BEACON Researchers at Work: The tale of the tail-less sea squirt

Posted on November 14, 2012 by Danielle Whittaker

This week’s BEACON Researchers at Work post is by University of Washington graduate student Max Maliska.

Photo of MaxI have found my work as a PhD. candidate in Billie Swalla’s lab at University of Washington in Seattle to be highly integrative; spanning the disciplines of molecular biology, marine biology, evolutionary biology, and computer science. I would have never thought I would have been able to integrate these disciplines and learn a diverse set of training during my PhD. or been able to travel to the places I have been. As a student-researcher member of BEACON since its inception, I have also been able to gain a great amount of knowledge in areas that are becoming necessity in big-data science.

As a kid growing up in the Chicago suburbs, I was a book-worm infinitely interested in the weird and bizarre (I still am). I remember hoping that one day it would become clear that The X-Files actually existed, and I would be hired on and groomed by Mulder and Scully. I would routinely collect frogs and fish at our nearby creek, spending hours wading with a small plastic sand bucket. I remember at the age of 12 being very concerned about what I would do when I grew up. I then came to the quick realization that I would be a biologist, which made perfect sense, and have felt comfortably about being so ever since (X-Files contact me if you are legit).

I moved to University of Florida for college with the specific interest of studying animals in their own element, specifically herps (reptiles and amphibians). It was not until my sophomore year of college that I took a course and discovered a group of animals much crazier, much more alien than I thought would ever exist on this planet: marine invertebrates. I later learned that many movie aliens were actually often based on marine invertebrates (see Alien queen).

At the end of my sophomore year I was encouraged to take a course at UW’s Friday Harbor Labs on marine invertebrate zoology, one of the best courses taught on the subject in the world (go here to check out FHL’s student opportunities). Working in the San Juan Islands of Washington, I fell in love with the temperate waters, lush greenery, and the strange and diverse fauna of the northern Pacific. I knew I had to come back. After completing an undergraduate thesis studying the species relationships of several undescribed species of a tropical sea cucumber in the Florida Museum of Natural History, I applied to University of Washington and was accepted as a PhD. student in the Biology Department.

My dissertation research at UW has been under the advisement of Dr. Billie Swalla. The main interests of my research have been trying to understand how changes in the swimming larval phase of species of sea squirts have occurred and how this affects the evolution of these species. Sea squirts or ascidians are the closest related group of invertebrates to animals with a spine, or vertebrates (fish, reptiles, amphibians, birds, and mammals); understanding their evolution gives us insight into what the common ancestor of invertebrates and vertebrates was like and what kinds of changes have occurred between these groups.

Sea squirtsSea squirts live as adults cemented to the substratum, filter-feeding little particles from the water into a mucous-net inside their pharynx. The only phase of their lives that sea squirts have the ability to disperse, which improves their success of finding a mate or finding a place to grow up favorably as an adult, is when they are a little larva. I have been studying a group of sea squirts, which includes several species that have lost the tail during their larval phase. We have showed that this group has evolved taillessness multiple times independently. As this is the only dispersive phase for these organisms, being tailless results in larvae to often not disperse as far as the tailed species. Therefore, it is somewhat of a conundrum as to why this has evolved many times.

In 2011, I was able to travel to Panama to take a course, involving researchers from all over the world, on sea squirt biology through a National Science Foundation program sponsored by the Pan-American Studies Insititute. I was stationed at the Smithsonian Tropical Research Institute in Bocas del Toro, Panama. The diversity of sea squirts in the Caribbean is quite amazing and we were able to snorkel and SCUBA dive daily and bring different forms back into lab, identify, and understand them.

Tailless and tailed sea squirt larvaeTo understand how genetics and development have played roles in tailless larvae in certain species of sea squirts we have taken advantage of a collaboration through BEACON with Dr. Titus Brown’s lab at Michigan State University. We have taken advantage of recent technological advances to sequence the entire genome scale of data for a species with a normal tailed larva, a species with tailless larva, and the hybrid of these two species, which shows an intermediate of half a tail in its larval phase.

This has been a great collaboration for me because I have been fortunate enough to work with Dr. Brown and also a student in the Brown lab, Elijah Lowe. These two have been patient enough to help me while I have learned computational methods that have advanced my understanding of ascidian and evolutionary biology.

Using these methods, we have been able to determine that species with tailless larvae have changed when they activate the metamorphosis genetic program. This is a program that turns on and allows a larva go through metamorphosis into the very different adult form. These tailless species activate their metamorphosis genetic program earlier and we hypothesize this has been moved earlier and starts “breaking down the tail” before it actually forms.

I will defend my dissertation this spring and I am excited about what is next. I hope to better understand the population scale changes that occur between species of marine invertebrates when they evolve alternative larval forms, which affect their dispersal. While it’s not The X-Files, solving these actual evolutionary mysteries have been exciting to determine.

For more information about Max’s work, you can contact him at mem24 at u dot washington dot edu.

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BEACON Summer REU Students Shine

Posted on November 14, 2012 by Danielle Whittaker

BEACON’s summer Research Experience for Undergraduates (REU) Program is a 10-week intensive residential program targeting the recruitment of underrepresented students to conduct research with a faculty mentors. This past summer, BEACON funded sixty-seven students across the partner schools at six different research sites.

Tobias and DannyMany of these students successfully submitted and presented their summer research at national conferences across the United States. For example, Tobias Ortega-Knight and Danny Lynch (University of Virgin Islands) attended the Annual Biomedical Conference for Minority Students (ABRCMS), which is the largest undergraduate research conference in the nation. Both Tobias and Danny won the category for outstanding presentation; additionally, Tobias won a second award for interdisciplinary research.

Please contact Judi Brown Clarke, BEACON Diversity Director (jbc@egr.msu.edu) with any questions about the REU program, or fill out this form to receive more information. 

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BEACON Researchers at Work: Reproducing the evolutionary path to human-level intelligence

Posted on November 6, 2012 by Danielle Whittaker

This week’s BEACON Researchers at Work post is by MSU graduate student Randal Olson.

Photo of Randy OlsonFor well over a decade, I have been fascinated with the idea that computers could achieve the same level of intelligence as humans. I would often ask my friends, “How cool would it be to combine human-level intelligence with the massive computing power of computers? A machine that could think like us, but infinitely faster… imagine the possibilities!” Usually my friends responded by rolling their eyes, or started talking about how that kind of Artificial Intelligence (AI) would kill us all. (Thanks to Terminator, and about every other popular movie that features an evil AI!) Since I have never been content to just think about what an Artificial Intelligence would be capable of, I set out on a long journey to figure out how I could help make a (not-so-evil) AI a reality in my lifetime.

Any good story begins with a prologue, so let’s start there.

Ever since computers were created, scientists have tried to figure out how a computer could achieve human-level intelligence. By the 1960’s, AI researchers had already discovered that the computer could perform many intelligent tasks that humans were capable of, at speeds that were previously considered impossible.

Indeed, the leading AI researchers at the time claimed that,

“Machines will be capable, within twenty years, of doing any work a man can do.”
Herbert Simon

and

“Within a generation … the problem of creating ‘artificial intelligence’ will substantially be solved.”
Marvin Minsky

Unfortunately, creating an AI with human-level intelligence (called “strong AI”) inside a computer turned out to be a much harder problem than everyone thought, but that hasn’t deterred AI researchers. Since the 1960’s, AI researchers have made tremendous strides in creating intelligent machines whose abilities rival (and oftentimes outperform) those of humans. Today, we have AIs that defeat world chess champions in chess, act as electronic personal assistants (with an attitude!), drive cars without human intervention, and even help diagnose and predict health issues in patients.

Despite this remarkable progress, an AI with human-level intelligence has eluded AI researchers for over half a century. A growing number of researchers, particularly those with a neuroscience background, claim that strong AI will continue to elude AI researchers as long as they try to “hard-code” AI; to create a true general Artificial Intelligence, AI researchers must first understand the biological mechanisms that ubderly human intelligence.

This is where my story comes in.

Along with my colleagues in Dr. Chris Adami’s lab and the Evolved Intelligence research group at the BEACON HQ, I have been working on understanding how human-level intelligence evolved in nature using Artificial Life as a research platform. We are trying to learn about the problems that early creatures faced that made intelligent behavior a favorable trait to evolve, with the goal of eventually working our way up to understanding how early forms of intelligence evolved into the complex forms of adaptive, social, and predictive intelligence that humans are capable of. In essence, we are attempting to reproduce the evolutionary path to human-level intelligence in silico.

Lately, I have been studying the evolution of swarming behavior in animals. Swarming behavior is a particularly interesting intelligent behavior because it enables groups of animals to accomplish tasks that would otherwise be difficult or even impossible to accomplish alone. Take this group of European starlings under attack by a Peregrine falcon, for example.

If the starlings didn’t work together, they would make easy prey for the falcon. However, by swarming together, they are better able to defend themselves against the falcon’s attacks. In a recent paper, my colleagues and I addressed one of many possible reasons why the starlings evolved this swarming behavior.

Understanding grouping behavior and group decision making in animals is an important part of understanding the evolution of human-level intelligence. Humans are social creatures that evolved to work in groups, so understanding how and why animals work and live in groups will give us a better grasp of the interplay between grouping behavior and human-level intelligence. It could very well be that grouping behavior is a trait that creatures must have before they can evolve higher-level intelligence.

By following this path to strong AI, I hope to one day turn from SciFi into reality the intelligent machines that will transform our society for the better.

For more information about Randy’s work, you can contact him at rso at randalolson dot com.

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BEACONites around the blogosphere

Posted on October 22, 2012 by Danielle Whittaker

The BEACON blog moderator is taking a brief hiatus to finish compiling our annual report to the National Science Foundation. For your weekly dose of evolution blogging, we recommend checking out some other BEACON-related blogs:

  • Variation Selection Inheritance: a blog and podcast by NC A&T faculty member Randall Hayes. VSI is a podcast about evolution, broadly defined as the behaviors of any system that displays the trifecta of variation, selection, and inheritance.
  • Living in an Ivory Basement: MSU faculty member C. Titus Brown’s stochastic thoughts on science, testing, and programming.
  • Pleiotropy: An evolution blog by MSU postdoc Bjørn Østman.
  • Notes from Kenya: MSU Hyena Research: Michigan State University students in Kay Holekamp’s lab blog about their experiences in Kenya, research on spotted hyenas and adventures in the field.
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Evolution 101: Synthetic Biology

Posted on October 15, 2012 by Danielle Whittaker

This week’s Evolution 101 blog post is by UW graduate student Bryan Bartley.

Synthetic biology is a new frontier in biological research where scientists and engineers are creating living systems out of molecular chemistry.  In the last half century, the fundamental biochemical pieces and processes that comprise the phenomena of life have been isolated and studied by scientists in the laboratory.  This reductionist approach to molecular biology has yielded enormous insight into the basic molecular units that govern life, such as genes encoded on DNA.

Today, a new approach, a synthetic biology, is possible in which basic units of biochemistry are re-assembled into new living systems, using platform technologies such as DNA synthesis, genome engineering, simulation tools, and computer-aided-design.  Some present examples of synthetic biological systems that have been built are artificial microbial ecosystems (Shou 2007) and a self-replicating RNA microcosm (Lincoln and Joyce, 2009).  In the future, foundational technology like solar energy, biofuels, and medicines may be synthesized out of wetware.

Synthetic Gene Circuit diagram

Synthetic Gene Circuit Designed By TinkerCell. Credit: Jeff Johnson, Graduate Student, UC Berkeley

Synthetic biologists use both principles of rational and evolutionary engineering to construct biological systems. Rational design principles such as standardization, modularity, and mathematical simulation have helped engineers build automobiles, airplanes, and computers.  These rational approaches are now being adapted to help design living processes at the molecular and cellular scales.  However, biological systems, even ones like E. coli that have been studied for decades, are so complex that classical engineering concepts may have practical limits in a biological context. This matter is of philosophical interest among BEACON researchers.  Therefore, evolutionary approaches such as directed evolution, distributed computation, and stochastic simulation, are necessary and complementary to the rational approach in synthetic biology.  Some BEACON researchers are interested in accelerating the evolution of microbial strains in the laboratory.  For example, it may be easier to evolve a desired phenotype rather than design it de novo.

Over billions of years, evolution has generated a vast reservoir of creative biodiversity which is of great importance to synthetic biology.  Many life forms have evolved solutions to problems of great importance to human beings, such as the capture of energy from sunlight, the production of medicinal compounds, and the neutralization of toxic chemicals.  Many projects in synthetic biology are attempts to take some of nature’s best biochemical designs and transfer them to new host microorganisms where they can be harnessed in a more direct manner.  For example, microbes can be genetically modified to produce rare and precious molecules that are otherwise difficult to synthesize through industrial chemical synthesis.   In the near future, biologically-based modes of energy production may become sustainable, renewable, and compatible with an ecologically stable economy.

The question, What is life?  is one of the most fundamental questions in all of science.  Synthetic biology and evolutionary theory are playgrounds for imagining life’s origins.  For example, using synthetic biology, researchers are attempting to construct minimal living systems that might explain how molecular life first emerged on earth.  In the near future, they may demonstrate that it is possible to assemble a self-replicating system from molecular components that are readily available on Mars or the moons of Jupiter.  Synthetic biology also challenges us to think deeply about the interfacing relationships between living and artificial systems.  Should fallible humans attempt to meddle with nature’s designs?  Here is the perspective of one synthetic biology researcher, Dr. Jingdong Tian of Duke’s Department of Biomedical Engineering, taken from an interview with Discover magazine: “Dinosaur was unable to control its fate and, as a result, became extinct. The human race is trying to gain some control by taking over its own evolution, therefore it studies synthetic biology. Synthetic biology is a science aimed at accelerating natural evolution.”

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Evolution 101: Fitness Landscapes

Posted on October 8, 2012 by Danielle Whittaker

This week’s Evolution 101 blog post is by MSU postdoc Arend Hintze and MSU graduate student Randy Olson.

While fitness landscapes are generally thought to be more of a theoretical construct, they are in fact quite tangible and underly every evolutionary process that we know of. Unfortunately, fitness landscapes are often difficult to visualize, and in many cases in biology it is unclear what they actually look like. But don’t worry, I am going to talk you through it.

As a first step, imagine what a genotype looks like. For biologists, a genotype can be composed of nucleotides and look like:

ACGCGCTCATATGACA… 

Since we are also computational scientists, genotypes can also be represented with numbers, such as:

0.9 0.1 0.1 0.4

If you are familiar with Avida, a genotype could be a sequence of program instructions and look like this:

fczczcczrucanqqqpqpqppjcovv

Whichever way the genotype is represented, each genotype encodes a phenotype, and each phenotype is assigned a fitness value depending on how well it performs. In biology, we count the mean number of offspring (fecundity) and assign that as the phenotype’s fitness, whereas in computational simulations we either use fecundity or assign another numerical value indicating how well the phenotype performs. (For example, how far a robot walks before falling down.) Regardless of the system, this fitness value, W, will always determine the mean number of offspring that the phenotype produces. This means that each genotype is associated with exactly one fitness value. In reality, the mapping between genotype and phenotype is not so straight forward, and depends on several factors, but we will keep things simple for now.

Imagine the first genotype again (ACGCG…), and imagine a mutation being applied to it. If we only consider point mutations, each possible site will have three alternatives (e.g., A can mutate to T, C, or G), and each of these alternatives will have a new fitness value associated with it. Since each mutated genotype experiences only one mutation, we say that all of the mutated genotypes with one mutation have a mutational distance of one from the original genotype. The more the genotype is mutated, the further the mutational distance between the mutated genotype and original genotype. If we enumerate every possible genotype (and every genotype has its own fitness value), we can start drawing a fitness landscape, where the height of the landscape is defined by the fitness, and the place on the map is defined by the mutational distance from the original genotype.

You might have spotted the issue with this method of creating fitness landscapes: while such a landscape grows nicely, it only has two dimensions, with the third dimension representing the fitness value. In reality, fitness landscapes are highly dimensional and impossible to visualize; however, the two-dimensional space we defined suffices as a simple, low-dimensional representation of the landscape. Thusly defined, an entire fitness landscape might look like this:

Fitness Landscape

There are peaks and valleys, and by examining this landscape you can imagine the direction a genotype may evolve. Every time the genotype mutates, it alters its location in the landscape a little, and experiences the fitness value assigned to its new genotype. The higher the fitness value, the better the genotype performs, and the more likely it will create offspring into the next generation. By continuing this process over many generations, the genotype will eventually end up on a peak in the landscape.  If mutations allow the genotype to make large steps across the landscape (e.g., more than one point mutation per generation), it could cross valleys easier (blue and green trajectory), or discover a different path entirely that leads to the genotype encoding with the highest possible fitness value (red trajectory). The shape of the landscape and how far mutations can move the genotype across it will determine the evolutionary path and the final peak the genotype will end up at.

One nice way of visualizing fitness landscapes is with Luis Zaman’s hands-on simulation of “finger painting” fitness landscapes, which can be found here: http://www.cse.msu.edu/~zamanlui/processingJS/draw_fitness/

The paper about the finger painting landscapes simulation can be found here: http://mitpress.mit.edu/books/chapters/Alife13/978-0-262-31050-5-ch065.pdf

 
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BEACON High School Summer Program

Posted on October 5, 2012 by Danielle Whittaker

BEACON High School StudentsIn the summers, BEACON hosts a week-long residential high school program at Kellogg Biological Station. Students develop, carry out, and report on a research project focusing on evolution in action. 30 students participated this year, including two from Utica Community Schools in Michigan – see the writeup here!

 

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Rich Lenski explains the Long Term Evolution Experiment [VIDEO]

Posted on October 4, 2012 by Danielle Whittaker

In episode 61 of MicrobeWorld Video, filmed at the American Association for the Advancement of Science Meeting in Vancouver, Canada on February 17th, 2012, Dr. Stan Maloy talks with BEACON’s Richard Lenski Ph.D., Hannah Professor of Microbial Ecology, Michigan State University, about his research into the evolution of bacteria and the new frontier of digital evolution. 

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Best practices for scientific computing

Posted on October 3, 2012 by Danielle Whittaker

BEACON’s Titus Brown co-authored a paper now available as a pre-print on arXiv:

Best Practices for Scientific Computing
D. A. Aruliah, C. Titus Brown, Neil P. Chue Hong, Matt Davis, Richard T. Guy, Steven H. D. Haddock, Katy Huff, Ian Mitchell, Mark Plumbley, Ben Waugh, Ethan P. White, Greg Wilson, Paul Wilson
(Submitted on 1 Oct 2012)

Scientists spend an increasing amount of time building and using software. However, most scientists are never taught how to do this efficiently. As a result, many are unaware of tools and practices that would allow them to write more reliable and maintainable code with less effort. We describe a set of best practices for scientific software development that have solid foundations in research and experience, and that improve scientists’ productivity and the reliability of their software.

A quick preview is available here. The first key practice is “Write programs for people, not computers.”

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Evolution 101: Natural Selection

Posted on October 1, 2012 by Danielle Whittaker

This week’s Evolution 101 post is by an interdisciplinary group of BEACONites, all of whom rely on the principles of natural selection in their research: MSU graduate student Nikki Cavalieri (Zoology), MSU postdoc Prakarn Unachak (Evolutionary Computation), and NC A&T graduate student Patrick Wanko (Industrial & Systems Engineering).

If animals are closely adapted to their habitats, why do we see overlap?

Photo credits: Gray Tree Frog by Heidi Bakk-Hansen; Green frog by Trish Coxe; Background by Kahunapule Michael Johnson; Illustration by Prakarn Unachak

For instance, Gray Treefrogs (Hyla versicolor) and Green Treefrogs (Hyla cinerea) in the Southern United States seem to  be ecologically equivalent. Both species eat insects, live off the ground on vegetation and lay their eggs in small pools. So why is there not just one species?

Adapted Roger Conant and Joseph T Collins. 1998. A Field Guide to Reptiles & Amphibians of Eastern & Central North America (Peterson Field Guide Series).

While Gray Treefrogs and Green Treefrogs seem to occupy the same habitat, when we look closer we can see that though their ranges overlap, Gray Treefrogs live farther north than Green Treefrogs. We can also see that Gray Treefrogs prefer wooded areas with temporary pools, while Green Treefrogs prefer more open wetland areas with cattails and other aquatic vegetation.

CC google Hyla versicolor (LeConte, 1825). Cryptic colored adults clinging to a tree trunk. Photo © Painet, Inc.

 On a tree in a wooded area, the Gray Treefrog is much harder to detect.

Photo by Richard Crook

In a wetland, the situation is reversed.

What is Natural Selection?

Natural selection is the process in nature by which organisms better adapted to their environment tend to survive and reproduce more than those less adapted to their environment.

For example, treefrogs are sometimes eaten by snakes and birds. Gray Treefrogs blend well in dark wooded areas on tree bark and Green Treefrogs blend in well with green vegetation found in marshes and swamps. A Green Treefrog on the bark of a tree is easier for a predator to find, compared to a Green Treefrog on a green leaf. So, Green Treefrogs that go into habitats where they are not camouflaged are more likely to be eaten by predators. Since Treefrogs that have been eaten do not live to have any more baby Treefrogs, natural selection has favored Treefrogs that live in habitats in which they are more camouflaged.

This explains the distribution of Gray and Green Treefrogs. The wooded habitat of the Grey Treefrog is larger and extends farther North, while the Green Treefrog’s swamp and marsh habitat is concentrated in the South.  In the area in which Gray and Green Treefrogs overlap, both habitats occur but in different places.

However, natural selection does not always go to the optimum. It only goes to what works. For instance, rabbits are herbivores, which have hind gut fermentation (fermentation of food after passing through the stomach). They have a special organ called a caecum which helps them digest their food. Unlike other animals, the caecum of the rabbit is located too far down the intestines of the rabbit for the rabbit to get all the nutrients out of its food. So when digested food is expelled from the body, it still contains a high quantity of nutrients. To compensate for losing these nutrients, rabbits are coprophagous (they eat their own fecal pellets).  They have two types of fecal pellets: 1) pellets which have been digested only once, which they place in a special latrine to consume later, and 2) those than have been digested twice and are not stored. Rabbits have evolved to get maximum nutrients from their food despite having a non-optimum arrangement of digestive organs.

To be more general, natural selection is a process that results in some animals and plants with certain characteristics being better adjusted than others to their natural environment. Those plants and animals then have a higher chance to survive, reproduce, and increase their population more than the ones that are less adapted to their environment. The better adapted plants and animals are therefore able to pass on their advantageous characteristics (coded for by genes) to their offspring through inheritance.

However, genes are not always passed on to the offspring in the exact same form as the parents’ genes. Change in a gene sequence can occur through two mechanisms known as crossover and mutation.

Crossover? Mutation? What are those?

We cannot see genes with our naked eyes, but we can observe the products of them through physical traits, known as phenotype (type of hair, color of eye/skin, sex…).  Gregor Mendel, the “father of modern genetics,” experimented with pea plants between 1856 and 1863. Mendel showed that by fertilizing a given shape of green pea plant with the pollen of a different shape of yellow pea plant, one would get a variety of green and yellow peas of many shapes. The resulting peas will share their color or their shape with the original peas. What Mendel did is today called cross-pollination, and the fact that the resulting peas will share some common traits is due to inheritance.

Genes are grouped together on chromosomes. For crossover to occur, we need two chromosomes that exchange material.  A mutation, on the other hand, is a change or error within a gene or chromosome that can result in a change of genetic functions and expressions. When that error occurs, it modifies a gene which can change the phenotype of the plant or animal, which may be more than just a change in appearance. Mutations can involve deletions, duplications, insertions, inversions or translocations of sections of genetic sequence. Mutations and crossovers supply the raw material for natural selection to work with by creating variation among organisms.

Crossover

Mutation

How Do We Get So Many Different Organisms by Natural Selection?

Natural selection results in organisms with different characteristics (caused by mutations and crossovers) thriving in different environments. Beside the Green and Gray Treefrog (our example above, showing adaptation through camouflage), there are many ways natural selection shapes organisms:

  • Some bacteria can live at temperatures of 60°C (140°F) and higher. One species, Methanopyrus kandleri, can even prosper under extreme heat as high as 120°C (248°F)! Other bacteria also adapt to seemingly inhospitable environments –  acidic, radioactive, or under the deepest part of the sea, where there are no conventional source of food. No matter how hostile an environment is, it is very likely that you will find some kind of microorganisms evolved to live there.
  • Penguins, at first glance, are birds that cannot fly, which does not seem to make them good candidates for survival. However, in place of flying, penguins have adapted to be master swimmers, which benefits them greatly in finding food and escaping predators.  Furthermore, in Antarctica, and other places penguins live, there are no natural predators on land, thus losing the ability to fly is not a disadvantage. There are other flightless birds, and all have adapted to compensate their lack of flight in other ways. Either by being a fast runner (Ostrich), hiding well (Invisible Rail), or able to defend themselves effectively (Cassowary).

  • Some plants, such as the Venus Fly Trap, are carnivorous. Usually plants obtain nitrogen, a chemical element vital to a plant’s survival, from the soil through their roots. These plants, however, usually grow in areas where soil is lacking in nitrogen. They cannot get enough nitrogen just by taking it out of the ground. In order to thrive in such an environment, these carnivorous plants capture insects in trap-like leaves. These insects become an alternative source of nitrogen for the plant allowing it to survive in a nitrogen-poor habitat.

Environments change over time, and natural selection acts on the genetic diversity in species. Individuals with better traits for the new environment have more offspring. After many generations in this new environment, the current population may not look like their ancestors because natural selection has changed them – they have evolved – to survive in the new environment.

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