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BEACON Researchers at Work: The mystery of tropical diversity: testing a forgotten idea

Posted on June 3, 2014 by Danielle Whittaker

This week’s BEACON Researchers at Work post is by MSU graduate student Carina Baskett. Carina blogs at Wandering Nature.

Here in Michigan, the hummingbirds are coming back for the summer. If you see one, it’s likely a Ruby-Throated, since it’s the only common hummingbird here. Within the continental US, there are 17 species of hummingbirds, compared with 51 in Costa Rica, and 127 in Peru. The numbers for global tree distributions are even more amazing: there are over 22,000 species of trees in the Amazon, and only 620 in the US and Canada.

This pattern of higher species diversity in the tropics is ubiquitous across many types of organisms, across the globe, and throughout the fossil record. The reason these numbers surprise and interest me is that we don’t really know why the tropics house so much of Earth’s biodiversity. Sure, there are hypotheses (in fact, over 100!), but we’re not very close to agreeing on which ones are more or less correct.

I think our inability to explain this pattern highlights a gap in our understanding of biological processes. It’s a difficult question to answer because the scale of time and space is enormous. To experimentally test some of the hypotheses, we would need other Earths and many millions of years!

One of the early ideas to explain high tropical diversity focused on how temperate and tropical environments could differentially influence evolution. Theodosius Dobzhansky said in 1950 that, “Any differences between tropical and temperate organisms must be the outcome of differences in evolutionary patterns.” (Dobzhansky would say that! His quote, “Nothing in biology makes sense except in the light of evolution,” is practically a required first slide in any class that mentions evolution.)

Specifically, by differences in evolutionary patterns, he means that species are formed at higher rates in the tropics. Why and how? The idea has two parts. The first is that interactions between species (like pollination, competition, or diseases) are more intense in the tropics because of a lack of evolutionary pressure from harsh winter weather.

Intense tropical herbivory

For example, as any tropical traveler knows, you need more than sunscreen to get ready for a trip to the equator. You need shots and medicine to prevent typhoid, yellow fever, and malaria. You need to budget for plenty of bottled water, and whatever you do, don’t eat the lettuce. These precautions aren’t just because of a lack of sanitation in tropical countries. Human diseases and parasites are actually more diverse and severe closer to the equator.

Species could form at higher rates in a region with more intense species interactions because the players in an interaction can evolve, while the weather, which is the main environmental challenge in a place like Michigan, cannot.

The great tropical ecologist Dan Janzen stated this succinctly when he said, “The winter experienced by a northern pine or oak has probably not changed in the past 60 million years, nor has the good solution to its challenge; the [biological] challenge to a rainforest [tree] Dipteryx panamensis most decidedly has changed.”

Imagine if two populations of Dipteryx panamensis were separated from each other for 10 million years, and the main challenge in each location is a fungal disease. Even if the fungus starts out the same in each locale, it will quickly change as the trees in each place evolve different ways to resist it, and the fungi evolve different counter-attack strategies. The two tree populations might have changed so much as to be unrecognizable to each other after 10 million years—that is, they will have become two separate species—compared to two populations of white oak, whose main challenge is the unchanging winter.

Dobzhansky’s idea has been relatively under-studied. Scientists have focused more on testing ecological and historical explanations for global diversity patterns, rather than evolutionary explanations. But something is missing, because the answer still eludes us.

Existing evidence is not sufficient to say whether Dobzhansky was right, but nor has his idea been disproven. Some recent studies, using both fossils and evolutionary family trees, have found patterns of higher speciation rates in the tropics. There are studies showing that species interactions are more “intense” in the tropics, but it’s a difficult concept to define and measure, and there is not yet enough data to conclude anything definitive. Whether or not evolutionary outcomes driven by species interactions are somehow different from those driven by the weather is almost completely unknown. With my dissertation, I am trying to test these latter two parts of the hypothesis.

Pokeweed in common garden at Kellogg Biological Station

I did a preliminary study of whether pokeweed, Phytolacca, is more reliant on pollinators at lower latitudes. To test this, I planted seeds near MSU from the temperate Phytolacca americana that were collected from southern Florida, northern Florida, Tennessee, and Michigan. I also planted some seeds from its close relative Phytolacca rivinoides, which lives in Central America. The lower-latitude plants invested more in pollinator attraction and reward. They had bigger flowers and larger overall floral displays, and they produced more nectar. Soon, I’ll head out to the field to make similar measurements on pokeweed in natural populations.

To test the idea that evolutionary outcomes differ depending on whether the main evolutionary challenge can evolve or not, I’m collaborating with current and former BEACON students and professors on an experimental evolution project in the lab, using bacteria, viruses, and antibiotics.

Phytolacca americana (pokeweed) fruits

I realize that my dissertation isn’t going to solve the mystery of why the tropics are so diverse, but I’m excited to chip away at it, now that we have new tools to test Dobzhansky’s idea from 1950. It’s also important to understand how the tropics might differ, in terms of ecology and evolution, from the temperate zone. Most conservation and restoration research takes place in the temperate zone. What if the lessons we’ve learned about conservation here don’t translate to the tropics?

Compared to working in a lab with bacteria that I can’t see, I’m much more comfortable in the rainforest, even with its venomous snakes and unbearable heat. But I’m looking forward to learning my way around the petri plates and Bunsen burners—as long as I get to spend some time each year in the tropics, getting an intense dose of biodiversity (but hopef

ully not parasites).

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

Posted in BEACON Researchers at Work | Tagged BEACON Researchers at Work, biodiversity, Biological Evolution, Kellogg Biological Station, plant biology, speciation, tropical diversity | Leave a comment

BEACON Researchers at Work: The big picture of virulence factors and bacteria

Posted on May 19, 2014 by Danielle Whittaker

This week’s BEACON Researchers at Work blog post is by NC A&T graduate student Alshae Logan.

My name is Alshae Logan and I am a master’s student in the Department of Biology at North Carolina A&T State University. My project investigates virulence factors. Virulence factors are expressed molecules of colonizing micro-organisms that effect progression of disease within their host. In general, virulence factors can enable various phenotypes of motility, adhesion, and toxicity. Motility associates with virulence, biofilm development and invasiveness. Motile bacteria move to favorable locations through chemotaxis. Once bacteria come into contact with the surface, there can be attachment and the formation of biofilms. The initial attachment of cells occurs in two steps, transport to the surface and absorption to the surface. The production of toxins can lead to tissue damage and disruption of host physiology. Virulence factors can vary in relationship to different selective forces of, for instance, niche adaptation and evasion of host defenses. In order to investigate these different selective forces, I have developed a study within the theme of my mentor’s lab (Dr. Scott Harrison) which is to bridge between bioinformatics and biotechnological approaches.

How evolutionary analysis can investigate variation

In my research, we are analyzing two bacterial strains, Pseudomonas syringae and Pseudomonas aeruginosa, for differences of plant versus animal-host adaptations. The particular virulence factor we are examining is the bacteria flagellum. A primary factor to the evolution of flagella genes in animal versus plant host contexts is the presence of an adaptive immune system in animals. The fliC gene encodes for the flagellin protein, a major component of the flagellar filament structure located outside of the cell boundary. The middle region of the expressed flagellin polypeptide is on the outer surface of the filament, and is expected to be subject to recognition by the adaptive immune system. A hypothesis in our study is that this would lead to antigenic variation due to diversifying selective pressure for animal host-associated bacteria compared to plant host-associated bacteria.

To develop this comparative analysis, we assembled a data set of fliC gene sequences from 18 animal bacterial pathogen strains and 18 plant bacterial pathogen strains.

For this study, each of these strains has a fully sequenced genome and is from the class Gammaproteobacteria with the exception of the two Ralstonia solanacearum strains which are from the class Betaproteobacteria. This selection of strains was based on the strong degree of prior investigation and unambiguous association with pathogenicity found in the literature. Even though I am investigating one of the most well-studied bacterial taxonomic classes, it was difficult to attain a perfectly balanced comparison across paired subgroups (color coordinated by family in the above figure). It is interesting to ponder what this comparison might have been like five years ago or what it would be five years hence. As a strategy for connecting our plans for bioinformatics analysis to laboratory work, I settled upon Pseudomonas syringae and Pseudomonas aeruginosa, which each have multiple strains with fully sequenced genomes. The general objective we have had is to examined conserved patches of homology across the fliC gene and whether there is a predictable relationship with animal host-related pathogenicity. Shown below is an alignment and structured comparison across fliC genes that I have used for measures of dN/dS and quantitative trait loci analysis.

In general, I have been finding promising results (to be published, stay tuned!) that have been connecting bioinformatics methods to our strategies of DNA extraction and sequencing from our P. syringae and P. aeruginosa controls and real-world samples. It’s been challenging to develop a workflow that traverses the many different possible choices for bioinformatics resources, algorithms and laboratory protocols. In my cross-disciplinary training as a BEACON graduate student, I have taken coursework in computational science and have found Python to be a helpful tool for constructing some of the steps for this analysis. As I complete my master’s work and prepare for my next steps into a PhD program, I am excited that this work will live on through the adoption of its different methods and protocols into our undergraduate research training courses and experiences.

For more information about Alshae’s work, you can contact her at arlogan1 at aggies dot ncat dot edu.

Posted in BEACON Researchers at Work | Tagged bacteria, BEACON Researchers at Work, bioinformatics, Biological Evolution, virulence | Leave a comment

BEACON Researchers at Work: The evolution of sociality in a large cat

Posted on May 14, 2014 by Danielle Whittaker

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

Of the 37 extant species of cats, lions (Panthera leo) are the only species in which females live gregariously in groups (Caro 1989, Packer 1986). Most other cats live solitarily, with females only associating regularly with their dependent offspring. By contrast, female lions live in prides of 2-18 (Schaller 1972) individuals, usually all closely related. One or few males defend prides of females and their shared offspring from other males. The prevalence of asociality among felids begs the question: what forces have led to the evolution sociality in lions but none of the other cats?

The African lion (Panthera leo) lives communally in prides of 2-18 females (Schaller 1972). Of 37 species of extant cats, the lion is the only species that is truly gregarious. Here, cubs from multiple mothers feed on a kill made by one of the pride’s lioness. Photo taken by Aurelia DeNasha.

At first it was proposed that group living in lions evolved in response to selection for cooperative hunting (Schaller 1972). While lions do hunt cooperatively, this hypothesis fell out of favor after Packer (1986) showed that hunting group size doesn’t increase the per capita food intake of the hunting party, suggesting that selection on cooperative hunting is not a strong enough force to lead to group living. As an alternative, Packer (1986) proposed a hypothesis suggesting that a confluence of ecological conditions catalyzed the development of sociality in lions. His hypothesis is as follows. Because lions prefer to hunt large prey, kills produce food sources that are too large to be immediately consumed by a single individual, and are thus likely to persist for several days. Because the African grassland ecosystem has good visibility and supports high densities of individuals, most carcasses will be located and partially consumed by another individual. This means that the original hunter is bound to lose some of its kill to kleptoparisitism, or food stealing. Individuals who live in groups benefit because they lose the food they acquire to kin rather than non-kin, and consequently their loss of food is partially compensated by the indirect benefits of helping kin. In summary, the hypothesis states that large prey size, high population densities, and good visibility produce sociality in large carnivores.

Dr. Arend Hintze (from Chris Adami’s lab at MSU) and I are testing Packer’s hypothesis using a simulated population of lions that hunt and reproduce in environments with varying visibility, prey size, and population densities. The lions are encoded as Markov Network Brains (MNBs), which are evolving networks of binary nodes. These networks include input nodes (allowing the lions to detect their environment), output nodes (allowing them to interact with their environment), and hidden nodes, which store information (see http://adamilab.msu.edu/markov-network-brains/ for more information on MNBs). Over the course of rapid evolution, the structure of the MNB evolves, allowing inputs to be linked to outputs in arbitrary ways. As such, the lions evolve the ability to respond to their environment.

Lotka-Volterra oscillations of predator (red) and prey (green) populations in our simulated environment. Note that the oscillations are slightly offset. Predator populations are initially large but drop quickly until they learn to hunt and reproduce. Prey populations are maintained at a minimum of 100 to prevent extinction.

In our experiment, an environment is seeded with a population of randomly moving prey and naïve lions. The lions begin with a random MNB, and need to learn to hunt for prey. When a prey item is killed it becomes a carcass, and any individual may feed on the carcass until it has been fully consumed. The digital lions reproduce after they have consumed a certain amount of food, and the offspring inherit the parents’ MNB, with some number of mutational changes. Environments are created with different properties (e.g. good visibility vs. bad visibility, large vs. small prey) and we observe whether certain environments lead to kin associating more closely than others. Data collection is ongoing, but thus far we have found that our simulated predator-prey ecology is similar to natural predator-prey ecologies. Populations of simulated lions and their prey undergo localized and global population fluctuations such as those described by the Lotka-Volterra equations, which characterize the linkage between populations of predators and prey. In our experiments, as in the natural world, prey and predator populations undergo similar but slightly offset population size oscillations, where peaks in prey populations are associated with increasing predator populations, and peaks in predator populations are associated with increasing prey populations. It remains be seen whether there are differences in sociality among our experimental treatments.

Are the three ecological conditions proposed by Packer sufficient to produce sociality in our simulated lions? Is any one of these conditions alone sufficient to produce sociality? Digital evolution provides a powerful platform for asking evolutionary questions that are difficult to answer with empirical observation alone.

References:

Caro, T. M. (1989) “Determinants of asociality in felids.” Comparative socioecology: the behavioral ecology of humans and other mammals Staden V., Foley RA 41-74.

Packer, C. (1986) “The ecology of sociality in felids.” Ecological aspects of social evolution 104: 429-451.

Schaller, G. B. (1972) The Serengeti Lion: A Study of Predator-prey Relations. University of Chicago Press.

For more information about Eli’s work, you can contact him at straussed at gmail dot com.

Posted in BEACON Researchers at Work | Tagged BEACON Researchers at Work, Biological Evolution, Digital Evolution, predator-prey, sociality | Leave a comment

BEACON Researchers at Work: Patterns and processes of community assembly of plants in oceanic and alpine island ecosystems

Posted on May 6, 2014 by Danielle Whittaker

This week’s BEACON Researchers at Work blog post is by University of Idaho graduate student Hannah Marx.

“I love my job.” – written by me, at 12,009 feet in the summit log on Hyndman Peak, Pioneer Mountains, Idaho.

Although I never thought that I would grow up to be a scientist, it is clear looking back that I have always been a naturalist at heart—boxes of rocks, various types of seed pods, shells, leaves, and fossils that I began collecting and identifying as early as I can remember attest to that. Now, I feel incredibly fortunate that I have found a career in which I can combine work with my personal passion for exploring alpine regions and attempt to shed light on questions that have sparked human curiosity for ages.

I suspect the most used word in ecology and evolution would be “diversity”. It seems that biologists have some universal fascination for understanding why some groups of organisms or particular places are more or less diverse than others. I am no exception to this rule. My research aims to explore processes that drive the patterns of biodiversity that we observe in nature. I am especially interested in questions that relate ecological processes to the evolution of species and communities, typically at macro-scales and taking advantage of the vast amount of datasets that are publicly available.

Since Darwin began writing about his travels in the Galapagos, islands have been of particular interest to evolutionary biologists, resulting in the description of many testable hypotheses of community assembly in natural systems (e.g. MacArthur and Wilson 1967). Most broadly defined, an island can be described as “a self-contained region whose species originate entirely by immigration from outside” (Rosenzweig 1995). Although traditionally considered to be landmasses surrounded by bodies of water, islands can also be viewed as habitats that are isolated from the surrounding ecosystems due to extreme climates, edaphic factors, or any other isolating mechanisms. Alpine ecosystems are often thought of as ‘sky islands’, isolated from the lowlands by an altitudinal gradient. Harsh climates that characterize this ecosystem drive the evolution of novel traits to cope with pressures from ultraviolet radiation, extreme cold, drought, and short growing seasons (Körner 2003). These two types of island systems share similar geographic characteristics that have made them attractive study sites—manageable in size and separated from the mainland at a known geologic date, they are ideal systems for understanding questions such as the forces driving speciation, diversification dynamics, and ecological composition. However, they differ in other characteristics, notably the number and distribution of invasive species.

Ranges of invasive species are increasing—especially on islands (Pyšek and Richardson 2006) and in alpine communities (Pauchard et al. 2009)—and in the face of accelerating global change it is of economic importance to predict which species are most likely to become invasive and their potential for future spread and impact. However, due to the severity of harsh and variable environments in alpine communities, most introduced species reach their distributional limits in sub-alpine and alpine zones (Becker et al., 2005). Combined with their remote location, invasion in alpine “islands” is much less common than in oceanic and continental islands. Modern species introductions are an important process to study not only for their economic and conservation concerns, but also, when considered as recent colonizers in an ecological community, invaders can be used to understand the processes of community assembly in nature (Tilman 2004).

The objective of my PhD dissertation is to provide insight into generalizable processes that produce biodiversity by comparing patterns of diversity and species invasions between highly invaded oceanic islands and relatively ‘pristine’ alpine plant communities. Typically in a comparative research approach such as this, we observe patterns in nature and then form hypotheses about the processes that generated them. I take a similar approach, beginning by describing and summarizing patterns of diversity in natural systems, and then using these patterns to test classic hypotheses of community assembly.

In oceanic and continental islands, we often have a much better understanding of the flora and I am able to gather occurrence data from publicly available sources for my studies. This was the case for my first graduate research project, which dissected patterns of community assembly in the San Juan Islands of Washington State. I compared phylogenetic and functional diversity (measured by ecologically relevant functional traits) between invasive and native species on uninhabited islands within the continental archipelago, and used this ‘ecophylogenetic’ approach to deconstruct Darwin’s Naturalization Hypothesis in the San Juan Islands.

Due to their remote nature, basic aspects of many alpine habitats are poorly understood. This is especially true for the alpine habitats in Idaho, where we have a very poor understanding of the species that occur above treeline. Therefore, I get to combine my research interests with one of my favorite personal interests—alpinism. Over the last few summers I have been climbing peaks across the Sawtooth National Forest in central Idaho, surveying the plants that occur from the summit down to treeline. I also collect leaf tissue that will be used in molecular studies to summarize the genetic diversity in these remote and unique ecosystems. Much of this work has involved multi-day excursions into some of the most remote areas of the continental United States with the help of the Sawtooth Mountain Guides and other graduate student volunteers, without whom these collections would not be possible. From these important collections, I will follow a similar approach as I did with the San Juan Islands to summarize phylogenetic and functional diversity, and compare dominant diversity dynamics between the two island systems.

This line of research is now leading to adventures beyond the Idaho wilderness. Currently, I am spending a year as a visiting graduate researcher at the Laboratoire d’Ecologie Alpine in Grenoble, France. Through a NSF Graduate Research Opportunities Worldwide program, I am collaborating with Dr. Sébastien Lavergne on a large comparative project investigating patterns of diversity between the European Alpes and the Rocky Mountains. Here, we are continuing to combine phylogenetic methods with functional trait-based community ecology at a much larger spatia

l scale.

I am grateful that I have found a way to combine research with my passion for alpine adventures. Hopefully though this synchronous lifestyle and comparative framework, I will contribute something to our understanding of the processes that generate assemblages of plants on islands, and hope to clean up the “mess” that is community ecology.

References:

Becker T., Dietz H., Billeter R., Buschmann H., Edwards P.J. 2005. Altitudinal distribution of alien plant species in the Swiss Alps. Perspectives in Plant Ecology, Evolution and Systematics. 7:173–183.

Körner C. 2003. Alpine plant life : functional plant ecology of high mountain ecosystems. Berlin; New York: Springer.

MacArthur R.H., Wilson E.O. 1967. The theory of island biogeography. Princeton, N.J.: Princeton University Press.

Pauchard A., Kueffer C., Dietz H., Daehler C.C., Alexander J., Edwards P.J., Arévalo J.R., Cavieres L.A., Guisan A., Haider S., Jakobs G., McDougall K., Millar C.I., Naylor B.J., Parks C.G., Rew L.J., Seipel T. 2009. Ain’t no mountain high enough: plant invasions reaching new elevations. Frontiers in Ecology and the Environment. 7:479–486.

Pyšek P., Richardson D.M. 2006. The biogeography of naturalization in alien plants. Journal of Biogeography. 33:2040–2050.

Rosenzweig, M.L. 1995. Species Diversity in Space and Time. Cambridge: Cambridge University Press.

Tilman D. 2004. Niche tradeoffs, neutrality, and community structure: a stochastic theory of resource competition, invasion, and community assembly. Proceedings of the National Academy of Sciences of the United States of America. 101:10854-10861.

For more information about Hannah’s work, you can contact her at h dot marx dot h at gmail dot com.

Posted in BEACON Researchers at Work | Tagged alpine, BEACON Researchers at Work, biodiversity, community ecology, ecology, evolution, Field Biology, plant biology | Leave a comment

BEACON Researchers at Work: Mating System and Molecular Evolution

Posted on April 28, 2014 by Danielle Whittaker

This week’s BEACON Researchers at Work blog post is by University of Texas at Austin graduate student Rayna Harris.

Research in Hans Hofmann’s lab at UT Austin is best known for its studies of the neurogenomic basis of male social behavior, but Hans and his entourage have never shied from using comparative approaches to study the evolution of social behavior (listen to Hans on NPR’s Academic Minute). My research in the Hofmann lab has fallen into the second category, as two of my favorite comparative experiments investigated 1) the behavioral evolution from a monogamous to polygynous mating system and 2) the molecular evolution of the neuropeptide pro-opiomelanocortin (pomc).

Mating System Evolution in Herichthys Cichlid Fishes

A H. cyanoguttatus pair defends their territory.

In 2010 and 2011, I investigated the neuroendocrine mechanisms that underlie differences in social behavior between two closely related North American cichlid fishes (1). Herichthys cyanoguttatus, the Rio Grande cichlid, lives in rivers and drainages of the Gulf Coast of northern Mexico and southern Texas. They are sexually monomorphic (see photo at right), monogamous, and provide bi-parental care for offspring, often over multiple breeding cycles. Herichthys minckleyi, the Cuatro Ciénegas cichlid, lives in the pristine spring-fed ponds and streams in the desert valley of Cuatro Ciénegas in northern Mexico. They are sexually dimorphic and polygynous, with males providing little paternal care.

Neuroendocrine changes. This graph shows how expression of arginine vasotocin (avt) and its receptor (avtr), isotocin (it), prolactin (prl) and its receptor (prlr) differ by brain region and species as compared to hypothalamic expression in H. cyanoguttatus. 18S rRNA expression is stable. Bars show median expression (log2fold change) and whisker are 95% confidence intervals.

We collected blood, brains and gonads in the “field” (our field stations were set up on the running path along Shoal Creek Trail just north of 6^th Street in downtown Austin and in two outdoor pools at UT’s J.J. Pickle Research Campus). In the lab we micro-dissected the brains and processed the blood for quantitative real-time PCR and hormone assays, respectively. We found that two circulating androgens are differentially regulated between males of each species. I analyzed my qPCR data with an awesome new R package called MCMC.qpcr and created bar graphs to visualize the species- and brain region- differences relative to H. cyanoguttatus hypothalamic expression (see figure at left). A few significant results include increased expression of prolactin (prl) in the hypothalamus and prolactin receptor (prlr) in the telencephalon H. minckleyi, indicating increased activity of the prolactin pathway in this species. Next, I used a correlation network approach to identify patterns of conserved endocrine activity in these two species (below). This covariance network suggests a relationship between circulating androgen levels and neuroendocrine receptor expression in the brain. Up-regulation of gene networks, not individual genes, appears to be important for driving changes in behavior. Future research is needed to disentangle neural differences associated with habitat independent of social behavior.

Covariance network. This network shows the relationship between the androgens testosterone and 11-ketotestosterone (11-KT) and gene expression in the telencephalon (TEL) and hypothalamus (HYP) across all individuals in the analysis.

This study focused on prolactin, vasopressin, and isotocin pathways, but other research in our lab was investigating the role of the melanocortin system in regulating social behavior and other phenotypic traits in cichlid fishes.

Molecular Evolution of the Pro-opiomelanocortin Gene Family

In 2011, the genomes of five African Cichlid species were made publicly available, and I became engrossed with understanding to which extent the melanocortin system underlies polymorphisms in color, behavior, and physiology across species. I designed a research project to examine the molecular evolution the neuropeptide pro-opiomelanocortin (pomc) gene family (2). Most teleosts have a duplicate pomc gene from the teleost whole genome duplication (pomc β), but the pomc α paralog recently duplicated in multiple lineages independently (pomc α1 and α2). I was isolating and sequencing these paralogs in A. burtoni when I obtained a curious result in the pomc β gene: a portion of the α-melanocyte stimulating hormone (α-MSH) region was duplicated! After confirming that it wasn’t a technical artifact (the duplication is present in African- and American cichlids and damselfish), I devoured the literature and learned that the α-MSH region of pomc gene had four other times in evolution. I had just uncovered the fifth example, so I named the region “ε-MSH” to follow the established nomenclature on naming peptides with a melanocortin binding site (cleaved peptide sequence: S Y R M E H F R W G K P A G L K M R E P K L K A R S D E). Next, I used bioinformatic approaches to understand how changes in regulatory sequence gave rise to differential tissue expression of pomc orthologs and paralogs. This analysis of subfunctionalization of duplicated genes inspired me to use functional approaches to study genome regulation as part of my dissertation, which aims to examine evolution of steroid hormone response elements in the genome. So

, stay tuned for more on that the subject of cis-regulatory evolution of steroid hormone receptors.

Outreach & Networking

International networking. Hans and I (center) had a great time participating at the conference and workshop in Bergen, Norway.

I gained a lot of practical experience working on the two projects above, but perhaps even more importantly, they provided many opportunities for honing my writing and speaking skills. I presented the mating system evolution story to my peers at the 2^nd Annual Brain, Behavior & Evolution Annual Symposium and to the public during a Darwin Day outreach event and on the radio program “They Blinded Me with Science”. I presented the molecular evolution story at a conference in Bergen, Norway and at the Gordon Research Conference: Genes & Behavior. All of these activities provided a stimulating environment to discuss my research with the intellectual and the curious, and they opened new doors for opportunities in research, teaching, and outreach. Who knows, maybe one day I’ll give a TED talk about the role of molecular evolution in shaping behavioral polymorphisms.

Oldfield RG, Harris RM, Hendrickson DA, Hofmann HA (2013) Arginine Vasotocin and Androgen Pathways are Associated with Mating System Variation in North American Cichlid Fishes. Hormones and Behavior 64: 44–52.
Harris RM, Dijkstra PD, Hofmann HA (2014) Complex structural and regulatory evolution of the pro-opiomelanocortin gene family. General and Comparative Endocrinology 195,: 107–115

For more information about Rayna’s work, you can contact her at rayna dot harris at utexas dot edu or like the Hofmann lab Facebook page for research updates.

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BEACON Researchers at Work: The Evolution of Regeneration in the Deuterostomes

Posted on April 21, 2014 by Danielle Whittaker

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

Shawn Luttrell at Friday Harbor Laboratories Open House, showing invertebrates to the public. Photo courtesy of Kathy Ballard.

Regeneration has captured the interest and imagination of people for centuries. Popularized in myths, science fiction, and even horror movies, regeneration of missing and damaged tissue is anything but fictional in the animal kingdom. Nearly every animal phyla contains at least some species that consistently regenerate all or certain tissues and structures. All deuterostome groups, with the possible exception of cephalochordates and xenoturbella, have at least some species with the capacity to regenerate (Sánchez Alvarado, 2000). Numerous chordates, like tunicates, frogs, fish, salamanders, and even humans are able to regenerate to some degree. Every extant class of echinoderms have been reported to regenerate, while some species of hemichordates, which are a sister group to the echinoderms, are capable of regenerating all body structures (see figure below; Candia Carnevali at al., 2009; Rychel and Swalla, 2008; Humphreys et al., 2010). When and in what animal lineage did the ability to regenerate first evolve? Was regeneration a stem metazoan trait that was subsequently lost or reduced in numerous taxa or has regeneration evolved independently several times across the metazoans? These questions, which remain unanswered after years of study, fascinate me and form the foundation of my Ph.D. graduate thesis research.

Deuterostome phylogeny showing the hemichordates in a sister group to the echinoderms. Photo modified from Swalla et al., 2001.

Deuterostome phylogeny showing the hemichordates in a sister group to the echinoderms. Image modified from Swalla et al., 2001.

Understanding the morphological and genetic mechanisms for regeneration may yield clues to unlocking regeneration in animals with limited or no regenerative abilities, like humans. Millions of people suffer from neurodegenerative diseases, spinal cord injuries, and limb amputations. Furthermore, aging and age related diseases affect every person on the planet. Regeneration may slow the aging process and stem cells present feasible ways to combat a multitude of diseases and injuries. If regeneration is a stem deuterostome trait, it is likely that humans possess many, if not all, of the genetic switches controlling regeneration, but those switches have been modified or inactivated in some way over evolutionary time. It may therefore be possible to re-activate those pathways in humans using genetic models made from animals with extensive regenerative capabilities.

The solitary hemichordate, Ptychodera flava

To help elucidate the origin of regeneration, I am working with Dr. Billie J. Swalla at the University of Washington to identify the genetic underpinnings controlling regeneration in the hemichordate, Ptychodera flava, which is a basal deuterostome and capable of regenerating all anterior and posterior structures when bisected (Rychel and Swalla, 2008; Humphreys et al., 2010). We are collaborating with Dr. Alejandro Sánchez Alvarado of the Stowers Institute for Medical Research to sequence, assemble, and analyze the transcriptome of P. flava. This is part of an overarching transcriptome project, across numerous animal phyla, to determine whether there is a common gene or set of genes expressed across all species during regeneration. If the same genetic mechanisms are used, this would supply data to support the theory that regenerative powers evolved once from a common ancestor to all animals studied. If the data shows little to no overlapping expression in genes of interest, this may support the hypothesis of multiple, independent, evolutionary nodes of regeneration. As it is much easier evolutionary to lose a trait rather than gain, we believe the data will support a single line of descent.

Regenerating proboscis and collar on Ptychodera flava at 7 days post amputation.

I am also working to characterize expression patterns of neural genes and then map that to morphological changes accompanying nervous system regeneration in P. flava to supplement the transcriptome data. As a basal deuterostome with a bona fide central nervous system and remarkable regenerative powers, this animal presents an exciting model to study nervous system regeneration. With this and the transcriptome data, we can directly compare regeneration of the neural tube to early development of the neural tube and ascertain whether regeneration is a morphological and genetic recapitulation of development. Preliminary results suggest regeneration may employ morphological pathways not invoked during early development. If confirmed, this result is exciting because it means there are multiple ways to achieve the same biological structure and demonstrates developmental plasticity during regeneration.

Another component of my thesis work will be to determine the origin and character of stem-like cells employed during hemichordate regeneration. Are these cells multi-potent stem cells with similar genetic expression as vertebrate stem cells or are the cells coming from a population of differentiated cells that have returned to a progenitor cell state and are assigned new cell fates in regenerating tissue? To answer this question, I will use in situ hybridization with known vertebrate stem cell markers, like piwi, vasa, and nanos. Expression of these markers, coupled with functional identity, would support hemichordates having genuine multi-potent stem cells. If basal hemichordates and vertebrates both possess multi-potent stem cells, then the common ancestor of the deuterostomes likely had these cells as well. If, on the other hand, expression and functional relatedness cannot be confirmed, this might suggest that differentiated cells are undergoing cell fate reassignment. In humans, when a cell differentiates into a particular cell fate, the cell’s condition cannot be reversed or altered. If cells are undergoing cell fate reassignment in hemichordates, this will have implications for creating stem cells from differentiated cells in humans by revealing cell signaling pathways necessary for the gain and loss of cell fates.

Though I am in the early stages of my research, I have already obtained exciting preliminary data. I have collected live P. flava and performed various regeneration experiments. I have isolated total RNA at different stages of
regeneration for the transcriptome. Sequencing and assembly is currently underway. I have also fixed numerous animals and I am making sections of the regenerating tissue for staining with particular biomarkers to obtain a clear picture of how and when missing neural structures are elaborated. My research may reveal key evolutionary mechanisms of regeneration in the deuterostomes.

References

Candia Carnevali, M.D. (2006). Regeneration in echinoderms: Repair, regrowth, cloning. Invert Surv J. 3, 64-76.

Humphreys, T., Sasaki, A., Uenishi, G., Taparra, K., Arimoto, A,. Tagawa, K. (2010). Regeneration in the hemichordate Ptychodera flava. Zoolog Sci. 27(2), 91-95.

Rychel, A.L. and Swalla, B.J. (2008). Anterior regeneration in the hemichordate Ptychodera flava. Dev. Dyn. 237(11), 3222-3232.

Sánchez Alvarado, A. (2000). Regeneration in the metazoans: Why does it happen? Bioessays. 22, 578-590.

For more information about Shawn’s work, you can contact her at shawnluttrell at gmail dot com.

Posted in BEACON Researchers at Work, Uncategorized | Tagged BEACON Researchers at Work, Biological Evolution, development, genetics, nervous system, regeneration | Leave a comment

BEACON Researchers at Work: Solar and geomagnetic activity forecasting using evolved Markov networks: Darwin vs. space weather hazards

Posted on April 14, 2014 by Danielle Whittaker

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

“Space Weather” hazards have achieved a great international scientific and public importance in recent years due to their catastrophic effects on modern technologies such as satellites and other distributed facilities. Today, space weather is a branch of science that will give new insights into the complex influences and effects of our violent Sun and other cosmic sources on interplanetary space, the Earth’s magnetosphere, ionosphere, and thermosphere that can influence the performance and reliability of space-borne and ground-based technological systems, and beyond that, on their endangering affects to life and health (Bothmer & Daglis, 2007; Moldwin, 2008). The Sun–Earth system is a complicated time varying system, ranging from magnetic field reconnection and accelerated solar wind as hot plasma to impact of charged particles on manmade electronic devices and biological systems.

Known space weather hazards on manmade technologies (Lanzerotti, Thomson, & Maclennan, 1997).

The effects of our violent sun, as the main source of space weather disturbances, on our space environment ranging from producing faults in spacecraft operations to disruptions of distributed electrical power systems to the manufacturing of precision equipment have been well documented for more than 35 years (Kane, 2006). Space weather hazards on average cause annual losses of the order of more than $100 million (Maynard, 1990). The figure at right shows the known space weather effects on manmade technologies.

Considering the catastrophic effects of space weather on human technology, accurate predictions of space weather indexes seems to be an urge for modern society. During the past two decades, scientists have been working on this problem and have introduced different approaches to predict solar and geomagnetic activity indexes (Feynman and Gabriel, 2000; Vassiliadis, 2000).

In this research, we evolve Markov networks (MNs) (shown in figure below), which are probabilistic finite state machines (Edlund et al., 2011; Marstaller, Hintze, and Adami, 2013), to predict one of the famous “space weather” indexes in long-term: Sunspot number (SSN) (shown in graph below and are caused by intense magnetic activity, which inhibits convection by an effect comparable to the eddy current brake, forming areas of reduced surface temperature). These networks do not have the mentioned limiting assumptions on model structure and inputs. These networks choose the most informative inputs and the optimal structure through the course of evolution for a given problem; therefore, evolution helps us to solve input selection and structure system identification problem simultaneously.

A Markov network with 12 nodes and two Probabilistic Logic Gates (PLGs). Once the nodes at time t pass binary information into the PLGs, the PLGs activate and update the states of the nodes at time t+1.

Sunspot number time series from 1600, showing the 11-year cycles of solar activity. Before 1750, the record is yearly and sporadic, after that we have monthly and daily data.

By using evolutionary algorithms, we are able to discover Markov networks that are able to predict SSN index accurately close to its theoretic prediction limit imposed by the chaotic nature of the signal. We evolved Markov networks and predict daily SSN one-step ahead for different years and states of solar activity to compare its performance with other well-established methods. We found that on average Markov network had the best performance (shown below).

One step ahead prediction of daily sunspot number: (a) blue: actual sunspot number, red: one step ahead prediction. (b) one step ahead prediction vs. actual SSN time series.

Our ultimate goal in this project is to apply evolutionary algorithms to evolve Markov networks that are able to predict the solar and geomagnetic activity indexes near to their theoretic prediction limit imposed by the chaotic nature of space weather. By having an accurate prediction of these indexes, we can have an alarm system to avoid hazards of forthcoming geomagnetic storms on modern technologies. Moreover, we hope that by analyzing the structure of evolved networks, we are able to find time dependencies between lags of these indexes, which are difficult to capture with existing physical models.

References:

M. Moldwin, An Introduction to Space Weather. Cambridge University Press, 2008.
V. Bothmer and I. Daglis, Space Weather: Physics and Effects. Springer Praxis Books / Environmental Sciences, Praxis Publishing Limited, Chichester, 2007.
I. A. Daglis, Space storms and space weather hazards, vol. 38. Springer, 2001.
R. Kane, “The idea of space weather–a historical perspective,” Advances in Space Research, vol. 37, no. 6, pp. 1261–1264, 2006
L. J. Lanzerotti, D. J. Thomson, and C. G. Maclennan, “Wireless at high altitudes environmental effects on space-based assets,” Bell Labs technical journal, vol. 2, no. 3, pp. 5–19, 1997.
P.-N. Mayaud, Derivation, meaning, and use of geomagnetic indices, vol. 22. American Geophysical Union, 1980.
J. Feynman and S. B. Gabriel, “On space weather consequences and predictions,” Journal of Geophysical Research: Space Physics, vol. 105, no. A5, pp. 10543–10564, 2000.
D. Vassiliadis, “System identification, modeling, and prediction for space weather environments,” Plasma Science, IEEE Transactions on, vol. 28, pp. 1944–1955, Dec 2000.
J. A. Edlund, N. Chaumont, A. Hintze, C. Koch, G. Tononi, and C. Adami, “Integrated informa- tion increases with fitness in the evolution of animats,” PLoS computational biology, vol. 7, no. 10, p. e1002236, 2011.
L. Marstaller, A. Hintze, and C. Adami, “The evolution of representation in simple cognitive net- works,” Neural computation, vol. 25, no. 8, pp. 2079–2107, 2013.

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

Posted in BEACON Researchers at Work | Tagged BEACON Researchers at Work, evolutionary algorithms, Evolutionary Computation, Markov networks, space weather | Leave a comment

Using fitness landscapes to visualize evolution in action

Posted on April 9, 2014 by Danielle Whittaker

BEACONites Bjørn Østman and Randy Olson created a video to visualize evolution in action using fitness landscapes. Read about it below!

Fitness landscapes were invented by Sewall Wright in 1932. They map fitness, or reproductive success, of individual organisms as a function of genotype or phenotype. Organisms with higher fitness have a higher chance of reproducing, and populations therefore tend to evolve towards higher ground in the fitness landscape. Even though only two traits can be visualized this way, we can actually observe evolution in action. Here we explore three phenomena in evolutionary dynamics that can be difficult to comprehend.

First we show dynamic landscapes with two fluctuating peaks in which the population track the peaks as they appear at difference locations in phenotype space. We also demonstrate negative density-dependent selection, which causes the population to split into distinct subpopulations located on separate peaks, illustrating how speciation can occur in sympatry. Lastly, we show the survival of the flattest where the population prefers a tall narrow peak at low mutation rate, but moves to the lower but wider plateau at high mutation rate. These examples highlight how visualizing evolution on fitness landscapes fosters an intuitive understanding of how populations evolve.

Posted in BEACON Researchers at Work | Tagged evolution in action, evolutionary dynamics, fitness, fitness landscapes, speciation | Leave a comment

BEACON Researchers at Work: Holey Fitness Landscapes

Posted on April 7, 2014 by Danielle Whittaker

This week’s BEACON Researchers at Work post is by MSU postdoc Bjørn Østman, and is also posted on his research website.

What do real fitness landscapes look like? Do they look more like the image on the left, a nearly-neutral holey fitness landscape, or the one on the right, a rugged fitness landscape with many distinct peaks?

Those are only in two dimensions, so the question is also if depicting anything in two dimensions conveys intuitions that are at all correct.

Holey fitness landscapes (Gavrilets and Gravner, 1997, Gavrilets 1997) are approximations of real fitness landscapes where all genotypes are assigned a fitness value of either zero or one. After normalizing fitnesses to be between zero and one, those that lower than one are assigned a fitness of zero¹. Because real fitness landscapes are of extremely high dimensionality², and assuming that genotypes have fitnesses that are randomly distributed³, it follows that there exist a nearly-neutral network on genotypes connected by single mutations that has fitness (effectively) equal to one.

The proposition is then that this holey landscape model is a good approximation of real fitness landscapes. It hypothesizes that the evolutionary dynamics on real fitness landscapes is similar to that on holey landscapes, and that distinct peaks like in the image on the right do not really exist. And this is a testable prediction.

Take a look at these videos. They depict populations evolving in two-dimensional fitness landscapes at a very high mutation rate. (You can also download the videos from my research website.)

In all three cases the population size is 2304 (that’s (3*16)², in case you’re wondering), mutation rate is 0.5, the grid is 200×200 pixels (i.e. genotypes), and mutations cause organisms to move to a neighboring pixel. Ten percent of the population is killed every computational update (which gives an approximate generation time of 10 updates), and those dead individuals are replaced by offspring from the survivors selected with a probability proportional to fitness (asexual reproduction). Top: neutral landscape where all genotypes have the same fitness. Middle: Half-holey landscape with square holes of 10% lower fitness (size of holes is 14×14 pixels). Bottom: Holey landscape where the genotypes in the holes have fitness zero.

The proposition is that the dynamics of the populations should be the same no matter how deep the holes are. The populations in the half-holey and in the holey landscapes should evolve in comparable ways if the holey landscape is a good approximation.

So what do you think?

What I think is that the evolving population in the top (neutral) and middle (half-holey) landscapes resemble each other, whereas they look nothing like the bottom (holey) landscape. In the half-holey landscape the population takes advantage of the holes all the time, meaning that many individuals who are in them reproduce, even though they have a clear fitness disadvantage. The lesson is that being disadvantaged is just okay, and populations can easily cross quite deep valleys in the fitness landscape. But obviously not when the valleys consist of genotype with zero fitness; evolution in holey landscapes are much impeded compared to rugged landscapes, which is why I think they are not a good approximation.

Caveats: These populations are evolving at a very high mutation rate. When I redid it with a much lower mutation rate (0.05), the neutral and half-holey landscapes stop resembling each other, and the half-holey and holey landscapes look more alike. However, evolution happens so slowly in this case that it is difficult to distinguish the dynamics, so the matter is unresolved so far (however, I have other evidence that lower and more realistic mutation rates do not change this conclusion – some preliminary data in √òstman and Adami (2013)). A second caveat is that the whole holey landscape idea relies on the fitness landscape being multidimensional, and so how can I even allow myself to compare evolution of populations in half-holey and holey landscapes in just two dimensions? That is valid question: the intuitions we get from these animations may lead us to think we know something about evolution in multi-dimensional landscapes, while the original premise of Gavrilets’ idea was that we exactly cannot. Unfortunately, while this is an empirical question – meaning that it could be tested – the holey landscape model posits that the neutral network appears at very high dimensionality. What this dimensionality is is unclear, so even if I were to evolve populations in 2,000 dimensions (which is not computationally feasible – the limit is a little over 30 binary loci), one could always claim that not even that many are enough. Sighs.

¹ Genotypes with fitness greater than 1 divided by the population size, N, are effectively the same, because selection cannot “see” differences smaller than 1/N.

² High dimensionality means a large number of genes (loci) or number of nucleotides.

³ We already know that this is not a very good assumption, as there are indications that fitness landscapes are non-randomly structured with high fitness genotypes clustered with other fit genotypes (Østman et al, 2010), but we don’t know if it is enough to render the holey landscape model useless.

References

Gavrilets S, and Gravner J (1997). Percolation on the fitness hypercube and the evolution of reproductive isolation. Journal of theoretical biology, 184 (1), 51-64 PMID: 9039400

Gavrilets S (1997). Evolution and speciation on holey adaptive landscapes. Trends in ecology & evolution, 12 (8), 307-12 PMID: 21238086

Østman B and Adami C (2013). Predicting evolution and visualizing high-dimensional fitness landscapes, in Recent Advances in the Theory and Application of Fitness Landscapes” (A. Engelbrecht and H. Richter, eds.). Springer Series in Emergence, Complexity, and Computation DOI: 10.1007/978-3-642-41888-4_18

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BEACON Researchers at Work: Measuring natural selection in flowers

Posted on March 31, 2014 by Danielle Whittaker

This week’s BEACON Researchers at Work post is by MSU graduate student Raffica La Rosa.

Novel traits differ qualitatively from the characters from which they arise, and are generally thought to be adaptive. I study adaptive novel traits by combining studies of present-day natural selection to identify which traits are likely adaptive, and phylogenetic comparative analyses to understand the past evolution of those traits. My study system is the milkweed genus Asclepias. Milkweeds have amazing flowers that are distinct from all other other flowers on Earth. They have unique floral traits that likely influence how they interact with their insect pollinators, but little is known about how the floral traits might be adaptive.

Milkweed flowers have a number of unusual floral structures. Typically, an angiosperm flower is made up of four whorls. The sepals are the outermost whorl, are often green, and typically form the bud before the flower opens. The next whorl in is made up of petals, which we usually think of as the colorful, attractive part of the flower. The innermost whorls consist of the reproductive organs—the stamen produce pollen (male) and the carpels contain ovules (female). Milkweeds in the genus Asclepias have sepals and petals, but their male and female whorls have fused together and are almost unrecognizable.

In the center of the flower is the gynostegium, which forms a chamber containing two carpels. Rather than having loose pollen that might stick to pollinators, the pollen has been clustered together into five pairs of waxy pollen sacks called pollinia. The pollinia reside in the walls of the gynostegium between nectar-holding hoods that often have horn-like protuberances. The exposed dark gland (corpusculum) that attaches adjacent pollen sacks has a tapered slit, like the back of a hammer, that catches onto the hairs, claws, or mouthparts of pollinators, and allows the pollinia to be slipped out and transferred between flowers.

For pollination to occur, a single pollinium must be deposited into one of five slits around the outside of the gynostegium that lead to the central chamber. Once there, each pollen grain can grow a pollen tube to fertilize an ovule. The pollinium contains enough pollen grains to fertilize all of the ovules within a carpel, so the milkweed fruit (pod) that develops often contains 40-200 seeds that all share the same father. This can be very convenient for researchers, such as myself, who want to study natural selection through female fitness and male fitness, which is rarely done in plants. Measuring male fitness in plants is often very difficult because loose pollen from many individuals is easily jumbled, resulting in a fruit containing seeds sired by many different fathers; in milkweeds however, I can collect a pod, sprout just one seed, run genetic tests to figure out paternity, and then know the paternity of all of the seeds in that pod. This gives me the ability to find the paternity of up to 100% of the seeds in a population by only sampling about 3% of them!

With this handy feature of milkweed flowers, I can measure selection on floral traits through female fitness and male fitness separately to see if the unusual floral traits of milkweeds function more to help the plants produce more seeds (female fitness), or sire more seeds (male fitness). To do so, I just need just three things to run a selection gradient analysis: trait measurements, female fitness measurements, and male fitness measurements.

To collect trait measurements, I first determine which floral traits might be influencing the attraction, reward, and efficiency of pollinators, since Asclepias species depend on insect pollinators to transfer their pollen. After observing the flowers and their interactions with pollinators in nature, I choose floral traits that I think might be influencing pollinators, and ultimately affecting fitness. For instance, the size of the hoods and gynostegium most certainly affect the visibility of the flowers, the dimensions of the hood could affect the volume of the nectar reward, and the horns and spacing between hoods could influence how easily pollinators remove and deposit pollinia. To measure the traits, I collect several flowers per plant in the population, digitally photographed them, and later measure them from the photographs.

To measure fitness, I collect all of the pods in the population and record how many each plant produces. Later, I count the number of seeds in each pod. From these data alone, I can quantify female fitness, because I can say how many seeds each plant has produced. Measuring male fitness is a much longer process that starts by sprouting one seed from each of the pods. Once the seedlings are large enough, I collect them and extract their DNA. During the summer, I also collect leaf tissue from every possible parental plant in the population and extract DNA, so that I can match the offspring to their parents. I already know who the mothers are, but I can use genetic paternity tests to identify the fathers.

Regression of relative fitness onto a trait; the slope of the fitted line is the selection gradient. Each point represents an individual within the population.

I measure natural selection on each of the traits by using a multiple regression to regress relative fitness onto all six of the traits at once to account for any correlations between traits. The resulting coefficients are the selection gradients. Positive selection gradients mean that individuals with larger trait values will have higher fitness, and negative selection gradients mean that plants with smaller values of that trait have higher fitness. The larger the absolute value of the selection gradient is, the stronger selection is. Finding selection on a trait is a large first step toward knowing if a trait is adaptive.

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

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