2017 Darwin Day Roadshow – Apply Now!

Posted on October 18, 2016 by hageyt

Starting in 2016, BEACON began collaborating with three scientific societies (ASN, SSE, SSB) to continue support for a variety of outreach opportunities that needed a new home after NESCent ended. Today we’re highlighting one of these opportunities – the Darwin Day Roadshow!

The roadshow is a way for scientists and educators to share their excitement about the science of evolution. Each year around the time of Charles Darwin’s birthday (February 12), teams of scientists talk to students, teachers, and the general public about their research in evolutionary science, their career path, and why evolutionary science is relevant to everyone. The roadshow has visited over 24 states so far since 2011!

We are currently planning for the 2017 Roadshow and are looking for local hosts, typically teachers! These hosts – known as ‘Darwin Day Scholars’ – work with the roadshow staff and scientists to design a set of activities that best serve their school and community. If you are a teacher or know of a teacher who may be interested, we invite you to read more about the roadshow on our website. The deadline for host applications for the 2017 Darwin Day Roadshow is Friday, December 2, 2016. HOSTS APPLY HERE!

A scientist visiting a classroom during a previous roadshow

If you are a scientist interested in participating in the roadshow, we would love to have you involved! Please fill out the ‘Roadshow Scientist’ application form HERE. The deadline for scientist applications for 2017 is Wednesday, December 21, 2016.

Any questions about the program can be directed to Alexa Warwick.

 

 

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Scientific Symposium at the SACNAS National Conference

Posted on October 13, 2016 by hageyt

The Society for Systematic Biologists and the BEACON Center for the Study of Evolution in Action have collaborated to organize a scientific symposium at the Society for the Advancement of Chicanos/Hispanics and Native Americans in Science national conference in Long Beach, California on October 14, 2016.

(Day and) Night at the Museum: Exploring Research in Ecology and Evolution behind the Scenes of Natural History Museums

Natural history museums house the world’s past and present biological diversity. Beyond the specimens and materials displayed to the public, these critical collections inform numerous research questions that seek to understand processes of evolution and ecology. This symposium offers a behind-the-scenes glimpse into the lives and research of museum-based scientists.

Speakers:

  • Corrie Moreau, PhD — How I Became a Rainforest Explorer: Ant Genomes to Microbiomes
  • Andreas Chavez, PhD — My personal experiences with admixture and what admixture can tell us about speciation and adaptation
  • Seema Sheth, PhD — Harnessing the power of herbarium specimen data for ecological and evolutionary studies
  • Lauren Esposito, PhD — Can arachnids save the planet? A journey through natural history and conservation
  • Scott Edwards, PhD — Using museum collections to study the genomics and evolution of birds

SACNAS is a national organization focused on increasing the proportions of underrepresented minorities in science, technology, engineering, and math (STEM) fields. In 2015, the National Conference was attended by 3,746 scientists from diverse backgrounds, with almost 60% of the participants members of ethnic/racial groups that are significantly underrepresented in STEM fields. This year promises to surpass 4,000 attendees!

For many people, a visit to a natural history museum may represent their first exposure to the marvels of science. What they may not realize is just how much scientific research is being conducted behind the walls and awe-inspiring displays. The goal of this symposium is to reveal the exciting biological research conducted within different major natural history museums across the US. By attending this session, participants will learn about the use of natural history collections in ecology and evolution research such as plant ecology, population genetics in mammals, avian comparative genomics, arachnid biogeography, and the interactions between ants and their gut microbiomes. Additionally, the speakers will discuss their paths as scientists and museum researchers, giving a behind-the-display glimpse from public natural history museums like the California Academy of Sciences and the Field Museum in Chicago, as well as university-affiliated museums/herbaria at UC Berkeley, Harvard, and Ohio State University.

For anyone attending the SACNAS conference, please join us on for the symposium on Friday, October 14, 10:15-11:45 in room 201B!

In addition to the scientific symposium, BEACON and SSB are also contributing the following activities to the 2016 conference:

 

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Meiotic Recombination: Crossing-over into Livestock Species

Posted on October 11, 2016 by hageyt

This post is by Kimberly Davenport, first year graduate student in Animal Science with Dr. Brenda Murdoch at the University of Idaho and Brenda Murdoch, assistant professor of animal genetics at the University of Idaho.

Kim Davenport

With each research project comes its own challenges, rewards, and quirks. My research project in Dr. Brenda Murdoch’s laboratory involves characterizing homologous recombination in different mammalian species. Homologous recombination is an extremely important process in gametogenesis that not only contributes to genetic variation but also ensures proper chromosome segregation during cell division in meiosis I. Errors due to failure or improper placement of recombination represent a significant contribution to aneuploidy, developmental disabilities, fetal loss and infertility [1]. Despite the importance, we know very little about the factors that control and/or influence global meiotic recombination in mammals.

Work in model organisms; yeast, fruit flies, and mice, have outlined the steps in the recombination pathway [2]. For example, we have gained insights on the sequential relationship of chromosome pairing, sister chromatid cohesion, synapsis and recombination. Meiotic recombination is initiated by a topoisomerase like protein (SPO11) that establishes breaks in the chromosomes. Chromosome breaks are resected with the aid of strand invasion proteins RAD51 and DMC1 and ultimately produce double Holliday junction intermediates. These are resolved as either crossovers or non-crossovers. The majority of non-crossovers are generated early in the pathway and only a subset of the breaks are resolved as crossovers. Crossover repair protein MLH1 localizes the majority of crossovers [3]. Interestingly, only a few of the breaks are resolved as crossovers and the rules that govern the frequency and sites remain unclear.

Over many years of research, scientists have identified a few general guidelines associated with meiotic recombination. First, one recombination event, or crossover, is required per chromosome arm pair for proper cell division. Second, we know from previous studies that the placements of recombination events are not random. Crossovers exhibit preferences in the genome called “hotspots” and experience “interference” in that one crossover cannot occur too close in proximity to another [4]. Third, crossover numbers have been shown to be sexually dimorphic (different between males and females) in many different species. And lastly, the number of crossovers is correlated with the size of the genome and number of chromosome arms (i.e. the more chromosome arms, the more crossovers are likely to occur).

Although recombination occurs in many different species, our lab focuses primarily on livestock species, specifically sheep and cattle. These two domestic species are not only important for agricultural food production, but also provides an interesting biological comparison and insight into how recombination has evolved in ruminants. While sheep and cattle have the same number of chromosome arms and a similar genome length, they do not have the same number of genome-wide recombination events. This poses a number of important questions about how genomic meiotic recombination levels are controlled in different domesticated species.

In the Murdoch lab we identify and characterize crossover events using our optimized immunohistochemistry method. This cytogenetic approach allows us to directly observe where chromosomes recombine through the microscope. We identify crossovers with an immunofluorescent antibody which specifically binds to MLH1, a protein that is known to be involved in repairing DNA breaks into crossovers.

Figure 1: Meiotic cross-overs in sheep and cattle. A) Representative images of MLH1 foci on synatonemal complex from a Gelbvieh bull (46 MLH1 foci), and B) Targhee ram (71 MLH1 foci) spermatocyte.

So, how do we get samples to collect the data we need? Much like a surgeon is on call for patients who need assistance, I serve as the “on call” graduate student for samples! Since we study primary spermatocytes, we require testicular samples from males who have reached puberty or older. However, finding males that have not been castrated provides a real challenge. Male sheep and cattle are usually castrated early in life to provide a safer working environment for both the animals and the people who care for them. Some males are kept intact (not castrated) for breeding purposes, but these are the few “best” animals. So, any time an intact male is at the end of his breeding career and is harvested for food, I retrieve a testicular sample. But there is one more problem: these samples cannot be frozen, and we need to extract the cells and fix them on microscope slides within 24 hours. Otherwise, the integrity of protein we use to identify crossovers degrades.

Prepping these samples is a race against time. If we receive the samples from a local source, I pick them up as soon as I get a phone call. If they are shipped overnight from elsewhere, I wait impatiently for the samples to arrive the next day. In a way, I am considered “on call” for receiving and prepping our samples. And since these samples only last for about 24 hours on ice, I prep them almost immediately upon arrival. It is exciting in our lab to receive a new sample because the data we collect from one ram or bull may be very different from another!

The reward for timely but precise preps of our samples is that we are able to learn more about the process of meiotic recombination, and impact the way the scientific community understands how genes are passed from generation to generation. Since meiotic recombination contributes to genetic variation, understanding differences as well as the mechanism behind the process gives us deeper insight into how genetic diversity is evolving.

References:

  1. Hassold, T., and Hunt, P. (2001). To err (meiotically) is human: the genesis of human aneuploidy. Nature reviews. Genetics 2, 280-291.
  2. Murdoch, B., Owen, N., Stevense, M., Smith, H., Nagaoka, S., Hassold, T., McKay, M., Xu, H., Fu, J., Revenkova, E., et al. (2013). Altered cohesin gene dosage affects Mammalian meiotic chromosome structure and behavior. PLoS genetics 9, e1003241.
  3. Baudat F and de Massy B (2013). Meiotic recombination in mammals: localization and regulation. Nature Reviews Genetics 14, 794-806.
  4. Jones, G.H., and Franklin, F.C. (2006). Meiotic crossing-over: obligation and interference. Cell 126, 246-248.
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Pareto Improvement of Pareto-Based Multi-Objective Evolutionary Algorithms

Posted on October 5, 2016 by hageyt

This week’s BEACON Researchers at Work blog post is by Prof. Lihong Xu and Prof. Erik D. Goodman

Prof. Lihong Xu and Prof. Erik D Goodman

The Greenhouse research team of BEACON, led by Prof. Lihong Xu (from Tongji University, China) and Prof. Erik D. Goodman, has been doing work on BEACON’s international collaboration projects to model, optimize and control the microclimate inside greenhouses with the aid of evolutionary techniques. We explore the capabilities of evolutionary algorithms for solving problems with multiple conflicting objectives, beginning by adapting some Multi-Objective Evolutionary Algorithms (MOEAs), including NSGA-II, developed by BEACON’s Prof. Kalyanmoy Deb, to explicitly tradeoff the greenhouse production and the associated energy cost. This strategy worked pretty well until we tried to take more objectives (such as control precision) into consideration, and one major setback was the undesired deterioration of MOEA performance as the problem dimension increased.

NSGA-II and many other MO optimization techniques use the concept of Pareto domination. One solution dominates another if it is not worse than the other solution with respect to ALL objectives and is better with respect to at least one. We typically seek a set of solutions that are not dominated by others in the set and which are well distributed throughout the solution space. But it is well known that as the number of objectives increases in a multi-objective optimization problem, the fraction of points in the space that are non-dominated rises rapidly, because there are so many objectives in which a point can be better than another point, so not be dominated, since domination requires the dominating solution to be better in at least one objective and not worse in the others. (Here we ignore the effects of constraints, which make solutions feasible or infeasible, and under which any feasible solution dominates any infeasible solution.) As shown in Fig. 1, Pareto optimality orthogonally partitions the objective space into three subspaces—i.e., “better”, “worse”, and “indifferent” (that is, better in some dimensions and worse in others). It is easy to calculate that the percentage of the “better” (or “worse”) solutions decreases exponentially with the problem dimensionality, that is to say, more and more solutions are mutually non-dominating as the number of objectives grows.

Fig. 1. Demonstration of the Pareto optimality widely adopted in conventional MOEAs (a solution is Pareto optimal means there is no other solution that is better in at least one dimension and not worse in all others), and a probabilistic analysis of its deficiency (its discriminability drops exponentially with the problem dimensionality).

To prevent this Pareto homogenization tendency, Prof. Xu’s former graduate student and former BEACON Visiting Scholar Chenwen Zhu generalized the Pareto optimality notion by controlling the size proportions of these subspaces, essentially by changing the orientations of the boundaries from being orthogonal to a more relaxed condition. However, he has done a symmetric generalization, a property not shared by previous generalizations. The main idea of the new optimality criterion was to offer MOEAs consistent solution discriminability so that all the objective spaces could be treated as “pseudo-two-dimensional” spaces, and therefore lessen the aforementioned performance deterioration. To quantitatively compare the selection pressure of different optimality criteria, we proposed the concept of the dominating ratio and adopted the Monte Carlo Method to estimate it. Experiments on the greenhouse problem and several benchmark functions showed that this relaxed Pareto optimality did greatly improve the convergence capabilities of Pareto-based MOEAs. However, as pointed out by David Wheeler, though problems in computer science can always be solved by adding another layer of abstraction, it will usually create another problem. Here, what the symmetric generalization of Pareto optimality cost were an increase in runtime and possible loss of solution diversity.

To address these difficulties, and given the qualitative nature of the Pareto optimality, our solution to the runtime issue was to add another abstraction layer (which we called the ordinal space) to presort all the solutions in each dimension before the dominance calculation procedure. Instead of repeatedly conducting pairwise comparisons of the objective values, we exploited semi-ideal solutions and the concept of joint order to complete the same solution evaluation task with only some simple arithmetic calculations of the ordinal information. By taking advantage of this trading-space-for-time technique, three fast algorithms were developed and then tested on both artificial and evolving solution datasets. According to the results collected, the speedup of Pareto-based MOEAs was quite significant, and the runtime overhead of the generalization process could easily be recovered.

As for the issue of diversity loss, a distributed evolution architecture with adaptive parameter settings was proposed to implement a complementary diversity compensation. In this case, Pareto optimality was further generalized to its asymmetric form to provide MOEAs with the flexibility of modifying their evolving directions. Multiple slave processors with independent generalization parameters were first clustered to achieve the maximum coverage of the original Pareto optima, and a master processor was then introduced to adaptively tune each parameter to minimize the overlap between different slave processors. The scheme validation was performed on benchmark functions with different types of known Pareto optima (e.g., concave, convex, and disconnected), and for each problem, a relatively complete set of solutions was always be obtained.

Fig. 2. Pictorial demonstration of the algorithm framework that improves Pareto-based MOEAs in a Pareto sense. (a) Conventional MOEAs; (b) Symmetric generalization of the Pareto optimality to increase the convergence probability; (c) Ordinal optimization to reduce the runtime; (d) (e) Adaptive distributed evolution to maintain the solution diversity.

Here, a natural question arose: since the common performance indexes of MOEAs —i.e., convergence, runtime and diversity—could be separately taken care of, how would MOEAs perform if we put all the proposed techniques together? To answer this question, the Pareto optimality generalization, the ordinal optimization, and the adaptive distributed evolution were then synthesized as a unified but modularized framework, GOOD-MOEA (see Fig. 2). Theoretically, this is a versatile algorithm framework that applies to most Pareto-based MOEAs. And just as expected, experimental results on the greenhouse problem and several benchmark functions all suggested that it is a framework that could improve the convergence, the runtime, and the solution diversity of Pareto-based MOEAs at the same time—that is, this is an algorithm framework that is capable of making Pareto improvement of Pareto-based MOEAs.

Notes:

The main result above has been published in the Journal— IEEE Transactions on Evolutionary Computation, 2016, 20(2):299-315

For more information about this work, you can contact Prof. Lihong Xu (the corresponding author of the published paper above) at email: xulihong@msu.edu

Reference:

[1] C. Zhu, L. Xu(*) and E. D. Goodman, Generalization of Pareto Optimality for Many-Objective Evolutionary Optimization, IEEE Transactions on Evolutionary Computation, 2016, 20(2):299-315

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A Network Analysis of BEACON Co-authorship 2010-2015

Posted on October 3, 2016 by hageyt

This post is by Sarah Fitzgerald (doctoral candidate), Alex Gardner (doctoral student), Patty Farrell-Cole, Ph.D. and, Marilyn Amey, Ph.D.,

Inter-institutional and interdisciplinary research has become an increasingly popular and important area of study as funders, tenure committees and others seek to better understand the work of faculty, staff, students, postdocs and other researchers. While this is still an emerging area of inquiry, one thing is clear, there is much to learn about how researchers research. In the case of scientists, what they discover about the natural world is often shaped by the social networks that define their lives. Their ability to advance knowledge is dependent upon their access to subject matter experts, funding, or equipment. To make progress in science, researchers need access to methodologies or knowledge they have not used before with support from their departments and institutions. As third party evaluators, we seek to study the connections made between these different stakeholders.

The primary goal of BEACON is to unite biologists, computer scientists and engineers in a joint study of natural and artificial evolutionary processes to solve real-world problems (BEACON Strategic Plan, 2013). BEACON collects self-reported output data from its members for annual reports. For example, BEACONites have reported 849 BEACON-related publications since the start of the Center in 2010 (BEACON 2015 NSF Report) but the data do not provide insight into the structure of BEACON collaborations between disciplines and institutions. In our recent evaluation of BEACON we elected to use Social Network Analysis (SNA) to study the relationships and outputs of the BEACON members by roles, institutions, and disciplines. SNA involves mapping and measuring relationships and information networks between individuals, groups, organizations, and other connected units.

We set out to explore who participates in cross-boundary collaboration and which types of boundary crossing BEACON participants engage in with regularity. We used NetDraw, a Network Analysis program, to create sociograms showing the co-authorship ties in BEACON and statistical analysis through SPSS to identify which member attributes were correlated with co-authorship.

The sociogram below shows co-authorship between BEACON members who collaborate within the Center. Triangles represent members who collaborate inter-institutionally; circles represent members who collaborate only within their institution. Colors delineate institutional affiliations as displayed in the key. The size of the shape representing each member is dependent on the number of collaborative outputs they produced. In general, BEACON members tend to collaborate with people from their own institutions more than they do with those from other institutions.

In the sociogram below, triangles represent members who collaborate interdisciplinarily (meaning researchers conduct research with those in other disciplines) and circles represent members who collaborate only within their discipline. In contrast to the clustering by institution above, collaboration across disciplines is much more intermingled. Most interinstitutional work is also interdisciplinary, but most interdisciplinary work involves only one institution. Individuals who work on interdisciplinary projects are more likely to cross institutional boundaries, and vice versa.

Looking at graph on the right, interdisciplinary work is more than twice as common as inter-institutional work. In addition, interdisciplinary co-authorship is growing at BEACON while inter-institutional research appears to have reached a plateau.

Institutional Differences

Scholars at NCA&T State were least likely to collaborate among all the BEACON institutions.

Scholars at NCA&T State are the most likely to engage in interinstitutional collaboration with other Center members.

 

Scholars at NCA&T State are also the most likely to engage in interdisciplinary collaboration, which may be a result of being the only institution in the STC with nearly equal distribution of members from all three of the center’s core disciplines: life sciences, computer science, and engineering.

Disciplinary Differences

Differences in collaboration practices tend to be dependent upon institutional affiliation, but researcher discipline did not reveal any patterns in collaboration. Members of all three core disciplines participate in collaboration and interinstitutional collaboration fairly equally.

5-

Members in the life sciences are least likely to collaborate interdisciplinarily. This may be because so many of the departments involved in BEACON are classified as life sciences and they may be collaborating with members in other life sciences departments.

Role Differences

A greater proportion of graduate students and postdocs collaborate than faculty.

A smaller proportion of graduate students and postdocs collaborate inter-institutionally.

Although there are more graduate students than faculty members in BEACON, the majority (54%) of collaborations involve at least one faculty member and at least one graduate student. In total, 38% of collaborations involve multiple faculty members and only 6% of collaborations do not involve faculty members. The majority (62%) of interdisciplinary collaboration and the majority (65%) of inter-institutional collaboration involves multiple faculty members.

Gender Differences

Of BEACON collaborators whose gender was known, 38.4% are women. Women are somewhat more likely than men to collaborate within BEACON. A higher percentage of men who collaborate within BEACON do interi-nstitutional work than women. In addition, a higher percentage of male BEACONites participate in interdisciplinary collaboration than females. The reasons for gender differences in collaboration is an area that requires further exploration, and could include factors such as representation within faculty rank, discipline, and role within BEACON since such a high percentage of women are students and staff.

Citizenship Differences

Non-U.S. citizenship and permanent residency do not appear to be barriers to collaboration within BEACON. Non-U.S. citizens tend to collaborate less interinstitutionally while permanent residents do not have the same problem. Non-U.S. citizens are less likely to participate in interdisciplinary collaboration within BEACON than U.S. citizens or permanent residents.

Race Differences

A lower percentage of Hispanic, Black, and Asian BEACONites participate in collaboration within BEACON than White BEACONites.

11-coauthorshipbyrace

For members who collaborate within BEACON, Hispanic and Black members tend to collaborate between institutions more than White and Asian members. Black and African American BEACONites are more likely than White or Asian BEACONites to collaborate across disciplines within BEACON. Hispanics are less likely than Whites or Asians to do so. Sixty percent of the African Americans are at NCA&T State which could explain why they are more likely to do interdisciplinary and inter-institutional work.

We would like to hear BEACONites’ perspectives on the following questions. Please email Sarah Fitzgerald (fitzge81@msu.edu) and/or Alex Gardner (gardn179@msu.edu)

  • What explanations do you see for these patterns?
  • How could BEACON encourage more boundary crossing?
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Lobsters out of water: Scientists at film camp in Maine

Posted on September 27, 2016 by hageyt

This post is by MSU grad student Carina Baskett

Carina and her fellow science communicator Klara Scharnagl making a stop at Niagara Falls on the way back from a film workshop in Maine.

My colleague Klara Scharnagl had a great idea. “Let’s shoot it from the perspective of a vegetable!” As a scientist, I don’t usually go to work expecting to hear a sentence like that! But yes, we did end up shooting a short video at a farmer’s market from the perspective of a love-struck melon, all in the name of science education.

Klara and I were at a weeklong film workshop in Maine the first week of September to improve our filmmaking skills. We are working on a BEACON-funded project with Melissa Kjelvik, Liz Schultheis, Travis Hagey, and Anna Groves to make videos for classrooms about scientists. The videos will accompany Data Nuggets (DNs), which are exercises for K-12 and undergraduate students to practice working with data from real, current research. DNs were co-developed by MSU graduate students and K-12 teachers.

The goals of the videos are two-fold. First, we aim to redefine how students see science and scientists by featuring researchers from diverse backgrounds, giving students more face time with the scientists than they can get from a photo in a DN. Second, we aim to enhance evolution education by showing how data is collected and presenting information in an alternative media to the standard written descriptions.

A Maine lobster dinner was the cherry on top of the film workshop sundae!

On top of those goals, there is the overriding need for the videos to be engaging, and the first, somewhat invisible step toward that goal is to be technically proficient. Klara and I each have experience with science outreach and a smattering of the requisite technical skills for filmmaking, but we needed more training and experience with videos. So we found a workshop, “Documentary Camera” at a school called Maine Media.

Klara and I were the only scientists out of the 11 students in the class. In fact, some of the students said that we were the only scientists they had ever met. Being in a classroom where I was clueless and surrounded by people more expert than me was a lot like being a first-year graduate student again! But it was fun to learn so much.

To practice the techniques that we would be using for the DN videos, Klara and I made a “pilot.” We decided that it had to be about plants or lichens (the organisms that we study), not humans or animals, because a major challenge of the DN videos will be to tell engaging stories about organisms and questions that aren’t inherently exciting to most of the population. Personally, I find plants and lichens to be a lot more exciting than, say, sports, but I realize I’m in the minority with that view.

The closest we could come to interviewing a plant expert was to go to an “herbal apothecary,” a pharmacy where all the medicines and remedies come from plants. The message of the video was to get viewers excited about the chemicals that plants make, by pointing out that traditional and many modern medicines come from plants, and then slip in some biology by asking why plants make these chemicals (generally to defend themselves from pests and disease).

We visited the apothecary on short notice, and were able to snag a quick interview with a gardener. When asked, “Plants don’t make these chemicals for human use. Why do they?” she said, “How do we know they don’t make them for humans? Hmm, I’ll have to think about that.” This was an informative moment for us in a couple ways.

First, it was a good reminder that a lot of the scientific knowledge we take for granted, and even the questions that scientists think to ask, are not common sense. Even someone whose job it is to work with plants and the chemicals they manufacture was not considering the evolutionary explanation for why plants have these adaptations that we are co-opting. Yet it would be helpful for someone working with plant medicine to have an understanding that related plants might manufacture similar compounds and that the environmental context (such as an outbreak of caterpillars on a plant) might affect the drugs that they are harvesting. That’s why evolution education and outreach are so important!

Second, the interview was good practice for the DN videos because we aren’t always going to get a nice, video-ready sound bite from everyone we talk to. Some of the scientists we interview might use too much jargon and be unable to make their research approachable. But that’s why we will include narration and drawings to guide the narrative. We ended up using the gardener’s quote about why she thinks plants are amazing and exciting, and we provided the explanation of why plants make chemicals that we use for medicine.

So was our science communication effective? On the last day of the workshop, participants from several classes ate an amazing dinner of Maine lobster, and then watched each other’s projects. It was funny to see our educational video mixed in with a beautifully shot piece showing a nearby harbor as the catch was being brought in; with a portrait of a pair of local artists whose house is covered in drawings; and with some dramatic fictional pieces from another class. When I asked everyone afterward, “So why do plants make chemicals that we use for medicine?” almost all of them answered correctly. If we can reach a group of filmmakers who didn’t even know there would be a quiz, hopefully we can have an impact on students, by helping to make Data Nuggets just a little more delicious.

To see the 5-minute video, click here! And if you have an extra few minutes and wouldn’t mind giving us some feedback, please click here.

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Collaborations with K-12 teachers first inspired Data Nuggets, and continue to today

Posted on September 18, 2016 by hageyt

This post is by MSU postdocs Melissa Kjelvik and Liz Schultheis

Liz modeling the process of science within a Data Nugget.

Back when we were biology graduate students, the GK-12 program at the Kellogg Biological Station (KBS) exposed us to science education for the first time. When we signed up to work with K-12 teachers and go into schools as the “classroom scientist” we knew there would be benefits, such as time to hone our science communication skills, a venue to share our research with broad audiences, and of course saving us the hour and a half drive to MSU’s main campus to TA. However, we had no idea what we had really gotten ourselves into.

We were each assigned a partner teacher whose classroom we would visit a few times a week, and who would mentor us as we attempted to share our research with students for the first time. The experience of standing up in front of 30 sixth graders was intimidating at first. Yet, it was necessary to realize how hard it was to simplify and explain a topic, while also making it engaging for an audience who may never have thought about these ideas before. The teachers’ infectious enthusiasm boosted the passion we had for our research. They pushed us to describe why the things we were doing day-to-day mattered for the big picture. They were willing to stand in the back of the classroom and wave their arms when students checked out because we’d used too much jargon or started to nerd-out. These teachers had such a clear passion for improving student learning; they constantly stepped out of their comfort zone to try new ways to improve their teaching and integrate the latest effective science teaching strategies into their classrooms. Working with these teachers and their students quickly became our favorite time of the week.

Melissa running a professional development workshop for high school math and science teachers.

These same teachers originally inspired Data Nuggets; they shared that their students were struggling to make sense of data in most applications, but especially data from classroom inquiry projects that turned out messy or did not follow predictions. Students should not feel they have failed when their data has variation around the mean or does not support their hypothesis. Typically, students are only exposed to research and data published in textbooks, leading to the misconception that all science is a completed product with well established ideas and clear results. To get students to think like scientists, they need to be exposed to the process of science itself and how scientists work to develop, test, and refine their ideas. As early-career scientists, we knew that along the way, experiments often fail or yield unexpected results.

For continued support, we turned to BEACON, whose education objectives align with the Data Nuggets vision. Using these seed funds, we were able to work with Louise Mead and other BEACON scientists to develop Data Nuggets that connect students to real data and the motivation and passion of the scientists behind the research. Today we have 46 Data Nuggets (and counting) up on our website, freely available to teachers and students, many written by women and early career scientists.

As we wrapped up as graduate students, we realized there was so much more we wanted to do to improve and expand Data Nuggets. The support from BEACON allowed us time to fully develop our ideas and submit an NSF DRK-12 grant with Louise. As BEACON postdocs we are excited to have time to integrate all these great ideas into Data Nuggets. The main objective of the collaborative NSF DRK-12 grant, between MSU and Biological Science Curriculum Study (BSCS), is to assess whether Data Nuggets increase students’ quantitative reasoning abilities, along with their understanding of, and engagement with, science. In preparation for this efficacy study, we are currently revising each Data Nugget and integrating new ideas and feedback from our collaboration.

High school math and science teachers working to complete a Data Nugget during a professional development workshop.

This summer we worked with 4 teachers – Marcia Angle, Cheryl Hach, Ellie Hodges, and Kristy Campbell. Marcia and Cheryl have been with us since the beginning, and were among those who first helped us develop Data Nuggets. They were thrilled to see that we continued to develop Data Nuggets and were happy with how far they’d come since the original inception. This summer we had many insightful conversations about students’ struggles with certain scientific practices, including data interpretation and constructing explanations. The teachers shared their different teaching strategies, and researched new ones, in order to write guides to help other teachers cover these difficult topics. As a group we read through student responses to Data Nuggets piloted in the spring. This was a powerful way to think deeply about the areas students could improve, and ways for us to provide more context in our teacher guides to encourage rich classroom discussions. Along with BEACON postdoc Alexa Warwick, the teachers developed a grading rubric to help teachers score Data Nuggets and identify areas where their students need more practice. While reading student responses, the teachers collectively noticed that students had a difficult time using evidence to support their claims, so they worked on a new tool to ease students into this process. They presented this tool, along with other strategies, at professional development workshops for the KBS K-12 partnership teachers and all Kalamazoo Public Schools high school science teachers.

This year we are finalizing preparations for our Data Nugget efficacy study, taking place in 2017. Preliminary observations in classrooms, and feedback from teachers, indicate Data Nuggets effectively increase students’ quantitative and scientific literacy while engaging them with the story behind the research and building a connection to scientists. However, as scientists, we are of course not satisfied with anecdotal evidence and want data to support our claims! We are excited for the upcoming study to determine the ways in which Data Nuggets might contribute to a strong science education curriculum!

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Life Isn’t Fair

Posted on September 7, 2016 by hageyt


This post is by MSU PhD candidate Eli Strauss

PhD candidate Eli Strauss

“That’s not fair!” These were the words I uttered as a child anytime I felt that someone or something had unjustly slighted me. “Life isn’t fair,” my parents would tell me, which always seemed an unsatisfying solution to the real world problems of a spoiled kid. Flash forward a few decades, and I find myself doing a PhD motivated by that exact question: why isn’t life fair?

Certainly, we can point to many unfair aspects of modern and ancient human societies—racism, sexism, or feudalism, for example—in which some members of society benefit more than others. In these cases, the criteria for being a privileged member of society are arbitrary: a descendent of the royal bloodline rules a kingdom based entirely on birthright with no consideration of their abilities as a leader. The inheritance of socioeconomic status from parents means that children in our society don’t all start on an equal playing field, regardless of the innate abilities of the child. It is clear that humans are familiar with an unjust life, but is this specific to humans? Is “life not fair” for other animals as well? The short answer is yes.

The composition of a single hyena clan. Notice the difference in the sizes of high-ranking and low-ranking matrilines. High-rankers get priority while feeding and reproduce more quickly and successfully than low-rankers. Figure from Holekamp et al. 2012.

Social animals usually live in groups structured by a dominance hierarchy, where some members of society get priority when it comes to feeding or reproducing. This means that a low-ranking female paper wasp may never get to lay eggs, or that a high-ranking hyena may eat her fill before her lower-ranking sister gets to feed. Some of these hierarchies are organized based on size or fighting ability, where the dominant animals earn their status through physical prowess. In the societies of many primates and spotted hyenas, however, social rank isn’t earned; instead, it’s inherited. Although this inheritance is not mediated genetically, this means that the dominant female hyena gets to be the queen of her clan simply because of who her parents were. Sound familiar?

A current project of mine is focused on understanding what forces promote unfair societies in the animal kingdom. I’m using spotted hyenas as a model organism to answer these questions because their social standing is inherited and their societies are dramatically unfair. In a hyena clan, a high-ranking hyena can easily steal food and other resources from a lower-ranking group-mate. This means that  hyena that hunts and kills a gazelle may lose it to a higher-ranking individual without getting a single bite, even if the higher-ranking hyena is smaller than the animal who killed the gazelle! Rank in spotted hyenas has a large effect on fitness, and higher-ranking (and better fed) individuals reproduce around twice as much as lower-rankers (Holekamp et al. 1996). Clearly, low-rankers would stand to gain a lot by attempting to improve their social standing, but rank changes in hyenas are extremely rare. Fortunately, long-term research from the MSU Mara Hyena project has yielded a large dataset that allows for the study of such rare behaviors. Now, using nearly thirty years of data on multiple hyena clans, I have identified instances of rank change in order to understand why they are so uncommon.

An example of coalitionary aggression. The two smaller hyenas on the left are displaying aggressive postures (tail bristled, ears cocked forward, head high) while the larger hyena on the right displays submissively (ears plastered back, mouth open).

Why don’t low-ranking hyenas challenge the status quo and attempt to improve their rank? It is possible that they don’t have enough allies. In societies where social status is inherited, it is common to see coalitionary aggressions from multiple individuals to a single target. These aggressions are thought to either strengthen the status quo (‘conservative’ coalitions, directed from high-rankers to a single low-ranker) or challenge it (‘revolutionary’ coalitions, directed from low-rankers to a single-high ranker; Bissonnette et al. 2015). If coalitions are the key to maintaining or challenging the status quo, then low-rankers may rarely have the chance to challenge high-rankers because high-ranking hyenas have more social allies. High-rankers reproduce more quickly than low rankers and thus have more close kin with which to ally, and high-rankers also have mutual direct interest in maintaining the status quo (Holekamp et al. 2012, Smith et al. 2010). If rank change hinges on coalitionary aggression, we expect to see the instances of rank change associated with cases where a high-ranker loses many of its allies, thus exposing itself to revolutionary coalitions.

Alternatively, it is possible that low-rankers rarely challenge the status quo because they suffer a cost by doing so. This cost could be punishment inflicted by the challenged groupmates or could be due to subtler costs of living in unstable conditions. Dominance hierarchies are thought to exist because they bring stability and decrease conflict within a group (Schjelderup-Ebbe, 1922). Thus, challenging the status quo should carry costs associated with increased conflict. If the direct costs to challengers are deterring hyenas from challenging the status quo, we expect to see elevated amounts of wounding and high levels of stress hormones in hyenas that are involved in rank changes and unstable hierarchies. Even if we detect costs incurred by hyenas attempting to improve their social status, they will have to be weighed against the rate of success and the benefits gained by hyenas that successfully improve their rank.

Hopefully, this research will begin to shed light on the forces that maintain social inequality in animal societies. Hyena societies are similar to many primate societies, and are thus wonderful models to use to understand the inequality that exists throughout the animal kingdom. Viewing human inequality in the context of evolutionary patterns can help us understand why life isn’t fair, both for us and for our animal relatives.

Bissonnette, A. et al. 2015. Coalitions in theory and reality: a review of pertinent variables and processes. Behaviour 152:1–56.

Holekamp, K. E., L. Smale and M. Szykman. 1996. Rank and reproduction in the female spotted hyaena. Journal of Reproduction and Fertility 108:229–237.

Holekamp, K. E., J. E. Smith, C. C. Strelioff, R. C. Van Horn and H. E. Watts. 2012. Society, demography and genetic structure in the spotted hyena. Molecular Ecology 21:613–632.

Schjelderup-Ebbe T. Beitrage zur Sozialpsychologie des Haushuhns. Z Psychol 1922, 88:225-252.

Smith, J. E. et al. 2010. Evolutionary forces favoring intragroup coalitions among spotted hyenas and other animals. Behavioral Ecology 21:284–303.

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You Say “Cow” and I Hear “Milk” – The Joy of Interdisciplinary Work

Posted on August 31, 2016 by hageyt

This post is by MSU faculty Arend Hintze

While I was listening to the many interesting talks of this year’s BEACON congress (2016), I was pondering about the journey that we took together to get here. Fortunately, I was around to not only witness the first BEACON congress, but all others since then, except for 2015. Amazing, exciting, controversial, and interdisciplinary were only some of the words that popped into my mind, and I deeply enjoyed reflecting on that ride. But why? What is the thing I liked most, what made this so special?

That was the moment I had the idea for the title of this blog post, because it characterizes so absurdly what makes working with the people in BEACON so exciting and rewarding. It is the misunderstanding between the different disciplines. When I talk to biologists, for example about mate selection, or navigation, or foraging then this person has a specific animal with a specific repertoire of behavior and methods to study it in mind – the “cow” – and also the limitations of said model system. I can make our computational model systems without constraints, but also, most often, I have no experience or knowledge about the animal my collaborators are talking about. I know what it means to have the “feeling for the animal”[1,2], but that’s it, I don’t have that about model organisms in general (with the exception of C. elegans maybe). I have a feeling about abstract systems, selection pressures, and how to design experiments in the computer, and I code worlds and environments that are loose enough analogies to animal system to get things to evolve – the “milk”.

This necessarily leads to misunderstanding, and in the process of picking up the pieces, we typically both learn things. I understand the animal better, you understand the modeling process, and together we find the right abstractions, and are able to form the exact hypotheses and experiments to do in the future. It is enlightening and rewarding, and we haven’t even conducted experiment yet, but at least we think we know what is going on, until the results come in.

In many cases, these results shake both of our understandings, not because we again communicated poorly, but because neither of us understood what was going on in the first place; or I go and fix a bug and we meet next week, hoping for new and surprising results. However, it is exactly this dialog, where we try to explain to each other what we do and how our system works that allows us to be creative. When I was listening to many talks, I could see how well we now understand each other. Having results from computational systems right between results from organismal systems, and the audience doesn’t even flinch, is amazing. We reached a state where we talk each others language, and appreciate what everyone can bring to the table, without hearing “milk” or “cow” but knowing that we talk about bovine evolution.

I also had the feeling that we might start to lose out on exactly this quality that made us strong. The presentations were great, but also much more focused on each topic, without giving the broader context we all add when we know that the audience isn’t too familiar with what we are working on. It is a sign that the past made a difference, and we indeed learned from each other, and maybe we just rose to a new level? In one of our workshops about AVIDA and Markov Brains/MABE we tried to go back to the basics and explain things from scratch. While that might have been preaching to the choir, I also had the feeling that we could do this more often. The strength of our computational model systems doesn’t come from what we did with them in the past. Their strength comes from what we can do with them in the future. In summary, I think we came very far, we should just make sure that we keep misunderstanding each other in the familiar productive way I learned to love!

Cheers Arend

[1] Holmberg, T. (2008). A feeling for the animal: On becoming an experimentalist. Society & Animals, 16(4), 316-335.

[2] Keller, E.F. (1983) A feeling for the organism. The life and work of Barbara McClintock. New York: Owl Books

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STC Directors annual meeting invited keynote Rush Holt

Posted on August 30, 2016 by hageyt

We were very excited to meet our invited keynote Rush Holt (center) at this year’s STC Directors annual meeting, which BEACON organized. In his keynote address, Dr. Holt discussed how NSF-funded Science and Technology Centers like BEACON can help take on one of the greatest challenges facing the scientific community: communicating science and promoting evidence-based thinking among policymakers and the public.

Holt started his career as a physicist, heading a lab at Princeton. He then served as New Jersey’s U.S. Representative for 16 years. He is now the CEO of the American Association for the Advancement of Science, and Executive Publisher of the Science family of journals. Holt also beat the IBM computer Watson on an exhibition match of Jeopardy!

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