BEACON Researchers at Work: The Original Social Gaming

This week’s BEACON Researchers at Work blog post is by University of Texas at Austin postdoc Tessa Solomon-Lane.

Me SCUBA diving in the kelp forests offshore of Catalina Island, CA to collect bluebanded gobies. The last dive of my dissertation warranted wearing a lab coat. Photo credit: Megan Williams & Jenny Hofmeister (2014)

Me SCUBA diving in the kelp forests offshore of Catalina Island, CA to collect bluebanded gobies. The last dive of my dissertation warranted wearing a lab coat. Photo credit: Megan Williams & Jenny Hofmeister (2014)

I can trace the beginning of my fascination with social behavior to the summer I was ten. That summer, I started volunteering as a teacher’s aide at an arts-based school for kids with learning and developmental differences. As I gained experience at the camp, the scientist in me started to recognize patterns and ask questions. One observation intrigued me most: seemingly independent of a wide variety of special needs, kids who could interact socially with other campers and adults seemed to have a particular advantage. There was an ease with which these campers moved through their day that we, as educators, struggled to instill in campers who didn’t already have that ability. In the long run, one teacher told me, these socially savvy kids would be fine. Although ‘fine’ is overly simple, and some social skills can be coached, I think there was an important kernel in my early observations that still drives my research today: members of highly social species who can play the social game well are likely to have an advantage.

Field biology beyond the playground. Fast forward to a college semester abroad in Australia when, for the first time, it clicked for me that scientific research was the way to ask and answer my persistent questions about social behavior and why individuals behave the way that they do. What led to this realization? I learned there were fish that changed sex depending on their social environment.

Most of the animals we encounter in our everyday lives —humans, dogs, cats, birds, and even zoo animals—are genetically either male or female. Males typically remain males for their entire lives, and females typically remain female. For many species of fish, however, sex is much more flexible. Females that produce eggs can transform into fully functioning males in just days or weeks (protogynous, sequential hermaphrodite). In species like grouper and clownfish, males can sex change into females (protandrous, sequential hermaphrodite), and there are even species that can change from female to male and back again (bidirectional, sequential hermaphrodite). This transformation can dramatically increase lifetime fitness and involves coordinated changes at multiple biological levels. The first changes occur in the brain and behavior of the sex changer. External morphology, such as coloration and genitalia shape, can also shift. Steroid hormones, particularly androgens and estrogens, are central to the reorganization of the reproductive system. And in the gonad itself, an ovary becomes a testis (or vice versa) through a combination of cell birth and cell death.

Remarkably, this whole transition is socially regulated. In a protogynous species, for example, female to male sex change occurs when a female establishes dominance in a social group. In nature, this might happen if the previous dominant male dies or if an all-female group forms. Within minutes of the social environment becoming permissive to sex change, the behavior of the dominant female changes. She may even take on the male role so rapidly that she performs male reproductive behaviors before she has sperm to release.

Why did sex changing fish set off a light bulb for me? Without a doubt, social behavior is critically important for all of the diverse social species found throughout the animal kingdom, including humans. But here were animals that, over the course of a single lifetime, must generate social behaviors appropriate for a low ranking female, a middle ranking female, a high ranking female, and even a dominant male! How do the fish accomplish this behavioral range and context specificity?

My field crew of undergraduate research assistants from Agnes Scott College: (left to right, back) Cierra Lockhart (2014), Megan Williams (2012-2014), Alma Thomas (2013-2014), and Alyssa Millikin (2014), with me in front. Taken at the Wrigley Institute for Environmental Studies on Catalina Island, CA.

My field crew of undergraduate research assistants from Agnes Scott College: (left to right, back) Cierra Lockhart (2014), Megan Williams (2012-2014), Alma Thomas (2013-2014), and Alyssa Millikin (2014), with me in front. Taken at the Wrigley Institute for Environmental Studies on Catalina Island, CA.

In my doctoral research, I addressed this overarching question by studying the bluebanded goby (Lythrypnus dalli), a bidirectional sex changer. I was fortunate to work in the field on Catalina Island, CA and in the laboratory at Georgia State University with a number of excellent undergraduate researchers, including two field teams I led from Agnes Scott College. Over the course of my dissertation, I formed many different kinds of social groups and watched hours upon hours of social interactions with the goal of understanding which factors affected social behavior and how. I formed social groups of different sizes and sex ratios, with adults and juveniles. Sometimes I selected females that were gravid and ready to lay eggs, gave individuals controlled social experiences, or chose fish with specific levels of aggression. In other experiments, I first implanted steroid hormones or injected neuromodulators, such as corticotropin-releasing factor and arginine vasotocin, into the brain. And like any good behavioral biologist, highly accurate reenactments of the behaviors I observed occasionally make their way into conference presentations and K-12 science outreach activities.

A male, juvenile, and female bluebanded goby (left to right).

A male, juvenile, and female bluebanded goby (left to right).

Male bluebanded goby caring for his eggs laid on the inside of the shell. The black dots are developing eyespots.

Male bluebanded goby caring for his eggs laid on the inside of the shell. The black dots are developing eyespots.

The good, the bad, and the adaptive. There is often an impulse to anthropomorphize animal social behavior and label behaviors as ‘good’ or ‘bad’ depending on their connotations for humans. But gobies are not tiny, underwater humans. Subjective classifications can impede our understanding of behavioral evolution because ‘good’ behaviors may or may not actually increase fitness. Furthermore, the fitness consequences of some behaviors, such as aggression, differ depending on the context and the species. Even the individual expressing the behavior can influence the outcome. In my research, I identified adaptive behaviors and successful individuals by directly measuring a component of fitness: reproductive success. For females, I counted the number of eggs she laid in the male’s nest, which ranged from 55 to 2,200 eggs per clutch. For males, I counted the total number of eggs (from multiple females) that hatched from his nest. The most productive male hatched 4,995 eggs in just 2 weeks! By identifying the strong connections between social behavior and reproductive success in bluebanded goby social groups, my research can provide insight into how these important behaviors evolved.

As a new postdoctoral fellow in the Hofmann Lab (cichlid.biosci.utexas.edu), my goal now is to go into the brain. Although I am no longer studying a sex changer, the African cichlid Astatotilapia burtoni is highly social and expresses fascinating and flexible social behaviors. I am excited to begin investigating how a highly conserved network of brain regions regulates the expression of adaptive, context-specific social behavior.

For more information about Tessa’s work, you can contact her at tksolomonlane at utexas dot edu.

Posted in Uncategorized | Tagged , , , , , | Leave a comment

BEACON Researchers at Work: Same behavior, same genes?

This week’s BEACON Researchers at Work blog post is by University of Texas at Austin research associate Rebecca Young. 

Me as a teenager in 1996 (Austin, TX).

Me as a teenager in 1996 (Austin, TX).

From an early age I spent my time outside – chasing lizards, riding horses, and begging to go to a zoo. As this fascination matured I found it was the diversity of life, as Darwin so eloquently describes the “endless forms most beautiful and most wonderful”, which intrigued me most. Where does this variation come from? How are these differences generated during development?

Ironically, while my curiosity and decision to pursue a career in biology arose from a fascination with animal diversity, my study of biology is largely centered on what is shared among organisms. It is now known that the tremendous diversity of animal species on earth develop from remarkably conserved ‘toolkits’ of gene regulatory networks redeployed in context- and species-specific ways. In hindsight, some may argue this is not surprising. The foreleg of a horse and wing of a bat look and function differently, but at their core they are quite similar. Both are appendages, out growths from a remarkably similar bilaterally symmetrical bodyplan; their differences (e.g., their length, width, and arrangement of bones) can be achieved by changes in growth and adjustments in apoptosis during development. However, the underlying developmental and genetic similarities among organisms go beyond homologous traits like limbs – characters that occur in multiple species derived from the same ancestral trait. Developmental genetic similarities appear in non-homologous traits as well. For example, in cases of parallel evolution, where similar features evolve independently in multiple lineages, similar developmental mechanisms can be found (e.g., the electric organ in electric fishes). In some cases traits that have seemingly no similarities evolutionarily or functionally (e.g., human diseases and yeast phenotypes) can share gene regulatory networks (read about ‘phenologs’ here: http://www.phenologs.org/). That traits such as these, having no business being developmentally similar, are in fact “deeply homologous” – i.e., share gene regulatory mechanisms – is a principal discovery resulting from evodevo thinking in biology.

Paired monogamous and non-monogamous species used in our research.

Paired monogamous and non-monogamous species used in our research.

For the majority of its history, the field of evolutionary developmental biology has focused on morphological and physiological characters. However, other types of traits, such as behaviors, should likewise share gene regulatory mechanisms. My work as a research associate in the Hofmann Lab at the University of Texas at Austin (http://cichlid.biosci.utexas.edu/) asks whether similar behaviors, in this case monogamy, that have evolved independently in multiple taxa result from deployment of the same ‘deeply homologous’ gene regulatory mechanism. To answer this question we are taking a comparative transcriptomic approach. Specifically, we quantify expression of genes in the brains of reproductive males in paired, closely-related monogamous and non-monogamous species of voles, mice, song birds, dendrobatid frogs, and cichlid fishes using the next generation sequencing approach RNA-seq. By comparing neural gene expression between monogamous and non-monogamous species within a group (e.g., within voles) we can identify the genes that are up-or down-regulated in the monogamous species. If we do this in all of the groups we can identify the genes that are differentially regulated across all monogamous species examined. This is a jumping off point. From here, we can further examine this list of differentially expressed genes to identify groups of co-expressed genes. We can assess the known interactions and functions of the differentially expressed genes to identify the types of neurogenomic changes that accompany the transitions to monogamy in all of these groups generating functional hypotheses for future experiments.

Illustration of orthologs and paralogs.

Illustration of orthologs and paralogs.

Getting a list of genes differentially regulated in all these independent evolutionary transitions to monogamy is no small feat. Outside of the efforts required to recruit a consortium of experts who can provide the appropriate tissue for each of these distinct species, comparative approaches in next generation sequencing data analysis are in their infancy. To date, comparative ‘omics research has focused largely on closely related species. When studies do span large evolutionary distances they focus on model systems with well-developed genomic resources (e.g., a well-annotated, published genomes and functionally annotated genes) that facilitate comparisons. Much of our effort has focused on improving these approaches in non-model species like those explored for this project. One of the major challenges is identifying the ‘same’ (orthologous) genes in each species. This problem comes from differences in genome complexity across organisms. For example, gene, gene family, and whole genome duplications have occurred in some lineages and not others, meaning that quite often a gene has more than one copy – called paralogs. How can you compare expression of one gene in one species with two genes in another? To resolve this problem, I have worked in collaboration with the Center for Computational Biology and Bioinformatics (CCBB: http://ccbb.biosci.utexas.edu/) and the Texas Advanced Computing Center (TACC: https://www.tacc.utexas.edu/) to establish an analysis pipeline based on OrthoMCL (http://www.orthomcl.org/) in a high performance computing environment. Rather than focusing on individual genes, we use OrthoMCL to generate ‘orthologous gene groups’ that contain all of the paralogs for a particular gene. These gene groups are generated by comparing sequences within and between species. When gene sequences within species are more similar to each other than to genes in another species, they are grouped as paralogs into these orthologous gene groups. Expression values of the orthologous gene groups can be calculated and compared directly across species. This is not only critical for our research interests, but facilitates comparative ‘omics in general as next generation sequencing approaches are applied to more and more non-model organisms in a diversity of empirical and experimental contexts.

My 2 year old daughter at the Austin Zoo and Animal Sanctuary .

My 2 year old daughter at the Austin Zoo and Animal Sanctuary .

Right now the scientific pursuits that drive my curiosity are best explored indoors, in a lab or on a computer rather than in the field. Until my research takes me outside again, and from time to time it does (read about my other research directions here http://devoevo.ccbb.utexas.edu/), the observations of the beautiful and wonderful natural variation I have made on horseback as a teenager, in the field as a scientist, and exploring nature with my kids have generated endless questions on the developmental and evolutionary origins of animal diversity.

For more information about Rebecca’s work, you can contact her at youngrl at utexas dot edu.

Posted in BEACON Researchers at Work | Tagged , , , , , | Leave a comment

BEACON Researchers at Work: Long-Term Ecological Research Sites as Evolutionary Experiments

This week’s BEACON Researchers at Work blog post is by MSU faculty member Jen Lau. 

Jen Lau in 1988

Research and blogpost author Jen Lau (now an Associate Professor at MSU; then an angst-ridden middle schooler)

1988 was a good year. MSU won the Rose Bowl; Miami Vice, 21 Jump Street, and Family Ties were still on prime-time, and I was a gangly, angsty adolescent entering middle school with a hideous perm and fluorescent clothes. Who could imagine 1988 being any more exciting? But it was–1988 also saw the beginnings of Lenski’s long term E. coli evolution experiment, one of the most exciting experiments in evolutionary biology. In the same year, sixty-five miles down the road, Kellogg Biological Station (KBS) became one of the first Long-Term Ecological Research (LTER) sites. And that’s how all the magic of 1988 collides. Unbeknownst to me back in 1988 with my permed hair and adolescent angst, that combination of scientific starts would motivate much of my research a quarter century later.

Just as Lenski’s long-term evolution experiment has taught evolutionary biologists much about the limitless potential for evolution, long-term ecological research sites have contributed an amazing amount to our understanding of community and ecosystem ecology. Less well-appreciated is the potential contribution of LTER experiments to the study of evolution. Since Lande & Arnold’s seminal 1983 paper, thousands of studies have measured natural selection on wild populations; yet, we still have a limited understanding of the selective agents causing that evolution or the evolutionary and genetic mechanisms driving evolution in local populations. That’s where LTER experiments come in–just like Lenski’s flasks, LTER sites contain replicated populations evolving for many generations, often under multiple experimentally manipulated ecological conditions. Although in most cases we do not have genotypes of the wild populations inhabiting these LTER sites back in 1988 tucked away in -80 degree freezers, we can still compare populations evolving in experimentally-controlled treatments to test for evolutionary changes in response to a known experimental factor.

Jen’s less gangly sidekick Katy Heath (now an Assistant Professor at Univ. Illinois; then a 4th grader).

Jen’s less gangly sidekick Katy Heath (now an Assistant Professor at Univ. Illinois; then a 4th grader).

My good buddy and collaborator Katy Heath (Univ. Illinois) and I are doing just that. We are using a long-term nitrogen (N) addition experiment set up by Kay Gross at the KBS LTER back in 1988 to test basic predictions about the stability of mutualisms (positive interactions between two organisms). In resource mutualisms, two organisms trade resources in a manner that benefits both partners. For example, in the legume-rhizobium resource mutualism, plants in the legume family trade carbon fixed through photosynthesis for N fixed by their rhizobium symbionts. Theory predicts that changes in the availability of traded resources can destabilize such resource mutualisms. For the legume-rhizobium mutualism, in particular, increased soil N is predicted to cause evolutionary changes that will destabilize this mutualism. First, high soil N availability is predicted to cause legumes to abandon the mutualism. Second, most theories also predict that high soil N will cause the evolution of less cooperative rhizobium mutualists that provide fewer growth benefits to their plant hosts.

Satellite image of the KBS LTER Main Cropping System Experiment; Photo Credit: SPOT Image

Aerial view of the KBS LTER main site experiments. Photo Credit: Spot Images.

By studying rhizobium populations that have been evolving since 1988 in N-addition vs. control plots at the KBS LTER, we find that N-addition has indeed caused the evolution of less cooperative rhizobia. When plants are inoculated with rhizobium strains isolated from N-addition plots they produce about 30% less aboveground biomass than plants inoculated with rhizobium strains isolated from lower N control plots (check out our recent paper). By sequencing our strains, we can test additional hypotheses about the genetic and evolutionary mechanisms driving these evolutionary shifts. We find that the evolution of reduced cooperation results from genetic changes on the pSym plasmid, an area of the bacterial genome containing numerous genes related to N-fixation and rhizobium-legume signaling. A variety of evolutionary mechanisms could cause such evolutionary changes: 1) High nitrogen may simply relax many of the selective pressures that exert purifying selection for high quality rhizobium strains in lower nitrogen environments (e.g., sanctions or partner choice), 2) Low quality rhizobia could actually be selected for in high N environments if there are trade-offs between rhizobium quality and survival in the soil environment. Patterns of nucleotide diversity at known symbiosis genes support the latter hypothesis; high nitrogen appears to actively select for low quality mutualists.

In short, by using an LTER experiment as an extra-large test tube for studying bacterial evolution, we can quantify evolutionary responses to known, experimentally manipulated environmental changes. What we find with the legume-rhizobium mutualism does not bode well for natural N-fixation. The legume-rhizobium mutualism was once responsible for nearly 98% of N inputs to terrestrial systems; our results suggest that less cooperative mutualists that are probably fixing much less N are actively favored by natural selection as synthetic nitrogen availability increases. What we don’t know are the limits to this evolution and just how widespread these phenomena are. Will rhizobium quality continue to decline just as Lenski’s strains continue to evolve, shifting this once important mutualism to parasitism? And is this phenomenon unique to KBS, or are other legume-rhizobium mutualisms facing similar evolutionary fates? Luckily for some gangly tween currently in middle school, hopefully these LTER studies will continue for another few decades, allowing for tests of evolutionary responses over even longer time scales. Luckily for Katy and me, there are numerous other LTER N-addition experiments all over the country for us to study next to determine the generality of our results.

And this is why next time you see me wandering around campus, I’ll be blaring some REM on my Sony Walkman, pegging my pants, and wearing fluorescent pink leg warmers and a matching scrunchie. Not only am I a typical academic fashion failure who hasn’t bought new clothes in decades, I’m also paying homage to the 80s because it was one of the happiest decades in recent American history and also because it was a windfall (accidentally or intentionally) for experimental evolution. 

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

Posted in BEACON Researchers at Work | Tagged , , , , , , , | Comments Off

BEACON Researchers at Work: Evolutionary Optimization and the Open Source Community

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

Brian visits Austria as part of his research collaboration.

Brian visits Austria as part of his research collaboration.

I teach computers to be better at guess, check, and revise. At least, that is the short version of my research. More precisely, I develop optimization algorithms based on evolutionary principles for the purpose of finding high quality solutions to challenging real-world problems. Most of the time this involves staring at white boards, pacing back and forth, and muttering to myself, with occasional rushes of pure excitement caused by some new revelation. However, thanks to months of hard work and the generous support of BEACON, I recently got to travel to Austria to share my ideas with the open source community.

For the last 18 months I have been working to develop what I call the Parameter-less Population Pyramid, or P3 for short. P3 is built on the idea that it is often easier to determine how good a solution is than to find the best possible solution. To understand how P3 works, let’s apply it to the problem of packing a moving truck. It’s pretty easy to measure how good a packing configuration is (how much stuff fits in the truck) but pretty hard to find the best configuration (getting everything in the truck). To solve this problem, P3 starts with an initial guess (random solution) for how to pack the truck, and then makes minor changes to that guess (hill climbing) until doing so can’t make it any better. For instance, you might get to the point where moving any chair will not make things better, but if you could move all of them at once into a big stack it would be a lot better.

Algorithm overview of P3.

Algorithm overview of P3.

Unfortunately, trying to test all ways to make larger changes to a guess can take a lot of time. To overcome this problem, P3 stores multiple good guesses (population) and tries to learn what makes them good. To further improve, P3 tries to take the good parts of previous guesses and combine them to create new ones (mixing). In our example, this could mean putting together the box stacking from one guess and the furniture layout of another. In order to focus learning, P3 filters guesses based on how much effort went into producing each guess, storing them separately (pyramid of populations). This design also lets P3 learn as it goes (parameter-less), unlike most evolutionary systems where you need to know how many guesses to store before starting optimization.

This method of iterative solution improvement is designed to be applicable to a wide range of problems. The exact same algorithm can, for instance, be applied to making better crumple zones in cars and to reducing power consumption in electronics. Unlike many previous methods which rely on people to provide problem specific information, P3 is focused on learning everything it needs from the problem itself. On top of being easier to use, all of the initial results suggest that P3 is actually more effective than existing methods for performing this kind of optimization.

Brian and members of the HEAL group.

Brian and members of the HEAL group.

The challenge now is to get P3 in the hands of people who have problems they need solved, which brings us back to my trip to Austria. HeuristicLab is an open source tool developed by the Heuristic and Evolutionary Algorithms Laboratory (HEAL) at the University of Applied Sciences Upper Austria. Built to be user friendly, HeuristicLab provides a graphical interface which allows users to test out a wide variety of optimization algorithms and is currently in use by many researchers and practitioners world wide.

Example P3 optimization using Heuristic Lab.

Example P3 optimization using HeuristicLab.

Last December I flew to Austria to further BEACON’s collaboration with HEAL and to integrate P3 into HeuristicLab. This in-person interaction was essential to ensuring the smooth transition of my work into their toolbox as I was the first non HEAL member to significantly expand HeuristicLab. That is to say I asked a lot of questions and got a lot of help, but I think in the process we made it easier for future researchers to add their work. I am proud to say we were completely successful, and P3 is now available for download as part of HeuristicLab 3.3.11. By integrating into their software, I gained access to their real time data visualization and analysis tools, which will definitely help me better understand how P3 works and how to make it even better. Doing so also makes P3 more accessible to the optimization community, a critical step toward increasing its utilization.

For more information about Brian’s work, you can email him at goldma72 at msu dot edu.

Posted in BEACON Researchers at Work | Tagged , , , , | Comments Off

BEACON Researchers at Work: How the chicken crossed the sea

This week’s BEACON Researchers at Work blog post is by MSU postdoc Eben Gering.

Biotic invasions (the Disney version)

Some ecologists have likened invasive species to the army of enchanted brooms in Disney’s Fantasia. In the movie, Mickey Mouse portrays a sorcerer’s bumbling apprentice who uses borrowed magical powers to bring his broom to life. Once the broom has completed an epic chore (intended for Mickey), it divides into millions and millions of brooms that the apprentice is powerless to stop.

A native of Southern Asia, the small Asian mongoose (Herpestes javanicus) was introduced to Pacific and Caribbean islands in a misguided effort to control invasive rats. Image modified from Wikipedia.

A native of Southern Asia, the small Asian mongoose (Herpestes javanicus) was introduced to Pacific and Caribbean islands in a misguided effort to control invasive rats. Image modified from Wikipedia.

Like those pernicious magic brooms, invasive species often begin as small and seemingly harmless propagules that quickly reach high densities via unchecked population growth. And while invaders usually disperse as stowaways, sometimes like Mickey’s broom, they begin as hopeful experiments.

 

A male and female Lesser ʻakialoa (Hemignathus obscurus), last seen in the year of Fantasia’s theatrical premier (1940). This species is one of several dozen endemic Pacific songbirds driven to extinction by introduced predators and other anthropogenic stressors. Image source: Walter Rothschild. The Avifauna of Laysan and the neighbouring islands with a complete history to date of the birds of the Hawaiian possession. London: R.H. Porter, 1893-1900).

A male and female Lesser ʻakialoa (Hemignathus obscurus), last seen in the year of Fantasia’s theatrical premier (1940). This species is one of several dozen endemic Pacific songbirds driven to extinction by introduced predators and other anthropogenic stressors. Image source: Walter Rothschild. The Avifauna of Laysan and the neighbouring islands with a complete history to date of the birds of the Hawaiian possession. London: R.H. Porter, 1893-1900).

The small Asian mongoose, for example (above), won a free trip to several remote archipelagos in order to help regulate invasive rats. Perhaps you remember the mongoose from Rikki Tiki Tavi1, in which the bold little predator massacres two cobras along with all of their eggs. This predisposition to ovivory (egg eating) came in handy for invasive mongooses2. As diurnal hunters, they seldom encountered island rats (which prefer to forage at night). So instead they ate eggs of native songbirds, many of which lacked evolutionary histories with (and defenses against) terrestrial mammalian predators.

Now cited as one of the world’s 100 worst invaders3, the mongoose has already abetted several dozen bird extinctions… each species irreplaceable, each irrevocably gone.

How the chicken crossed the sea (a case study of invasion)

Most of Hawaii’s non-native birds have faired better than native counterparts. For example, wild chickens have overrun Kauai – perhaps helped by the island’s lack of mongoose and other predators. These birds are somewhat difficult to classify (invasive? exotic? Polynesian legacies?) since neither their origins nor their ecological impacts have been established. Museum specimens indicate that Red Junglefowl (Gallus gallus; the chicken’s closest living relative), were introduced to Hawaii by ancient Polynesians, but it’s unclear if they persisted. According to many Kauai locals, modern wild chickens instead descend from livestock that went feral after recent hurricanes (see figure below). Scientific studies of Pacific chickens (both morphological and genetic) have reached similarly conflicting conclusions4,5,6.

Census data from feral chicken populations on Kauai confirm reports by Kauai locals of recent, exponential growth coinciding with hurricane events.  For additional details, refer our recently published study7 (from which this figure was modified).

Census data from feral chicken populations on Kauai confirm reports by Kauai locals of recent, exponential growth coinciding with hurricane events. For additional details, refer our recently published study7 (from which this figure was modified).

Our team recently completed in depth analyses of feral chicken genomes, morphologies and behaviors7. Here are our key findings:

  1. Some individuals’ genomes “matched” DNA sequences from ancient Kauai fossils (consistent with ancient Polynesian origins).
  2. Other individuals’ genomes match European breeds domesticated for food production and later distributed worldwide (consistent with recent feralisation).
  3. Individuals’ genomes, behaviors and morphologies exhibit tremendous variation, and show patterns consistent with hybridization between Red Junglefowl and domesticated chickens.

A feral rooster from Kauai displaying the plumage phenotype that is typical of Red Junglefowl (Gallus gallus), the ancestor of domesticated chickens. Red Junglefowl were spread throughout the Pacific by ancient Polynesians prior to European contact, and before the development of modern, food production G. gallus breeds. We found molecular, morphological, and behavioral signatures of Red Junglefowl ancestry, but also derived traits, such as yellow legs (pictured here), that are unique to domesticated breeds. These patterns are consistent with an invasion of domesticated genes into a Red Junglefowl reservoir population in the Pacific, and with the hypothesis that feralization may have contributed to the exponential growth of Kauai’s G. gallus population during the late 20th century (photo by Dominic Wright).

A feral rooster from Kauai displaying the plumage phenotype that is typical of Red Junglefowl (Gallus gallus), the ancestor of domesticated chickens. Red Junglefowl were spread throughout the Pacific by ancient Polynesians prior to European contact, and before the development of modern, food production G. gallus breeds. We found molecular, morphological, and behavioral signatures of Red Junglefowl ancestry, but also derived traits, such as yellow legs (pictured here), that are unique to domesticated breeds. These patterns are consistent with an invasion of domesticated genes into a Red Junglefowl reservoir population in the Pacific, and with the hypothesis that feralization may have contributed to the exponential growth of Kauai’s G. gallus population during the late 20th century (photo by Dominic Wright).

Kauai’s colorful modern flocks may thus descend from both intentional and accidental introductions, each originating in different places, at different time points, and from different selective environments.

Why we are crowing about feral chickens

While feral chickens pose significant threats to agriculture and human health, Kauai’s G. gallus seem fairly benign (e.g. compared to mongoose). Chickens have even been adopted by locals as their island’s unofficial mascot, which enjoys limited regulatory protections on public land. Perhaps someday our team’s data will prove useful for revisiting local management priorities. Meanwhile though, we have broader (and exciting!) motivations to continue our studies:

  1. Advancing invasion biology. When we left off with Mickey Mouse (above), he was inundated by monsters of his own making. Fortunately his skilled mentor arrives in time to intervene, and easily puts the brooms to bed. One can hope, Fantasia suggests, that by understanding our errors we obtain the power to correct them. But even if that’s so, we are leagues from understanding population biology well enough to mitigate invasions8. If invasive species are analogous to Mickey’s demonic brooms, then today’s ecologists are well-meaning apprentices at best, fumbling against nature’s complex interdependencies.
  2. Biosecurity. There are more than 20 billion chickens on earth right now, which comprise humanity’s leading source of animal protein. In contrast, the Red Junglefowl has disappeared throughout its native range due to habitat loss and “contamination” of gene pools by hybridization. It is crucial that we identify and conserve the genetic variation that still remains in the Red Junglefowl. This variation could soon be essential for the improvement and/or evolutionary rescue of commercial chicken breeds. Recent years found chicken producers combating both rapidly evolving pathogens, and fertility issues believed to be products of inbreeding. An exciting new collaboration between MSU and UT biologists will use Kauai’s feral birds to obtain insight to these issues, combining molecular, biophysical and evolutionary approaches.
  3. Studying evolution in action. Darwin drew heavily from his studies of domesticated species to develop his theory of evolution9. He did this because domesticated taxa display many traits that are readily apparent as product of artificial selection regimes. Broiler chickens are 3x larger than Red Junglefowl, yet somehow mature 2x as fast. Hens from certain egg-laying varieties exceed 300 eggs/year, while Red Junglefowl females produce only a dozen. In Kauai, hybrid G. gallus can potentially inherit both domestication-related enhancements to growth and fecundity, and ancestral abilities to survive and compete in complex natural and social environments. We are eager to learn which combinations of genes and traits are emerging from this ‘evolutionary experiment,’ and to see whether our findings can translate to gains in the sustainability or efficacy of egg and poultry production.

Disney’s Fantasia was based on an 18th century poem by Goethe (German poet, naturalist, philosopher and statesman). The plots of Der Zauberlehrling and of the derivative scenes from Fantasia are basically the same… except for one detail. Whereas Goethe’s sorcerer is unruffled by his pupil’s mistake, his Disney counterpart becomes enraged when he stumbles upon Mickey’s magic mess. Here are two wild guesses as to the reason for that discrepancy: 1) By the time Fantasia was made (1940), we better understood what can happen when people like Mickey muck with nature expecting simple outcomes. 2) While growth of human knowledge and experience might have made us more cautious, it has also produced newer, better, and more idiot-friendly tools for the mucking-inclined. Or maybe, like me, something about Mickey Mouse just irritated him. We stand as little chance of knowing this as we do of deciding if the chicken preceded its egg. My parting gift is this: if anyone ever asks you which came first? and you happen to be standing on Kauai, then you can answer with reasonable conviction: Red Junglefowl did. 

 

1Ricki-Tikki-Tavi is one of the stories from The Jungle Book. It was not included in the Disney version.

2mongooses is, indeed, the plural form of mongoose

3http://www.issg.org/database/species/search.asp?st=100ss

4Condon T (2012). Morphological detection of genetic introgression in red junglefowl (Gallus gallus). MS Thesis, Georgia Southern University.

5 Storey AA, Ramırez JM, Quiroz D et al. (2007) Radiocarbon and DNA evidence for a pre-Columbian introduction of Polynesian chickens to Chile. Proceedings of the National Academy of Sciences, 104: 10335–10339.

6 Thomson VA, Lebrasseur O, Austin JJ et al. (2014) Using ancient DNA to study the origins and dispersal of ancestral Polynesian chickens across the Pacific. Proceedings of the National Academy of Sciences, 111, 4826–4831.

7Gering E, Johnsson M, Willis P, Getty T, & Wright, D (2015). Mixed‐ancestry and admixture in Kauai’s feral chickens: invasion of domestic genes into ancient Red Junglefowl reservoirs. Molecular ecology.

8Bock, Dan G., Celine Caseys, Roger D. Cousens, Min A. Hahn, Sylvia M. Heredia, Sariel Hübner, Kathryn G. Turner, Kenneth D. Whitney, and Loren H. Rieseberg. (2015) “What we still don’t know about invasion genetics.” Molecular ecology

9Darwin C. (1871). On the origin of species by means of natural selection. Murray. London.

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

Posted in BEACON Researchers at Work | Tagged , , , , | Comments Off