Search Results for: lter

Dangerous Aquatic Prey: Can Predators Adapt to Toxic Algae?

Figure 1: Scientist Finiguerra collecting copepods at the New Jersey experimental site.

Figure 1: Scientist Finiguerra collecting copepods at the New Jersey experimental site.

The activities are as follows:

Phytoplankton are microscopic algae that form the base of all aquatic food chains. While organisms can safely eat most phytoplankton, some produce toxins. When these toxic algae reach high population levels it is known as a toxic algal bloom. These blooms are occurring more and more often across the globe – a worrisome trend! Toxic algae poison animals that eat them, and in turn, humans that eat these animals. For example, clams and other shellfish filter out large quantities of the toxic algae, and the toxic cells accumulate in their tissues. If humans then eat these contaminated shellfish they can become very sick, and even die.

One reason the algae produce toxins is to reduce predation. However, if predators feed on toxic prey for many generations, the predator population may evolve resistance, by natural selection, to the toxic prey. In other words, the predators may adapt and would be able to eat lots of toxic prey without being poisoned. Copepods, small crustaceans and the most abundant animals in the world, are main consumers of toxic algae. Along the northeast coast of the US, there is a toxic phytoplankton species, Alexandrium fundyense, which produces very toxic blooms. Blooms of Alexandrium occur often in Maine, but are never found in New Jersey. Scientists wondered if populations of copepods that live Maine were better at coping with this toxic prey compared to copepods from New Jersey.

Figure 2: A photograph of a copepod (left) and the toxic alga Alexandrium sp. (right).

Figure 2: A photograph of a copepod (left) and the toxic alga Alexandrium sp. (right).

Scientists tested whether copepod populations that have a long history of exposure to toxic Alexandrium are adapted to this toxic prey. To do this, they raised copepods from Maine (long history of exposure to toxic Alexandrium) and New Jersey (no exposure to toxic Alexandrium) in the laboratory. They raised all the copepods under the same conditions. The copepods reproduced and several generations were born in the lab (a copepod generation is only about a month). This experimental design eliminated differences in environmental influences (temperature, salinity, etc.) from where the populations were originally from.

The scientists then measured how fast the copepods were able to produce eggs, also called their egg production rate. Egg production rate is an estimate of growth and indicates how well the copepods can perform in their environment. The copepods were given either a diet of toxic Alexandrium or another diet that was non-toxic. If the copepods from Maine produced more eggs while eating Alexandrium, this would be evidence that copepods have adapted to eating the toxic algae. The non-toxic diet was a control to make sure the copepods from Maine and New Jersey produced similar amounts of eggs while eating a good food source. For example, if the copepods from New Jersey always lay fewer eggs, regardless of good or bad food, then the control would show that. Without the control, it would be impossible to tell if a difference in egg production between copepod populations was due to the toxic food or something else.

Featured scientists: Michael Finiguerra and Hans Dam from University of Connecticut-Avery Point, and David Avery from the Maine Maritime Academy

Flesch–Kincaid Reading Grade Level = 10.6

There are three scientific papers associated with the data in this Data Nugget. The citations and PDFs of the papers are below. 

Colin, SP and HG Dam (2002) Latitudinal differentiation in the effects of the toxic dinoflagellate Alexandrium spp. on the feeding and reproduction of populations of the copepod Acartia hudsonicaHarmful Algae 1:113-125

Colin, SP and HG Dam (2004) Testing for resistance of pelagic marine copepods to a toxic dinoflagellate. Evolutionary Ecology 18:355-377

Colin, SP and HG Dam (2007) Comparison of the functional and numerical responses of resistant versus non-resistant populations of the copepod Acartia hudsonica fed the toxic dinoflagellate Alexandrium tamarense. Harmful Algae 6:875-882

Is chocolate for the birds?

Cocoa beans used to make chocolate!

Cocoa beans used to make chocolate!

The activities are as follows:

About 9,000 years ago humans invented agriculture as a way to grow enough food for people to eat. Today, agriculture happens all over the globe and takes up 40% of Earth’s land surface. To make space for our food, humans must clear large areas of land, which creates a drastic change, or disturbance, to the habitat. This land-clearing disturbance removes the native plants already there including trees, small flowering plants, and grasses. Many types of animals including mammals, birds, and insects depend on these native plants for food or shelter. Large scale disturbances can make it difficult to live in the area. For example, a woodpecker bird cannot live somewhere that has no trees because they live and find their food in the trees.

However, some agriculture might help some animals because they can use the crops being grown for the food and shelter they need to survive. One example is the cacao tree, which grows in the rainforests of South America. Humans use the seeds of this plant to make chocolate, so it is a very important crop! Cacao trees need very little light. They grow best in a unique habitat called the forest understory, which is composed of the shorter trees and bushes under the large trees found in rainforests. To get a lot of cacao seeds for chocolate, farmers need to have large rainforest trees above their cacao trees for shade. In many ways, cacao farms resemble a native rainforest. Many native plant species grow there and there are still taller tree species. However, these farms are different in important ways from a native rainforest. For example, there are many more short understory trees in the farm than there are in native rainforests. Also, there are fewer small flowering plants on the ground because humans that work on cacao farms trample them as they walk around the farm.

rainforest and cacao plantation

Part I: Skye is a biologist who wanted to know whether rainforest birds use the forest when they are disturbed by adding cacao farms. Skye predicted she would see many fewer birds in the cacao farms, compared to the rainforest. To measure bird abundance, she simply counted birds in each habitat. To do this she chose one rainforest and one cacao farm and set up two transects in each. Transects are parallel lines along which the measurements are taken. She spent four days counting birds along each transect, for a total of eight days in each habitat. She had to get up really early and count birds between 6:00 and 9:00 in the morning because that’s when they are most active.

Part II: Skye was shocked to see so many birds in cacao farms! She decided to take a closer look at her data. Skye wanted to know how the types of birds she saw in the cacao farms compared to the types of birds she saw in the rainforest. She predicted that cacao farms would have different types of birds than the undisturbed rainforest. She thought the bird types would differ because each habitat has different types of food available for birds to eat and different types of plants for birds to live in.

Skye broke her abundance data down to look more closely at four types of birds:

  1. Toucans (Eat: large insects and fruit from large trees, Live: holes in large trees)
  2. Hummingbirds (Eat: nectar from flowers, Live: tree branches and leaves)
  3. Wrens (Eat: small insects, Live: small shrubs on the forest floor)
  4. Flycatchers (Eat: Small insects, Live: tree branches and leaves)

skyecacao

Featured scientist: Skye Greenler from Colorado College and Purdue University

Flesch–Kincaid Reading Grade Level = 8.5

Additional teacher resources related to this Data Nugget:

  • The research described in this activity has been published. The citation and a PDF of the scientific paper can be found here:
  • The complete dataset for the study has been published to a data repository and is available for classroom use. This dataset has even more data than what is in the Data Nugget activity. While the Data Nugget has data for just two habitats (cacao and rainforest), the full dataset also includes two other agroforest habitat types. The dataset also includes data for every species (169) recorded during the study, whereas the Data Nugget only has data for four families (toucans, wrens, flycatchers, hummingbirds).
  • Study Location: Skye’s study took place in a 10 km2 mixed rainforest, pasture, agro-forest, and monoculture landscape near the village of Pueblo Nuevo de Villa Franca de Guácimo, Limón Province, Costa Rica (10˚20˝ N, 83˚20˝ W), in the Caribbean lowlands 85 km northeast of San José.
  • For more background on the importance of biodiversity, students can eat this article in The Guardian – What is biodiversity and why does it matter to us?

About Skye: As a child Skye was always asking why; questioning the behavior, characteristics, and interactions of plants and animals around her.  She spent her childhood reconstructing deer skeletons to understand how bones and joints functioned and creating endless mini-ecosystems in plastic bottles to watch how they changed over time.  This love of discovery, observation, questioning, and experimentation led her to many technician jobs, independent research projects, and graduate research study at Purdue University.  At Purdue she studies the factors influencing oak regeneration after ecologically based timber harvest and prescribed fire.  While Skye’s primary focus is ecological research, she loves getting to leave the lab and bring science into classrooms to inspire the next generation of young scientists and encourage all students to be always asking why!

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Resources



Concepts to cover when using Data Nuggets in the classroom

What is a Hypothesis?

The Process of Science

Reading Scientific Texts

Constructing Explanations using Claim-Evidence-Reasoning (CER)

Graphing

Basic Statistics

Data Literacy



Science communication materials to tell your science story & create a Data Nugget



Other great data and education websites

Repositories for Teaching Resources

Organizations and great teaching resources!

Datasets: Collection of websites that offer freely available online data.

Data & Data Visualization Tools: Collection of websites that offer free platforms for data visualization.

  • DataClassroom – our partner on Digital Data Nuggets, activities where studies easily explore large datasets and make beautiful graphs, develop their data literacy abilities, do statistics, and more!
  • FieldScope by BSCS – interactive online platform that provides citizen scientists with the means to collect and analyze data. Emphasizes spatial citizen science datasets. Allows users to visualize data both spatially and graphically.
  • Common Online Data Analysis Platform (CODAP) by Concord Consortium – easy-to-use web-based data analysis platform, geared toward middle and high school students, and aimed at teachers and curriculum developers. Designed to help students summarize, visualize and interpret data, advancing their skills to use data as evidence to support a claim.
  • Tuva Labs – online research-based tools and inquiry-based tasks enhance students’ learning and application of essential mathematical, statistical, and probability concepts. Subscription necessary to access materials.
  • Harvard Forest Schoolyard LTER Program – A platform developed to easily visualize the data collected by students at the Harvard Forest. The graphing tool can be found here. The data can be downloaded here.
  • Serenity – easy-to-use web-based data analysis interface for data analytics in R, built using Shiny.
  • Laboratory for the study of exoplanets (ExoLab) – Free, online astronomical laboratory. Based on cutting-edge astronomy research and provides students with data from telescopes. Designed to increase study data literacy, while engaging them in the search for habitable worlds beyond earth.
  • NASA Earth Observations (NEO) – Here you can browse and download imagery of satellite data from NASA’s constellation of Earth Observing System satellites. Over 50 different global datasets are represented with daily, weekly, and monthly snapshots, and images are available in a variety of formats including JPEG, PNG, Google Earth, and GeoTIFF.
  • Data USA – Visualization of US Public Data
  • TerraScope – worldwide environmental and social demographic data in a manipulative digital platform that can drive inquiry investigations (TerraPopulus datasets synthesized from a variety of sources for broad application).
  • iDigBio – large set of digitized natural history collections, along with educational resources and ideas for classroom use.
  • TinkerPlots – software for dynamic data exploration
  • StatKey – collection of web-based statistics apps, written to pair with the statistics textbook “Statistics: Unlocking the power of data” by Lock^5. Has datasets available or you can upload your own.
  • Dryad Lab – collection of free, openly-licensed, high-quality, hands-on, educational modules for students to engage in scientific inquiry using real data.

Video Series & Podcasts

The ground has gas!

Measuring nitrogen (N2O) gas escaping from the soil in summer.

Measuring nitrogen (N2O) gas escaping from the soil in summer. Photo credit: Julie Doll, Michigan State University

The activities are as follows:

If you dig through soil, you’ll notice that soil is not hard like a rock, but contains many air pockets between soil grains. These spaces in the soil contain gases, which together are called the soil atmosphere. The soil atmosphere contains the same gases as the atmosphere that surrounds us above ground, but in different concentrations. It has the same amount of nitrogen, slightly less oxygen (O2), 3-100 times more carbon dioxide (CO2), and 5-30 times more nitrous oxide (N2O, which is laughing gas!).

Measuring nitrogen (N2O) gas escaping from the soil in winter.

Measuring nitrogen (N2O) gas escaping from the soil in winter. Photo credit: Julie Doll Michigan State University.

Nitrous oxide and carbon dioxide are two greenhouse gasses responsible for much of the warming of global average temperatures. Sometimes soils give off, or emit, these greenhouse gases into the earth’s atmosphere, adding to climate change. Currently scientists are working to figure out why soils emit different amounts of these greenhouse gasses.

During the summer of 2010, Iurii and his fellow researchers at Michigan State University studied nitrous oxide (N2O) emissions from farm soils. They measured three things: (1) the concentration of nitrous oxide 25 centimeters below the soil’s surface (2) the amount of nitrous oxide leaving the soil (3) and the average temperature on the days that nitrous oxide was measured. The scientists reasoned that the amount of nitrous oxide entering the atmosphere is positively associated with how much nitrous oxide is in the soil and on the soil temperature.

Featured scientist: Iurii Shcherbak from Michigan State University

Flesch–Kincaid Reading Grade Level = 9.2

More information on the research associated with this Data Nugget can be found here

Data associated with this Data Nugget can be found on the MSU LTER website data tables under GLBRC Biofuel Cropping System Experiment. Bioenergy research classroom materials can be found here. More images can be found on the LTER website.

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Fertilizing biofuels may cause release of greenhouse gasses

An aerial view of the experiment at MSU where biofuels are grown

An aerial view of the experiment at MSU where biofuels are grown. Photo credit: K. Stepnitz, MSU

The activities are as follows:

Greenhouse gases in our atmosphere, like carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), trap heat from the sun and warm the earth. We need some greenhouse gases to keep the planet warm enough for life. But today, the majority (97%) of scientists agree that the levels of greenhouse gases are getting dangerously high and are causing changes in our climate that may be hard for us to adjust to.

Scientist Leilei collecting samples of gasses released by the growing of biofuels

Scientist Leilei collecting samples of gasses released by the growing of biofuels. Photo credit: K. Stepnitz, MSU

When we burn fuels to heat and cool our homes or power our cars we release greenhouse gasses. Most of the energy used today comes from fossil fuels. These energy sources are called “fossil” fuels because they come from plants, algae, and animals that lived hundreds of millions of years ago! After they died, their tissues were buried and slowly turned into coal, oil, and natural gas. An important fact about fossil fuels is that when we use them, they release CO2 into our atmosphere that was stored millions of years ago. The release of this stored carbon is adding more and more greenhouse gases to our atmosphere, and much faster than today’s plants and algae can remove during photosynthesis. In order to reduce the effects of climate change, we need to change the way we use energy and think of new ways to power our world.

One potential solution could be to grow our fuel instead of drilling for it. Biofuels are a potential substitute for fossil fuels. Biofuels, like fossil fuels, are made from the tissues of plants. The big difference is that they are made from plants that are alive and growing today. Unlike fossil fuels that emit CO2, biofuel crops first remove CO2 from the atmosphere as the plants grow and photosynthesize. When biofuels are burned for fuel, the CO2 is emitted back into the atmosphere, balancing the total amount that was removed and released.

Scientists are interested in figuring out if biofuels make a good replacement for fossil fuels. It is still not clear if the plants that are used to produce biofuels are able to absorb enough CO2 to offset all of the greenhouse gases that are emitted when biofuels are produced. Additional greenhouse gases are emitted when producing biofuels because it takes energy to plant, water, and harvest the crops, as well as to convert them into fuel. In order to maximize plant growth, many biofuel crops are fertilized by adding nitrogen (N) fertilizer to the soil. However, if there is too much nitrogen in the soil for the crops to take up, it may instead be released into the atmosphere as the gas nitrous oxide (N2O). N2O is a greenhouse gas with a global warming potential nearly 300 times higher than CO2! Global warming potential is a relative measure of how much heat a greenhouse gas traps in the atmosphere.

Leilei is a scientist who researches whether biofuels make a good alternative to fossil fuels. He wondered what steps farmers could take to reduce the amount of N2O released when growing biofuel crops. Leilei designed an experiment to determine how much N2O is emitted when different amounts of nitrogen fertilizer are added to the soil. In other words, he wanted to know whether the amount of N2O that is emitted into the atmosphere is associated with how much fertilizer is added to the field. To test this idea, he looked at fields of switchgrass, a perennial grass native to North America. Switchgrass is one of the most promising biofuel crops. The fields of switchgrass were first planted in 2008 as a part of a very large long-term study at the Kellogg Biological Station in southwest Michigan. The researchers set up eight fertilization treatments (0, 28, 56, 84, 112, 140, 168, and 196 kg N ha−1) in four replicate fields of switchgrass, for a total of 32 research plots. Leilei measured how much N2O was released by the soil in the 32 research plots for many years. Here we have two years of Leilei’s data.

Featured scientist: Leilei Ruan from Michigan State University

Flesch–Kincaid Reading Grade Level = 10.1

Additional teacher resources related to this Data Nugget:

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Fair traders or freeloaders?

Measuring chlorophyll content in the greenhouse

Measuring chlorophyll content in the greenhouse

The activities are as follows:

When two species do better when they cooperate than they would on their own, the relationship is called a mutualism. One example of a mutualism is the relationship between a type of bacteria, rhizobia, and legume plants. Legumes include plants like peas, beans, soybeans, and clover. Rhizobia live in bumps on the legume roots, where they trade their nitrogen for sugar from the plants. Rhizobia fix nitrogen from the air into a form that plants can use. This means that legumes that have rhizobia living in their roots can get more nitrogen than those that don’t.

Under some conditions, this mutualism can break down. For example, if one of the traded resources is very abundant in the environment. When the plant doesn’t need the nitrogen traded by rhizobia, it doesn’t trade as many sugars to the rhizobia. This could cause the rhizobia to evolve to be less cooperative as well. Less-cooperative rhizobia may be found where the soil already has lots of nitrogen. These less-cooperative bacteria are freeloaders: they fix less nitrogen, but still get sugars from the plant and other benefits of living in nodules on their roots.

Photo by Tomomi Suwa, 2013

Rhizobia nodules on plant roots. In exchange for carbon and protection in the nodules from plants, rhizobia provide fixed nitrogen for plants.

One very important legume crop species is the soybean. Soybeans are used to produce vegetable oil, tofu, soymilk, and many other food products. Soybeans trade with rhizobia for nitrogen, but often farmers add more nitrogen into the field as fertilizer. Since farms use a lot of nitrogen fertilizer, researchers at KBS were interested in how different types of farming affected the plant-rhizobia mutualism.

They grew soybean plants in a greenhouse and added rhizobia from three different farms: a high N farm, low N farm, and organic farm that used no N fertilizer. After four weeks, the researchers measured chlorophyll content of the soybean plants. Healthy plants that have lots of nitrogen will have high chlorophyll content, and plants with not enough nitrogen will have low chlorophyll content. Because high nitrogen could lead to the evolution of less-cooperative rhizobia, they expected that rhizobia from organic plots would be most cooperative. They predicted rhizobia from high N plots would be the least cooperative, and rhizobia from low N plots would fall somewhere in the middle. More-cooperative rhizobia provide more nitrogen, so the researchers expected plants grown with cooperative rhizobia to have higher chlorophyll content than plants receiving less-cooperative rhizobia.

Featured scientist: REU Jennifer Schmidt from the Kellogg Biological Station

Flesch–Kincaid Reading Grade Level = 10.1

For more information on the evolution of cheating rhizobia, check out these popular science articles:

If you are interested in performing your own classroom experiment using the plant-rhizobium mutualism, check out this paper published in the American Biology Teacher describing methods and a proposed experimental design: Suwa and Williamson 2014

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Cheaters in nature – when is a mutualism not a mutualism?

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The activities are as follows:

Mutualisms are a special type of relationship in nature where two species work together and both benefit. Each partner trades with the other species, giving a resource and getting one in return. This cooperation leads to partner species doing better when the other is around, and without their partner, each species would have a harder time getting resources.

One important mutualism is between clover, a type of plant, and rhizobia, a type of bacteria. Rhizobia live in small bumps on the clovers’ roots, called nodules, and receive protection and sugar food from the plant. In return, the rhizobia trade nitrogen to the plant, which plants need to photosynthesize and make new DNA. This mutualism works well when soil nitrogen is rare, because it is hard for the plant to collect enough nitrogen on its own, and the plant must rely on rhizobia to get all the nitrogen it needs. But what happens when humans change the game by fertilizing the soil? When nitrogen is no longer rare, will one partner begin to cheat and no longer act as a mutualist?

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Worldwide, the nitrogen cycle is off. Not that long ago, before farmers used industrial fertilizers and people burned fossil fuels, nitrogen was rare in the soil. Today, humans are adding large amounts of nitrogen to soils. The nitrogen that we apply to agricultural fields doesn’t stay on those fields, and nitrogen added to the atmosphere when we burn fossil fuels doesn’t stay by the power plant that generates it. The result is that today, more and more plants have all the nitrogen they need. With high nitrogen, plants may no longer depend on rhizobia to help them get nitrogen. This may cause the plant to trade less with the rhizobia in high nitrogen areas. In response, rhizobia from high nitrogen areas may evolve to try harder to get food from the plant, and may even cheat and become parasitic to plants. If this happens, both species will no longer be acting as mutualists.

When Iniyan was a college student, he wanted to study human impacts on the clover-rhizobia mutualism. To find out more, he contacted Jen Lau’s lab at the Kellogg Biological Station one summer, and joined a team of scientists asking these questions. For his own experiment, Iniyan chose two common species of clover: hybrid clover (Trifolium hybridum) and white clover (Trifolium pretense). He chose these two species because they are often planted by farmers. Iniyan then went out and collected rhizobia from farms where nitrogen had been added in large amounts for many years, and other farms where no nitrogen had been added.

Iniyan completed this research as an REU at KBS.

Iniyan completed this research as an REU at KBS.

To make sure that there were no rhizobia already in the soil, Iniyan set up his experiment in a field where no clover had grown before. He then planted 45 individuals of each species in the field. He randomly assigned each plant to one of three treatments. For each species, he grew 15 individuals with rhizobia from high nitrogen farms, and 15 with rhizobia from low nitrogen farms. To serve as a control, he grew the remaining 15 individuals without any rhizobia. To add rhizobia to the plants he made two different mixtures. The first was a mix of rhizobia from high nitrogen farms and water, and the second was a mix of rhizobia from low nitrogen farms and water. He then poured one of these mixtures over each of the plants, depending on which rhizobia treatment they were in. The control plants just got water. No nitrogen was added to the plants.

After the plants grew all summer, Iniyan counted the number of leaves and measured the shoot height (cm) for each individual plant. He did not collect biomass because he wanted to let the plants continue to grow. He then averaged the data from each set of 15 individuals. Plants with fewer leaves and shorter shoots are considered less healthy. He predicted rhizobia that evolved in high nitrogen soils would be worse mutualists to plants, while rhizobia that evolved in low nitrogen soils would be good mutualists.

Featured scientist: REU (NSF Research Experience for Undergraduates) Iniyan Ganesan from the Kellogg Biological Station

Flesch–Kincaid Reading Grade Level = 9.5

For more information on the evolution of cheating rhizobia, check out these popular science articles:

If you are interested in performing your own classroom experiment using the plant-rhizobium mutualism, check out this paper published in the American Biology Teacher describing methods and a proposed experimental design: Suwa and Williamson 2014

Data Nuggets: Small activities with big impacts for students

See the original article on the LTER webpage (reproduced below)

LTERHEADER

The landscape of science education is undergoing a fundamental shift. Updated standards, to be followed by all science teachers in Michigan, emphasize that science is an active process: instead of the memorization of facts in textbooks, students should be taught the ability to generate new knowledge by testing hypotheses and interpreting data. In other words, students should be taught how to use the scientific method and make arguments from evidence. However, with the scientific method comes uncertainty in results. Educators, like K-12 Partnership teacher Connie High (Delton-Kellogg High School), express concern that teachers and their students are not comfortable with the messy data that can result from inquiry, such as results that do not support a hypothesis, or data with lots of variability: “We often found that although lab experiences were fun for students, they lacked skills in summarizing the meaning or goal [of their research]… we needed some data sets for students to practice on.”

As graduate students, we have experienced our fair share of unexpected results and experiments gone awry, and have no problem reassuring teachers that this is nothing to be afraid of. Often, we learn more from data that goes against our original hypothesis, but this can be intimidating in a classroom setting with limited time or ability to conduct follow-up experiments. Because we cannot go into every classroom to share this message, we thought that it would benefit students to work with data collected by scientists, instead of just seeing the polished results presented in textbooks.

Along with other fellows in the K-12 Partnership at KBS, we developed an educational tool aimed to do just that. Data Nuggets are worksheets that give students the chance to work with real data – and all its complexities. Each Data Nugget includes a brief background to a scientist and their study system along with a small, manageable dataset. Students are then given the scientist’s hypothesis and must use the data to construct an argument as to whether the data does or does not support it. One of our priorities has been to provide resources for students of all ages and skill levels because we recognize that students are often overwhelmed with data interpretation. As such, we have created Data Nuggets for students ranging from grades K-16, with varying levels of difficulty. We hope providing this structure will allow teachers to build these skills throughout a student’s entire education, ultimately preparing them for a career in science.

As we present Data Nuggets in classrooms and at national conferences, we continue to get great feedback from teachers on ways to improve the Nuggets and how teachers see them as fitting into their classrooms. “As we get our students ready for ACT testing, Data Nuggets are wonderful sets to use in our classroom because they are relevant and introduce “real” research to our students who might not have this type of exposure otherwise.” (K-12 Partnership teacher Marcia Angle, Lawton Middle School).

Melissa and Liz presenting their research to elementary students.

Melissa and Liz presenting their research to elementary students.

As scientists, we also see Data Nuggets as a great way to share our research with the public. We have each created Nuggets from our own dissertation data (Liz – invasive species, Melissa – animal behavior). Because we believe Data Nuggets could be a great way for all researchers to communicate their work as scientists, our future plans are to hold workshops to help scientists make Data Nuggets of their own. Communicating science to broad audiences is a skill that is becoming increasingly desirable for acquiring jobs and grants. We think Data Nuggets will help develop these skills in scientists of all disciplines and help them to think broadly about the societal importance of what they do.

Data Nuggets are currently funded by NSF’s BEACON Center for the Study of Evolution in Action, and originated through MSU’s GK-12 program at the Kellogg Biological Station.

Publications & Research


Publications: Below are publications related to the Data Nuggets program – including our research into the efficacy of our resources, best practices when sharing the stories of scientists in the classroom, and teaching data literacy.

  1. Strode, P.K., L.S. Mead, M. Stuhlsatz, M.K. Kjelvik, E.H. Schultheis, A. R. Warwick, A. Mohan, J.A. Morris, and R. Mayes. 2025. Quantitative Reasoning in the Context of Science Phenomena. The American Biology Teacher 87:308-312. Click here for a PDF!
  2. Costello, R.A., S.N. Ewell, P.E. Adams, M.L. Aranda, A. Curry, M.M. De Jesus, R.D.P. Dunk, M.E. Garcia-Ojeda, SJ. Gutzler, L.R.A. Habersham, M.K. Kjelvik, M. Mateen, K.J. Metzger, K.X. Mulligan, M.T. Owens, R.M. Pigg, K. Quillin, M.M. Rice, S. Sovi, E.H. Schultheis, J. Schultz, E.J. Theobald, E.Tracey, B. Tripp, S. Yang, A. Zemenick, C.J. Ballen, and D. Ovid. (2025). Highlighting counterstereotypical scientists in undergraduate life science courses. CBE Life Sciences Education 24:es1. Click here for a PDF!
  3. Costello, R. A., E. P. Driessen, M. K. Kjelvik, E. H. Schultheis, R. M. Youngblood, A. T. Zemenick, M. G. Weber, and C. J. Ballen. (2025). More than a token photo: humanizing scientists enhances student engagement. Proceedings. Biological sciences / The Royal Society 292:20240879. Press release here. More press here. Click here for a PDF!
  4. Schultheis, E. H., A. T. Zemenick, R. M. Youngblood, R. A. Costello, E. P. Driessen, M. K. Kjelvik, M. G. Weber, and C. J. Ballen. (2024). “Scientists are people too”: Biology students relate more to scientists when they are humanized in course materials. CBE Life Sciences Education 23:ar64. Click here for a PDF!
  5. Schultheis, E. H., M. K. Kjelvik, J. Snowden, L. Mead, and M. A. M. Stuhlsatz (2022). Effects of Data Nuggets on student interest in STEM careers, self-efficacy in data tasks, and ability to construct scientific explanationsInternational Journal of Science and Mathematics Education 21:1339-1362. Video summary here. Click here for a PDF!
  6. Rosenberg, J., E. H. Schultheis, M. K. Kjelvik, A. Reedy, O. Sultana. (2022). Big data, big changes? The technologies and sources of data used in science classrooms. British Journal of Educational Technology 53:1179-1201. Blog summary here. Click here for a PDF!
  7. Schultheis, E. H. and M. K. Kjelvik (2020). Using messy, authentic data to promote data literacy and reveal the nature of science. The American Biology Teacher 82(7): 439-446. Click here for a PDF!
  8. Kjelvik, M. K. and E. H. Schultheis (2019). Getting messy with authentic data: Exploring the potential of using data from scientific research to support student data literacyCBE Life Sciences Education 18(2): es2. Click here for a PDF!
  9. Schultheis, E. H. and M. K. Kjelvik (2015). Data Nuggets: Bringing real data into the classroom to unearth students’ quantitative and inquiry skills. The American Biology Teacher 77(1):19-29. Click here for a PDF! 

NSF IUSE Research Study (2024-2028): This grant continued our collaboration with Auburn University to develop DataVersify materials. Sadly, this grant was terminated by the NSF in 2025. It would have funded Data Nuggets for 4 years and built off our previous research looking at how sharing scientists stories help students relate to scientists and in turn increases their engagement in the classroom. We hope to continue our work identifying factors that help students view science careers as attainable and promote an interest in STEM in the next generation.

Check out the MSU press release here, and the Auburn press release here.


NSF IUSE Research Study (2020-2024): We collaborated with Project Biodiversify and Auburn University to increase the representation in our scientist role-models found in Data Nuggets. We added new DataVersify activities that combine a Data Nugget with a Scientist Profile that adds additional humanizing information about the person behind the data, such as their hobbies and interests. In undergraduate science classrooms, we found that DataVersify activities:

  1. Increased the extent to which students related to scientist role models.

  2. Changed how students related. Students shifted from relating to professional details, like the scientist’s passion for a particular topic, towards more personal information like their hobbies, identities, and obstacles they overcame.

  3. Increased student engagement with data literacy course materials.

  4. Effects were strongest when the identities of students matched an element of the scientist’s identity (and was even stronger for a double-match).

Check out the MSU press release here, and DataVersify activities here!


NSF DRK-12 Research Study (2015-2020): Our first research study was in partnership with BSCS. Using a fully randomized design, we evaluated the effectiveness of integrating Data Nuggets into high school biology curriculum. We found that students in classrooms using Data Nuggets:

  1. Spent 2x more time in class engaged in the practices of science.

  2. Were better at constructing scientific explanations, including their ability to support claims using data as evidence.

  3. Had greater confidence (self-efficacy) in their science and data literacy abilities.

  4. Showed greater motivation to engage in science and pursue STEM careers.

Check out the MSU press release here. All study materials and a summary of findings here!


BEACON: Data Nuggets was awarded several seed grants through the BEACON Center at Michigan State University. The goal of this collaboration was to develop and provide science communication professional development workshops across all 5 BEACON institutions, create evolution-themed curriculum to use in K-12 and undergraduate classrooms, and disseminate BEACON research to teachers and students.


LTER Data Nuggets: Data Nuggets began in 2011 as a collaboration between scientists and teachers through the K-12 Partnership at the Kellogg Biological Station. Since its start, the KBS LTER has supported the Data Nuggets program to develop new activities that focus on long-term data, share scientists’ stories, and expand students’ data literacy abilities. To explore these resources, check out this page!

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Data Nuggets in the classroom

TitleContent LevelScience Concepts / KeywordsQuantitative Concepts / StatisticsGraph Type(s)Variable Type(s)Data Type(s)
Dangerously bold1animals, animal behavior, tradeoff, fish, predation, biological significancepercent, standard error (SE), predictionsbarcategoricalsummarized, Digital Data Nugget
Coral bleaching and climate change1climate change, coral reef, marine, mutualism, temperature, animals, algae, adaptation, evolutionratiobarcategoricalsummarized, Digital Data Nugget
Won’t you be my urchin?1coral reef, herbivory, marine, sea urchin, water, animals, competition, food webmean, models, standard error (SE), standard deviation (SD)barcategoricalraw
Springing forward1 & 3climate change, phenology, plants, temperaturemean, standard error (SE), Julian datebarcategoricalsummarized, full dataset available, Digital Data Nugget
Do urchins flip out in hot water?1 & 3animals, climate change, marine, heatwaves, urchins, behavior, invertebrates, environmental changeaverage, mean, standard error (SE), calculationbarcategorical, continuoussummarized, two levels available,
Do insects prefer local or foreign foods?2herbivory, invasive species, plants, insects, enemy release, ecologymean, variance, standard deviation (SD), standard error (SE), confidence intervals (CI), predictionsbarcategoricalsummarized, full dataset available, Digital Data Nugget
Spiders under the influence2animals, invertebrates, habitat, chemical pollution, aquatic, streams, scientist profilemean, multiple variablesmultiple barcategoricalfull dataset, students summarize
Do invasive species escape their enemies?2herbivory, invasive species, plants, insects, enemy release, ecologymean, percentbarcategoricalsummarized
Lake Superior Rhythms2amplitude, aquatic, atmosphere, environmental, physics, student research, wave period, wavescycle, sine wave, amplitude, change over timesine wavecontinuoussummarized, full dataset available
All washed up? The effect of floods on cutthroat trout2animals, disturbance, ecology, fish, water, stream, floods, alternative hypotheses, limnologyregression, ratio, rate, graph choice, unnecessary variables, long-term datascattercontinuousraw, Digital Data Nugget
Float down the Kalamazoo River2Kalamazoo River, water, suspended solids, dam, reservoir, limnologymean, ratio, rate, standard deviation (SD), standard error (SE), Julian date, unnecessary variablesbar, linecategorical, continuoussummarized, Digital Data Nugget
Finding a foothold2animals, ecology, marine, substrate, waterfrequency, proportionbarcategoricalsummarized
Is chocolate for the birds?2experimental design, agriculture, animals, birds, biodiversity, rainforest, succession, disturbance, transect, habitataddition, unnecessary variablesbarcategoricalraw, full dataset available, Digital Data Nugget
Fish fights2animal behavior, animals, fish, matingmean, proportion, regressionscattercontinuoussummarized, Digital Data Nugget
Marvelous mud2ecology, environmental, fertilization, mud, phosphorus, substrate, water, wetland, limnologypercent, regression, graph choicescattercontinuoussummarized
Which guy should she choose?2animal behavior, animals, fish, matingfrequency, regression, correlation vs. causationscattercontinuousraw, summarized
Sexy smells2adaptation, animal behavior, animals, birds, matingpercent, regression, correlation vs. causationscattercontinuousraw, Digital Data Nugget
Shooting the poop2adaptation, animal behavior, animals, insects, predation, alternative hypothesesmean, standard error (SE)barcategoricalraw
Invasive reeds in the salt marsh2disturbance, invasive species, plants, wetland, limnology, transectmean, percentbarcategoricalraw, summarized
A tail of two scorpions2animal behavior, animals, predationaddition, proportion, ratio, graph choicebar, stacked bar, pie chartcategoricalraw, Digital Data Nugget
Green crabs: invaders in the Great Marsh2animals, invasive species, substrate, wetland, erosion, limnologyaddition, range, mapmapcategorical, spatialraw, summarized
Guppies on the move2animals, aquatics, behavior, ecology, genetics, migration, movement, tropicsregressionline, scattercategorical, continuousraw, full dataset available
The birds of Hubbard Brook, Part I2animals, biodiversity, birds, climate change, succession, disturbance, ecologycount, long-term dataline, scattercontinuousraw, full dataset available, Digital Data Nugget
Beetle battles2adaptation, animals, behavior, competition, evolution, insects, matingstandard error (SE)barcategoricalsummarized
How do brain chemicals influence who wins a fight?2animals, behavior, competition, insects, aggression, brain chemistry, physiologymeanbarcategoricalraw, summarized
Deadly windows2animals, animal behavior, birds, environmental, urban, alternative hypothesesaddition, proportionbarcategoricalsummarized
Which would a woodlouse prefer?2experimental design, animals, behavior, ecology, predationcount, Chi-square test, replication, sample sizebarcategoricalraw, Digital Data Nugget
Tree-killing beetles2animals, biodiversity, disturbance, ecology, environmental, insects, plantsmean, percent, proportion, regressionscattercontinuoussummarized
Alien life on Mars – caught in crystals?2astrobiology, salt, solution, Mars, extraterrestrial life, chemistry, physical sciencemean, time series linecontinuoussummarized, visual, full dataset available
Beetle, it’s cold outside!2animals, climate change, ectotherm, insects, temperaturemean, standard error (SE), modelslinecontinuoussummarized, Digital Data Nugget
Can a salt marsh recover after restoration?2disturbance, salinity, transect, invasive species, plants, wetland, restoration, limnologymean, percent, frequencybar, linecontinuoussummarized
Fast weeds in farmer’s fields2evolution, adaptation, agriculture, plants, fitness, heredity, geneticsfrequency, percent, mean, replication, sample size, unnecessary variablesbar, scattercontinuous, categoricalsummarized
The carbon stored in mangrove soils2carbon, climate change, disturbance, ecology, environmental, nutrients, greenhouse gasses, plants, transectproportion, mean, unnecessary variablesbarcontinuous, categoricalsummarized
Where to find the hungry, hungry herbivores2herbivory, plants, insects, ecology, latitude, longituderegression, standard deviation (SD), standard error (SE)scattercontinuoussummarized
A window into a tree’s world2climate change, dendrochronology, ecology, plants, temperaturemean, relative growth, graph choice, regression, correlation vs. causation, trend line, line, scattercontinuous, categoricalsummarized
Corals in a strange place2adaptation, coral reef, mangrove, morphology, structure and functionvisual data, countbar, stacked bar, pie chartcontinuoussummarized, full dataset available
Mangroves on the move2climate change, disturbance, ecology, environmental, fertilization, nitrogen, nutrients, phosphorus, plantsmean, standard error (SE)barcategorical, continuoussummarized
Getting to the roots of serpentine soil2soil, plasticity, limiting factors, plants, ecology, scientist profilemean, range, standard deviationbarcontinuous, categoricalsummarized
Little butterflies on the prairie2butterflies, prairie strips, prairie, agriculture, crops, farmers, animals, ecology, scientist teamPollard Walk, transect, abundance, count, mean, long-term data, T-testbarcontinuoussummarized, full dataset available
Blinking out?2agriculture, insects, population, ecology, biodiversity, fireflies, scientist profilemoving window, long-term data, standardize, sampling effort, division, count, unnecessary variablesline, scattercontinuous, categoricalsummarized, full dataset available, Digital Data Nugget
PFAS: Our forever problem2PFAS, water, teacher, pollution, watershed, Human impactconcentration, sampling, dilution, bar plotbarcontinuous, categoricalraw, full dataset available
Buried seeds, buried treasure2germination, plants, seed bank, seed viability, scientist profilelong-term data, trendscattercontinuousraw
Mowing for monarchs, Part I2community science, citizen science, animals, behavior, biodiversity, community science, disturbance, ecology, plants, insects, alternative hypothesesaverage, time, rate, fractionbarcategoricalsummarized, full dataset available
A difficult drought2agriculture, biofuels, climate change, plants, carbon, fermentation, ethanol, chemistrymean, range, variability, replication, sample sizebarcontinuous, categoricalsummarized, full dataset available
Farms in the fight against climate change2Carbon, Soil, LTER, climate changeaverage, standard deviation, percent, bar plotbarcategorical, continuoussummarized, raw
Mowing for monarchs, Part II2community science, citizen science, animals, behavior, biodiversity, community science, disturbance, ecology, plants, insects, predation, alternative hypothesesaverage, time, rate, fractionbarcategoricalsummarized, full dataset available
Does more rain make healthy bison babies?2animals, ecology, keystone species, plants, prairie, precipitationmean, time, regression, long-term data, unnecessary variablesline, scattercontinuoussummarized, full dataset available
Benthic buddies2adaptation, animals, arctic, biodiversity, ecology, environmental, invertebrates, lagoons, marinemeanbarcategoricalsummarized
Did you hear that? Inside the world of fruit fly mating songs2animals, insect, process of science, reproducibility, communication, volume, social, behaviorcalculations, index, standard deviation, average, replicatebarcategorical, continuoussummarized
A burning question2biodiversity, canopy, ecology, fire ecology, forest, human impact, keystone species, land management, natural resourcesaverage, timebarcategorical, continuoussummarized
What grows when the forest goes?2fire, forest, invasive species, long-term, plantsaverage, percent, proportion, error barsbarcategorical, continuoussummarized
Anole’s new niche2adaptation, animals, evolution, niche, population, introduced species, native species, behavior, habitat, competitionmean, grand mean, standard deviation (SD), count, unnecessary variablesbarcategorical, continuoussummarized
Salmonberries in our future2Arctic, wetlands, climate change, ecology, disturbance, water, marinemean, standard deviation (SD)multiple barcategorical, continuoussummarized
Can kelp help the plovers? 2marine, beach, herbivory, kelp, preference, behavior, invertebrate, student research, food webaverage, multiple trials, calculationsmultiple barcategorical, continuoussummarized
Are plants more toxic in the tropics?3herbivory, diversity, plants, insects, ecology, adaptation, chemistrystandard deviation (SD), standard error (SE), index, formulabarcategoricalsummarized
Does a partner in crime make it easier to invade?3legume, plants, mutualism, rhizobia, invasive species, soil, scientist profilemeanbarcategoricalsummarized
Fair traders or freeloaders?3evolution, legume, plants, mutualism, rhizobia, nitrogen, fertilizationmean, standard error (SE)barcategoricalsummarized
Fertilizing biofuels may cause release of greenhouse gasses3agriculture, biofuels, climate change, fertilization, greenhouse gases, nitrogen, plantsregressionscattercontinuoussummarized, full dataset available, Digital Data Nugget
The ground has gas!3climate change, temperature, greenhouse gases, nitrogen, plants, chemistryregressionscattercontinuousraw, summarized, full dataset available
Growing energy: comparing biofuel crop biomass3agriculture, biofuels, climate change, fertilization, plants, carbonmean, standard error (SE)barcategoricalsummarized
Microbes facing tough times3drought, enzymes, microbes, mutualist, agriculture, climate change, cropsmean, standard deviation, negative valuesbarcontinuoussummarized
How the cricket lost its song, Part I3adaptation, animal behavior, animals, rapid evolution, mating, parasitism, scientist profilepercentbarcategoricalraw, summarized, Digital Data Nugget
The mystery of Plum Island Marsh3fertilization, fish, food web, marine, mollusk, water, wetland, limnologymeanbarcategoricalraw
Invasion meltdown3climate change, ecology, invasive species, plants, temperaturemean, range, replication, sample sizebarcategoricalsummarized, full dataset available
Is your salt marsh in the zone?3climate change, ecology, plants, sea level rise, substrate, wetland, limnologymeanbarcategoricalraw
Lizards, iguanas, and snakes! Oh my!3animals, biodiversity, disturbance, restoration, urban, transectcount, additionbarcategoricalraw
What do trees know about rain?3climate change, dendrochronology, ecology, plants, precipitation, temperature, watermean, formula, equation, addition, multiplicationlinecontinuousraw, full dataset available
CSI: Crime Solving Insects3animals, insects, parasitismweighted meanbarcategoricalraw, Digital Data Nugget
Does sea level rise harm saltmarsh sparrows?3animals, birds, sea level rise, climate change, disturbance, ecology, wetland, limnologymean, standard deviation (SD)linecontinuoussummarized
Keeping up with the sea level3climate change, disturbance, ecology, sea level rise, plants, substrate, wetland, limnologyformula, equation, rateline, scattercontinuous, categoricalraw
The birds of Hubbard Brook, Part II3animals, biodiversity, birds, climate change, succession, disturbance, habitat, ecologycount, long-term dataline, scattercontinuous, categoricalraw, full dataset available, Digital Data Nugget
How the cricket lost its song, Part II3adaptation, animal behavior, animals, rapid evolution, mating, parasitism, scientist profilemeanbarcategoricalraw, summarized. Digital Data Nugget
Feral chickens fly the coop3adaptation, animals, behavior, birds, ecology, evolution, invasive species, mating, heredity, geneticsproportion, percentbarcategoricalraw, summarized
Raising Nemo: Parental care in the clown anemonefish3animals, behavior, coral reef, ecology, fish, marine, mating, tradeoff, plasticity, scientist profilemean, standard error (SE)barcategoricalraw
When a species can’t stand the heat3animals, climate change, disturbance, ecology, environmental, mating, temperature, sex ratioaddition, percent, ratio, regressionscattercontinuousraw, full dataset available, Digital Data Nugget
Are you my species?3adaptation, animals, behavior, biodiversity, competition, evolution, fish, matingformula, equation, addition, subtraction, division, regressionscattercontinuousraw, Digital Data Nugget
Marsh makeover3bodiversity, disturbance, ecology, greenhouse gases, mud, plants, restoration, wetland, limnologystandard error (SE)bar, linecategoricalraw, summarized
To bee or not to bee aggressive3animals, behavior, genes, insects, tradeoff, plasticity, aggressionmean, effect size, percent change, rangebarcategoricalsummarized, full dataset available, Digital Data Nugget
Why are butterfly wings colorful?3adaptation, animals, insects, models, predation, alternative hypothesesfraction, proportion, probabilitybarcategoricalsummarized, Digital Data Nugget
City parks: wildlife islands in a sea of cement3animals, biodiversity, ecology, urban, island biogeography, parks, camera trapShannon Wiener Index, formula, equation, sum, proportion, regressionscattercontinuoussummarized, full dataset available, Digital Data Nugget
Is it better to be bigger?3adaptation, animals, evolution, predation, natural selectionmean, percent, rate, regressionscattercontinuoussummarized, Digital Data Nugget
Is it dangerous to be a showoff?3adaptation, animals, evolution, predation, tradeoff, natural selectionpercent, rate, regressionscattercontinuous, categoricalsummarized
What big teeth you have! Sexual selection in rhesus macaques3animals, evolution, sexual selection, sexual dimorphism, anatomy, form and function, primate, scientist profilemean, standard deviation (SD)barcontinuous, categoricalraw, Digital Data Nugget
Bringing back the Trumpeter Swan3animals, biodiversity, birds, ecology, environmental, restorationmean, long-term data, countlinecontinuous, categoricalraw, full dataset available, Digital Data Nugget
Are forests helping in the fight against climate change?3climate change, carbon, ecology, greenhouse gasses, photosynthesis, plants, decomposition, respirationregression, long-term datascattercontinuousraw, Digital Data Nugget
Can biochar improve crop yields?3agriculture, environmental, fertilization, plants, soil, water, biochar, carbonpercent, mean, standard deviation (SD), yield, replication, sample size, randomizationbarcontinuous, categoricalsummarized
Hold on for your life! Part I3adaptation, animals, disturbance, evolution, natural selection, genetic drift, hurricane, biological significance, alternative hypothesesargumentation, mean, standard error (SE)barcontinuous, categoricalsummarized
Hold on for your life! Part II3adaptation, animals, disturbance, evolution, natural selection, genetic drift, hurricaneargumentation, visual datavisualraw, photo, video
Testing the tolerance of invasive plants3ecology, herbivory, invasive species, plants, tolerancestatistical interaction, mean, standard error (SE)barcategoricalsummarized, full dataset available
Picky eaters: Dissecting poo to examine moose diets3animal behavior, animals, ecology, foraging, herbivory, parks, predator-prey1:1 line, proportion, mean, unnecessary variablesscattercontinuous, categoricalsummarized, full dataset available
Candid camera: capturing the secret lives of carnivores3animals, biodiversity, carnivores, ecology, island biogeography, richness, camera trap, parksregressionmap, scattercontinuoussummarized, Digital Data Nugget
Crunchy or squishy? How El Niño events change zooplankton3algae, animals, marine, El Niñooutlier, correlation vs. causation, unnecessary variablesline, scattercontinuousraw, Digital Data Nugget
Streams as sensors: Arctic watersheds as indicators of change3climate change, ecology, environmental, carbon, nitrogen, permafrost, limnologyunnecessary variables, regression, long-term datascattercontinuoussummarized
The end of winter as we’ve known it?3climate change, ice cover, student researchJulian date, mean, regression, messiness, variabilityscattercontinuoussummarized, full dataset available, Digital Data Nugget
Working to reduce the plastics problem3plastics, synthetic materials, chemistry, biodegradable, elastomer, polymer, monomer, stress, strain, physical sciencepercent, ratiolinecontinuoussummarized
Limit by limit: Nutrients control algal growth in Arctic streams3nitrogen, nutrients, phosphorus, nutrient limitation, law of the minimum, Arctic, limnologyresponse ratio, graph choice, standard deviation (SD)barcategoricalsummarized
To reflect, or not to reflect, that is the question3albedo, arctic, climate change, environmental, ice, temperature, waterequation, unnecessary variables, regressionline, scattercontinuoussummarized
How milkweed plants defend against monarch butterflies3herbivory, evolution, coevolution, plants, insects, ecology, scientist profilemean, regression, best fit line, trend line, multiple dependent variables, messiness, outlierline, scattercontinuoussummarized
Purring crickets: The evolution of a new cricket song3adaptation, animal behavior, animals, rapid evolution, mating, parasitism, scientist profilemean, percent, Chi-square testbarcategoricalraw, Digital Data Nugget
Round goby, skinny goby3local adaptation, animals, biodiversity, rapid evolution, fish, Great Lakes, habitat, invasive species, Kalamazoo Rivermean, standard error, replication, sample sizebarcategoricalsummarized, full dataset available
David vs. Goliath3aggression, animals, behavior, brain chemistry, competition, insects, physiology, biological significancefrequency, proportion, percent, unnecessary variablesbarcategoricalraw, summarized
Size matters - and so does how you carry it!3adaptation, animals, evolution, insects, sexual selection, tradeoffsresiduals, trend, multiple graphs, standardizescatter, linecontinuousraw, summarized, full dataset available
Do you feel the urban heat?3urban, climate change, extreme heat, environmental justice, human impacts, socioeconomic, sensorsmaximum, minimum, averages, time, baselinemultiple linecontinuous, categoricalsummarized
Ant wars!3aggression, animals, behavior, competition, insectsdensity, ratio, percent, regression, countbar, line, scattercontinuousraw, summarized
Salty sediments? What bacteria have to say about chloride pollution3bacteria, chemistry, disturbance, environmental, microbes, pollution, salt, urban, water, habitat, time, toxicitymean, concentrationbarcategoricalsummarized
Going underground to investigate carbon locked in soils 3climate change, ecology, environmental, greenhouse gasses, soil carbon, microbes, chemistrymean, standard deviation (SD), regression, best fit line, trend line, correlation vs. causationline, scattercontinuoussummarized
Nitrate: Good for plants, bad for drinking water3agriculture, environmental, fertilization, nitrogen, soil, water, plants, human health mean, time, date, Julian date, concentrationline, scattercontinuous, categoricalsummarized, full dataset available
Trees and the city3biodiversity, ecology, environmental justice, social demographics, urbanspatial data analysis, percent, binned data, average, median, histogrammultiple scatter, spatial mapcontinuousspatial, summarized, full dataset available
Collaborative cropping: Can plants help each other grow?3agriculture, environmental, plants, cropsreplicates, correlation vs. causation, regression, trendmultiple scattercontinuousraw
The sound of seagrass3acoustics, sound, photosynthesis, marine, productivity, decibels, physicsaverage, mean, standard deviation, trend, timemultiple scatter, linecontinuoussummarized
Which tundra plants will win the climate change race?3climate change, nutrients, long-term data, competition, plants, ecologymean, trend, time, series, control, long-term datalinecontinuoussummarized
The prairie burns with desire3ecology, prairie, plants, fire ecology, human impact, reproduction, land managementtrend, time, multiple plots, multiple variables, long-term data, proportion, averagescatter, linecontinuoussummarized, full dataset available
Seagrass survival in a super salty lagoon3climate change, ecology, environmental, long-term, marine, plants, salinitydouble y-axis, trend, time, multiple variablesscattercontinuoussummarized
Sink or source? How grazing geese impact the carbon cycle3carbon cycle, Arctic, wetlands, primary production, photosynthesis, respiration, climate change, birds, ecosystemequation, calculation, subtraction, negative values, source, sinkbarcategorical, continuoussummarized
Poop, poop, goose!3wetlands, Arctic, carbon cycle, climate change, disturbance, ecology, environmental, greenhouse gasses, birdsmean, standard deviation (SD), fluxbarcategoricalsummarized
Too hot to help? Friendship in a changing climate3mutualism, algae, coral, genotype, photosynthesis, respiration, climate changecalculations, negative values, net, mean, average, standard errorbarcategorical, continuoussummarized
Does the heat turn caterpillars into cannibals?3animal behavior, insect, virus, disease ecology, temperature, cannibalism, caterpillarcalculation, percents
barcategorical, continuoussummarized
climate change, disturbance, ecology, environmental, growth, human impact, plants, water, wetlands3climate change, disturbance, ecology, environmental, growth, human impact, plants, water, wetlandsmean, average, mass, barquantitative, qualitative
summarized
A plant breeder’s quest to improve perennial grain4genetics, artificial selection, DNA, selective breeding, phenotype, genotype, nucleotides, sequencingcalculations, average, predictions, standard error, standard deviation, barcontinuous, categoricalsupplemental activity available,
Cheaters in nature – when is a mutualism not a mutualism?4evolution, legume, plants, mutualism, parasitism, rhizobia, nitrogen, fertilizationmean, standard error (SE)barcategoricalsummarized
Dangerous aquatic prey: can predators adapt to toxic algae?4adaptation, algae, evolution, marine, predationmeanbarcategoricalsummarized
Salmon in hot water4adaptation, animals, climate change, evolution, fish, genes, genome, temperature, DNA, heredity, genetics, QTLmeanlinecontinuoussummarized
Urbanization and estuary eutrophication4algae, eutrophication, fertilization, marine, nitrogen, phosphorus, wetland, urban, photosynthesis, respiration, limnologymean, standard error (SE), subtraction, modelbarcategoricalraw
How to escape a predator4adaptation, animal behavior, animals, predation, physiologymean, standard error (SE)barcategoricalraw, summarized
The flight of the stalk-eyed fly4physics, moment of intertia, adaptation, animals, flight, physiologymean, standard error (SE), formula, equation, multiplicationcontinuoussummarized
Make way for mummichogs4animals, biodiversity, disturbance, fish, restoration, wetland, limnology, student researchmeanbar, linecontinuousraw, summarized
The Arctic is melting – so what?4climate change, marine, temperature, water, weather, ice, Arctic, albedopercent, modelsdiagramcategorical, modeled datasummarized
Gene expression in stem cells4gene expression, genes, stem cells, DNA, genetics, human healthmeanbarcategoricalsummarized, Digital Data Nugget
Bon Appétit! Why do male crickets feed females during courtship?4adaptation, animals, behavior, competition, insects, mating, feeding, alternative hypotheses, scientist profilecount, proportion, regression, multiple regression, unnecessary variablesscattercontinuousraw, Digital Data Nugget
Winter is coming! Can you handle the freeze?4ecology, evolution, genes, plants, local adaptation, QTLpercent, standard deviation (SD), standard error (SE)bar, linecategoricalraw, summarized
Finding Mr. Right4animals, animal behavior, biodiversity, birds, evolution, genes, mating, local adaptationmeanbarcategoricalraw
Why so blue? The determinants of color pattern in killifish, Part I4adaptation, animals, biodiversity, evolution, fish, genes, mating, heredity, genetics, close reading activitymean, standard deviation (SD), standarad error (SE)barcategoricalraw, summarized
Why so blue? The determinants of color pattern in killifish, Part II4adaptation, animals, biodiversity, evolution, fish, genes, mating, heredity, geneticsmean, standard deviation (SD), standarad error (SE)barcategoricalraw, summarized
Sticky situations: big and small animals with sticky feet4adaptation, animals, biomimicry, chemistry, physics, scalemean, ratio, multiplication, formula, equation, surface area, mass, volumescatter - logarithmic axescontinuoussummarized
When whale I sea you again?4climate change, marine, temperature, water, whales, DNA, PCR, sex ratiofraction, percent, ratioline, stacked barcontinuous, categoricalraw, Digital Data Nugget
The case of the collapsing soil4climate change, carbon, ecology, plants, phosphorus, sea level rise, respiration, substrate, wetland, limnologyregression, concentrationscattercontinuousraw, Digital Data Nugget
Clique wars: social conflict in daffodil cichlids4animal behavior, animals, competition, fishcount, standard deviation (SD), standarad error (SE)barcategoricalsummarized, Digital Data Nugget
Fishy origins4community science, citizen science, DNA, evolution, fish, PCR, marine, microsatellitespercent, proportion, addition, divisionbar, stacked barcontinuous, categoricalraw
Fertilizer and fire change microbes in prairie soil4biodiversity, diversity, grassland, microbes, plants, prairie, soilunnecessary variables, Shannon Wiener Index, meanbarcontinuous, categoricalsummarized
Breathing in, Part I4photosynthesis, carbon accumulation, carbon sequestration, climate change, forest, habitatmean, confidence, global databasebarcontinuous, categoricalsummarized, full dataset available
Breathing in, Part 24climate change, photosynthesis, respiration, carbon, climate modelprecision, percent, model prediction, mean, calculation, equationbarcontinuous, categoricalsummarized, full dataset available
Stop that oxidation! What fruit flies teach us about human health4insects, model species, cell biology, genetics, cellular processes, oxidation, genetics, scientist profilemeanbarcontinuous, categoricalraw, summarized
Love that dirty water4environmental, urban, water, GIS, landscapes, impervious surfaces, ecosystem services, land acknowledgement, human healthmodel, web-tool, simulation, percent change, calculation, mapbar, line, mapcategorical, continuoussummarized
Trees and bushes, home sweet home for warblers4animals, biodiversity, disturbance, ecology, birds, succession, transect, habitatregression, best fit line, trend line, percentscattercontinuoussummarized
Changing climates in the Rocky Mountains4citizen science, climate change, community science, ecology, environmental, plants, precipitation, temperaturemean, trend, timeline, double y-axiscontinuous, categoricalsummarized, photo
Surviving the flood4disturbance, urban, stream, floods, photosynthesis, respiration, stormwaterreference line, percent, negative values, additional variables, difference, unnecessary variables, outlierscatter, linecontinuousraw, summarized
Eavesdropping on the ocean4acoustic ecology, physics, whales technology, mammals, marine biology, renewable energy, population, human impactproportions, calculation, detectionsscatter, barcategorical, continuoussummarized, full dataset available
Reconstructing the behaviour of ancient animals4anatomy, animals, behavior, form and function, fossils, nocturnal, paleobiology, primatemean, average, range, maximum, minimum, box and whisker plotbar, box and whisker plotcategorical, continuoussummarized
CO2 and trees, too much of a good thing?4Carbon dioxide, climate change, photosynthesis, plants, respirationmass, averagebarcategorical, continuoussummarized
Catching fish with sound4climate change, fish, ocean, soundvolume, depth, temperaturescattercontinuoussummarized
Bear Necessities: A genetic panel for bear identification4Animals, DNA, Genetics, ConservationgenotypeNAcategoricalsummarized