago, I already knew that I would be working with a fantastic group on the research of robotic fish. I could still remember every detail the first time I came into our lab in the Fall of 2009. John Thon, a passionate researcher in our lab as well as a teacher at Holt Junior High School, gave me a warm welcome and guided me through the existing work. Our research began with an outreach activity to stimulate interest of precollege students in the fields of science and engineering through demonstrations of fish-like robots. These robots soon turned into a much more serious set of scientific projects. Later that day, Michael Carpenter, an undergrad research assistant, and Freddie Alequin, a graduate research assistant, showed me a demo of the robotic fish working in a 15-foot-long tank holding 6,000 gallons of water in the Smart Microsystems Laboratory. This is the first time I saw a real robotic fish prototype – a shell of green plastic with a rigid tail capable of swimming straight and making simple turns. I promised myself that I could and would make substantial contributions to propel these projects forward. From then on, I have been working with John, Freddie and Cody Thon, an optimistic and accommodating undergrad research assistant, all of whom are now my close friends.

Exhibit "Swimming with Robotic Fish" at the inaugural US Science and Engineering Expo in Washington DC. (L to R): Prof. Tan, Freddie, Cody and Jianxun

One year later, we developed a 6-inch fishlike robot with gray and yellow stripes modeled after a panfish. It had a GPS unit mounted on its head, a 3D compass embedded inside the front of the body, a wireless communication component on top of the shell, and a dissolved oxygen sensor suspended from its bottom. This robotic fish was designed to patrol a pond or a lake, while collecting and sending data about water temperature and the dissolved oxygen level. The robotic fish will provide a level of spatial and temporal sensing resolution that traditional water quality measurement approaches cannot match. Thanks to Felix Adisaputra, an undergrad research assistant working with me, we had a user friendly Graphical User Interface with Labview for remotely operating the robotic fish.

At nearly the same time, I started to shift part of my research time to work on the modeling of robotic fish. The main focus of my contributions are the control of individual and groups of them. The mathematical model I developed is currently being used for evolutionary design of robotic fish, which is a collaboration with Prof. Philip McKinley’s group. There, I met Tony Clark, a smart and hardworking Ph.D student of Prof. McKinley. We work together on the challenges existing in the development of autonomous robotic fish, which include realizing high maneuverability and high energy efficiency at the individual robot level and achieving adaptive coordinated movement (such as schooling) at the group level. Live fish and evolution computation provide a source of inspiration for effectively addressing these challenges. Consequently, in this project, Liliana Lettieri and Jason Keagy (two knowledgeable research associates from Prof. Jenny Boughman’s group) and Tony and myself are working together to create autonomous robotic fish by merging bio-inspiration, evolutionary design, and experimental prototyping. In particular, Tony has shown that the dynamic models I developed for robotic fish can be used successfully in evolving waypoint-following control strategies for these robots.

Recently, Osama En-Nasr, an excellent undergrad research assistant, John, Cody and I have been developing a so-called “predator robotic fish”, which will be used to study cooperation and social behavior in stickleback fish in collaboration with Prof. Boughman’s group. The idea is to use the robotic fish as a predator to elicit animal responses, since this “predator fish” could be controlled to demonstrate many complicated and repeatable behaviors. In this prototype, I have to acknowledge a very important and impressive technique: 3D printing. Supported by an NSF grant called “Evolution Park,” we luckily have this football table size 3D printer. With 3D drawing and selection of material, this 3D printer can provide us with arbitrary three-dimensional objects that have varying stiffness within a same object, which makes it much easier to create robotic fish prototypes. In the near future, we will print different types of robotic fish just like real fish. From this semester, we have two new members joining in this special group, Jared Moore and Sanaz Behbahani, and I am sure we can make some fascinating advances in the area of robotic fish with their contributions.

For more information about Jianxun’s work, you can contact him at wangji19 at msu dot edu.

Posted in BEACON Researchers at Work | Tagged BEACON Researchers at Work, Evolutionary Applications, Robotic Fish | Leave a comment

BEACON Researchers at Work: Evolution of Higher Mutation Rates

Posted on December 12, 2011 by Danielle Whittaker

This week’s BEACON Researchers at Work blog post is by University of Washington graduate student Tasneem Pierce.

When I started as an undergraduate in BEACON a year ago, I kept hearing about Avida and how powerful it is to study evolution in action. I decided to teach myself how to use the Avida software, and I quickly discovered that there are no tutorials for biologists interested in the more complex aspects of Avida. Fortunately for me, I was in the heart of BEACON, surrounded by people who were willing to take time to teach me how to use the software. Every single one of the people in the photo below, most of whom are in Dr. Ofria’s Digital Evolution lab, helped me in some aspect of my research with Avida. Now, I am working on creating a tutorial targeted at researchers with no computational background.

Photo of people who helped with this research

Front row (L to R): Charles Ofria, Owen Pierce, and Tasneem Pierce

BEACON’s collaborative atmosphere allowed me to start an Avida project of my own. I started my Avida project when I was doing research in Dr. Lenski’s Experimental Evolution Lab. Dr. Lenski’s long-term evolution experiment studies the genetic changes in twelve populations of Escherichia coli that have evolved for over 50,000 generations. If you are wondering how long that is, I’m 23 years old, and Dr. Lenski’s experiment started just a couple of months before I was born. There have been many cool discoveries in the long-term populations, one being that six of the twelve populations have increased mutation rates. These populations are called mutator lines, as they have damaged methyl-directed DNA mismatch repair systems, which have increased their effective mutation rates by a hundred fold. A high mutation rate alone will typically be maladaptive (more mutations are detrimental than beneficial), but if a mutator causes a rare beneficial mutation, that mutator may hitchhike to fixation meaning that the mutator becomes the dominant organism in the population.

How can a higher mutation rate fix in a population? Again, it is more likely that a higher mutation rate will break something important instead of making something better. What circumstances would lead to the fixation of a higher mutation rate? Using Avida, we can identify conditions under which a population will fix a higher mutation rate if it is easy to knock out a mutation-repair mechanism, but difficult to re-evolve one.

E. coli in nature cycles between the nutrient-rich gut and the external, nutrient-limited environment. In the Lenski lab lines, E. coli starts every 24 hours in fresh media, and by the end of the day, it is in a nutrient-depleted environment. In both of these situations, the E. coli face changing environments. A strategy in a nutrient-rich environment might not be beneficial in a nutrient-limited environment and vice versa. We tried to recreate the changing environments in Avida by rewarding Avidians for task set 1 and punishing them for doing task set 2 during one cycle, and then reversing this by rewarding task set 2 and punishing task set 1 in the next cycle.

Our hypothesis was that a moderate environmental change will select for organisms with a higher mutation rate. We had a variety of environments: a static environment where all of the tasks were consistently rewarded and 6 dynamic environments where there was a toggle between rewarding and punishing traits at different rates (100, 250, 500, 1000, 1500, and 2000 updates).

The starting organisms had a divide instruction with a low mutation rate. Organisms could mutate to have a divide instruction with a higher mutation rate as compared to the starting organism (2x, 3x, 10x higher). The Avidians could evolve a higher mutation rate, but they could not re-evolve a lower mutation rate, similar to how it is much harder to fix a DNA repair mechanism once it is broken.

Our preliminary results indicate the digital organisms can fix a higher mutation rate if they are subjected to a dynamic environment. We see that the ideal environmental change is not too short (not enough time to mutate) or not too long (no incentive to change as the environment is changing slowly). As seen in the graph, populations are more likely to fix a higher mutation rate if the change in the mutation rate is smaller (ex. 2x higher mutation rate is favored over fixation of a 10x higher mutation rate). Our initial runs were 100,000 updates long. When we increased the length of the runs, we found that there is an exponential decay in the number of populations at the lower mutation rate, and gradually most of the populations will fix the higher mutation rate. It is possible that, as long-term line E. coli populations go through more generations, more of the twelve lines will fix a higher mutation rate.

I am currently a first year graduate student in the Kerr lab at University of Washington. My new Avida runs will more closely mimic the Lenski lines as the environmental change will switch to a limited resource system instead of rewards/punishments system and the population size will be limited by requiring resources for successful replication. We hope to find conditions that lead to populations fixing a mutation rate that is a hundred fold higher similar to long term E. coli lines. Stay tuned!

For more information about Neem’s work, you can contact her at neem at uw dot edu.

Posted in BEACON Researchers at Work | Tagged Avida, BEACON Researchers at Work, Digital Evolution, E. coli, long term evolution experiment | Leave a comment

BEACON's Kalyanmoy Deb wins Cajastur Mamdani Prize for Soft Computing

Posted on December 8, 2011 by Danielle Whittaker

Professor Kalyanmoy Deb has been awarded the Fifth Edition of the Cajastur Mamdani Prize for Soft Computing by the European Centre for Soft Computing, in consideration of his contributions to the development and application of Evolutionary Multi-objective Optimization. (Press release, in Spanish, here.)

Dr. Deb is currently in residence at BEACON at Michigan State University, and during Fall 2011 has been teaching Introduction to Evolutionary Computation at BEACON.

Posted in BEACON in the News | Tagged Awards and Honors | Leave a comment

BEACON Researchers at Work: Experimenting with predation

Posted on December 5, 2011 by Danielle Whittaker

This week’s BEACON Researchers at Work blog post is by MSU graduate student Michael DeNieu.

The Dworkin lab is like a certain popular energy drink…it gives you wings. There’s a wide range of topics ongoing in the lab spanning functional and quantitative genetics, evolution, and development, but the common thread is that the Drosophila wing is used as a model to understand these processes. It is a multivariate trait. It’s easy to measure, and there is an abundance of natural variation. There are powerful tools for elucidating genetic and development mechanisms underlying phenotypic traits. What could possibly be lacking? In a word: ecology. Very little is known about the natural history of the fruit fly Drosophila melanogaster. For a lot of questions, that isn’t an issue. However, a major goal of the research in the lab is to gain a better understanding of the processes underlying adaptive evolution, and it is hard to infer evolutionary implications without knowing the functional significance of the traits we study. We want to know what variation exists in natural populations, what forces maintain it, and what that variation means for future evolutionary trajectories.

As an undergraduate student, I was always interested in how traits are shaped by natural selection. I was particularly interested in behavioral evolution because of the complex integration of various functions required to produce an appropriate behavioral response. The organism must detect relevant stimuli from the environment, process the information, and then initiate the response. The organism must also possess the morphological traits required to execute the behavior. It’s clear Dr. Ian Dworkin, my adviser, was having the same thoughts around the same time. He had devised a system in which flies were exposed to predators so that he could measure selection for natural variation in wing morphology.

In addition to providing the ability of flight, the wing plays an important role in many behaviors. The rapid “beating” of the wing produces the male courtship song, and is also used in aggressive displays between males. Yet it is still just one part of the fly. We wanted to take the approach even further to discover what traits might be involved in anti-predator behavior in order to begin to understand the selective forces present not just on the wing but on the whole organism in natural environments. When I joined the lab as a PhD student we did just that. The predation assay was adapted in order to initiate populations experimentally evolved under predation pressure. Our predation populations undergo selection by nymphs of the Chinese mantis (Tenodera aridifolia sinensis). Only surviving flies are able to pass their genes to the next generation. Left behind are the half-eaten remains of flies that were unable to avoid or escape the quick strike of the mantids. Comparing the predation populations to control populations – free from selection by the predator – allows us to identify behaviors and morphology as they evolve. Because we save the surviving populations, we have a morphological and more importantly a genetic record of the adaptive process.

One of the advantages of actively evolving populations is that they are free to adapt along any available trajectories. As experimenters, we are not forced to hypothesize and guess at the traits we predict might be important. We allow selection to identify them for us. We have performed over 70 generations of experimental evolution, and the predation populations have evolved morphologically and behaviorally, but not always in ways we would have predicted. Both replicate predation populations have evolved to increase their survival in the presence of the mantids. Surprisingly though, the populations have different wing morphology – both in size and shape – suggesting that despite the same selective pressure, they are evolving along different trajectories. Observations of their behavior during predatory selection show that they behave differently as well. Flies in one population favor ambulatory locomotion while the other is more apt to fly from place to place. Preliminary tests of the startle-induced behavior of these populations supports these observations.

This might mean that escape may not be the primary trait responsible for the increase in survival. There is a large body of research demonstrating the profound effects predation has on populations. In particular, many studies have focused on risk effects of predation. Risk effects result from modifications of behavioral patterns by prey organisms in order to reduce the likelihood of encountering and potentially being eaten by a predator. Because much of our data point to the conclusion that avoidance behaviors play a significant role in the evolutionary response, we investigated changes in potentially risky behaviors, including aggression and foraging.

Mantids are ambush predators. They sit still waiting for prey to move in front of them and strike quickly before it can escape. Any behavioral change that reduces the probability of encountering a mantis is likely to evolve. Consequently, we predicted that foraging and aggression levels would be reduced in our predation populations. When we measured foraging behavior we saw the predicted decrease. When we measured aggression, we were surprised yet again. The predation populations have increased their aggression level. Our current hypothesis is that the increase in aggression is a plastic response to predation risk. As you will see in the first part of the video below, aggression is measured in the absence of the predators. We think the flies are reducing aggression when predation risk is high, and increasing it when risk is low.

Whatever the answer turns out to be, one thing that I enjoy most about this project is that evolution always has the ability to surprise you. Part of the excitement of experimental evolution is that you get to watch it happen. The challenge is to make sure that you’re paying attention to the right things and asking the right questions.

For more information about Michael’s work, you can contact him at denieumi at msu dot edu.

Posted in BEACON Researchers at Work | Tagged BEACON Researchers at Work, Biological Evolution, Drosophila, experimental evolution, predator-prey, video | Leave a comment

BEACONites generating more data than we can handle

Posted on December 1, 2011 by Danielle Whittaker

It sounds like a good problem to have, but it is still a very real problem: we are now producing genomic and metagenomic data much faster than we can analyze it. Two of BEACON’s own scientists are quite familiar with this problem, and were quoted in today’s New York Times article, “DNA Sequencing Caught in Deluge of Data.”

C. Titus Brown, one of BEACON’s thrust group 1 leaders (Evolution of Genomes, Networks, and Evolvability) and who teaches one of BEACON’s core multidisciplinary graduate courses, points out the problem of too much data:

The Human Microbiome Project, which is sequencing the microbial populations in the human digestive tract, has generated about a million times as much sequence data as a single human genome, said C. Titus Brown, a bioinformatics specialist at Michigan State University.

“It’s not at all clear what you do with that data,” he said. “Doing a comprehensive analysis of it is essentially impossible at the moment.”

…

Professor Brown of Michigan State said: “We are going to have to come up with really clever ways to throw away data so we can see new stuff.”

E. Virginia Armbrust, University of Washington professor and BEACON researcher, comments on the overwhelming amount of data produced by metagenomics projects:

E. Virginia Armbrust, who studies ocean-dwelling microscopic organisms at the University of Washington, said her lab generated 60 billion bases — as much as 20 human genomes — from just two surface water samples. It took weeks to do the sequencing, but nearly two years to then analyze the data, she said.

“There is more data that is infiltrating lots of different fields that weren’t particularly ready for that,” Professor Armbrust said. “It’s all a little overwhelming.”

One thing is clear: BEACON is at the forefront of bioinformatics research, and is poised to figure out new solutions to this unusual problem.

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BEACON Researchers at Work: Effects of rapid environmental change on evolution

Posted on November 28, 2011 by Danielle Whittaker

This week’s BEACON Researchers at Work blog post is by University of Washington postdoc Jenna Gallie.

Left: Haley Lindsey; right: Jenna Gallie.

Life has existed on Earth for nearly four billion years. Given that organisms have been evolving continuously, why are they still not perfectly adapted to their environments? One reason is that environments are constantly changing. This is particularly evident at present, as Earth’s climate is changing rapidly. In addition, there are many other situations where the survival of organisms depends on their capacity to adapt to dynamic environments. For example, drug treatment regimes drastically alter the environment experienced by infectious microbes.

A metaphor that can aid understanding of the evolutionary effects of changing environments is that of the ‘fitness landscape.’ The fitness landscape is a map from genotype to fitness for a given environment, where elevation gauges fitness (see Fig. 1 for a simple example). The genotypes involved form a network where neighbours are mutationally accessible. Populations take ‘steps’ through mutation, and are driven up slopes by natural selection (as fitter genotypes prevail). The operation of mutation and selection generates an evolutionary ‘path’ on the fitness landscape. This path may lead to an ‘adaptive peak’, a genotype where all mutational neighbours are less fit (e.g. ‘ab’ in Fig. 1B). Once a peak is reached the population can remain static if the environment is constant.

Fig. 1: The fitness landscape (an example with two mutations). (A) Four genotypes (AB, aB, Ab, and ab) are linked by single mutations. (B) The landscape results from the inclusion of the fitness ‘heights’ of the genotypes. To get from genotype AB to ab, there are two ‘paths’ available (red and blue). The blue path is accessible (each successive genotype has higher fitness), whereas the red path is not. (Figure created by Ben Kerr and Jenna Gallie).

Since the topography of the fitness landscape is dependent on the environment, the evolutionary path can be altered by environmental change. When the environment shifts, the relative fitness of genotypes may change, altering the landscape. If the environmental shift is instantaneous, any corresponding change in the landscape is immediate. Conversely, more gradual environmental changes may result in transitions through several forms of the landscape as each stage of the environmental change is realized. These additional landscape forms may alter the accessibility of mutational paths. In this way, the rate of environmental change can have profound effects on evolutionary outcomes. This is a particularly pressing issue in the face of modern climate change, which is occurring faster than ever before. Many predictions focus on how today’s organisms will survive in conditions projected by global warming models in many years time. However, for organisms with short generation times (e.g. annual plants), many generations will pass – and so populations may evolve – before the projected conditions are realized. To improve predictions regarding the biological effects of climate change, it is essential to understand how the rate of environmental change affects evolution.

For the past 1.5 years, I have been a postdoctoral researcher in Dr. Ben Kerr’s laboratory at the University of Washington. Haley Lindsey, Ben Kerr and I have been using a combination of experimental evolution and molecular genetics to explore the effect of environmental change on adaptation, with particular emphasis on how the rate of environmental change influences evolutionary outcomes. In our research we use a well-established microbial model system: populations of Escherichia coli and the antibiotic rifampicin. Microbial systems are often used in experimental evolution as a combination of short generation times (minutes to hours) and large population sizes (~10¹⁰ organisms) enable evolution to be directly observed in real time. Additionally, microbial populations can be frozen in a state of suspended animation almost indefinitely, and then revived for comparison with derived genotypes. We chose rifampicin as an environmental stressor because its effects on E. coli populations are well understood; mutations conferring resistance to rifampicin usually occur in specific regions of the rpoB gene, which encodes the major subunit of RNA polymerase (the enzyme that catalyses transcription).

Fig. 2: Representation of the three differing rates of drug addition during our experiment.

Using the E. coli-rifampicin system, we devised an experiment to explore the effects of the rate of environmental change on adaptation: increasing amounts of rifampicin were added to replicate E. coli populations that were propagated over many generations. The populations were divided into three treatment groups that differed only in the rate at which rifampicin was added (Fig. 2). The first treatment group (‘Rapid’) received the full concentration of rifampicin immediately, while in the second group (‘Gradual’) the rifampicin concentration was slowly increased over the course of the experiment. The final group (‘Moderate’) was subjected to an intermediate rate of rifampicin increase. Importantly, the final concentration of rifampicin was the same in each treatment group. All surviving populations from each treatment group were periodically frozen throughout the experiment, allowing the evolutionary changes occurring in each treatment group to be analysed. By comparing the changes found in survivors from each treatment group, we aimed to determine if and how evolutionary outcomes were influenced by the rate of rifampicin addition.

The results obtained so far are promising. Notably, faster addition of rifampicin led to a lower survival rate, showing that rapid environmental change can lead to higher rates of extinction. A second interesting finding was that survivors from the Moderate and Gradual treatment groups contained a wider variety of rpoB mutations than those from the Rapid treatment group. We are currently exploring whether specific evolutionary paths taken under the Moderate and Gradual treatments were accessible to populations exposed to the Rapid treatment. Preliminary data suggests that a greater variety of mutational paths were available under the Moderate and Gradual treatments than under the Rapid treatment. Together, these findings indicate that rapid environmental shifts can severely constrain evolutionarily outcomes. Our findings highlight the need to consider the rate of environmental change in other situations. In particular, rates of change should be considered when making predictions about the biological effects of climate change.

For more information about Jenna’s work, you can contact he

r at jgallie at uw dot edu.

Posted in BEACON Researchers at Work | Tagged BEACON Researchers at Work, Biological Evolution, E. coli, experimental evolution | Leave a comment

BEACON Researchers at Work: Teaching Computational Evolution

Posted on November 22, 2011 by Danielle Whittaker

This week’s BEACON Researchers at Work post is by University of Texas at Austin postdoc Art Covert.

My name is Art Covert and I’m a teacher. Specifically, I teach the “Computational Evolution Stream” in the Wilke Lab as part of the Freshman Research Initiative (FRI) at the University of Texas at Austin. The FRI is an innovative program at UT Austin that places freshmen interested in research into “streams” which over two semesters train students to do research. Over the two semesters, students develop their own research projects under the guidance of a post-doc level instructor. Students are able to participate in research, in actual research labs, for which they get course-credit, and research groups get many helping hands.

Computational Evolution is a new stream started last year with BEACON support, and is taught by yours truly. In my stream we focus on using the evolution of digital organisms as a proxy for evolution in organic study systems. Digital evolution captures the essence of the evolutionary process with self-replicating computer programs that undergo mutation and selection in a simulated world with limited resources. Researching evolution with computational instantiations lends itself to teaching the scientific method because it’s easy to do controlled experiments, with high statistical power, that will directly answer a well-reasoned hypothesis. Students in my stream not only see evolution occurring before their eyes, but also acquire valuable skills such as doing large-scale data analysis with the python scripting language and learning to use the Sun Grid Engine, the software that powers most of the worlds super-computers, to do their experiments. I use digital-evolution research to engage my students and in the process teach them a variety of skills that they can take with them to almost any research group in undergrad or grad school, as well as industry. Now that we are nearing the end of the first full year of running this stream, I want to share with you three of the projects that my students have been working on.

Figure 1: Two experiments from Terrel's work with paired treatment of an island model at the same migration rate but with different populations structures. On the top, populations of 10,000 digital organisms are divided into 200 subpopulations of size 50. In the bottom plot, populations of equal total size are divided into 25 subpopulations of size 400. We see that as population size of individual sub-populations increases, the optimal migration rate decreases.

Terrel Roane is an undergrad in my stream who has taken over a project I started at Michigan State, examining how genetic drift and migration combine in island models to help populations reach higher fitness than they would achieve otherwise. In island models, populations are subdivided into relatively small subpopulations and are connected by the migration of single organisms, which carry genetic variance between the otherwise isolated groups. Terrel has spent the summer and fall working to understand exactly how these structured populations improve fitness. He has found that when total population size is held constant, larger subpopulations can achieve higher fitness at lower migration rates then smaller subpopulations (Figure 1). Terrel’s results suggest that drift is not the only important factor driving the evolution of island models, and that populations in nature which are structured in similar ways may be able to take advantage of island models with a broader range of migration rates than previously thought.

Jared Carlson-Stevermer has been working in my stream to understand what role deleterious mutations may play in temporally changing fitness landscapes. Digital organisms receive additional bursts of energy if they perform certain logical tasks; therefore, we can manipulate the organism’s fitness landscape by changing which tasks are rewarded. Jared evolves digital organisms which do one task extremely well, then places the best of those organisms in new environments which reward the organisms for doing different tasks (Figure 2, below). By using some clever tests, we can selectively prevent deleterious mutations from entering the evolving populations and determine how important deleterious mutations are to adaptation in changing environments. Jared has found that deleterious mutations tend to play a greater role in environments that are more complex, i.e., those that have several tasks rewarded rather than just one or two. The implication is that deleterious mutations play little or no role when adapting to single-peaked landscapes, but become increasingly more important when adapting to more complex changes in the fitness landscapes. In other words, the more drastic the environmental change, the more important neutral drift may be.

Figure 2

Figure 2 (right): Jared tests how changing the environment affects the evolution of digital organism and gives us information on the shape of the fitness landscape. The top plot illustrates the environment all of Jared’s organisms start out in, a single-peaked environment with little or no epistasis, fitness is derived primarily by being able to do task A. We then place a second task in the environment, B or C (middle and bottom plots), which can change the environment in one of two ways. Some new environments correspond to the middle landscape, where peaks A and Bare neutral plateaus to peak AB, and some environments are more like the bottom landscape where peaks A and C are separated by a small deleterious valley which must be traversed to reach peak AC. Jared’s work shows adding more tasks to the environment increases the chance of having a landscape like the bottom plot and increases the role of deleterious mutations, which are needed to cross fitness valleys.

Figure 3: A disheveled post-doc helps Lane optimize the code for his research. FRI students work directly with post-doc level researchers and grad students in the lab.

Lane Smith is a student who is studying the effects of sign-epistasis in sexual organisms. Epistasis is a term used to describe mutations with have different effects on different genetic backgrounds. In their most extreme form, sign-epistatic mutations have the opposite effect when they are combined, individually deleterious mutations can become beneficial. Lane’s project entails tracking all the sign-epistatic interactions in a sexual population between the original ancestor and the final dominant (the most abundant genotype at the end of an experiment). Lane’s project requires that he reconstruct the genealogy over the entire experiment and test every deleterious mutation and their progeny for sign-epistatic interactions, a process that entails looking at millions of genotypes. So far Lane has been able to identify expressed sign-epistatic interactions in several short experiments (with tens-of-thousands of genotypes)

and is working to scale up his experiments to larger runs, and to examine how sign-epistasis is impacted when the pivotal mutational combinations are disrupted by increasing linkage-disequilibrium (Figure 3).

Each of the projects I have described above have a strong potential for publication. We plan to send several of them to the Alife XIII conference. The projects also offer many obvious avenues for future research for the next batch of students to work on. The work being done in this stream has been made possible in large part by our BEACON support, which has allowed us to pool resources from FRI and UT to start a new and exciting research stream that fits squarely within the research and educational goals of both BEACON and FRI. In our next BEACON proposal we plan to ask for funds so that our summer fellows can do digital research at BEACON partner sites during the summer and bring those projects back to UT to complete in the Wilke lab over the fall. It is our hope that students who participate in research at other sites will help to foster collaboration between labs at UT and other BEACON institutions.

For more information, you can contact Art at covertar at gmail dot com.

Posted in BEACON Researchers at Work | Tagged BEACON Researchers at Work, Digital Evolution, Education, Freshman Research Initiative | Leave a comment

BEACON Researchers at Work: How do geckos stick to the wall?

Posted on November 14, 2011 by Danielle Whittaker

This week’s BEACON Researchers at Work post is by University of Idaho graduate student Travis Hagey.

While doing fieldwork in the Dominican Republic helping collaborators look at habitat use of rare and unique anole lizards, I found a very rare species of anole (Anolis fowleri).

I am a fourth year grad student in Dr. Luke Harmon’s lab at the University of Idaho. My thesis looks at how geckos (and some other lizards) use their sticky toes to adhere to surfaces. I also study the patterns of ecological and evolutionary diversification of gecko toes. By studying the details of geckos’ adhesive pads, we can understand the physics and mechanics of how toes stick (biomechanics). We can also begin to understand long-term evolutionary patterns across padded lizards, including adaptation, convergence and evolutionary constraint. We can also consider how adhesive toes may change how a lizard may use its environment.

Ecology and evolution have been topics of interest to me in since I was an undergrad, and mechanics is something I’ve been excited about since I was a kid. My bedroom was covered in Legos and I often took apart electronics and other toys just to figure out how they worked. I think about problems, including evolutionary problems, from a mechanical point of view. This is the main reason why I find the mechanics behind gecko toe pads to be so interesting.

Photographs of five different types of gecko toe pads

There are many different types of gecko toe pads. Some species have fan-shaped pads (upper left), some have divided pads (upper center), undivided pads (upper right), no adhesive pads at all (lower left) or only pads on the very tips of the toes (lower right).

There are three different groups of lizards with adhesive toe pads: geckos, anoles and a few species of Pacific island skinks. These groups are distantly related, which tells us that toe pads most likely evolved independently in all three groups – and probably multiple times within geckos. The adhesive pads of these lizards have important characteristics at multiple size scales. At a broad scale, different lizards have differently shaped pads. Some species have undivided lamellae (the horizontal scales on the bottom of lizard toes), others have divided lamellae, some have fan shaped lamellae or only one or two pads on the very tip of their toe. Some species that spend their lives on the ground don’t have any pads at all. When we look closer at the adhesive pads using electron microscopy, we see millions of very small structures called setae. They may look like hairs, but only mammals have hair. Instead, setae are actually modifications of the outer layer of skin. Reptile skin (and setae) are made mostly of beta keratin, a protein similar to the material from which our fingernails and hair are made. Lizard setae are much, much smaller then a human hair. The length of most setae is equal to the width of our hair (about 100 micrometers). Setae are so small that the atoms in the tip make very weak chemical bonds with the surface they’re walking on (Van der Waals force). This weak force is what makes them stick to a surface. This type of adhesion is very different from Velcro or suction cups or glue or most other sticky things we commonly encounter.

We can use an electron microscope to look at gecko setae. These setae are about 100 micrometers tall and cover the adhesive pads of geckos. Setae are branched at the tips, kind of like a bushy tree, with each tip making a weak chemical bond with whatever the gecko is walking on. When all the setae tips are added together, the entire gecko toe pad can actually make really strong adhesive forces.

The shapes of gecko toe pads and setae are valuable in understanding how they function. That observation is interesting not only to biologists, but also engineers. There are a lot of researcher teams working to recreate structures similar to lizard setae using synthetic materials. If successful, these inventions would be valuable in many different industrial fields. Researchers have already developed robots that can climb vertical walls and small racecars with synthetic setae-like structures on their wheels that can drive up vertical racetracks. Gecko setae have also been used to pick up and place very small computer chips to build tiny computer components.

In my work, I am trying to understand how setae shapes affect their function. I measure how setae are shaped (using microscopy), measure how they work (by measuring the friction and adhesion with really sensitive force sensors), and look for patterns. For example, we have found that geckos with longer setae can create more adhesion with less effort. One shortcoming of only comparing shape and performance is that it doesn’t explain why longer setae work the way they do. In order to understand the mechanical relationship, we need to use biomechanical models. Using our understanding of physics, we simplify setae into shapes that are easier to work with mathematically, like solid, straight rods. We can then write mathematical equations describing exactly how setal adhesion works. We make predictions based on these models and compare them to our actual measurements in the lab or the field to see how well our models describe real life. Through this process of building models and testing them, we can make increasingly accurate models to help us understand the mechanics underlying how gecko toe pads work. Testing current biomechanical models using lab and field observations is a significant part of my thesis.

One way we can measure the stickiness of a gecko’s toe pad is to measure the angle that their toe falls off of a piece of glass. Stickier geckos can hang on through steeper and steeper angles.

Another focus of my thesis is to study how the ecology of geckos may be related to their toe pads. Our biomechanical models give us some guesses about how toe pads should be adapted to different environments. I’m currently developing a project in Australia that will investigate whether our predictions of form and function apply to geckos on the Cape York Peninsula. For example, previous work suggests species that live on rough surfaces (like sharp rock or rough bark) need stickier toes because it’s harder to make good contact with a lot of the surface. On the other hand, species that live on smooth rock or bark might not have to be as sticky. Also species that live higher off the ground may be stickier in order to have a larger safety margin so they’re less likely to fall out of a tree and hurt themselves. By testing the relationships between a gecko’s habitat and toe pads, we can find evidence of adaptation.

We can also look across the gecko family tree, or phylo

geny, to find interesting evolutionary patterns. Geckos first evolved around 200 million years ago, and have since colonized every continent expect Antarctica. By studying how toe pad characterizes are spread out across the phylogeny, we can learn about the evolutionary processes guiding toe pad change. Do we see lots of change early in the tree? This might suggest that toe pads played an early role as geckos spread across the Earth and formed new species. How different are the toe pads of different groups of geckos? We have already found multiple examples of very distantly related geckos having very similar toe pads. Why is this?

By combining biomechanical models, habitat, and large-scale evolutionary patterns, I hope that my work will help us build a stronger understanding of how gecko toe pads work and how they may have adapted to each species’ environment. We can also highlight interesting evolutionary patterns that describe how gecko toe pads have changed through time.

For more information about Travis’ work, you can contact him at thagey at uidaho dot edu.

Posted in BEACON Researchers at Work | Tagged BEACON Researchers at Work, Biological Evolution, Biomechanics, ecology, Field Biology, geckos | Leave a comment