The word “protein” is quite familiar to all of us, and yet most of us have a very limited understanding of the role of proteins in our bodies. If you assume, as many people do, that the main role of proteins in your diet is to build and maintain strong muscles, then the following discussion will be full of surprises. You will find that there are thousands of different proteins in your body, located in every cell in your body, and that they greatly influence nearly everything that happens in your body.

The Essential Roles of Proteins in Your Body

Most of us think of protein as the material that muscles are made of. It is indeed true that your muscles are composed primarily of protein — not just the skeletal muscles that allow you to move around, but also the muscles associated with internal organs, such as the heart. However, there are actually many different kinds of proteins in the human body, which perform many different roles. To clarify this situation, let’s group these roles into six large buckets:

1. Contractile proteins

Contractile proteins allow living cells to change shape quickly, which enables motion. In particular, the contractile proteins in your muscle cells — primarily myosin and actin — give your muscles the ability to contract. Your skeletal muscles, most of which are anchored to the bones in your body, use this contracting action to move your body around — allowing you to walk, run, throw a ball, and so on. Additional proteins also play a role in muscle action. For example, the proteins troponin and tropomyosin hide and expose the binding sites where myosin molecules attach to actin molecules, thereby controlling when the muscle fibers contract.

2. Structural proteins

Although this second category of proteins is less familiar to most of us, it is present in large quantities throughout your body. Structural proteins comprise the matrix of the connective tissue that provides structure and support to cells and tissue throughout your body. These proteins — which include collagen and elastin — give shape to your bones, tendons, skin, cartilage, and other tissues, and quite literally hold your body together. Collagen alone constitutes about a quarter of the protein in your body. Another structural protein called keratin is the principal component of hair and fingernails.

3. Enzymes

An enzyme is any protein molecule that catalyzes a chemical reaction. In other words, an enzyme facilitates a chemical change without getting used up in the reaction. The enzymes in your body are incredibly important and amazingly diverse. Enzymes catalyze thousands of different processes in your body, producing many of the chemical compounds needed by your body. Enzymes mediate the cellular metabolism that keeps you alive, and they play a huge role in the development of a human body from a single-celled zygote to a fully formed adult.

Enzymes are essential for digesting your food — breaking down proteins into amino acids, starches into simple sugars, and fats into fatty acids. For example, pepsin is one of the enzymes that breaks down proteins in your food. Enzymes also break down spent molecules in your bloodstream and in your cells. However, only a small proportion of the enzymes in your body are dedicated to disassembling large molecules. Many of the enzymatic processes in your body involve the modification of existing molecules, with little impact on their size. These small changes are central to many of the metabolic processes that occur in your body, often involving a long chain of sequential steps, each mediated by a specific enzyme. Enzymes are also involved in the assembly of individual molecules into large complex structures — such as the construction of the plasma membrane that surrounds each of the trillions of cells in your body.

Most enzymes are named after the reactions they catalyze, with the suffix “-ase” at the end of the name. This is especially true of the enzymes that have long names — as in phenylalanine hydroxylase, an enzyme that changes phenylalanine (an amino acid) into tyrosine (another amino acid) via the process of hydroxylation. Because enzymes are not used up in the reactions they catalyze, small quantities can have a huge impact.

4. Signaling and sensory proteins

These two categories of proteins have related roles, so I’ve put them into a single bucket.

Signaling proteins carry signals between distant parts of the body, coordinating biological processes that involve multiple cells. Most of these proteins are classified as hormones. For example, the hormone insulin is a protein that tells the cells in your body when to absorb glucose from the blood, thereby regulating your blood sugar level. Hormones are produced by endocrine tissue — such as the pituitary gland, adrenal gland, pancreas, and gonads — and are carried by the bloodstream to interact with cells all over the body.

Sensory proteins are embedded in the plasma membrane that surrounds each cell in your body. The role of these proteins is to detect and react to signals and stimuli that arrive from outside the cell, triggering a response within the cell. These sensory proteins respond to specific molecules in the bloodstream — some of which are hormones, but many of which are non-protein molecules. Other proteins play important roles in specialized cells called receptors, which allow you to detect various physical phenomena. For example, the opsins in the cells of your retina respond to distinct wavelengths of light, the first step in the complicated process that results in vision.

5. Transport proteins

Transport proteins move small but vital molecules from one place to another, either within cells, across cell membranes, or between cells. A great example is hemoglobin, found in red blood cells, which carries molecules of oxygen from your lungs to all parts of your body. Transport proteins embedded in the plasma membrane of each cell help to move essential molecules from the bloodstream into the cell. If these proteins were absent, then the plasma membrane would present an impenetrable barrier to many essential nutrients. Other transport molecules help to remove excess molecules from the cell. Still others ferry molecules within the cell, moving them to locations where they are needed.

6. Defense proteins

Defense proteins help you fight infection and heal damaged tissue.

Antibodies, also called immunoglobulins, are proteins that attach to foreign material in your bloodstream — such as viruses and bacteria — a first step in destroying them. Your white blood cells are capable of creating an incredibly wide variety of antibodies, each of which is specialized to bind to a certain molecule or category of molecules. Vaccines train your immune system to recognize dangerous microbes and proteins, so that your body can quickly produce the appropriate antibodies if you should become infected.

Another protein called fibrin responds to wounds by forming blood clots and scabs to seal the wounds. Your body is able to respond quickly to such damage because the precursor protein fibrinogen always circulates in your blood. Physical and chemical changes at the site of a wound cause the enzyme thrombin to act on the soluble fibrinogen molecules, linking them together into an insoluble mesh of fibrin that binds to blood platelets to form a clot.

Where Do Proteins Come From?

People usually assume that the proteins in our bodies come from the proteins we eat. In one sense this is indeed correct, but in another sense it is quite wrong. In reality, all of the proteins in your body were built by your own body. However, most of the ingredients for building these proteins come from the proteins you eat. To make sense of this, we need to discuss the general structure of a protein.

So what exactly is a protein? A protein is a large molecule consisting of a long chain of amino acids, strung together in a linear sequence. Amino acids are rather small molecules, each containing between 10 and 27 atoms. Our bodies use 20 distinct amino acids — with names such as leucine, glutamine, and tryptophan — as the building blocks for assembling proteins. Each protein consists of a specific sequence of these amino acids — typically a chain of 250 to 500 amino acids, although some proteins can be shorter or much longer.

Most proteins fold up into a compact shape as soon as they are created, and the specific role the protein plays is often connected to the shape of the folded molecule, especially in the case of enzymes. So while a protein can be described as a chain of amino acids, the final shape of a protein is more likely to resemble a crumbled ball of paper than a necklace. However, the contractile proteins in your muscle fibers are exceptions, and do not fold up. These proteins maintain their linear shape, while forming temporary linkages with adjacent muscle proteins.

Whenever you eat any kind of protein, your digestive system breaks it into little bits, separating all the amino acids from one another. It is these individual amino acids, rather than entire proteins, that are absorbed into your bloodstream. Therefore your body builds every protein it needs from scratch, using the amino acids circulating in your blood as the raw components. This might make you wonder: How many kinds of proteins does your body actually make? Ten different proteins? A hundred different proteins? A thousand different proteins?

In fact, your body knows how to build tens of thousands of different protein molecules, probably at least 80,000. Some of these are contractile and structural proteins, which are typically needed in large quantities. But most of the proteins made by your body fall into the other categories — enzymes, hormones, antibodies, and so on. Even if these proteins are only manufactured in small quantities, they are essential to the proper functioning of your body. In fact, life would not be possible without them.

Each cell in your body manufactures its own proteins. The tiny factories where the protein molecules are assembled are called ribosomes. There are up to 10 million ribosomes in each of your cells. It has long been thought that nearly all the ribosomes in a cell are functionally identical. In other words, any ribosome is capable of building any protein. Recent studies have suggested that there might be some degree of specialization among ribosomes — but even if this is true, we can still say that any ribosome is capable of building multiple kinds of proteins. This raises two key questions:

1) What mechanism allows ribosomes to be so versatile?

2) After a ribosome has finished building a protein molecule, how does it know what protein to build next?

The short answer to both questions is that a ribosome builds a protein molecule whenever it receives a message from one of your genes. This message contains the entire recipe for building a specific protein, which means that the ribosome simply needs to follow the instructions contained in the message. This allows any ribosome to assemble any protein, simply by stringing together the exact sequence of amino acids specified by the message.

The Relationship Between Proteins and DNA

Your genes are composed of DNA, which contains the recipes for all the proteins that your ribosomes can make. Each recipe is stored as a coded message, which is why you hear references to the genetic code. In each gene, the coded message is stored as a sequence of four small molecules called nucleotides, which we traditionally designate as A, C, G, and T (which are the first letters of the four nucleotides — adenine, cytosine, guanine, and thymine). Because DNA is a linear molecule, we can read off these nucleotides as a sequence of letters, such as “GATCCTCCAT…”. Each set of three consecutive nucleotides is a code for a specific amino acid. For example, CAT represents the amino acid histidine.

Every form of life — plant, animal, fungus, or single-celled organism — uses essentially the same coding system in its DNA. Because each code represents a specific amino acid, this means that all living creatures use the same 20 amino acids to build the proteins they need, with a few rare exceptions. Furthermore, every form of life — even plants and fungi, which have no muscles — must assemble proteins in order to stay alive. At the very least, every living thing requires a diverse set of enzymes for its metabolism and to maintain the integrity of its cells.

In fact, the only information in your genes is a set of around 20,000 recipes for building proteins. Each gene is essentially a recipe for building a single protein — although in many cases several closely related proteins can be built from the same gene (by selectively reading only parts of the gene). When a gene is “expressed”, the coded message is copied onto a strand of messenger RNA (mRNA). This RNA molecule then travels to a ribosome located elsewhere in the same cell, which uses the information in the message to build the specified protein. After the protein has been built, enzymes might make modifications to it, further extending the number of proteins that your body can build.

So now we have reached a counter-intuitive point. On the one hand, your DNA is responsible for most of the details of your body, and for almost everything that happens within each cell of your body. On the other hand, nothing in your DNA directly specifies any of those details — except for what proteins your ribosomes should make. Everything that DNA does, it does indirectly by determining what proteins are built. It is these proteins that mediate all the biochemical processes that occur within your body. These processes not only keep you alive, they also direct your growth and development from a zygote to an adult human being.

The upshot is that proteins are far more than simply the stuff that muscles are made of. And just as all life depends upon DNA, all life also depends upon proteins.

Choose a Protein, Please

These days, when you visit a fast casual restaurant, it is not unusual to be asked to “choose a protein”. Perhaps you have ordered a burrito or a salad bowl, and now you must choose between chicken, beef, pork, fish, or tofu. A decade ago, you would have been asked to choose a meat. Because of the increased popularity and variety of meatless options, the language we use in this context has changed, even if most of options are still meat.

Many of us understand that “Choose a protein” is simply a verbal shortcut for “Choose a source of protein”. None of the available options is actually the name of a protein, and none of the options consist of 100% protein. But for the purpose of ordering a meal at a restaurant, the abbreviated wording simplifies the verbal transaction. This new use of the word “protein” has proven to be quite convenient. For example, the person taking your order might ask “Would you like to add a protein to your salad?” — and the intent of the question is perfectly clear.

I have no problem with the use of verbal shortcuts, and in fact I applaud linguistic changes that make our conversations more efficient. However, our understanding of science is often influenced by the words and phrases we commonly use. Therefore I wonder if this wording might affect how we think about protein in our foods. Do some people now assume that chicken consists of a single type of protein? Do some people now assume that tofu is 100% protein?

It is that latter question that most concerns me. By using the phrase “choose a protein”, we are implicitly dividing all foods into “proteins” and “not proteins”. This is a highly misleading mental model. Each source of protein in your diet includes molecules that aren’t proteins. Especially when it comes to plant products (such as tofu), it is better to think of these foods as consisting of a mix of nutrients, not just protein. Furthermore, there are foods that contain moderate amounts of protein — such as beans, brown rice, quinoa, buckwheat, potatoes, etc. — that we don’t even consider when we say “Choose a protein”.

Even wheat can be an important source of protein. The main protein in wheat is gluten — a word that has become familiar to everyone. It is ironic that we now demonize gluten while extolling the virtues of protein. If you seek out gluten-free baked goods, then in many cases you are choosing a product with a higher concentration of carbohydrates and a lower concentration of protein. But if you do your own baking, you can avoid this pitfall by carefully choosing what types of flour to use.

It’s Not the Proteins, It’s the Amino Acids

As mentioned above, any protein you eat is broken down into its constituent amino acids by your digestive system. These amino acids are absorbed into your bloodstream and transported across the plasma membranes into your cells, where they are available for the construction of new proteins by your ribosomes — based on recipes contained in mRNA that is transcribed from your DNA. But what happens if your diet is lacking in one or more of the 20 amino acids?

The answer to that question depends in part on which amino acids are lacking. Your body is able to synthesize 11 of the 20 amino acids. The other 9 amino acids are often called essential amino acids, due to the importance of including them in your diet. The upshot is that it is possible to eat adequate amounts of protein and still suffer from a nutritional deficiency disease due to a shortfall in one of the essential amino acids. Even some of the “non-essential” amino acids can be essential at times, especially when your body is rapidly growing.

Most sources of protein that originate from animals — such as meat, eggs, and dairy — include a complete mix of amino acids. If you obtain much of your protein from animal sources, then you don’t need to worry about a lack of essential amino acids. In contrast, many proteins that come from plants are lacking in certain essential amino acids. For example, a diet that consists mostly of rice will be lacking in lysine. A diet that consists mostly of beans will be lacking in tryptophan and methionine. But a diet that includes adequate amounts of beans and whole-grain rice will include all of the essential amino acids, eliminating the need for animal proteins. Certain other plant-based foods, such as quinoa, contain all of the essential amino acids, and therefore can be quite helpful in crafting a vegan diet.

Note: To craft a truly balanced diet — whether vegan or not — you should also consider the other critical nutrients, including fats, carbohydrates, vitamins, minerals, and fiber.

Conclusion

Imagine if this essay had begun by asking you to answer the following two questions:

1. Why is it important to include proteins in your diet?

2. Name three proteins.

What would your answers have been? Now that you have reached the end of this essay, would your answers be different? Perhaps you now have a deeper appreciation of the many roles that proteins play in your body — and of the crucial importance of these many roles. Furthermore, you may now better understand that all the consequences of your body’s DNA are realized through the manufacture of proteins in your cells.

Before reading this article, you might have been stumped by the question “Name three proteins”. Answering this question should be a little bit easier now. At the very least, you have now encountered the names of many different proteins — including gluten, insulin, hemoglobin, collagen, keratin, actin, opsin, pepsin, myosin, elastin, fibrin, thrombin, troponin — and when you encounter any of these words in the future, you may remember that it represents a protein. You also know that many important categories of molecules in your body — including enzymes, antibodies, and hormones — consist of proteins.

In the future, whenever you see the name of a biological molecule that ends with “-ase”, you are likely to realize that this is probably an enzyme. Some of these names will short (such as protease, lipase, amylase, and maltase), while other will have longer names (such as RNA polymerase, glycogen phosphorylase, and pyruvate carboxylase). You might not immediately recognize what these enzymes do, but you know that their role is to catalyze important chemical processes in your body (or in some other living thing).

In short, your appreciation for the role of proteins in your body might have significantly increased. If so, then I am delighted to have helped.

For a deeper examination of how DNA works, see the chapter “The Blueprint of Life” in my book The Stickler’s Guide to Science in the Age of Misinformation.

We Underappreciate the Role of Proteins in Our Bodies was originally published in The Philipendium on Medium, where people are continuing the conversation by highlighting and responding to this story.

Three different ways to categorize living things: traditional terminology, biological classification, and cladistics

All of us try to make sense of the world around us, and a key part of that effort is naming the things we see. This allows us to talk about these things, to accumulate knowledge about them, and to compare experiences with other people. We feel much more in control when we are familiar with the things we see, and we have a better sense of how to react. It can be quite unsettling to see a large, unidentified creature roaming nearby — but the experience is quite different if you can confidently identify the animal.

However, we often find ourselves in a sort of muddled middle. You might end up saying “I think I saw a fox today, but I’m not sure.” Perhaps you only got a brief glimpse of the animal from a distance. Or perhaps you are not completely confident in your ability to distinguish a fox from other similar animals — especially if you are traveling in unfamiliar territory. This raises the question of “What is a fox?” What distinguishes a fox from all other animals?

The traditional criteria for distinguishing a fox from other similar creatures include the size of the animal, the shape of the body and the head, the presence of a thick brushy tail, the color pattern, and so on. However, traditional criteria are seldom universal; people simply needed to distinguish one type of local animal from all other local animals. Depending upon where you live, the local mix of animals might differ, which could mean that the criteria for distinguishing a fox from other animals might also vary.

We inherited the word fox from Old English, which means that we’ve been using the word for many hundreds of years, perhaps even a thousand. To put it another way, the word fox was in popular use long before anyone worried about a scientific definition. In fact, a lot of words we use to categorize natural things predate scientific terminology — including such words as fish, bird, fruit, and bug. But when these words are adopted for scientific use, they are given more precise definitions, which can easily conflict with the original popular meanings of the words.

For example, the word fish originally meant any animal that lives underwater. We can still see traces of this meaning in words such as shellfish and crayfish. The popular meaning has since narrowed to include only those creatures whose body shape conforms to our current stereotype of a fish. However, dolphins and whales have a similar shape — and as a result, small children often call them fish until taught otherwise. On the flip side, even adults may be reluctant to categorize seahorses or eels as fish because they don’t match their mental image of a fish — even though scientists classify them as fish.

So, if we assume that the word “fox” might now have a scientific definition — one that applies anywhere in the world — then what is that definition? What criteria do scientists use to distinguish between foxes and all other animals? To answer that question, we first need to take a look at the past and present state of biological classification. Then we can apply those concepts to the case of foxes.

Biological Classification

The ancient Greeks divided the world of nature into three large “kingdoms” — animals, plants, and minerals. This line of thinking remained deeply embedded in Western thought for millennia. In 1735, a scientist named Carl Linnaeus published a book that greatly expanded on this idea. He divided each of these three kingdoms into classes, which he subdivided into orders, then families, genera, and species — a hierarchy of six levels. He further refined this classification system in later editions of his books. The editions he published in the 1750s became the starting point for modern biological classification (which does not include minerals). We still use this system for dividing living things into groups and subgroups — although scientists long ago added a 7th level, called the phylum or division, just below the kingdom level. Interestingly, we still use some of the Latin species names that Linnaeus invented.

Our formal system of biological classification has now been in use for more than 250 years. During that time, the details have constantly evolved due to new discoveries about living things. But in recent decades, there has been a massive change in the criteria and methods that taxonomists use. Sixty years ago, we relied primarily on morphology (physical features) to categorize living things, and now we rely primarily on genomics instead. In other words, scientists compare the DNA of two creatures to identify the similarities and the differences. This has had a huge impact on the entire taxonomic system, at all levels of classification. Every year scientists continue to make updates to the scheme, affecting every level of the hierarchy.

Despite this long tradition of using seven levels of biological classification, the world of living things does not fall naturally into seven distinct levels. For the system to reflect reality, we would need dozens of levels. This has led to a never-ending debate on how best to categorize all the creatures of the world into seven fundamental levels. This problem has also inspired an alternative way of thinking called cladistics — a topic to which we will soon return.

Our Evolving Methods for Classification

When Linnaeus set up his classification system, the concept of biological evolution had no role. Two species could be considered “related” simply because they looked similar or had similar characteristics. Few people considered the idea that two related species might have evolved from a common ancestor. Therefore biological classification was considered to be an artificial system invented by the human imagination. Despite its artificial origin, the system was perceived as a helpful way to organize our knowledge of the world. Just as you and I might choose different criteria for organizing our kitchen drawers, the details of this classification system depended heavily on which criteria were chosen as the organizing principles.

For classifying animals, the main organizing principle was “body plans”. For example, vertebrates (animals with backbones and internal skeletons) have a body plan that is quite distinct from other animals (the invertebrates). Therefore all vertebrates were grouped together into a single high-level category (the phylum Chordata). However, the invertebrates are quite varied. A grasshopper has one body plan, a sea star has another, and an earthworm has still another. Each of these three examples needed to go into a separate high-level category. These high-level groups could be further subdivided according to increasingly subtle differences in the body plans and other details. For example, vertebrates have long been divided into fish, amphibians, reptiles, birds, and mammals — although current thinking is that birds and reptiles should be grouped together.

For classifying plants, a different organizing principle was chosen. Instead of grouping plants according to their body plans, they were grouped according to their reproductive structures — in other words, by their flowers or equivalent organs. If you have never studied botany, then this may seem an odd decision. After all, for most of us, our top-level criterion for categorizing plants is to divide them into trees versus smaller plants. Because the official classification system was based on reproductive structures instead of body plans, a single family of plants might include trees and non-trees. For example, every plant with a pea-like flower was put into the family Fabaceae, regardless of whether the species is a tree or a small herbaceous plant.

Note, however, that the “body plan” system for categorizing animals and the “reproductive structure” system for categorizing plants are both based on morphology — that is, the shape of the organism or of key parts of that organism.

After Darwin, as biologists came to accept evolution as the driving force behind the diversity of life, they saw that our system of biological classification provides a rough approximation of actual evolutionary relationships. It turned out that the Linnaean methods for categorizing plants and animals were fortunate choices. For example, we now know that the reproductive structures of plants are powerful clues regarding their evolutionary relationships. Biologists came to feel that any ongoing updates to the taxonomic system ought to reflect our latest understanding of phylogeny (evolutionary relationships). In addition to morphology, crucial data in the 19th and 20th century came from two other lines of inquiry:

• Historical geology (especially the analysis of fossils and the surrounding strata)
• Developmental biology (the way that various multicellular organisms grow from one-celled zygotes into complex multicellular structures)

By the 1970s another important tool was biochemical analysis — looking for similarities in the natural compounds found in related species, along with similarities in the biochemical pathways employed by the organisms. In recent years we have entered an era in which we can directly compare the genomes (the complete DNA) of two organisms — providing us with the best tools yet for discerning the evolutionary relationships between species. These tools now allow scientists to revise our biological classification system at an unprecedented pace.

As it turns out, all living things share certain genetic similarities with all other living things. For example, humans and earthworms may seem quite different, but we share a lot of genes. Humans share even more genes with fish, even more with lizards, even more with wolves, and even more with chimpanzees. So this gives us a good idea of how related we are to each of these other creatures. Also, it’s not just a matter of how many genes are shared. The shared genes exhibit slight differences between species, and the details of these differences provide great insight into evolutionary relationships. Even within a single species (such as humans), the DNA varies from one individual to another, and therefore comparing DNA is helpful for determining the degree of relatedness. This is especially true when comparing the entire genome, including the parts that don’t correspond to any genes.

What About Foxes?

Let’s now return to our discussion of foxes, to consider how we might distinguish a fox from all other animals.

People living in northern Europe, as Linnaeus did, tended to encounter just one species of fox (the Red Fox) and one species of wolf. The only similar animals in the vicinity were domesticated dogs. This made it rather simple for the average person to distinguish between foxes, wolves, and dogs. But if you consider the entire world, then there are dozens of similar species to consider. Worldwide, there are about 20 species that include “fox” as part of the common English name, including the Red Fox, Gray Fox, Arctic Fox, Kit Fox, and so on. There are several species of animals called wolves, several species called jackals, and several species called dogs. (These wild “dogs” are quite distinct from domesticated dogs, which are descended from wolves.) Altogether, there are around 35 species in this family of animals, which is called Canidae (or informally, the “Dog Family”).

The biological classification of Canidae started out as a formalization of commonsense concepts. The family was divided into several genera, such as Vulpes for foxes and Canis for wolves. As additional canid species from around the world received scientific scrutiny, some were placed in Vulpes or Canis, but many were placed in other genera. Currently Canidae is divided into 13 genera, several of which contain only one living species each:

The grouping of the 35 living species of Canidae into 13 genera provides a rough idea of how the species are related to one another. However, the fine evolutionary detail is completely missing. Contrary to the implications of this diagram, these 13 genera are not all equally related — some pairs are much more closely related than other pairs. The same issue exists within the genus Vulpes or any other genus that has more than two species — some pairs of species in the genus are quite closely related, while other pairs are less related.

To illustrate the evolutionary relationships within any group of living things, scientists now use a clade diagram. Such diagrams are a cornerstone of the new science of cladistics. For example, the diagram below shows how the 13 genera of Canidae are related to one another, according to a recent interpretation of the genetic data:

As you examine this diagram, you can trace a path from the box that says Canidae to any box that represents a genus. Each fork in the path represents the last common ancestor for two or more genera — in order words, it represents a point where a single ancient species diverged into two species. Note that the 13 genera did not split off from each other all at once. It would be more accurate to picture them splitting off one by one. You can see that the genus most closely related to Canis is Cuon, and the genus most closely related to Vulpes is Nyctereutes. But what else does this diagram tell us? And if this is a clade diagram, then what exactly is a clade?

To define a clade, you choose a single species that lived sometime in the past. That species, plus all the species that descended from it — including both living and extinct species — together form a clade. For example, all 12 living species in the genus Vulpes are descended from a single ancestral species of fox that lived about 9 million years ago, and all known descendants of that ancestor are included in this genus. Therefore the genus Vulpes is a clade. All 35 living species in the family Canidae are descended from an ancestral species of canid that lived about 30 million years ago, and all known descendants of that ancestor are included in Canidae. Therefore the family Canidae is also a clade.

However, a clade doesn’t have to correspond to a genus, a family, or any other of the 7 levels in the traditional system of biological classification. For example, in the diagram above, a clade named Vulpini consists of the three fox-like genera shown in purple: Vulpes, Nyctereutes, and Otocyon. Contained within that clade is a smaller clade consisting of just Vulpes and Nyctereutes. In fact, every horizontal black line in the diagram corresponds to a clade.

Of course, this only works if each of the genera listed above is a genuine clade. The past twenty years have seen many changes in how the 35 canid species are grouped into genera, in part to ensure that each genus does indeed correspond to a clade. Biologists now take it for granted that all genera and all families in our standard classification system ought to reflect actual clades. Whenever new research shows that a currently defined genus or family is not a clade, then taxonomists soon revise that genus or family — provided that enough genetic data is available to do so.

The clade diagram below is adapted from a 2012 paper (and is therefore just slightly out of date). It illustrates the evolutionary relationships between 11 of the 12 living species in the genus Vulpes:

As you can see, the genus Vulpes includes many smaller clades. For example, the Arctic Fox, Kit Fox, and Swift Fox together form a clade. The last common ancestor of these three species lived around 4 million years ago, much more recently than the last common ancestor of the entire genus. Note that every species is a member of countless different clades. Pick any ancestor of that species, and you have just defined a clade that includes your lucky species. Pick a different ancestor — and you have just defined a second clade that is either a subset or a superset of your first clade.

So far in this discussion, the diagrams have only shown extant (living) species. But a clade also includes all the extinct descendants of the ancestral species. It can be difficult to identify and properly categorize all the extinct species in a family, but Canidae is known to include well over 100 extinct species, many of which are in genera that are also extinct. Extant genera can also contain extinct species. For example, the genus Nyctereutes contains only one living species (the Raccoon Dog), but scientists are aware of 7 extinct species in the same genus.

Is There a Scientific Definition for “Fox”?

After all this discussion, we still don’t have a precise definition of what a fox is. Any dictionary will define the word “fox” — but that definition is unlikely to be useful for distinguishing a fox from other types of canids. One way out of this dilemma is to say that any creature that has “fox” in its common name must be a fox — but then you have issues such as the Flying Fox, which is actually a bat, not a fox.

In fact, there is no precise scientific definition for the word “fox” — but science will get you partway there. At the very minimum, the 12 species in the genus Vulpes are certainly all foxes. These are sometimes called the “true” foxes, because they share a genus with the Red Fox, the original fox of the Linnaean classification system. If you include the 6 species in the genus Lycalopex, the South American foxes, then you’ve got 18 species of fox — although they don’t form a clade. But this still omits several other canids that are popularly called foxes, such as the Gray Fox (Urocyon cinereoargenteus). Note that the most recent common ancestor of the Red Fox and the Gray Fox is ancestral to all living species in Canidae, including wolves and jackals.

Because foxes don’t form a clade, we can’t use evolutionary relationships to define the word “fox”, unless we limit our definition to the “true” foxes in the genus Vulpes. The alternative is to use morphological criteria. For example, we might say that a fox is any canid that has a narrow snout, a bushy tail, and whose adult height at the shoulder is less than a certain maximum. However, any such definition is likely to have exceptions — foxes that fail to meet the criteria, or other canids that meet the criteria. The upshot is that it is difficult to provide an unambiguous scientific definition that distinguishes foxes from all other canids. This doesn’t mean that you shouldn’t use the word “fox” — it just means that two reasonable people might disagree as to whether a particular species is covered by the term.

So feel free to argue that a Gray Fox is indeed a fox, or to argue that a Gray Fox is not a “true” fox. There are valid lines of thought behind both arguments. We use a lot of words that have fuzzy boundaries, and yet many of these words are still useful. The word “tree” is quite useful, even though there is a somewhat fuzzy boundary between trees and shrubs — and neither term is of much use for grouping plants according to their evolutionary relationships. As for me, I will continue to call a Gray Fox a type of fox — because to me it is indeed a fox!

For additional reading on topics related to evolution, see the chapter “Survival of the Fittest” in my book The Stickler’s Guide to Science in the Age of Misinformation.

What Is a Fox? was originally published in The Philipendium on Medium, where people are continuing the conversation by highlighting and responding to this story.

The term DNA (short for deoxyribonucleic acid) often appears in the media these days. Because of these frequent encounters, we all have a general idea of what the term means. When we think of DNA, we think of genes — and therefore we correctly associate DNA with genetic inheritance. In particular, we think of the human genome, which is the complete set of genes in a human being, distributed across 23 pairs of chromosomes. Yet our common understanding of DNA tends to be highly limited — and somewhat inaccurate. Consider, for the example, the following three statements. Which one of these assertions would you judge to be the most accurate?

#1: The human genome is much like a set of blueprints. Our living cells use these blueprints as guides to construct the human body.

#2: The human genome operates like a computer program, with coded instructions for building and maintaining a human body.

#3: The human genome is primarily a set of lists that specify the sequence of ingredients for assembling proteins. Our DNA does not actually contain any plans or instructions for building and maintaining a human body.

There are good reasons to quibble with all three of these assertions, but the most accurate of the three is #3. The human genome is not at all like a blueprint, as we shall see. The comparison with a computer program is somewhat more enlightening, but still highly misleading. But it is indeed true that the human genome is primarily a collection of ingredient lists for assembling proteins. DNA is a bit more than that, because DNA also includes templates for RNA molecules that help regulate the manufacture of these proteins. Therefore DNA is all about making proteins, without any specific plans or instructions as to how these proteins are supposed to yield a human body — or an elephant, or an oak tree.

(Note: DNA and RNA are very similar types of molecules. Your genetic inheritance is permanently stored in long DNA molecules. RNA molecules tend to be much shorter in both length and lifespan, serving as useful but temporary copies of certain stretches of your DNA.)

Statement #3, although mostly accurate, can still mislead our thinking — primarily because the word “proteins” can send our thoughts in the wrong direction. The missing concept is that proteins control most of the development and maintenance of the human body. Therefore our DNA, due to its central role in the creation of proteins, indirectly controls these processes. To make sense of this idea, we first need to take a closer look at the wide variety of proteins in our bodies, along with the crucial roles that these proteins play. Then we can return to the question of how DNA actually works.

The Essential Roles of Proteins in Our Bodies

Most of us think of protein as the material that muscles are made of — and which we must therefore include in our diet to build and maintain a strong body. It is indeed true that our muscles are primarily made of protein — not just the skeletal muscles that allow us to move around, but also the muscles associated with internal organs, such as the heart. However, there are actually many different kinds of proteins in the human body, which perform several distinct roles:

1) Enzymes drive most of the chemical reactions that occur inside our cells. There are thousands of such reactions, with an incredibly wide range of results. Enzymes produce most of the chemical compounds needed by the body, and they also play a huge role in the construction of the body. For example, the cell membranes in your body are primary built from lipid molecules — a multi-step process driven by several different enzymes.

2) Messenger proteins carry signals between different parts of the body, coordinating biological processes that involve multiple cells. For example, the hormone insulin is a protein that tells the cells in your body when to absorb glucose from the blood, thereby regulating your blood sugar level.

3) Transport & storage proteins move atoms and small molecules from one place to another, either within cells, between cells, or across cell membranes. These proteins also help store some of these materials. A great example of a transport protein is hemoglobin, found in red blood cells, which carries molecules of oxygen from your lungs to all parts of your body.

4) Antibodies are mostly found in your blood, where they bind to foreign particles, such as viruses and bacteria, thereby disabling them.

5) Structural components provide structural support for cells, as well as allowing parts of the body to move (due to the ability of muscle cells to contract). The proteins that allow contraction are actin and myosin.

In short, proteins control most of the processes that occur in the human body. Proteins keep our bodies running, and (rather amazingly) mediate most of the human development process — in which we grow from a single-celled zygote into an adult human being. But it is our DNA that tells our bodies what proteins to build.

DNA as a Template for Proteins

Every human has approximately 20,000 genes in his or her DNA — and two copies of most of these genes. For some of your genes, the two copies are identical — but in many cases the two copies have subtle differences. But what exactly is a gene? The term was invented before we had any understanding of DNA, but now we think of a gene as a coded DNA template for assembling a specific protein. For the most part, each gene is unique — which implies that our DNA contains the instructions for building as many as 20,000 different proteins. However, the picture is actually more complicated than this, because some proteins are built by using only selected parts of a gene — and leaving out other parts. As a result, a single gene can often serve as a template for several variant proteins. Another complication is that some proteins are assembled by linking together two or more smaller proteins, each separately coded in the DNA. Therefore the number of distinct proteins that your body can build is probably at least 90,000.

So what exactly is a protein? A protein is a long chain of amino acids, strung together in a linear sequence like beads on a string. Amino acids are rather small molecules, each containing between 10 and 27 atoms. There are 20 distinct amino acids that are typically used for assembling proteins (although under certain circumstances two other amino acids can also be used). These 20 amino acids have names such as leucine, glutamine, and tryptophan. Each protein consists of a specific sequence of these amino acids. A typical protein is a chain of 250 to 500 amino acids — although some proteins can be much shorter or much longer. Most proteins fold up into a compact shape as soon as they are created — and the specific role that the protein plays is often highly dependent upon the precise shape of the folded molecule.

Each gene in your DNA is actually a coded list of amino acids — like a shopping list or a list of ingredients, except written in code. This list tells your body what amino acids to use when it builds a specific protein molecule. The list also indicates the exact sequence in which these amino acids should be strung together. In other words, the whole point of a gene is to contain information. This information is used by a living cell to build the appropriate proteins.

Inside each cell, embedded in the cytoplasm, there is a collection of tiny protein-building machines called ribosomes. The odd thing is that nearly all of these ribosomes are identical. None of them are specialists. Any ribosome can build any kind of protein that you ask it to build. You simply have to give the ribosome a coded message — copied from a gene in the DNA — that contains the recipe for the desired protein. (The molecule that carries the coded message is called messenger RNA.) The information in the message is just a long list of amino acids, in the precise sequence for building the protein. If the required amino acids are available, then the ribosome will crank out a brand new custom-built protein molecule, according to the specifications contained in the message.

The DNA Coding System

The information for these protein recipes is stored in the DNA using a distinct coding system. To make sense of the coding system, it helps to picture the DNA molecule. You have probably seen illustrations of the DNA “double helix”, a spiraling ladder connected by evenly spaced “rungs”. There are four possible kinds of rung, which are called A, C, G, and T — and these rungs can appear in any order. These characteristics allow for encoded information to be embedded in the DNA molecule.

The core unit of information in a gene is a codon, which is equivalent to a 3-letter word, using this alphabet of just four letters (A, C, G, and T) — for example, “GGA” or “TAC”. In other words, each codon consists of exactly 3 small bits of information — stored in 3 consecutive rungs — and each bit can be any one of 4 distinct values. Each of the four possible values is represented by a tiny molecule that is incorporated into the rung. These four molecules are named adenine, cytosine, guanine, and thymine — but for practical reasons we refer to them by their first letters.

Now here is the fascinating thing about the DNA coding system. There are 64 different ways to string the 4 possible letters into a 3-letter codon — and each of these 64 combinations has a specific meaning. The codon “GGA” means that the next amino acid in the protein should be glycine. The codon “CAC” means that the next amino acid should be histidine. Sixty-one of the 64 possible codons represent specific amino acids. But because there are only 20 amino acids that are typically used, several different codons can indicate the same amino acid. For example, “CAA” and “CAG” both indicate glutamine. The other three possible combinations — the ones that don’t usually correspond to any amino acids — all mean “stop”, which tells the ribosome that the protein is now complete.

Not Like a Blueprint

So why is it incorrect to say that DNA is like a blueprint for your body? A real blueprint — such as the plans for a house or an office building — is a detailed visual representation of structure. A blueprint shows all the structural components of the planned building, indicating the precise location and dimensions (and sometimes composition) of each part. In the blueprints, you can see the dimensions of every space, the location and size of every column and beam, the routes of the plumbing and the air ducts, and many other key details. Before construction even begins, these blueprints specify the exact structure of the finished building — thereby allowing the project to proceed.

But DNA is nothing like that. DNA contains no depiction of the finished body. There is no schematic showing the skeleton, no sketch indicating the locations of the internal organs, and no diagram illustrating the routes of the blood vessels and nerves. There is nothing that depicts what the finished face should look like. There is no set of specifications indicating how long the finished arm and leg bones should be, nor how fine or coarse the hair should be. In fact, there are not even any specs to indicate how many fingers there should be, or how many vertebrae there should be. DNA is simply a coded list of the amino acids for each protein that the body can make — and somehow this information eventually yields the correct finished product: a human being, or an elephant, or an oak tree.

Not Like a Computer Program

So if DNA is not like a blueprint, then is it like a computer program? After all, a computer program is completely different than a blueprint. Like DNA, a computer program contains no depiction of the finished result. Instead, a computer program precisely defines a process, a set of steps to execute. The process encoded by a computer program is typically called an algorithm. An algorithm is seldom a straightforward list of steps. In addition to being extraordinarily detailed, it is also full of loops and conditional branches. A loop is when the algorithm repeats a certain set of steps over and over. A conditional branch is when the algorithm tests a certain condition, and then — based on the results of that test — either continues forward or jumps to a different point in the process.

To get a sense of what these loops and branches are like, imagine that you have somehow acquired an industrial robot — like those on an automobile assembly line — and you want to program this robot to make drop cookies in your kitchen. You promptly set to work writing the program. When you get to the point where the robot deposits cookie dough by the spoonful onto a greased baking sheet — a highly repetitive process — you realize that this part of the process is a set of three nested loops — that is, a loop inside a loop inside a loop. The inner loop directs the robot to deposit dollops of cookie dough onto the baking sheet, one cookie at a time, until a row of dollops has been completed. The middle loop directs the robot to create one row of dollops at a time, until the baking sheet is filled. The outer loop directs the robot to fill one baking sheet at a time, until there is no more dough. Note that each of the three nested loops contains a distinct process to repeat, plus a test to determine when to stop looping.

Now let’s compare a computer program to a blueprint. Suppose, as part of a woodworking project, you needed to create a square piece of plywood, nine inches on each side. In a blueprint, you would see a nice drawing of a square, with a notation indicating that each edge is nine inches long. In contrast, a computer algorithm might say:

1. Start with a large sheet of plywood.
2. With a circular saw, make a nine-inch straight cut in the plywood.
3. Turn the saw 90 degrees to the left.
4. Repeat the previous two steps three more times.

This algorithm does not depict the finished result — it doesn’t even mention the idea of creating a square. But the specified steps do indeed result in a square piece of plywood, nine inches on each side. Most of the processes in the human body are comparable, in that the end product is not defined in advance — it simply emerges as a result of the process.

In one sense, the comparison of DNA to a computer program is enlightening. There are countless biochemical processes going on within the human body at all times, each contributing to the development and maintenance of that body. These processes certainly involve a lot of looping and branching, and therefore they could validly be compared to the algorithms encoded in a complex computer program. The problem with this analogy is that these processes are not actually encoded in our DNA. The language of DNA does not contain codes to indicate the start or the end of a loop, nor when a branch should occur, nor what test to perform when a branch point is reached, nor where in the instructions to jump based on the results of that test.

Worse yet for our computer program analogy, there are no DNA codes for any of the individual steps in any of these thousands of biochemical processes. DNA is not capable of representing or communicating the steps in any of these processes. DNA is, for the most part, just a set of lists that specify sequences of amino acids. Therefore DNA is not like a computer program at all.

What Makes It All Work?

So how does DNA actually work? How does a collection of recipes for making proteins produce a human being, or an elephant, or an oak tree? It seems intuitive that something must be controlling the entire operation.

Well, you could say that there are three levels of management controlling this operation. The first layer of management consists of the molecules that control gene expression — in other words, turning the genes on and off. To put it another way, this first layer of management decides which proteins actually get made, and when they get made, and in which cells they get made. The second layer of management modifies these newly minted protein molecules as needed, making any final tweaks necessary for the proteins to start work. The third layer of management provides additional supervision, if needed, after the proteins are ready to begin work.

You might think of the entire operation as a huge ballet, with 90,000 roles (the various types of proteins) and trillions of protein molecules playing each role. Another analogy, perhaps a better one, is to think of the human body as a huge construction project, with each living cell serving as a job site within the project. The protein molecules are the workers — and each worker will have one of 90,000 specialties. Each cell hires its own workers and manages its own job site, but messages are constantly going back and forth between the cells to help coordinate it all. But who directs the whole enterprise? Who sits at the top, making sure that everything is going according to plan?

The best way to answer this question is to take a closer look at gene expression. At any given time, some of your genes are being “expressed” — copied onto messages for the creation of new protein molecules — and others are not. Furthermore, every cell in your body has its own copy of your DNA — and in each cell, gene expression operates independently, managed locally by the cell nucleus. Even for cells of the same kind, their specific location in the body (and many other factors) can result in a different expression of the genes. The upshot is that “upper management” in this enterprise is highly decentralized. As your body grows from a zygote into an adult human being, these differences in gene expression allow the cells to differentiate into various types — becoming skin, bone, muscle, blood, nerves, and so on. The differences in gene expression allow distinct organs to form within your body — heart, lungs, brain, liver, intestines, and so on. This grand ballet ultimately results in the final shape of your body, with two legs, five fingers on each hand, 12 pairs of ribs, 33 vertebrae, and so on.

Coordinating the Grand Project

This amazingly intricate process involves a huge number of distinct steps — along with a lot of looping and branching — just like an algorithm in an enormously complex computer program. Theoretically, one could imagine such an algorithm encoded somewhere in the body, directing all the processes of the body. But imagine what a beast that algorithm would be, in order to turn on and off each of the 20,000 genes in each of the 30 trillion cells in your body — based on an enormously long and intricate set of factors. Such an algorithm would be hopelessly complex — and far too long to store anywhere, regardless of the encoding system. Therefore the human body contains no central authority to micro-manage gene expression across all the cells. Instead, it all works as an emergent system. The details of the human body emerge as the result of all the behaviors of the contributing parts. It’s a bit like an ant colony, which functions as a coherent whole, even though no central authority — not even the queen of the colony — micro-manages the tasks performed by the individual ants. However, the development and maintenance of the human body is incredibly more complex than the behavior of an ant colony.

What about your brain? Doesn’t it control everything in your body? No, your brain controls a lot of things, but it does not control the expression of the genes within the cells of your body. And it does not control your development from a zygote to an adult.

Therefore, to make sense of how the body develops and maintains itself, we have to study the regulation of gene expression at the cellular level — knowing that each of the 30 trillion cells handles the process independently. This is a complicated topic, and there is much that we don’t yet know or understand. But due to the breakneck pace of scientific research, many of the key puzzle pieces are rapidly coming into view. First of all, there are those RNA molecules, copied from areas in your DNA that don’t code for proteins. One of the main purposes of these molecules is to help regulate gene expression. And then there are certain regulatory genes whose principal activity is to turn other genes on and off. Furthermore, there are yet other kinds of molecules that attach themselves to genes, affecting their expression.

A closely related issue is that cells need to cooperate with each other, especially with cells that are nearby. Therefore there are lots messages going back and forth between the cells, and most of these messages take the form of molecules. (Certain other messages are carried by electrical impulses in the nervous system.) Unlike messenger RNA, most of these molecular messages are not coded in any sort of language, nor are the messages divided into distinct words (comparable to codons). Instead, each message tends to have a very simple objective, usually meaning “start doing this”, or “stop doing this”, or “do this faster”, or “do this slower”.

Summary and Implications

Let’s review a few of the key ideas that we have covered, along with some of the implications of those ideas:

1. DNA is not like a blueprint.

DNA does not contain any schematics or other direct information about the structure or appearance of the organism. If you take a single gene from a fish, and insert it into an embryonic tomato plant, then you won’t get a plant that has fins or fish eyes. If the inserted gene is expressed at all, then you’ll simply get one additional protein appearing in some of the plant cells. This protein will probably have little effect on the plant — unless the protein interacts significantly with other molecules, in which case it could have a huge impact on how the plant develops and lives.

2. DNA is not like a computer program.

Human DNA does not contain any instructions for building or maintaining a human body. And while the processes that occur in the body can be roughly compared to algorithms, DNA does not contain any codes to define the individual steps in an algorithm, nor does it contain any codes to control the loops and branches of the algorithm. DNA simply contains recipes for building proteins, and it contains templates for RNA molecules that regulate the production of these proteins. Therefore, you cannot approach the editing of DNA in the same manner as you would approach the creation and editing of a computer program.

3. DNA controls the development and maintenance of the body indirectly, through the proteins that it encodes.

Proteins drive most of the processes in the human body, including our development from a zygote into an adult. DNA contains the recipes for these proteins — therefore DNA indirectly controls the development and maintenance of body, although in a manner that is far from straightforward. To get a reasonably complete understanding of how DNA controls these processes, it is necessary to know at least four key things:

• The molecules and processes that regulate the expression of each gene
• The proteins that are produced by each gene
• How these protein molecules interact with other molecules in the body
• How genes, and the products of genes, interact with each other to achieve various end results

By studying DNA and the proteins that it encodes for, we can draw correlations between the presence of certain genes and the effects of those genes. For example, by looking at someone’s DNA, it is possible to predict with a reasonable degree of certainty as to whether that person has brown eyes or blue eyes. But more often than not, the effects of our genes are quite complicated, and depend upon the interactions of many different genes, combined with various environmental factors.

4. There is no central authority, and no centralized set of instructions, that controls the development and maintenance of the human body.

Your body has trillions of cells, and every cell has its own copy of your DNA — including all 20,000 genes. Every cell independently regulates the expression of those genes. This gene expression is influenced by the molecules that come in contact with the outside surface of the cell membrane — but otherwise each cell does its own thing. There is no central authority — not in the brain, nor in any other part of the body — that drives the development and maintenance of the human body. Instead, the overall results are an example of an emergent system, in which the behaviors of all the component parts result in a distinct set of behaviors at a larger scale.

This has major implications for what could be feasibly accomplished with genetic engineering — without even getting into the moral and political issues. If DNA was like a blueprint — or like a computer program — then it would be possible for people to invent a completely new organism and to create the DNA necessary to produce that organism. Instead, the only viable approach is to start with the complete genome of an existing organism, and then to make very small modifications to the DNA. This, in fact, is how genetic engineering is currently conducted. Scientists take an existing genome — for example, that of a tomato plant — and then attempt to add or replace a single gene or a very small number of genes. A typical objective is to improve a single characteristic, such as cold hardiness or pest resistance. (Quite often, the inserted genes are found naturally in other members of the same species, or in closely related species.) If and when the objective is achieved, then the people conducting the research might try to improve another characteristic. It’s a gradual and incremental process — a feature that is shared with traditional plant breeding and with natural evolution.

Nevertheless, there are good reasons for society to engage in a serious debate about what specific limits to place on genetic engineering. But for that debate to be meaningful, it is important to have a basic understanding of how DNA actually works. To that end, perhaps this essay has been a helpful step in that direction.

For a more detailed discussion of this same topic, see the chapter “The Blueprint of Life” in my book The Stickler’s Guide to Science in the Age of Misinformation.

Is DNA Like a Blueprint, a Computer Program, or a List of Ingredients? was originally published in The Philipendium on Medium, where people are continuing the conversation by highlighting and responding to this story.

It’s not hard to find wacky ideas about science on the internet — bizarre concepts that stand out because they are so far removed from reality. However, when popular ideas about science are very nearly correct — but not quite — such subtle errors can be hard to spot. A fascinating example involves our popular explanations for the relationship between trees and carbon dioxide. It’s not that these popular explanations are completely wrong — in fact they are mostly correct — and yet the limitations of some of these models can lead to erroneous conclusions.

Below are six common mental models that we often use to explain the connection between trees and carbon dioxide. Many of us have been exposed to more than one of these concepts, although we might rely on a single model as our principal mental framework for the topic. All six of these models can be found in educational materials and on the internet. As we examine these six ideas, it may be useful for you to consider which model most closely aligns with what you have been taught.

The common thread among these models is that “trees absorb carbon dioxide”. This concept has recently grown quite popular — because forests help offset some of the human-caused increase in atmospheric carbon dioxide. In other words, there is a connection between forests — especially tropical rainforests — and global climate change. If we can slow down or reverse the worldwide reduction in the number of trees, then this will help to slow the increase in atmospheric CO2. But what does it really mean when we say “trees absorb carbon dioxide”? Each of the six mental models provides a concise — but different — explanation of what this phrase means.

By using the term “mental model”, we can focus on what happens in the mind of a person who learns and interprets a concept. This mental model might not exactly match what the author of the learning material had intended — in part because the learner is likely to “connect the dots”, drawing conclusions that are not explicitly stated in the learning material.

The first three models listed below are the simplest — which makes them quite popular — but they are also the mostly likely to lead to scientific misconceptions. The final three models are better at avoiding such misconceptions, but even these models can be slightly misleading if used in isolation. Therefore the strongest mental framework is a combination of models 4, 5, and 6:

Model 1 — Trees filter carbon dioxide from the air.

This mental model equates trees to an air filtration system, filtering out carbon dioxide and other “bad” substances from the air. Unlike some of the other models, this model offers no explanation as to what happens to the CO2 that has been removed. This can lead to the misconception that the extracted CO2 is completely destroyed. (Note: Any variation of this model that specifically mentions storing carbon dioxide is actually Model 2.)

One advantage of this model is that it is so easy to understand — and it is certainly true that trees remove carbon dioxide from the air, although the mechanism is different from that of a filtration system. However, there are two principal weaknesses to this model:

1) By failing to acknowledge that trees store massive amounts of carbon, this model suggests that the only harm in cutting down trees is that there are fewer trees to filter out carbon dioxide. There is no suggestion that destroying a forest can release huge amounts of carbon dioxide back into the atmosphere.

2) This model avoids the question of “Why do trees do this? What’s in it for the trees?” This oversight sharply limits the value of the model — because answering this question opens the door to several important insights about the relationship between trees and CO2.

Another weakness, common to all of the first three models, is the implication that trees are the only green plants that remove carbon dioxide from the air.

Model 2 — Trees absorb and store carbon dioxide.

This mental model equates a tree to a giant sponge that sops up carbon dioxide from the air. The underlying idea is that trees constantly absorb and store CO2.

Like Model 1, this model is very easy to understand — which is certainly an advantage. A second advantage is the acknowledgment that the carbon dioxide is not magically eliminated. And a third advantage is the implication that carbon dioxide will return to the atmosphere if the tree is destroyed.

Even though Model 2 is better than Model 1, it still has several weaknesses:

1) Like Model 1, this model avoids the question of “Why do trees do this? What’s in it for the trees?” Again this oversight sharply limits the value of the model.

2) It is just plain wrong to say that trees “store carbon dioxide”. Trees use carbon dioxide — they don’t store it. What is true is that a tree contains a large quantity of carbon-based compounds. In other words, a tree converts carbon dioxide into other carbon-based chemical compounds that are useful to the tree. The great mass of a tree consists primarily of just two things: carbon-based compounds (also called organic compounds) and water.

However, a tree does not actually store most of those organic molecules — at least not in the popular sense of the word “store”, which implies that unused material has been set aside for possible later use. On the contrary, most of those molecules have been turned into wood or leaves or other essential parts of the tree.

3) This model implies that any carbon dioxide “absorbed” by the tree remains locked away until the tree dies. (Some educational materials explicitly make this point, even though it is wrong.) In fact there are several mechanisms by which carbon dioxide is returned to the air even while the tree is alive — including the metabolism of sugars by plant cells, and the annual shedding of leaves by deciduous trees.

4) This model ignores the role of other plants in removing carbon dioxide from the air. It’s not just trees that do it! In fact, some non-forest ecosystems — such as peat bogs — are extremely good at removing carbon dioxide from the air.

Despite the weaknesses of this model, a person who learns this model will realize that destroying a forest has two negative effects connected to carbon dioxide. First, there are fewer trees to remove carbon dioxide from the air. And second, destroying a forest tends to release a lot of carbon dioxide into the atmosphere in a short period of time.

Model 3 — Forests are the lungs of the planet.

This mental model equates forests — especially tropical forests — to a set of lungs, allowing the planet to “breathe”. The idea is that a forest “purifies” the air by absorbing carbon dioxide and releasing oxygen. On a literal level, this is the opposite of what lungs actually do. Lungs take in “fresh” air and exhale the “stale” air — partially depleted of oxygen, but enriched in carbon dioxide. However, because the lung model is clearly a metaphor, it is easy to understand that trees do the opposite of what animal lungs do. Thus there is an implied balance between the forests of the world and the animals of the world. In fact, many educational materials contain graphics that illustrate such a balance.

The main strength of this model is its emphasis on gas exchange — the exchange of carbon dioxide and oxygen — which is an important concept. But if a forest has the equivalent of lungs, then where are these lungs? The answer is that most of the gas exchange occurs in the leaves. Pores on the surface of each leaf allow gases to move in and out. During the day, carbon dioxide enters through these pores, and oxygen escapes. This is consistent with the “reverse lungs” concept. But at night the opposite happens — oxygen enters through the pores, and carbon dioxide escapes — a phenomenon that Model 3 does not explain, or even acknowledge.

Despite the helpful emphasis on gas exchange, this model has several weaknesses:

1) Like Models 1 and 2, this model (in its simplest, most common form) avoids the question of “Why do trees do this? What’s in it for the trees?” A child who has been taught this model might answer this question by saying “Because people and animals need oxygen.” This answer confuses a benefit with a reason.

2) Like Model 1, the simplest version of this model fails to acknowledge that trees store massive amounts of carbon. There is no suggestion that cutting down a forest can release a huge amount of carbon dioxide back into the atmosphere.

3) Furthermore, by failing to explain what happens to the carbon, this model can promote the misconception that carbon dioxide is completely eliminated by conversion to oxygen.

4) This model diverts much of the attention away from the reduction of atmospheric carbon dioxide, shifting the attention to the production of oxygen. Indeed, some websites and educational materials suggest that if the world’s forests were to be cut down, then we would soon run out of oxygen to breathe. (“Forests are the lungs of the earth. If we destroy them, we destroy ourselves!”) Destroying the world’s forests would indeed be catastrophic, but it would not result in our suffocating.

5) Like the first two models, this model also undervalues the role of non-forest ecosystems in reducing atmospheric CO2.

Model 4 — Green plants use sunlight to convert CO2 and water into sugar.

This mental model explains the essence of photosynthesis quite succinctly. Unlike the first three models, this model provides a reason that plants remove carbon dioxide from air — to produce sugar. It also explains what happens to the carbon — it becomes part of the sugar (C6H12O6). This model also implies how green plants benefit from the process — they can use the sugar.

This model usually mentions that oxygen is given off as a waste product of photosynthesis. CO2 and water contain more oxygen atoms than are needed to make sugar, so the excess oxygen is released as a gas. That’s the reason that green plants give off oxygen — not because animals and humans need it. In fact, when early green plants began to pump oxygen into the atmosphere, the gas poisoned much of the existing life on earth — killing it off, but paving the way for the later evolution of oxygen-dependent creatures.

This simple mental model of photosynthesis — that green plants use sunlight to convert CO2 and water into sugar — provides a great foundation for understanding the relationship between trees and carbon dioxide. However, this model is incomplete without a second mental model that explains what happens to all that sugar. The simplest such model (although incomplete) is that the sugar produced by photosynthesis serves as food for the plant. This is a crucial concept. Every living cell needs energy to survive — and for most plant and animals cells, this energy is delivered as sugar. Therefore the sugar produced in the leaves of a plant must be transported to all the living cells in the plant — particularly the roots.

Once you fully grasp these two ideas — that every plant cell needs food in the form of sugar, and that a living plant must move sugar to where it is needed — it makes perfect sense that most land-based green plants have an internal water-based transport system. In fact there are two distinct transport systems. One system moves sugar water down from the leaves to the roots, and the other system moves mineral water up from the roots to the leaves.

So why do plant cells need energy? Cells use the chemical energy of sugar to drive the normal metabolic processes that keep the plant alive. When the cells use this energy, the sugar reverts to carbon dioxide and water — although oxygen is also consumed in the process.

The upshot is that every cell in a plant constantly consumes oxygen and gives off carbon dioxide — just as animal cells do. However, when the sun is shining, the chloroplasts in the leaves and other green surfaces do just the opposite — and they do it at a much faster rate. Thus, during the day, green plants are net consumers of carbon dioxide and net producers of oxygen. But at night, when photosynthesis shuts down, it is just the opposite.

Model 4 is therefore a powerful concept that is closely connected to several important details. But even if you remember all of these details, there is a crucial concept that is missing — the key concept underlying Model 5.

Model 5 — Green plants create biomass; animals and decomposers break it down.

The concept missing from Model 4 is that much of the sugar produced by green plants is not used to provide energy to the cells of the plant. Instead, the sugar is converted into other organic compounds that are useful to the plant. A surprisingly wide range of compounds are produced, including starches, fats, proteins, and many other classes of molecules. Some of these compounds, such as starches and fats, require nothing more than the atoms already present in sugar — carbon, hydrogen, and oxygen. But some compounds (such as proteins) require additional atoms (such as nitrogen) that arrive via the mineral water sent up from the roots. This wide range of molecules serves many different purposes in the life of a plant.

However, a very high percentage of the sugar is simply converted into cellulose — or in the case of woody plants, cellulose and lignin. These are the structural materials that give a plant its shape and allow it to stand upright. (Lignin, which is much stiffer than cellulose, is the compound that makes woody plants “woody”.) Therefore the dry mass of a woody plant is composed primarily of cellulose and lignin, and the dry mass of an herbaceous (non-woody) plant is usually composed primarily of cellulose. Humans cannot digest cellulose or lignin, so we tend to eat the parts of plants where the digestible compounds — such as sugars, starches, fats, and proteins — have been concentrated.

Biomass is any material that consists either of living tissue, or tissue that had once been living. In a forest ecosystem, most of the biomass consists of living trees or dead remnants of trees, such as the leaf litter on the forest floor. Some of the biomass is underground, including tree roots, fungus, other microorganisms, and the myriad little critters that live in the soil.

One component of biomass is water — embedded in living or dead tissue. But the rest of the biomass consists almost entirely of energy-rich carbon-based compounds. For that reason, dried biomass is flammable, and can be used as fuel. The most obvious example is firewood, but any dried plant material tends to burn easily. This fact reveals a key detail: that cellulose and lignin contain a lot of stored chemical energy. This energy was originally captured from sunlight and stored in sugar molecules that were later converted to other high-energy molecules. In fact, all the carbon-based compounds in a plant are high-energy, and this energy can be traced back to sugar created by photosynthesis.

The upshot is that green plants are the only organisms that can create biomass — because these are the only organisms that can use the energy of sunlight to manufacture sugar. (There is a minor exception for organisms that use the chemical energy of deep-sea hydrothermal vents.) Animals, like plants, can convert certain high-energy compounds into other high-energy compounds, but in doing so there is always a loss in biomass. In other words, when an animal eats biomass — plant or animal tissue — a small part of that biomass is often incorporated into the body of the animal, becoming muscle or other tissue. But a larger part of that biomass is simply metabolized for its energy. And a far larger part of the eaten biomass is wasted — especially if the animal is incapable of digesting cellulose. The key point here is that in a typical ecosystem, such as a forest or grassland, all of the biomass is originally created by plants.

When discussing the biomass of an ecosystem, it is helpful to consider how dense the biomass is. This can be expressed, for example, as tons of biomass per acre (or in metric tons per hectare). Not surprisingly, forests tend to have the densest biomass figures — especially tropical forests — because so much biomass is locked up in woody tree trunks, branches, and roots.

Model 6 — The forests of the world are a huge carbon sink.

Because all biomass consists of carbon-rich compounds — and the carbon in these compounds originated as atmospheric CO2 captured by green plants to create sugar — forests can be viewed as a major carbon sink. A “carbon sink” is anything that absorbs large amounts of carbon dioxide from the atmosphere, retaining the carbon in one form or another.

Of course, this is a two-way street — because carbon can move in either direction. The biomass of a forest becomes CO2 again whenever any of the following processes occur:

• Sugars are metabolized by plant or animal cells in order to access the stored energy.
• Dead biomass, such as fallen leaves or downed trees, decomposes into simpler compounds. (Decomposer organisms play a key role, consuming some of the stored energy while breaking down the organic compounds.)
• Fire races through a forest, burning the dead forest litter — and in the case of a crown fire, then also consuming parts of living trees.

In a typical forest, far more carbon is captured than is released — although the amount varies according to the type of forest, the age of the forest, and other factors.

Because trees can be very large, it seems intuitive that a forest would store more carbon per acre than any other type of ecosystem. But is that really true? If you only consider the above-ground storage of carbon, then the tropical rainforests of the world are the clear winners in terms of carbon mass. Forests in temperate climates also store a lot of carbon, but less than tropical forests.

However, if you consider the organic carbon stored in soils, then the picture becomes more complicated. There are extensive areas of peatlands in the world, where the density of carbon storage is as great as in tropical forests. However, much of this carbon is stored in a thick blanket of peaty soil, not in living vegetation. The acidic, waterlogged soils prevent fallen organic matter from decomposing, so it builds up over a long period of time. Peatlands are especially common in the far north — such as Canada, Russia, Scandinavia, and Alaska — but the tropics also contain significant areas of peatland.

Destroying peat bogs is as bad as destroying tropical forests, when viewed through the lens of preserving our major carbon sinks. Peat bogs are easily destroyed by draining away the water, which exposes the soil to air, allowing the organic matter to decompose. However, peatlands are not the only ecosystem with high levels of organic carbon in the soil — other examples include grasslands and mangrove swamps. In fact, worldwide there is more organic carbon in the top meter of soil than in all the above-ground biomass, including tropical forests.

Despite the crucial role of vegetation and soil as carbon sinks, they are not the only carbon sinks in the world. The ocean is also a major carbon sink, because carbon dioxide is soluble in water. In fact, there is far more carbon dioxide dissolved in the ocean than there is floating in the atmosphere. Therefore vegetation, soil, and oceans are the three major carbon sinks — but each is capable of returning carbon dioxide to the atmosphere, depending upon current conditions.

To round out this picture, it is also helpful to think about the former carbon sinks of the world, now locked away deep in the earth. There are two such former sinks:

1) Our fossil fuel reserves — oil, gas, and coal — are the remnants of ancient swamps in which large amounts of plant material accumulated without decomposing. This organic matter eventually became buried under deep layers of soil, which hardened into rock. This pool of carbon has been locked away for hundreds of millions of years — but now humans actively seek out these reserves to burn them as fuel, returning the carbon dioxide to the air.

2) The vast amounts of limestone in the earth’s crust are a result of carbon dioxide dissolving in the oceans. CO2 combines with water to form carbonate, which remains dissolved in the water. Many forms of sea life extract carbonate to produce shells, reefs, and other hard structures. Additional carbonate interacts with calcium that has weathered from continental rocks and washed into the ocean. Both of these processes result in a steady rain of calcium carbonate settling to the bottom of the ocean, forming thick layers of marl that eventually become limestone and related rocks. When limestone is processed to create cement, some of the carbon dioxide returns to the air.

Summary

We have now examined six popular mental models that attempt to explain the relationship between trees and carbon dioxide — each model consistent with the basic concept that trees remove carbon dioxide from the air:

1. Trees filter carbon dioxide from the air.

2. Trees absorb and store carbon dioxide.

3. Forests are the lungs of the planet.

4. Green plants use sunlight to convert CO2 and water into sugar.

5. Green plants create biomass; animals and decomposers break it down.

6. The forests of the world are a huge carbon sink.

Each of these mental models can help the learner to draw useful insights. However, the first three models all have serious weaknesses — including a failure to address the reason that plants absorb carbon dioxide, and a tendency to produce scientific misconceptions. The final three models are far stronger, but each in isolation only paints part of the complete picture. When combined, these last three models can provide a powerful understanding of the relationship between trees and carbon dioxide.

Of course, the forests of the world provide far more benefits than just capturing carbon — and the wholesale destruction of forests does far more harm than just releasing carbon dioxide into the atmosphere. But with the current emphasis on trees as part of the solution for fighting the rising levels of atmospheric carbon dioxide, it is helpful to have a good understanding of the underlying scientific concepts.

For a more detailed discussion of this same topic, see the chapter “The Lungs of the Planet” in my book The Stickler’s Guide to Science in the Age of Misinformation.

Trees and Carbon Dioxide: What Is the True Connection? was originally published in The Philipendium on Medium, where people are continuing the conversation by highlighting and responding to this story.

Quickly — name three ways in which water is intimately tied to energy. Ready? What answers did you come up with? If the word “energy” made you think of commercial energy production, then your first answer was probably hydroelectric dams. You might also have thought about the use of water to cool nuclear power plants, or the use of large amounts of water in hydraulic fracking and coal mining. If you are mechanically oriented, then you might have thought of steam engines or hydraulic systems. If you are a botanist or a biochemist, then perhaps you thought about the crucial role that water plays in photosynthesis — the mechanism by which green plants capture the energy of sunlight. But if the word “energy” made you think of the human body, then perhaps you thought about how your skin releases sweat when your body gets overheated, cooling your body by drawing away excess heat energy. The upshot is that there are many, many connections between water and energy — and any list of just three examples would barely scratch the surface.

Perspiration

Let’s start with the example of perspiration. Why does sweating cool the body? The sweat that we produce emerges from the skin at the same temperature as the skin, so it’s not quite the same as getting doused with a shower of cold water. You probably know the answer already — that sweat cools us by evaporating. But this answer just kicks the can down the road. Now we have to ask: How does the evaporation of water from our skin make us cooler?

The answer is that it takes a lot of energy to convert water from a liquid state to a gaseous state. As the sweat on your skin vaporizes into the air, it steals heat energy as it disappears. This process actually lowers the temperature of your skin — which then lowers the temperature of the flesh and blood directly beneath the skin. The cooled blood then circulates to other parts of your body, thereby cooling these places too. Meanwhile, additional hot blood replaces the cool blood near the skin, allowing the cooling of your blood to continue.

When I first heard this explanation many years ago, I was quite skeptical — the story did not make sense to me. If you put cold water on your skin, then it will steal heat energy by conducting the heat away, due to the difference in temperature between the skin and the water. But sweat is warm when it emerges from your skin — which, from my point of view, ruled out the possibility of heat conduction. Therefore what drives the heat energy to move from your skin into the evaporating water?

The answer is that the evaporation of water from your skin lowers the temperature of the water that is left behind. Consequently, the sweat on your skin soon becomes colder than the skin — which allows heat energy to be conducted away from your skin into the remaining water. But once again, this answer just kicks the can down the road. What makes the water evaporate, and how does evaporation reduce the temperature of the remaining water?

To answer this question, it is helpful to think about the individual molecules of water. All molecules, including water molecules, tend to move around. The amount of motion is tied to the temperature of the matter of which the molecule is a part. In fact, heat can be described as the movement of molecules within a substance, and temperature as a measure of the average amount of motion among those molecules. The word “average” is key here. At any given moment, some of the molecules are moving faster than the average, and some are moving slower. In a bead of water on your skin, the fastest-moving water molecules can fly off into the air — which is the definition of evaporation. Because only the fastest molecules escape, the remaining water molecules now have a lower average amount of motion — which means that the water temperature has been lowered. As bizarre as this explanation may sound, this is how a bead of water or sweat on your skin becomes colder than the skin.

Condensation

Now consider a situation that has some superficial similarities. It is a hot day in summer, and you pull a canned drink out of the refrigerator and put it on the table. You walk off, intending to come right back, but ten minutes pass before you actually return. You now see that the can is covered in beads of water, and that quite a bit of water has run off the can onto the table. Did all of this water condensation help to keep the can cold, in the same way that water on your skin cools you off? Or did the condensation on the can have the opposite effect?

Think about the details of what actually happened. The water that condensed on the can came out of the air, transitioning from water vapor to a liquid. This is exactly the opposite of evaporation, where the water changes from liquid to vapor. Instead of drawing away heat energy, as evaporating water does, the water condensing on the can gives up heat energy, thereby adding heat to the canned beverage.

Now consider these two scenarios — a sweating human body versus a “sweating” canned beverage — in two different climates. One locale is in the desert, with very low humidity. The other locale is lush and green, and is therefore quite humid. On this particular day, it is sunny and 86° F (30° C) in both locations. If you were to go for a vigorous hike in either location, then you would certainly sweat a lot, but in the desert the perspiration would evaporate quickly, keeping you relatively cool. In the humid location, the rate of evaporation would be much slower, preventing your body from cooling as much, and therefore you would suffer greater effects from the heat. Now suppose that you put a cold canned beverage on a shady table in both climates, and then returned to drink the beverage ten minutes later. In the humid climate, the can would be covered with heavy condensation, while the can in the desert would have little or no condensation. In both places the canned beverage would have experienced some warming due to factors other than condensation, but the condensation in the humid climate would have greatly speeded up the warming process. Therefore, as odd as it may seem, the desert is the better location for keeping your beverage cold.

Heating a Pot of Water

In our examples so far, we have seen that it takes a lot of energy to convert liquid water to a gas, but that the energy is recovered when gaseous water changes back into a liquid. Imagine now that you have placed a pot of very cold water on the stove top. You turn up the burner to its highest setting, and you place a kitchen thermometer into the water. Over the course of many minutes, you see the temperature of the water increasing at a fairly steady rate. Eventually the water reaches 212° F (100° C), and it visibly begins to boil. You leave the burner at the same high setting until nearly all of the water has evaporated. At the end of the experiment, you note that it took much longer to boil off the water than it took to heat the water to a boil. You also note that the temperature of the boiling water never exceeded the boiling point.

This means that for the first part of your experiment, most of the energy from the burner went into heating the water. But after reaching the boiling point, the water could no longer be heated. Instead, all of the energy went into evaporating the water. The fact that it took longer to boil off the water than to heat the water is another sign that a great deal of energy is needed to force the transition of water from a liquid to a gas. We can quantify this. It takes exactly 100 kilocalories of energy to heat one liter of water from the freezing point all the way to the boiling point. But it takes 226 kilocalories of energy to convert a liter of boiling water into steam.

As a side note, the very definition of a calorie is another connection between water and energy. A calorie is defined as the amount of heat energy required to raise the temperature of one gram of water by one degree Celsius. But in popular terminology, a calorie is a measure of the stored chemical energy in our food. Oddly, a food calorie is exactly equal to 1000 calories — a kilocalorie. In other words, a food calorie is the amount of energy required to raise one kilogram of water (2.2 pounds) by one degree Celsius (1.8° F). By the way, one liter of water (about a quart) happens to weigh exactly one kilogram, under standard conditions.

Water Behind a Dam

Now let’s turn our attention to water at a much bigger scale. Imagine a huge hydroelectric dam, such as the Grand Coulee Dam on the Columbia River in the western United States. (If you want to think even bigger, then imagine the Three Gorges Dam in China, or the Itaipu Dam on the border between Brazil and Paraguay.) Dams such as these can produce enormous amounts of electricity. But compared to our earlier examples, this is a different kind of relationship between water and energy. The electricity is generated by releasing water from a dam, allowing the water to flow down through an inclined shaft, striking and turning a set of turbine blades along the way. There is no need for the water to change state. Instead, the process is driven by the weight of the water, and by the fact that the water can be released to fall or flow to a lower location, powered by gravity.

The energy of water that is falling or flowing downhill can be used in other ways besides generating electricity. A couple of centuries ago it was common to build mills along rivers and streams, driven by water wheels. These mills could be used to grind grain or to drive factory equipment. In other words, the potential energy of the water was converted into mechanical energy, not electrical energy. This process can be compared to an old grandfather clock driven by a weight suspended inside the cabinet. Someone needs to lift the weight periodically — putting it back to its highest position — so that the clock continues to have the energy to keep ticking. Gravity pulling on the weight is what drives the entire clock mechanism.

Just as a grandfather clock needs a person to lift the weight back up from time to time, a hydroelectric dam needs to have its water replenished — which requires that the water be lifted back into the sky so that it can fall again as rain. The energy to lift the water comes from the sun, which drives the evaporation of the water, as well as driving the air circulation that lifts the evaporated water thousands of feet above the ground. It takes a huge amount of energy to lift all that water — and therefore the water in the atmosphere represents a massive amount of potential energy. This energy dissipates as the water falls back to the ground and then flows down to the ocean. However, some of that potential energy is intercepted by the hydroelectric dam, allowing us to generate electricity.

An interesting thing to consider here is that the water changes state twice during this process — from a liquid to a gaseous vapor, and then back to a liquid. A great deal of heat energy is sopped up by the water when it evaporates from the earth’s surface. Later, when that water vapor condenses to form water droplets in a cloud high above the earth, all of that heat energy is released into the atmosphere. The air containing the water vapor could have traveled a long distance before converting its moisture into droplets. This movement of heat energy through evaporation and condensation is an important factor in how the weather systems of the world behave.

Let’s clarify a key point that might have slipped by unnoticed. The water that evaporates from the surface of the earth captures the energy of sunlight by two distinct mechanisms: 1) by changing state from liquid to vapor, and 2) by getting lifted high above the surface of the earth. Therefore the water also gives up its stored energy by two distinct mechanisms: 1) by condensing into water droplets high in the sky, and 2) by falling back to earth and flowing down to the elevation from which it evaporated.

So far we have looked at cases where the phase change from liquid to gas (or the reverse) plays an important role in the transfer of heat energy, and we have looked at cases where the energy of falling water is used to generate electricity or power a mill. If we were to look at other connections between water and energy, would they fall into these same two patterns, or would we see additional patterns?

Water in Pipes and Tanks

Consider the case of a steam engine. As the name implies, such an engine depends upon steam — or more precisely, a phase conversion from liquid water to steam. The energy for the engine comes from burning some type of fuel, such as coal. The water and the steam are housed in a closed system, and as the boiler containing the water is heated by the burning fuel, some of the water turns to steam. Water expands greatly as it becomes steam, building up high levels of pressure inside the system. It is this pressure that can be put to work, driving the wheels of a locomotive or the propeller of a boat — or any of several other mechanical tasks. Thus a steam engine does indeed depend on a phase transition of water — but not to move heat from one place to another. Instead, it relies on the expansion of water during this change of state to generate mechanical energy.

Now consider a hydraulic system. Water or some other liquid is enclosed in a system consisting of pipes or tubes. Pressure is applied to the water at one place in the system, and this pressure is immediately distributed by the water to all parts of the system. This is analogous to the brakes in an older car, where the pressure of your foot on the brake pedal is distributed by the brake fluid lines to all four brakes. (Of course, these days everyone has power brakes, which means that the force applied by your foot is augmented by additional machinery.) In some ways a hydraulic system is like a steam engine, because in both cases an increase in pressure within a closed system does the work. However, in a hydraulic system, the pressure does not come from the expansion of water into steam, but by the application of an external force to the fluid in the system. In other words, a hydraulic system is not an engine, but a means of distributing a force, allowing the work to occur in some place other than where the force is applied.

Imagine a water distribution system that serves a town or small city — specifically a community that stores its treated water in water towers. It requires a lot of energy to lift the treated water up into the towers, but after it has been lifted, no additional energy is required to distribute the water. The weight of the water in the tower puts the entire distribution system under hydraulic pressure. Anybody anywhere in the town can open a water faucet, and the pressure will drive the water out of the open tap. Theoretically you could fit any one of these taps with a tiny turbine, thereby converting some of the potential energy into mechanical or even electrical energy.

Water and the Energy Budget of the Earth

There are yet other ways in which water is connected to energy. As you probably know, snow tends to appear blindingly white in bright sunlight. If you go skiing on a sunny day, then you should wear sunglasses to prevent snow blindness. This high degree of reflectivity means that much of the sunlight that falls on snow or ice bounces back into space. But if the snow or ice melts, exposing what lies beneath — bare rock, soil, plant material, or liquid water — then the exposed surface will instead absorb much of the sunlight that falls on it. Therefore the presence or absence of a snow cover has a great effect on whether a region will absorb sunlight and warm up, or instead reflect sunlight and stay cold. Either way, there can be a feedback loop, where the lack of snow cover causes additional warming which causes the loss of more snow cover — or where an unusually large extension of snow cover causes unusual cooling that results in further expansion of the snow cover.

There are other ways in which water affects the energy budget of the earth — that is, the balance between incoming solar radiation and the outgoing radiation returning to space. Clouds, like a cover of snow, are a bright white when seen from above — and therefore clouds also reflect a lot of solar energy back into space. The common attribute between cloud droplets and snow is the huge amount of surface area compared to the volume, thereby greatly increasing its reflectivity. In contrast, water vapor in the air — which has no surface to reflect light — is perfectly transparent to visible light. On a clear day you can easily see an aircraft several miles above your head, despite the water vapor in the air.

On the other hand, water vapor in the air is not transparent to all wavelengths of infrared radiation (IR). A high percentage of the energy that the earth sends back into space is in the IR band, and some of this energy is intercepted and absorbed by water vapor before it can escape. This is an important factor in the balance between incoming and outgoing radiation. This is why a cloud cover at night will prevent a region from cooling very much during the night, compared to a clear night. Water has still another big effect on this balance. Much of the carbon dioxide — a potent greenhouse gas — that enters the atmosphere eventually dissolves in the ocean. There is a constant balance between the CO2 in the atmosphere and the CO2 in the oceans. If you increase or decrease the CO2 in either place, then the equilibrium is affected, and CO2 will move from one to the other to restore the balance. This has several effects, including an effect on the energy budget of the earth. (For more details, see Are Greenhouse Gases a Myth?)

Photosynthesis

And now for one final example — yet another process in which water and energy are connected. As you know, green plants are able to capture the energy of sunlight through a process called photosynthesis. This process requires water, carbon dioxide, and sunlight as inputs. Chlorophyll in the plants uses the energy of sunlight to convert the water and carbon dioxide into molecules of sugar. (Oxygen is released as a by-product.) In other words, the plant converts light energy into chemical energy, in the form of high energy bonds within the sugar molecule (carbon-to-carbon and carbon-to-hydrogen bonds). This stored energy can be recovered at any time by converting the sugar molecules (along with some oxygen) back into water and carbon dioxide. However, this reverse process does not release light. Instead, it makes the energy available for the various metabolic processes that a plant must perform in order to survive. If an animal eats the plant, then it can make use of the stored chemical energy to drive its own metabolic processes. Therefore the living cells of an animal consume oxygen to release the energy, and these cells give off water and carbon dioxide as by-products.

We have now examined a wide variety of ways in which water is deeply connected to energy. And yet there are many other examples that we could have examined. Most of these unmentioned examples rely on the same principles discussed above, but a few rely on yet other principles. Can you think of additional examples of the connections between water and energy?

The Curious Connections Between Water and Energy was originally published in The Philipendium on Medium, where people are continuing the conversation by highlighting and responding to this story.

The recent talks with North Korea have brought renewed attention to that nation — and to the extremely repressive nature of its government. By any measure, North Korea is one of the most repressive countries in the world. However, North Korea is not the only country with a dismal human rights record. Several dozen of the world’s nations are highly oppressive, sometimes brutally so. Yet each nation is unique — and therefore direct comparisons between these countries can be tricky. Suppose it was your job to evaluate every country on Earth as to how repressive or free it is — and to give each nation a score based on your findings. How would you go about doing that? What criteria would you use? How would you find and collect the data that corresponds to each criterion? And after you had gathered all the data and performed your analysis, which 10 countries of the world would rank as the most repressive, and which 10 would rank as the most free?

As it turns out, there are several organizations that have taken on this very task — to evaluate every nation on Earth as to how repressive or free it is, and to give each nation a score that reflects the results. Each of these “Freedom Indices” produces a different set of rankings, but there are more similarities than differences among the various results. All of the rankings put North Korea at or very near the bottom of the list. All of the rankings place democracies such as Norway and New Zealand near the top of the list — or at least very high on the list. It is also worth noting that each of the principal indices is updated on an annual basis — because the world is always changing. Each year, some nations experience an erosion of freedom compared to the previous year, while some other countries experience an increase in freedom.

Therefore, to identify the 10 most repressive and 10 most free nations on earth, we will look for a consensus among six of the most respected Freedom Indices — using the most recent results from each index. We will start with three indices that use the most typical criteria for measuring freedom and democracy, and then we will incorporate three additional indices that take a more specialized approach.

The three initial indices for this analysis are:

• Freedom in the World (2018), published by Freedom House
• Democracy Index (2017), published by The Economist Intelligence Unit
• V-Dem Annual Democracy Report (2018), published by the V-Dem Institute

The Most Repressive Nations

What are the most repressive nations on earth, according to these three indices? All three indices agree that North Korea and Syria are among the three most repressive nations on the planet. In fact, two of the indices rank North Korea as the absolute worst.

All three indices agree that Turkmenistan is among the six most repressive countries, that Saudi Arabia and Equatorial Guinea are both among the eight worst, and that Uzbekistan is among the ten worst. Based on this consensus, we can conclude that six of the most repressive countries in the world are:

1. North Korea
2. Syria
3. Turkmenistan
4. Saudi Arabia
5. Equatorial Guinea
6. Uzbekistan

Beyond this consensus, each of the indices has chosen a different set of four nations to round out their list of the worst ten. Two of the indices consider Eritrea to among the three most repressive countries in the world, in the same league as North Korea and Syria. Other countries that scored in the bottom ten in one list or another are South Sudan, Somalia, Sudan, Chad, Central African Republic, Congo (Kinshasa), Tajikistan, Yemen, Bahrain, and Burundi.

NOTE: South Sudan was omitted from two of the indices, and Somalia was omitted from one of them. These omissions may skew the results slightly.

Three Other Indices

Before we look at the opposite question — the list of the freest countries on Earth — let’s take a look at three other indices, each of which presents a more specialized viewpoint regarding the questions of freedom and repression:

• World Press Freedom Index (2018), published by Reporters Without Borders
• Index of Economic Freedom (2018), published by the Heritage Foundation
• The Human Freedom Index (2017), published by a coalition of institutes, led by the Cato Institute and the Fraser Institute

The World Press Freedom Index focuses exclusively on press freedom, making it much more specialized than the first three indices. The Index of Economic Freedom (EF) is also quite specialized, focusing on “economic freedom”, as defined by the values of the political right. In other words, this index is a measure of how business-friendly the various nations of the world are. Another conservative index, the Human Freedom Index (HF), looks at freedom in the world through a libertarian lens. Fifty percent of the HF score is based on economic freedom, defined in a manner similar to the EF index. But the other half of the HF score is based on other key aspects of human freedom — thereby providing a broader and more balanced perspective, while still reflecting conservative values.

The Press Freedom index lists the four most repressive countries in the world as North Korea, Eritrea, Turkmenistan, and Syria — which is consistent with the first three indices. But after these worst-of-the-worst, the next five most repressive countries are listed as China, Vietnam, Sudan, Djibouti, and Cuba. All five of these countries scored poorly in the first three indices, but none (except Sudan) so poorly as to land in the bottom ten of any list. In other words, these countries are willing to allow certain very limited freedoms, but not freedom of the press. (Note that three of these countries — China, Vietnam, and Cuba — are communist nations.)

The Economic Freedom Index has a somewhat different list of the ten most repressive countries in the world. Due to a lack of data, this index omits Syria, Somalia, South Sudan, Yemen, Libya, and Iraq from it rankings — which skews the results somewhat, because some of these nations would certainly have appeared in the bottom ten. Consistent with the first four indices, this index lists North Korea as the worst country in the world, and Eritrea and Equatorial Guinea as among the ten worst. However, after North Korea, it considers the three worst countries in the world to be Venezuela, Cuba, and Congo (Brazzaville) — when evaluated in terms of economic freedom.

The third of our three specialized indices — the Human Freedom Index — is of limited use in identifying the most repressive countries in the world, because so many of the top candidates were omitted from the rankings — including North Korea, Turkmenistan, Eritrea, Equatorial Guinea, Somalia, Sudan, South Sudan, Cuba, Djibouti, Uzbekistan, and Iraq. (These countries were omitted due to a shortage of reliable statistics.) Of the countries that were indeed ranked, Syria and Venezuela are listed as the worst two.

If we consider all six lists, then the following countries appeared in the bottom twelve on three or more lists:

1. North Korea (5 of 5)
2. Syria (5 of 5)
3. Equatorial Guinea (5 of 5)
4. Turkmenistan (5 of 5)
5. Saudi Arabia (5 of 6)
6. Eritrea (4 of 5)
7. Yemen (3 of 5)
8. Uzbekistan (3 of 5)
9. Central African Republic (3 of 6)

This aggregated list therefore represents a consensus view — indicating the nine most repressive governments in the world. (Tenth place would probably go to Sudan, which appears in the bottom thirteen countries in 3 out of 5 indices.)

Now that we have this list, what should we do with it? First of all, we should remember which countries are on the list. In every one of these countries, a brutally repressive national government makes life miserable for a large part of the population. (In some of these countries, civil war magnifies the suffering several times over.) We should look for ways that the world might help the suffering people of these countries. Equally important, the democratic nations of the world should avoid pursuing policies that encourage further repression by undemocratic leaders. It would also be helpful if we all attempted to learn more about the specific situation in each of these countries.

What Does Freedom Actually Mean?

Let’s now consider the opposite question. If we were to list the ten freest countries in the world, then which countries would appear on that list? Just as there are far more than 10 repressive countries in the world, there are far more than 10 countries that qualify as free. Creating a list of the top ten is a bit tricky, because there are many different kinds of freedom. Before we can evaluate the state of freedom in a particular country, we have to decide which categories of freedom to consider. A typical list of our most important freedoms is the following:

• Freedom of expression (including free speech and freedom of the press)
• Freedom of association (including freedom of religion, freedom of movement, and freedom of assembly)
• A fair legal system (including equality before the law, and freedom from arbitrary arrest)
• Freedom from arbitrary seizure of property
• Free economic activity

Democracy is often considered to be essential to freedom. While freedom and democracy are not synonymous, the two concepts are closely related. We use terms like “self-determination” and “government by the people” to express our belief that government should answer to the people. Furthermore, democracy helps to ensure that government respects our other freedoms. A true democracy includes:

• Government by elected representatives of the people
• Free and fair multi-party elections, with universal suffrage
• Judicial and legislative constraints on the executive branch of government

The first three indices cited in this article (Freedom in the World, Democracy Index, and V-Dem) all put great emphasis on democracy, in addition to the other freedoms listed above. The three indices use similar criteria to evaluate the countries of the world, but each index uses its own methodology to match these criteria to various sets of measurable data. Therefore the results vary somewhat between the three indices.

Unlike our first three indices, our other three indices are far more specialized. The World Press Freedom Index focuses exclusively on press freedom. The Economic Freedom Index focuses exclusively on freedom of economic activity. The Human Freedom Index is a hybrid, with half the score focused exclusively on economic freedom, and the other half focused on several other categories of freedom.

Countries with the Most Freedom

So now let’s revisit our first three indices (Freedom in the World, Democracy Index, and V-Dem) to identify the freest countries in the world. All three of these indices list Norway as #1 — the freest, most democratic nation on Earth. All three indices list Sweden in the top three. In total, there are six countries that the three lists agree should be in the top ten:

1. Norway
2. Sweden
3. Finland
4. New Zealand
5. Denmark
6. Australia

There are two additional countries — Switzerland and Canada — that appear in the Top Ten in two different lists. Other countries that scored in the Top Ten in one list or another are Netherlands, Luxembourg, Uruguay, Iceland, Ireland, Estonia, Costa Rica, and Portugal.

Now let’s bring in the three specialized indices — Press Freedom, Economic Freedom, and Human Freedom.

The Press Freedom index agrees with the first three indices that Norway is the best country in the world, and that Sweden ranks #2. This index ranks Netherlands, Finland, and Switzerland in the top five, and it places New Zealand and Denmark in the top ten — all consistent with the first three indices. However, this index also lists Jamaica, Belgium, and Costa Rica as among the top ten countries of the world (with regard to press freedom).

The Economic Freedom (EF) index, with its narrow focus on friendliness to business, produces a distinctly different set of results. It lists Hong Kong and Singapore as the two freest countries in the world — even though both rank considerably lower in the first four indices. (Both countries are considered to be authoritarian regimes, with limited democracy and significant limits on freedom of speech and of the press.) However, there is a solid reason for placing Hong Kong and Singapore at the top of this particular index — because there is no disputing that these two places are both quite friendly to business.

NOTE: Hong Kong is not actually an independent country — it is a territory of China. However, due to its separate legal system and its semi-autonomous status, it makes sense to list Hong Kong separately from China in these rankings.

The other striking anomaly in the Economic Freedom index is that the United Arab Emirates is ranked among the ten freest countries in the world — while the first three indices all consider the U.A.E. to be a highly repressive dictatorship that ranks among the bottom 12% of all countries in the world. But again it is simply a matter of focus. The U.A.E. strives mightily to be friendly to business, while remaining highly repressive in most other regards.

On the other hand, the Economic Freedom rankings agree with the first four indices that New Zealand, Switzerland, Australia, and Canada all deserve to be the in the Top Ten. It also places Ireland, Estonia, and the United Kingdom in the top ten — all of which have very good scores in the other four indices. The EF index does not list either Norway or Sweden among its top ten, although it does give both countries very good scores. So despite the distinct anomalies, there are more similarities than differences between this index and the first four.

Our final index — Human Freedom — also presents a conservative viewpoint strongly influenced by concepts of economic freedom (that is, friendliness to business). However, only 50% of the score is based on economic freedom, while the other 50% is based on other human rights considerations. According to Human Freedom, the three best countries are Switzerland, Hong Kong, and New Zealand. Also in the Top Ten are Ireland, Australia, Finland, Norway, Denmark, Netherlands, and the United Kingdom. All ten of these countries appear in the Top Ten list of at least one other index.

Taking into account all six of our indices, the following countries appear in the Top Ten in three or more of the lists:

1. New Zealand (6)
2. Norway (5)
3. Finland (5)
4. Denmark (5)
5. Australia (5)
6. Switzerland (5)
7. Sweden (4)
8. Netherlands (3)
9. Canada (3)
10. Ireland (3)

Therefore we now have a consensus answer as to the ten freest countries in the world. In addition to appearing in several different Top Ten lists, all ten of these countries scored quite well in all six indices — almost always in the top twenty.

NOTE: Most of the six indices do not include extremely tiny nations such as San Marino and Andorra — and therefore such tiny countries are not eligible to appear in the consensus Top Ten list.

The Elephant in the Room

Some people might find it odd that the United States has not been mentioned in this discussion. In fact, all six indices included the U.S. in their rankings, but none of them ranked the U.S. in the Top Ten. The United States is often called “the land of the free” — so what happened? How well did the U.S. actually rank?

Not surprisingly, the ranking for the U.S. varied somewhat from one index to another. The highest ranking was 18 out of 180 countries, which is the 10th percentile. The lowest ranking was 45 out of 180 countries, which is the 25th percentile. Therefore, by any of these comparisons, the U.S. is among the freest 25% of all nations in the world. This is a respectable result, but certainly not outstanding. The general consensus among these indices is that the U.S. is not among the freest 10% of all nations in the world — which leaves the U.S. with some room for future improvement.

Conclusion

Now that we have a list of the 10 freest countries in the world — New Zealand, Norway, Finland, Denmark, Australia, Switzerland, Sweden, Netherlands, Canada, and Ireland — how can we make use of this list? First of all, it is worth paying close attention to these ten countries — with an emphasis on understanding why these countries are considered to be so free. All 10 of these countries are vibrant democracies, but they also differ from each other in certain key details. Therefore it is worth studying how these different flavors of democracy can all produce such outstanding results. If each of us looks carefully at these ten examples, then we are likely to find some valuable lessons that can be applied elsewhere.

The 10 Most Repressive Countries in the World — and the 10 Most Free was originally published in The Philipendium on Medium, where people are continuing the conversation by highlighting and responding to this story.

It seems a simple question: Are you and I related? When we phrase a question in this manner, we assume that there is a simple “yes” or “no” answer — either we are related, or we are not. This implies that there is a clear criterion for determining the answer. If the answer is yes, then we must have an ancestor in common. However, the hidden assumption is that any common ancestor must be within three generations (great-grandparents) or perhaps four generations (great-great-grandparents) — or else it doesn’t count. If we have to go back farther to find a common ancestor, then most people would say that we are not actually related.

A completely different mental model is to perceive all people in the world as being related. If you trace back two people’s ancestry for a sufficient number of generations, then you are guaranteed to find a common ancestor. Under this model, it is simply a matter of degree: I know that you and I are related — the question is how closely. This question can also be flipped on its head by asking how unrelated is it possible for two people to be. How many generations would you have to trace back to find a common ancestor to every person alive today?

There are also two different ways of thinking about how related two people are. In the first method, we consider only the family tree — in which case two people might be siblings, cousins, second cousins, and so on. In the second method, we consider how many genes two people have in common — which of course can be discovered through genetic testing. These two methods can provide somewhat different results. For example, you might have several first cousins — and under the family tree method, you are equally related to every one of them. But under the gene method, you might be slightly more related to one than to another. But for the moment, let’s simply consider some interesting mathematical issues related to tracing your family tree.

Consider, for example, when someone boasts “My ancestors came over on the Mayflower!” The Mayflower landed on the American coast in 1620, approximately 400 years ago. The average time between human generations is roughly 25 years — therefore the Mayflower landed approximately 16 generations ago. In other words, that would be the time of your great-great-great-great-great-great-great-great-great-great-great-great-great-great-grandparents. This is an awkward phrase at best, so let’s use the term “P-16” to represent all ancestors that are 16 generations back. (The P-1 generation represents your parents; P-2 is your grandparents, and so on.) How many P-16 ancestors does a person have?

We have two parents, four grandparents, eight great-grandparents, and so on — doubling the number with each generation. The result is that each of us should have 65,536 ancestors in the P-16 generation. So when I hear the phrase “My ancestors came over on the Mayflower”, my first thought is “That must have been a really big boat!” (The Mayflower actually had only 102 passengers, plus crew.)

Of course, the reality is that no one actually has 65,536 distinct ancestors in the P-16 generation, because there is always a certain degree of inbreeding after so many generations. There are indeed 65,536 available slots in the P-16 generation of your family tree, but some of your ancestors occupy more than one slot in that tree.

Now consider the P-30 generation, which would be approximately 750 years ago, around 1270 A.D. This would put us into mediaeval times in Europe. Each of us has more than a billion slots in our family tree for the P-30 generation — yet the entire population of the world at that time was less than a billion people. If you could actually construct a family tree going back to the P-30 generation, then you would not only have a billion slots in that generation, but you would also have a half billion slots in the P-29 generation, a quarter-billion slots in the P-28, and so on — a total of two billion names in the family tree. Of course, there would be a huge amount of repetition. Some of your ancestors will appear hundreds or even thousands of times in that tree.

Now let’s go back farther, to the time of the Roman Empire. Julius Caesar died more than 2000 years ago, in the year 44 BC. That’s approximately 84 generations back. Each of us has about 20,000,000,000,000,000,000,000,000 ancestors (20 septillion) in the P-84 generation. But back then, there were only about 200,000,000 people (200 million) in the entire world. This means that, on average, each person alive at that time must appear 10,000,000,000,000 times (10 trillion) in your family tree. Of course, some of the people alive at the time don’t appear in your family tree at all, because they had no children, or they produced lines that soon died out.

Such huge numbers may seem completely counter-intuitive, but it’s the simple result of doubling the number of ancestors in each generation that you trace back.

Suppose now that you were to go back 7000 years, to around 5000 B.C. By this time humans had migrated to every continent except for Antarctica. In several parts of the world people were already developing agricultural civilizations. The people alive at that time would be your P-280 generation of ancestors, more or less. However, the number of P-280 slots in your family tree is greater than the number of atoms in the entire universe. Numbers of this magnitude put a completely different spin on the concept of ancestry. Instead of thinking about individual ancestors and where they lived, it makes more sense to think about genes — and where the variants of different ancestral genes first arose in the world.

So how far back would we have to go to be absolutely sure that you and I have an ancestor in common? If you and I both have European ancestry, or both have Asian ancestry, or both have African ancestry, then 750 years (30 generations) is enough to be certain that we have many ancestors in common. After all, we each have a billion slots in the P-30 generation of our family tree. But suppose that you and I have roots in completely different parts of the world. Then how far back would be have to go to be sure of a common ancestor?

The key factor here is migration. Throughout human history, there have been people that have moved from one place to another — sometimes very long distances. Just as important, people have not consistently moved in one direction. Although there have often been waves of people moving in a particular direction, there have always been people moving in all other directions as well. People moved not only within continents, but also between continents. Even the Bering Strait, dividing the Old World from the New World, was a highly permeable barrier, with people often moving back and forth across it. The result is that over a period of a few thousand years, genes that arose anywhere in the world could easily have migrated to any other place in the world where people lived.

If you go back far enough, then you will come to a time when there was someone alive who is an ancestor to everyone in the world today. Based on careful mathematical modeling of historical patterns of human migration, it appears that the last common ancestor to all of today’s humanity lived between 2,000 and 5,000 years ago. Therefore, even if you and I appear to have roots in completely different parts of the world, we can be sure that we have common ancestors sometime in the past 5000 years.

If we go back a little farther, then the story gets even stranger. Consider the people who were alive 7,000 years ago, in 5000 B.C. Everyone who was alive at that time is either an ancestor to everyone alive today, or else is an ancestor to no one alive today. Therefore, the list of your living ancestors as of 7,000 years ago is identical to the list of my living ancestors as of 7,000 years ago — and the same goes for every other person in the world.

But if everyone alive today has exactly the same set of ancestors as of 7000 years ago, then why don’t we all look alike? How could there be any genetic differences between us? There are several reasons for this:

1) Remember that the P-280 generation in your family tree contains more slots than there are atoms in the entire universe. In other words, there are far more P-280 slots than you have genes in your DNA. Therefore, you did not actually inherit DNA from every single P-280 slot in your family tree.

2) The P-280 generation is so vast that each of your ancestors from 7000 years ago appears trillions of trillions of times in your family tree. But the frequency that one ancestor appears in your tree can be quite different from the frequency of another ancestor. If ancestor A appears millions of times more often than ancestor B, then this will have a major impact on your particular mix of genes.

3) New gene variants inevitably appear over time, and some of these variants have appeared within the past few thousand years. Such recent genes are more likely to be restricted to limited subpopulations of the world.

To look at it another way, there has always been a tradeoff between genes moving from one place to another due to migration, and genes staying local over a period of many generations. This tradeoff means that the mixing of genes around the world is always incomplete, allowing typical mixtures of genes to become associated with specific places in the world.

Still another way of looking at the issue is to trace the ancestry of a single gene. Every person has 23 pairs of chromosomes, and each chromosome has an average of approximately 900 protein-encoding genes. Therefore, a human has approximately 20,000 pairs of genes. The genes are in pairs because you have two copies of each gene (for most of your genes) — one copy inherited from your mother, and one copy inherited from your father.

In one sense, every one of us has exactly the same set of 20,000 genes. This is the human genome, which is what makes us human. However, many of these genes exist as two or more variants — and it is these variants (also called alleles) that result in the genetic differences between people. To put it in over-simplified terms, all of us have a human gene for eye color — but some of us have the brown-eyed variant of the gene, while some of us have the blue-eyed allele instead. To be more precise, each of us has two copies of this gene — and we might have two identical copies, or we might have two different alleles. (This example, while easy to understand, is actually erroneous, because eye color is controlled by the interaction of several different genes, not just one gene.)

A much more accurate example involves your ABO blood type. The human genome contains a gene for blood type, and there are three variants of this gene, called A, B, and O. You inherit one copy from your mother and one from your father — and these two copies may be identical or different. The six possible genetic combinations are AA, AB, AO, BB, BO, and OO — although there are only four blood types: A, B, AB, and O. (AO produces the same blood type as AA, and BO produces the same blood type as BB.)

Each of our genes consists of a string of base pairs, which are given the abbreviations A, C, G, and T. A typical gene contains around 25,000 base pairs, although this number can vary enormously from one gene to another. Each allele, or variant, of a single gene tends to be quite similar to all the other alleles, differing by a relatively small number of base pairs. These differences slowly accumulate over time, making it possible to reconstruct the history of a gene — that is, the sequence in which the changes appeared, producing the various alleles. By making some logical assumptions about the rate of mutation, it is possible to produce an estimated date as to when each allele first appeared.

As you trace the history of a particular gene in this manner, you will eventually come to a version of the gene that is ancestral to all the alleles currently in existence. The person in whom this gene resided is therefore an ancestor to every person in the world today. If this ancestor lived more than 7000 years ago, then this is no real surprise, because everyone in the world at that time was either an ancestor to everyone alive today, or to no one. The interesting aspect, therefore, is that we can attach a specific gene to this ancestor. By examining the distribution of alleles around the world, and considering when each allele first appeared, we can often identify the approximate location where this long-ago common ancestor lived.

Some of the easiest genes to trace in this manner are found in our mitochondrial DNA, rather than our nuclear DNA — and we inherit this DNA directly from our mothers. (There are only about 13 protein-encoding genes in mitochondrial DNA.) Researchers have concluded that the most recent common ancestor (MRCA) for our mitochondrial DNA lived in Africa about 100,000 to 150,000 years ago. This person has been nicknamed “Mitochondrial Eve”. It is important to note that other men and women alive at the same time also contributed to our modern human DNA — but no other mitochondrial DNA has survived into the present. However, genetic variants have appeared since the time of Mitochondrial Eve, and therefore we have at least eight major lines of human mitochondrial DNA in the world today.

In theory, we can go through a similar process for any of the genes in our nuclear DNA as well. First we choose a gene for which there are several different alleles. We study the sequence of base pairs in each allele, and use the results to reconstruct the history of that gene, tracing it back to a single variant in one of humanity’s common ancestors.

The upshot is that we have multiple methods to determine how related you and I are. One method is to compare your genes to my genes for each of the 20,000 pairs of genes — counting up how many times you and I have different alleles for the genes. The fewer differences there are, the more related we are. We could go even farther and take into consideration how related our different alleles are. Did most of our differing alleles diverge fairly recently, or a very long time ago?

From a practical standpoint, the inexpensive commercial DNA tests now available do not attempt to sequence all the DNA in your genome. Instead, each of these services chooses a limited set of genetic “markers” and sequences the DNA in these short segments. The theory is that these samples are adequate for the purpose — in the same way that opinion polls are based on the responses of a few thousand people, and yet are assumed to roughly represent the opinions of the entire nation.

Are You And I Related? was originally published in The Philipendium on Medium, where people are continuing the conversation by highlighting and responding to this story.

What do tomatoes, potatoes, and peppers have in common? One obvious answer is that these are all food plants. We can grow these plants in farm fields or home gardens and then collect the edible results. Or we can visit a grocery to purchase fresh produce from these plants. But what else do these three plants have in common? In fact, there are multiple answers:

• All three plants are members of the nightshade family (Solanaceae), which means that they are all related.
• All three crops were first domesticated in the Americas by Native Americans, but spread around the world after Europeans began to explore (and conquer) the New World.
• All three foods have become closely associated with various cuisines of Eurasia — although these plants were completely unknown in Europe and Asia just 500 years ago.

Can you imagine Italian cuisine without tomatoes? Yet Italians were unaware of tomatoes 500 years ago. Can you imagine German or Irish cuisine without potatoes? Yet there were no potatoes in Europe 500 years ago. And can you imagine Thai cuisine without spicy peppers? Yet such peppers were unknown in Asia 500 years ago.

Tomatoes

Domesticated tomatoes (Solanum lycopersicum) are the cultivated descendants of a wild plant that grows in the Andes region of southern Ecuador and northern Peru. This wild ancestor (Solanum pimpinellifolium) is often called a “currant tomato” or a “pimp”. This species, which is becoming increasingly scarce in the wild, should not be confused with escaped garden tomatoes. Wild currant tomatoes are tiny, typically less than a centimeter across — similar in size to a shelled green pea. This is notably smaller than our familiar cherry tomatoes. Yet currant tomatoes are quite flavorful, with a delightfully bright orange-red color.

The mystery behind tomatoes is that much of the early domestication of this crop occurred in Mesoamerica (Mexico and Central America), more than 1000 miles from its ancestral home. Apparently the species was first domesticated by Native American residents of the Andes, who collected the wild seeds and planted them in their gardens. Because of trade between people of the Andes and civilizations farther north, these same fruits were soon cultivated in Mesoamerica. It is not known when this cultivation began, but quite likely it was more than a thousand years before Columbus. When the Spanish arrived in Mexico on a mission of conquest, they soon became aware of domesticated tomatoes, which by this time were quite a bit larger than the wild ancestors. The Spanish took samples back to Europe, and eventually tomatoes were cultivated all over the continent.

In the following centuries, gardeners in Europe developed many new varieties — in various sizes, shapes, and colors — producing most of our “heirloom” tomatoes. These gardeners did not necessarily set out to develop new varieties. Instead, they simply saved seed each year for planting the following year. But of course they saved the seeds of the plants that performed the best or had the most pleasing fruits. This meant that gardeners in places with different climates — such as cool summers versus hot summers — were selecting for varieties that could successfully produce fruit in those specific climates. Furthermore, people in different locations tended to focus on certain sizes, shapes, and colors of fruit. Therefore the different heirloom varieties became distinct from one another.

You may think of heirloom tomatoes as representing a great deal of genetic diversity — and they are certainly more diverse than our commercial varieties. But in fact all of these heirloom varieties are genetically quite similar. Differences in just a tiny handful of genes produce the variations we see in these tomatoes. One reason for this lack of genetic diversity is that our domesticated tomatoes are twice removed from their original gene pool. First, a small sample of seeds was brought from South America to Mesoamerica — which means that all the varieties developed in Mesoamerica were based on a small gene pool. And then a small sample of seeds was brought from Mesoamerica to Europe, resulting in an even tinier gene pool.

In contrast, the gene pool in the wild currant tomatoes is vast. Furthermore, this wild species readily crosses with its domesticated brethren. This provides great opportunities for creating new varieties of tomatoes, with improved disease resistance and other desirable characteristics. But to take advantage of this genetic diversity, we must preserve many different examples of wild “pimps” before human activity pushes them to the brink of extinction. Furthermore, several closely related species of Solanum still grow wild in Latin America, providing additional sources of genes. Finally, old domesticated varieties of tomatoes in Latin America — including some that have escaped into the wild or hybridized with wild species — provide a third source of genes for plant breeding.

The issue of limited genetic diversity is not unique to tomatoes — although tomatoes provide an extreme example. Most of our popular crop plants, when compared to their wild ancestors, have very little genetic diversity. Therefore, for all major crop plants, agronomists are eager to identify and preserve populations of wild ancestors and other close relatives.

Today, the leading tomato-producing countries of the world are China, United States, India, and Turkey. China is by far the world leader, producing more tomatoes than the next three countries combined. Many of these tomatoes are eaten fresh, but a far larger quantity goes into making tomato sauce and canned tomatoes. It has been quite a leap from Native Americans harvesting the original wild currant tomatoes to our world of modern agriculture, with mechanized harvesting of vast acreages of large, domesticated tomatoes. This leap did not occur in a single bound, but in a long series of baby steps over many, many centuries, first in the Americas, and then in the world at large.

Potatoes

We think of potatoes and tomatoes as being quite different, and yet the two plants are closely related. Not only are both in the nightshade family (Solanaceae), but both are in the same genus (Solanum). There are approximately 2,700 species of plants in the family Solanaceae, about half of which are in the genus Solanum. The reason that these two names are so similar is that every botanical family is named after one genus in the family — and therefore Solanaceae got its name from Solanum.

The main reason that potatoes and tomatoes seem so different is that we eat a different part of the plant. In the case of tomatoes, we eat the fruit of the plant — in other words, the part that develops from a pollinated flower and that contains the seeds. In the case of potatoes, we eat a swollen, starchy part of the plant that is used for storing food and water underground. These reserves can help the potato plant survive periods of difficult conditions. Potatoes, because they are covered with tiny buds (the “eyes” of the potato), are capable of sprouting independently of the parent plant, even though they are genetically identical to the parent.

Domesticated potatoes (Solanum tuberosum) originated high in the Andes Mountains in southern Peru and northern Bolivia. The wild ancestor is Solanum brevicaule, along with several other closely related species. All of these related species are jointly called “the Solanum brevicaule complex”. Because the species in this complex are so closely related, they often interbreed, resulting in numerous hybrids. This makes it difficult to distinguish between the various species. A second difficulty is that some of the wild varieties have hybridized with ancient domesticated potatoes, making it difficult to determine which varieties are the pure wild ancestral potatoes and which are not.

Potatoes have been domesticated for at least 7,000 years, and perhaps even 10,000 years. This means that Native Americans were growing potatoes for many thousands of years before Europeans “discovered” the New World and brought samples of potatoes back to Europe. This long period of domestication, plus the continued gene exchange between wild and domesticated potatoes, resulted in several thousand varieties of domesticated potatoes prior to the European conquest. After the Conquest, potatoes spread to Europe and beyond. As a result, approximately 2000 varieties have been developed outside of South America. However, most of these later varieties — which dominate modern potato production — are descended from potatoes cultivated by Native Americans in the lowlands of south-central Chile, rather than the highlands of Peru and Bolivia. That’s nearly 2000 miles from where potatoes were first domesticated. So why is this?

It turns out that wild potato plants are quite sensitive to daylength. The plant can sense the length of a day (also called a photoperiod), and uses this information to determine when to bloom and when to grow potato tubers. This ability is not unique to potatoes. For example, most chrysanthemums are sensitive to daylength, and will start blooming in the autumn when the days become shorter than a particular threshold. This sounds almost magical, but the explanation is simple. During daylight hours a specific hormone builds up, while at night the hormone is gradually destroyed. The level of this hormone is therefore sensitive to daylength, and it is the level of the hormone that triggers key events in the lifecycle of the plant.

The original domesticated potatoes of the Andes, just like their wild ancestors, begin to produce tubers when the days grow short and the nights grow long. But this trigger is not appropriate for locations that are much farther from the equator, such as south-central Chile. Such a trigger would prevent the tubers from getting started until far too late in the autumn. The upshot is that Chilean potatoes do not have this trigger, and will begin to produce potatoes even when the days are still long. This characteristic is well-matched to the conditions in northern Europe and the northern U.S. The upshot is that most potatoes grown in cold-temperate zones are descended from samples collected in Chile.

Today the top five potato-producing countries of the world — in order — are China, India, Russia, Ukraine, and the U.S. China produces more potatoes than the next two countries combined.

Peppers

Peppers — including both hot peppers and sweet peppers — are yet another food crop in the family Solanaceae that originated in the Americas. However, unlike tomatoes and potatoes, peppers do not belong to the genus Solanum. Instead, they are members of the genus Capsicum.

Our word “pepper”, as applied to the genus Capsicum, is a confusing use of the term. The word itself has a very long history, and for many centuries referred only to pepper made from peppercorns — especially the black pepper that we put into pepper shakers. This kind of pepper is originally from Asia, and was known in Europe long before the discovery of the New World. Peppers from the New World are completely unrelated to pepper from the Old World, but because both are spicy, the same word was applied to both.

There are approximately two dozen wild species of Capsicum, all native to various parts of the Americas. Five of these species were domesticated by Native Americans. Of these five, the most commercially important species is Capsicum annuum — sometimes called “chili pepper” — which was originally domesticated in Mexico at least 9,000 years ago. This species has been bred to produce a wide range of varieties, both mild and spicy — including bell pepper, jalapeño, cayenne, paprika, serrano, sweet Italian, poblano, Hungarian wax, pimiento, Thai bird, and Santa Fe Grande. In other words, most of the peppers that you find in a North American grocery store are Capsicum annuum, descended from Mexican ancestors. Likewise, most of the spicy peppers that are routinely used in various Asian cuisines are also descended from Mexican ancestors, and were completely unknown in Asia 500 years ago.

However, there are four other species of Capsicum that were also domesticated by Native Americas, mostly in Peru and Bolivia where many different species grow wild. In particular, the species Capsicum chinense is the source of habaneros and scotch bonnets. The hottest peppers in the world, those with highest Scoville Heat Units, are all varieties of this species. Tabasco sauce is made from yet a third species, Capsicum frutescens. Other varieties of this species have become popular in Africa and in Asia.

Today, China is by far the world’s largest producer of Capsicum peppers, completely dwarfing the production of all other countries. The other four top producers of Capsicum peppers are Mexico, Indonesia, Turkey, and Spain. China produces almost ten times as much as Mexico, but China exports very little of its production. In contrast, Mexico is the #1 exporter of Capsicum peppers.

Other Foods from the Americas

A great number of other important foods crops are native to the Americas, and in most cases were domesticated by Native Americans long before the arrival of Europeans. These crops include corn (maize), common beans, pumpkins and squash, avocado, peanuts, sweet potato, quinoa, chocolate, pineapple, sunflower seeds, chia, amaranth, and cassava (also called tapioca, manioc, or yuca). Still other food crops native to the Americas include blueberries, strawberries, cranberries, wild rice, pecans, cashew nuts, and Brazil nuts. These later foods were well known to Native Americans, but were often collected in the wild rather than grown as domesticated crops. Furthermore, there are many other native fruits and vegetables that are popular in various parts of Latin America, but little-known in the United States.

Corn (Zea mays), also called “maize”, was first domesticated by Native Americans in Mexico more than 10,000 years ago. This crop gradually spread throughout the Americas, and was grown by Native Americans from Canada to Chile before the arrival of Europeans. With such a wide geographical range, and such a long period of domestication, Native Americans had already developed hundreds of varieties of corn prior to the Conquest. The wild ancestor of corn is teosinte (Balsas teosinte). But because an ear of corn looks so much different than an ear of teosinte, it took many years for scientists to piece together the history of domesticated corn. Today the top producers of corn are the United States, China, and Brazil.

Native Americans have grown domesticated beans for the past 8,000 years. The wild ancestors are all members of the genus Phaseolus, including at least four different species. The most commercially important species, by far, is the common bean (Phaseolus vulgaris), which was independently domesticated by Native Americans in two different places — in Mesoamerica, and also in South America. In a modern grocery store, you can typically find a wide variety of fresh, dried, and canned beans that have all been developed from this one species — including string beans (snap beans), kidney beans, white navy beans, black turtle beans, cannellini beans, and cranberry beans. A second native species of bean, Phaseolus lunatus, gave rise to lima beans and butter beans. Like corn, domesticated beans became a staple food among agricultural societies throughout the pre-Columbian Americas.

Squashes and pumpkins are the descendants of several different species of the genus Cucurbita, all domesticated by Native Americans long ago. The most commercially important species is Curcurbita pepo, first domesticated about 10,000 years ago in Mesoamerica. Varieties bred from this species include zucchini, yellow summer squash (straight-neck and crookneck), patty-pan squash, acorn squash, spaghetti squash, jack-o-lantern pumpkin, small sugar pumpkin, and certain ornamental gourds. The second most commercially important species is Curcurbita moschata, also domesticated about 10,000 years ago. Butternut squash is the best-known example of this species. All canned pumpkin in the United States is made from this species, using such varieties as Dickenson Field Pumpkin and Kentucky Field Pumpkin.

For more information on pumpkins and squashes, see Is That Really Pumpkin in my Pumpkin Pie?

Summary

Tomatoes, potatoes, and peppers have several attributes in common: All are members of the nightshade family (Solanaceae). All are native to the Americas and were first domesticated by Native Americans. After the European conquest of the Americas, all three crops gradually spread around the world. Now China is the world’s top producer for all three.

Given the American origin of these important crops, it is not surprising that all three are frequent ingredients in several Latin American cuisines. However, we often associate these foods with the cultures and cuisines of Europe or Asia — where these ingredients were completely unknown 500 years ago. Although surprising, in a way it makes sense — because these crops have indeed become essential to many cultures around the world. By the same token, certain Old World crops have become completely integrated into Latin American culture — including wheat, rice, bananas, oranges, and coffee.

Of the top 10 food crops of the world (ranked by tonnage), four were first domesticated by Native Americans — corn (maize), potatoes, cassava, and sweet potatoes. If you rank the food crops of the world by dollar value instead of weight, then tomatoes are also in the top ten. There are more than 20 distinct and well-known food crops — all familiar to the average North American — that originated in the Americas. All of these crops still have close relatives growing in the wild. If we find and preserve diverse wild populations for each of these crops, then we have made a huge investment in the future. These wild gene pools provide us with powerful tools to fight diseases and pests that attack our crops, to expand each crop’s ability to survive varied climatic conditions, and to produce new varieties with a wide range of helpful characteristics. Even without all of these economic benefits, it is nice simply to preserve aspects of the natural world that would otherwise be lost forever.

Tomatoes, Potatoes, and Peppers was originally published in The Philipendium on Medium, where people are continuing the conversation by highlighting and responding to this story.

The Oregon Trail is, by most measures, the most successful educational computer game of all time. Many versions of the game were created over the years, dating all the way back to 1971. However, the version that many people assume is the “original” — the version that constantly appears in memes and magazine articles — is the 1985 Apple II version. (The 1985 design was also implemented on the IBM PC, but it first appeared on the Apple II.) I was the lead designer and team leader of this most famous version of this most famous educational game, and therefore I am an excellent source for revealing exactly how this design came to be.

Because this is a success story — and “success has many fathers” — the names of several other people will appear in this account (not just my own name, despite the braggadocio in the title of this piece). All of these people played key roles in the success of The Oregon Trail.

There are several reasons behind the popular impression that the 1985 version was the “original”. One obvious reason is that the 1971 version was text-only, and relatively few people have seen or played the text-only version. A second big reason is that most of the features that people fondly associate with the game were first introduced in 1985. The 1985 product was far more than a new version — it was a complete reimagining of how to create a game about the Oregon Trail. I am quite certain that the 1985 design was the most original and creative version in the entire history of the product, excluding (of course) the completely original 1971 version. In other words, the game that ultimately became so successful was invented in two equally important stages — one in 1971, and the second in 1984–85.

The True Original Version

The very first version of The Oregon Trail began as a concept in the head of Don Rawitsch, who in 1971 was a student teacher in Minnesota. He imagined a board game that could be played by students in the class that he was scheduled to teach. Soon after Don began work on this concept, he shared his idea with his roommates — Bill Heinemann and Paul Dillenberger — who were also student teachers. They were enthusiastic about Don’s idea, but they suggested that it should be a computer game instead of a board game. The Minneapolis school district, where all three taught, had recently purchased a mainframe computer, and each school in the district had a teletype terminal for remote access to the computer. Furthermore, both Bill and Paul had learned BASIC, a simple programming language that was ideal for creating a small computer program such as the one they envisioned.

Don quickly agreed to the change in plans, and the three began to design and program the concept. Two weeks later, just in time for the class that Don was scheduled to teach, a first rough version of the game was ready to show the kids. Despite the awkwardness of the teletype machine, the kids clearly enjoyed playing the game. Bill and Paul observed similar results at their school. The game remained available, accessible to students in their spare time, for the rest of the semester. But at the end of the term, when his period as a student teacher came to a close, Don deleted the program from the computer system.

A few years later Don began work at MECC, a new Minnesota state agency tasked with providing computing access to all of the schools in the state. MECC’s computing model was similar to that of the Minneapolis school district — a centrally located mainframe computer that could be accessed via a teletype terminal in each school. But now, instead of serving a single school district, this new organization served the entire state. Don had saved a printout of the BASIC program from the 1971 game, so in 1974 he typed the same program into MECC’s computer system. Then he did some fine-tuning by tweaking the frequency of the various random events in the game. Finally, in 1975, he made the game available to all of MECC’s users. OREGON (as it was called) soon became the most popular educational activity on the system — and it remained so until MECC shut down its mainframe operations in 1983.

This early text-only version of The Oregon Trail was exceedingly simple — as were nearly all of the educational activities on the MECC mainframe. And yet seven of the core concepts that appeared in the 1971 game have re-appeared in every version since then. These core concepts are:

• The player buys supplies before starting the journey to Oregon.
• There are opportunities to hunt for food along the way.
• There are opportunities to make purchases at forts along the route.
• The player must manage the level of supplies to avoid running out.
• The rate of travel depends upon the current conditions.
• Misfortunes frequently occur.
• The game ends when the player reaches Oregon, or when the player dies along the way.

The 1971 game was structured as a repeating cycle. Each cycle represents two weeks of travel on the trail. Below is an example of a typical cycle, from an actual session of the game. Most of the text in this example was typed by the computer, but at times the computer stops to wait for a response from the player. The player’s responses are highlighted in yellow:

As you can see from this example, each cycle begins with a status update. Then the player decides whether to hunt. (Sometimes the option to visit a fort is also available, as shown here.) If the player chooses to hunt, then he shoots the rifle by typing “BANG” when told to do so. If the typing was fast and accurate, then the hunting is successful. The player now decides how much to eat. Finally, one or more “misfortunes” occur, which can reduce the supplies. Then the cycle repeats two weeks later, taking into account supplies used in the interim. The cycling continues until the player either reaches Oregon or dies trying. A successful journey requires approximately 12 cycles — depending upon various circumstances that affect the rate of travel.

For many players, the principal appeal of this game was the challenge of hunting — typing the word “BANG” fast enough to be successful, without making a typing mistake. Therefore the typical way to play was to give an identical response in every cycle — first type “2” to hunt, then type “BANG”, then type “2” to eat moderately. The only necessary deviation is when one of the supply items gets dangerously low, forcing the player to go shopping at a fort instead of hunting. The second appealing aspect of this game was the challenge of managing the resources — keeping an eye on the supplies to ensure that none of them run out, which would be a fatal mistake. The third appealing aspect is that there are so many different misfortunes that might occur. So you never know what will happen next — unless you run out of any supplies, in which case you will probably die quite soon.

The original design also featured random attacks from wild animals, bandits, and “hostile riders”, each of which provided additional opportunities to type “BANG” — and dire consequences for failing to type rapidly and accurately. In the late 1970s a small change was made to the game, so that the player would not know in advance what word he would be expected to type when it came time to shoot. Sometimes it would be “BANG”, but it could just as likely be “POW”, “BLAM”, or “WHAM”.

The World Changes

In 1978 Don Rawitsch published the BASIC code for OREGON in Creative Computing magazine. After that many people tweaked the code to run on various brands of microcomputers. Someone — I don’t know who — put a version of this program code on an Apple II. This version of the game was not a new design — in fact it mostly used the same BASIC code that appeared in the magazine. However, the author did make three important changes to the game, one of which was a reimagining of the hunting activity. Instead of being text-based — like all other parts of the game — this new activity presented a sketch of a deer, which the player could shoot at by pressing any key on the keyboard.

By 1979, MECC was buying Apple II computers in large quantities (at a discounted price), and reselling them at cost to schools all over Minnesota. In 1980 MECC also began to distribute disks containing Apple II software to these same schools. (These disks were provided for free to Minnesota schools, but sold at a significant profit to schools outside Minnesota.) Each disk contained a collection of programs ported over from MECC’s mainframe computer. The OREGON game appeared in two of these collections, one of which was named “Elementary Volume 6”:

MECC continued to distribute “Elementary Volume 6” for many years, and the game OREGON remained MECC’s most famous product, even after MECC shut down its mainframe operations to focus entirely on microcomputers (Apple II, Atari, IBM, etc.). However, the competitive landscape completely changed between 1980 and 1984. In 1980, all educational computer software was still being designed and programmed by amateurs — hobbyists and teachers who created these small, simple programs in their spare time. Furthermore, no large private companies had yet entered the educational software market — neither the school market nor the home market.

By 1984 the world of educational software was completely different. A great number of companies — both large and small — had begun to sell educational computer software. Some of these companies specialized in the school market, while others specialized in the home market. By 1984, all of the best educational software was designed and programmed by professionals — people whose full-time job was to design or program such software.

In order to remain competitive, the same transition occurred within MECC. By 1983 MECC had acquired a staff of professional designers and programmers tasked with creating top-quality original software for the Apple II. By 1984 the improvement in MECC’s products was quite obvious to everyone. This was the beginning of MECC’s “golden age” — a decade-long period in which MECC turned out exceptional new software year after year — mostly targeted to the school market, but also including a few titles for the home market.

Although MECC was now focused almost entirely on the creation of new, original titles, it also had three famous simulations from the 1970s — Oregon Trail, Lemonade, and Odell Lake — that it did not want to abandon. All three activities had been ported to the Apple II by 1980, but by 1984 they were embarrassingly outdated — especially in terms of their appearance. MECC decided that it would create new versions of all three activities.

The Decision to Break with the Past

It was in this context that I was chosen by MECC in October 1984 to lead the creation of a new version of The Oregon Trail. I had joined MECC’s staff in 1981 with a substantial background in the design and programming of computer-based simulations (although prior to 1981 all of my simulations had been targeted to college-level students). I was the only person on staff with this kind of experience, and therefore I was the logical choice for the role of lead designer and team leader. Still, I was surprised by the details of the mandate that I was given.

First of all, I was told to design a product for the home market, not the school market. Previously, MECC had always specialized in the school market. This would be MECC’s very first attempt to create a product specifically designed for the home market.

Second, my instructions were to do far more than just create a better-looking version of the original game. I was told to take the original concept and run with it. I was to expand upon the original concept, building a much more elaborate and robust game. I could do so in any way that I saw fit, provided that I preserved whatever magic had made the original so popular. However, I had to design the product specifically for the Apple II — which resulted in some serious restrictions, such as having to work with a palette of just six colors. (I would have preferred to use a more recent machine, such as the Commodore 64.)

My mandate to re-envision and redesign The Oregon Trail was almost overwhelming at first — the possibilities were endless, yet I had to get it absolutely right on the first release. For 13 years, from 1971 to 1984, the OREGON game had remained essentially unchanged. A few small details had been tweaked along the way, but never had the product been completely re-imagined and redesigned. Never had the underlying models been changed — the structures, algorithms, and assumptions upon which the game is based. For the very first time, we were going to throw out everything — including all of the existing software programming, which dated back to 1971 — and start completely from scratch. Every detail was up for reconsideration. Furthermore, I needed to create a much richer and more elaborate experience than the original OREGON — and this would require a great deal of new, original thinking.

To kick off the project, I defined a key metric to serve as the cornerstone of our approach: “Do the kids that love the old version like the new version even more?” The idea was to perform this test periodically throughout the project, to verify that we were still on track. Whenever we found ourselves deep in the weeds, this yardstick would bring us back to reality, allowing us to see the big picture again.

Because I was designing a home market product, I knew that I had to create a game that was highly entertaining, in addition to being clearly educational. I felt that it was possible to do both, without seriously compromising either — but I had to strike a careful balance in the details. In particular, I felt that both the educational value and the entertainment value should arise from immersing the player in a historically accurate experience.

From the very beginning, I saw that this project presented many challenges, but the issue of space constraints seemed especially challenging. The new game needed to have a rich set of color graphics, but the graphics would require a lot of space — both on the floppy disk and in the Apple II RAM (memory). I also hoped to add many new details and gameplay aspects, all of which would require space. Unfortunately, I was designing for a computer that had only 64K of RAM. Furthermore, the product would be distributed and played on a double-sided 5.25” floppy disk — providing a total of just 280K of storage space. (Note that the Apple II did not have a hard drive, only a floppy disk drive.)

As the project began, our core team consisted of five people. In addition to me (Philip Bouchard), there was also John Krenz (lead programmer), Charolyn Kapplinger (lead artist), Shirley Keran (research), and Bob Granvin (additional programming). All five of us played active roles in the early brainstorming and planning for the product, although MECC eventually moved Bob to a different project that needed his attention. And so the five of us began a project that became MECC’s biggest effort to date, lasting a total of 10 months, from October 1984 to the end of July 1985.

Developing the Key Concepts

In MECC’s traditional design approach (as of 1984), the instructional designer first imagines a computer-based student activity, and then creates a series of sketches that illustrate what should appear on the screen in each of the key steps of the activity. This collection of sketches is then packaged with written notes that provide additional details. The sketches and notes together constitute a “design document”. The designer then reviews the document with a small team of up to three other people — the lead programmer, the lead graphic designer, and an experienced programmer/analyst. Together they identify missing details in the design, and they help to resolve any other issues that the design presents. The designer then makes any additions or changes that are needed to complete the design, and the team gets to work creating the graphics and generating the program code.

This model worked well enough for the relatively simple products that MECC had been creating up to that time, but I could see that it would not work for the product that I intended to design. First of all, I envisioned the game as being based on a complex interlocking set of mathematical simulation models — a weather model, a health model, a travel model, and so on. Furthermore, each of these models would be based on a complex interlocking set of mathematical formulas. These models would require incremental design and tuning — which meant not only that I needed to crank out a great number of formulas, it also meant that the design needed to be specified and implemented in iterative stages, instead of all at once.

Second, I felt that the best way to build an innovative but successful design was to start with a an ambitious set of new ideas, and then to gradually build and test these ideas through successive refinement — first by creating a simple prototype, and then by building up the real product one aspect at a time. At each step of the process we should evaluate where we are, compared to where we want to be, and then prioritize what to do next. Some of our cherished ideas would have to be shed along the way, but by constantly prioritizing and improving, we would achieve a successful result in the end. (I first learned and applied the concepts of successive refinement when I studied computer science in graduate school in the 1970s. However, decades later, I saw a lot of overlap between these ideas and the concept of “agile programming”.)

But first, I needed to design the underlying framework for the game, and for a couple of weeks this had me stymied. The original OREGON was based on the concept of “turns”, each representing exactly two weeks of real time. This simple framework was well-adapted to its original purpose — a text-based game played on a teletype — but it was poorly adapted to the needs of the new game. I wanted to create a framework that was intimately tied to the actual geography of the Oregon Trail, and I wanted to give the player a much greater set of opportunities to make decisions — not just what to buy and how much to eat. It soon became clear that I would have to invent a completely different framework for the new game. But as I considered one idea after another, none of these new ideas seemed to work.

So I broke the problem into pieces, and I began to tackle each piece individually. Eventually, over a period of several weeks, I was finally able to generate a solution to every problem. The resulting new framework for The Oregon Trail was based on seven key concepts, none of which were present in the 1971 design:

1. To tie the new design to the actual geography of the Oregon Trail, the journey will consist of distinct segments of varying length, each ending at a unique major landmark along the trail. (Of all the many differences between the 1971 product and the 1985 design, this might be the most important — in part because it enabled so many other crucial changes.)
2. A set of activity modules is available at each landmark. In many cases there is a key activity tied to the landmark, such as crossing a river or purchasing goods at a fort.
3. The landmarks are grouped into distinct geographic zones, each with its own data about climate, terrain, and wild animals.
4. Most of the activity modules are data-driven and contain randomization, providing a different experience each time that the module is used. For example, each time that the hunting activity is launched, it uses the zone data in order to present animals and terrain that are appropriate to the current location on the trail. But even if the player hunts in the same zone over and over, the arrangement of the terrain will always be unique, and the animals will appear at unpredictable times and locations.
5. In addition to the landmark-to-landmark cycle, there is an independent daily cycle, which includes all of the resource management computations (supplies, health, etc.) along with the random events. Therefore the core computational cycle in the 1985 design is one day, instead of the two-week cycle used in the original design. This has huge ramifications for all the computations and all the interactions.
6. Between landmarks, the journey proceeds automatically, but the user can pause at any time. (This was an especially crucial concept, because it provides the user with access to many activities and events, without constantly interrupting the game.) However, the journey pauses automatically if a major event occurs.
7. Pausing between landmarks provides access to another full set of activity modules, some of which are distinct from the activities available at landmarks. (For example, you can talk to people at landmarks, and you can go hunting between landmarks.)

This new framework was dramatically different from the 1971 design, allowing me to create a much richer experience for the player. In the new design, the relationship between the activity modules looked like this:

With this highly flexible structure serving as backbone of the new product, we could now plan what activities would be accessible while traveling between landmarks (A-F in the diagram) and what activities would be accessible at each of the landmarks (G-L in the diagram). Each of these distinct modules (represented by the four rectangles and the 12 circles) could be designed and programmed separately. Furthermore, each of the half dozen mathematical simulation models (not shown in this diagram, but operating underneath it all) could also be designed and programmed separately.

My teammates were enthusiastic about this new framework, so I began the long process of fleshing out the details. First of all, based on my research and that of my colleague Shirley, I prepared a list of candidate landmarks. Then, by applying several selection criteria, I trimmed the set down so that the journey would consist of approximately 16 legs. I also introduced the concept of “cutoffs”, meaning that in addition to the principal route, the player would sometimes have the option to take an alternate route. Then I began work on creating a list of possible activity modules, systematically turning out “concept documents” for all of the most promising ideas.

Throughout this concept development stage, I conferred heavily with my teammates. Before writing up each concept document, I would usually discuss the ideas I had with my teammates. And then after I wrote each concept document, I would review the document with my teammates to get their additional feedback. The upshot is that even though I was the author of all but one of the concept documents, the ideas contributed by my teammates (Cheryl, John, Shirley, and Bob) were often reflected in my documents. This was especially true of the initial concepts for the hunting activity, which Bob, John, and I hashed out together.

Incorporating Humans into the Design

Early in the project, my second highest priority — second only to incorporating real geography in the product — was to include human characters in the design. The 1971 design omitted the concept of people, and therefore you travel the entire 2000-mile route without ever meeting or interacting with another person. I wanted to change that. I brainstormed many distinct concepts for how the player might interact with human characters along the way — or at least be aware of other humans— and I sincerely hoped to include nearly all of these ideas in the finished product. But as the project progressed, I had to cut some of my cherished concepts, because of limited space on the floppy disk and limited budget to design and build the product. However, I was able to incorporate four of the concepts into the design, each of which made a huge impact on the “human feel” of 1985 product:

• Before beginning the journey, you name four other people who will travel with you — typically family members or friends. Each member of your 5-person party is subject to illness and accidents. As you travel the trail, a principal goal is to keep all of these people alive and healthy. If any of them gets sick or dies, then they are mentioned by name. This collection of concepts was a crucial addition to the new design.

• As you purchase your goods before starting the journey, you enter a store (“Matt’s General Store”) and interact with the proprietor of the store. Matt provides helpful advice as you make your purchases.

• On the real Oregon Trail, people tended to congregate at each of the landmarks. These people included not only other travelers, but also Native Americans, local traders, and soldiers. Therefore, at each landmark in the game, the player can meet and talk to three different people — each of whom offers an interesting perspective on his or her experiences. Furthermore, many of these conversations provide helpful hints about how to survive the journey. (Unfortunately, due to limited disk space, we could not include pictures of these characters.)

• If you make it all the way to Oregon, then the high-score list is pre-populated with the names of actual people who made the journey to Oregon or who served as early explorers in the region.

Thanks to the lead graphic designer, Charolyn Kapplinger, people also appear in many of the large landmark graphics. This also adds significantly to the human side of the design.

My single biggest regret regarding The Oregon Trail is that I had to abandon most of my ideas for complex, nuanced interactions with Native Americans. Several such modules were included in the design concepts that I wrote up. However, a few of the simpler ideas did make it into the finished product. For example, you are much more likely to have a successful crossing of the Snake River if you hire a local Indian to guide you — which was also the case on the real Oregon Trail.

Other Top Priorities

Another top priority for me was to include river crossings — a key aspect of the Oregon Trail experience that was missing from the original design. I chose a set of representative crossings — a small subset of the actual number of crossings made by the travelers on the trail. At each river the player must choose between several methods of crossing, taking into account the current conditions at the crossing. A highly detailed simulation model underlies this module, controlling the current conditions of the river (based on the recent weather, among other factors) and also controlling the probabilities of various outcomes, based on conditions and the player’s choice of how to cross. The results are communicated in a nail-biting animation that puts many players on the edge of their seat.

Still another of my priorities was to include two more “action” activities, in addition to hunting. I soon modified this plan so that one of these activities would operate as an extended logic puzzle, rather than an action game. These two activities would be associated with the final leg of the journey to Oregon, the last 100 miles. The player could choose to raft down the Columbia River (the action activity), or to take the Barlow Toll Road, in which case the activity is the challenge of getting the wagon up and down through the difficult mountainous terrain along the route. Again, disk space and budget forced us to make cuts — but a stripped-down version of the rafting game did eventually make it into the product.

And finally, one other priority was to find as many ways as possible to build “replayability” into the product. This had seldom been a concern for MECC on any previous product, because all of these products had been targeted to the school market. For schools, where children far outnumbered computers and access time was highly limited, most of MECC’s products were designed so that the full value of the product could be experienced in a brief amount of time. But for the home market, I needed to design a product that a child could visit over and over — and it would still be interesting after 20 or 30 or 40 hours of play. To encourage replay, I incorporated a long list of features and details into the new design:

• The initial motivating factor — identical to the motivator in the original product — is to survive the entire journey to Oregon. As in the original, most players fail on the first attempt — but they perceive that it is possible to do better. And so they keep trying, doing better and better on most tries, until they finally succeed in reaching Oregon.
• Just as the player reaches Oregon for the first time, a surprise is unveiled — by surviving all the way to Oregon, the player has been awarded a score. This score is based on several distinct factors, which are clearly itemized. So at exactly the moment when the player achieves the initial goal — to reach Oregon — a new, highly motivating goal is revealed — to get a much better score.
• The player also sees that the high-score list is pre-populated with names — and most of these people have higher scores than what the player has achieved. This initial list creates a clear benchmark to strive for — to climb up and up the list until finally exceeding the top score on the list (that of the explorer Stephen Meek).
• As the player seeks to get higher scores, it becomes apparent that it is necessary to attempt the more difficult starting conditions — traveling as a carpenter (with less money) or a farmer (with even less money), rather than traveling as a banker. Although it is more difficult to reach Oregon when you have less money, you get more points for your efforts.
• The new game has far more options and alternative approaches to explore than the original game. Over time, many players become interested in exploring these alternatives: What if I start in a different month? What if I change the pace or the rations? What if I stop for rests every now and then? What if I talk to a lot more people to get advice? What if I float across each river instead of trying to ford any of them? What if I hunt less (which uses up a day of time in the new design) and shop more — or vice versa? What if I carry a greater number of spare wagon parts? What if I take the Barlow Toll Road instead of rafting the Columbia River — or vice versa?
• As in the original game, some players will want to play over and over just to go hunting. And to that end, I wanted the new hunting game to be both challenging and compelling.

All of these factors — and several others that I included in the design — resulted in a game with a very high degree of replayability. Without this characteristic, the game would not have been very successful in the home market.

The Mathematical Models

In both the original 1971 game and the new 1985 design, the simulation is driven by a set of mathematical formulas tied to state variables. Each state variable tracks the current value of some important quantity. In the 1971 design there were exactly eight state variables:

• Cash (in dollars)
• Food (as a dollar value)
• Ammunition (the number of bullets)
• Clothing (as a dollar value)
• Miscellaneous supplies (as a dollar value)
• Oxen (as a dollar value)
• Distance traveled so far (in miles)
• Current date (incremented by 2-week jumps)

Note that there is no state variable for health in the 1971 product. Therefore your health in the current turn does not depend in any way upon your health in the previous turn. The game ends if you die, and you can die from any of the following states:

• You run out of food and starve.
• You run out of “miscellaneous supplies” and you happen to get sick. (Cold weather and insufficient clothing increase the probability of becoming sick.) The rationale was that every time you get seriously sick, you must have medicine or else you will die.
• You run out of money and you get sick. The rationale was that every time you get seriously sick, you must pay a doctor or else you will die.
• You run out of bullets and are attacked by “hostile riders” who massacre you.

I strongly disagreed with the idea behind three of these four end-game conditions — only starving made sense to me. The year in which I set the game (1848) was long before the invention of modern antibiotics. The medicines available at that time had little effect on the diseases commonly contracted along the Oregon Trail. Therefore it made no sense that all diseases should be 100% curable with medicine, and 100% fatal without medicine. Likewise, it made no sense that you could always find an available doctor anywhere along the trail, and that you would always die without a doctor, but always live if a doctor was present — and that a doctor would never, ever treat you for free. I eventually eliminated medicine and doctors from the design entirely.

I also disagreed with the frequent attacks by bandits, wild animals, and “hostile riders” in the original game — and the need to drive all of them off by shooting at them. None of this was consistent with my historical research regarding the Oregon Trail. So I removed all of these interactions from the design.

For the new design, I completely reimagined how the underlying simulation model should work. Part of this change was necessary because of my switch from 2-week time increments to 1-day time increments. But most of the change was driven by a desire to provide a much richer model, and also by a completely new vision of what should trigger the deaths of the five members of the party. The new game would include a complex health model that employed several different state variables, allowing the game to track the general health of the party on a daily basis, as well as the state of recovery of each party member from any accidents or diseases.

Another important change was the inclusion of a complex climate and weather model, based on the actual month-by-month values for average temperatures and rainfall at various points along the trail. (In the original game, the month of the year has no effect upon the weather.) As the player travels along the trail, each day’s weather is based on the current month and the player’s current location along the trail. The simulation retrieves the corresponding average temperature and adds or subtracts a random deviation. The simulation also retrieves the odds of rainfall and then generates conditions that may be dry, rainy, or very rainy. (If the weather is very cold, then snow replaces rain.)

Instead of a single simulation model, I designed the following interlocking models, each consisting of several to many interlocking mathematical formulas:

• Climate and weather
• Health
• Progress on the trail
• Supplies (cash, food, oxen, bullets, clothing, and spare parts)
• River crossings
• Scoring system

I ended up with a huge set of state variables, and a huge set of mathematical formulas. With so many details affecting one another, I knew that I would have to do a lot of fine tuning of the gameplay as we tested the game. Especially in the final three months of the product development, I tweaked all of the formulas and starting values in order to produce a better and better balance in the gameplay.

The New Set of Core Concepts

By the end of July 1985, The Oregon Trail was complete and ready for release. To our delight, the product was an instant hit. In addition to becoming quite popular in the home market, it soon became the most ubiquitous educational software product in North American schools. Our new game, along with its successor versions, made millions of dollars for MECC’s owners over the following years. (MECC had begun as a state agency, but it was later sold to private investors.) Although these riches did not trickle down to the people who had designed and built the game, we were quite gratified to see what we had accomplished — the creation of a highly successful product that ultimately became a classic game and a cultural icon.

Designing and building this completely new version of The Oregon Trail had not been a straightforward, linear process. In my early design documents, I had proposed and considered all sorts of ideas, only some of which made it into the finished product. By the time we released the game, the following innovative new features had been incorporated:

1. Real geographic details. The original OREGON game included almost no geographic details at all. No rivers, mountains, forts, or other landmarks were ever mentioned by name. A key goal of my new design was to incorporate lots of real geography, woven throughout the game, illustrated with attractive color graphics.
2. Landmark-based travel cycle. The original OREGON employed a very simple 2-week game cycle, without any landmarks. I divided the primary route into 16 segments, each beginning and ending with a famous landmark. At each landmark, and also between landmarks, the player makes many decisions that were not available in the original game.
3. Continuous daily cycles. The journey to Oregon typically takes about 150 days. I needed to incorporate a daily cycle into the design, so that crucial events could occur on any day of the journey, and to allow the player to track the supplies on a daily basis. To avoid halting the game over and over, the game continues on “auto pilot” between landmarks, until the player interrupts or a major event occurs.
4. Choices as to the route. The original OREGON presented the Oregon Trail as a single continuous path, without any branches or choices of route. I introduced a more realistic situation where the player must occasionally make a decision as to which way to go — and this decision can have very important consequences for the player.
5. River crossings. In the original OREGON, there were no rivers and no crossings. I made river crossings a major feature of the new game. At each crossing, the player must make crucial decisions, based on the current conditions, which are strongly affected by the recent weather, which differs with each play of the game.
6. A re-imagined hunting activity. In the original OREGON, you hunt by typing the word “BANG”. Our goal was to turn the hunting activity into a bona fide skills-based arcade-style game, incorporating the actual terrain in which you are hunting, such as prairie, mountains, or desert. Likewise, the species of animals you find are based upon where on the trail you are currently located.
7. A point system. In the original OREGON there is no point system. In order to build a high level of replayability into the product, I designed a point system that is closely tied to the difficulty level you choose. Furthermore, as another motivator, the high-score list is pre-populated with a set of historical names covering a wide range of scores.
8. Family members. In the original OREGON, there is little mention that other people are traveling with you. To make the experience more personal, my design requires each player to provide four additional names as traveling companions. If you make bad decisions or encounter a streak of bad luck, then members of your family or group will die.
9. Talking to people. There is no conversation system in the original OREGON. In my new design I allow the player to talk to people along the way. This is an important method for obtaining helpful hints and discovering historical and geographic details of interest. Furthermore, it makes the game seem much more human.
10. River rafting game. The original OREGON does not mention rafting down the Columbia River, which I felt was a fascinating bit of history, as well as a great opportunity to include another arcade game in the product. Therefore we built the river rafting game, albeit a simpler version than what we had originally intended.
11. Tombstones. Another famous feature we added is the ability to create a tombstone for yourself after you die. This humorous interlude compensates for the player’s failure to complete the journey. But the real hook is that we store the tombstone data on the game disk, so that the next player who uses the same disk can see the tombstone.
12. A detailed health model. The original OREGON does not track the player’s health from turn to turn. My new design included an ongoing health model for the entire party, along with tracking individual illnesses and injuries. The weather, food rations, and pace all have cumulative effects on the party’s health, which can improve or decline over time.
13. A detailed weather model. The original OREGON did not include a true weather model, with seasons and data tied to several distinct climate zones. (The original had only two climate zones and no seasons.) In the new game, the program computes the weather every day, based on the current month and location. This in turn affects many other factors — such as health, river crossings, availability of water, and so on.
14. Robust resource management. My new design provided many new ways for a player to manage his resources, including: 1) adjust the pace of travel, 2) purchase specific types of spare parts, 3) rest at any time to improve the party’s health, 4) barter with other travelers to acquire essential resources, and 5) buy additional oxen at forts.
15. A captivating travel screen. The travel screen in the 1985 product — featuring a tiny animated ox and wagon — has become the iconic representation of the entire history of The Oregon Trail game. The combination of elements on this screen is both highly memorable and highly effective.
16. Matt’s General Store. In the original game, you never interacted with people. In the 1985 design, we introduced Matt’s General Store. Matt is the friendly store proprietor who assists you with your purchases, providing helpful advice as to what you should buy and how much you should buy.
17. Specific diseases. In the original game, if you get sick, then the disease is never named. (After you die, then pneumonia might be mentioned.) For the new design, I incorporated five diseases or symptoms often named by travelers on the Oregon Trail: typhoid, cholera, measles, dysentery, exhaustion, and fever. Oddly, one of these five has captured the popular imagination and become a huge meme. (“You have died of dysentery.”)
18. Difficulty levels. To encourage replay, I built three difficulty levels into the game. I chose three distinct professions to represent the three difficulty levels: banker, carpenter, and farmer. These choices have also become a meme.
19. Trading with people. At any time between landmarks, the player can attempt to swap items with passers-by. This can be quite helpful under certain circumstances, especially if a crucial wagon part has broken. However, this module is a pale shadow of the trading system I had intended to include. I later included the full, robust trading system in the game Lewis & Clark Stayed Home.
20. Options to rest or change the pace. The player can choose to speed up or slow down the pace, or to stop altogether for a few days of rest. These decisions represent a major tradeoff between the health of the party and progress on the trail.
21. Period music. At each landmark along the way, you can hear a different melody. Furthermore, every melody is an actual tune that was popular at the time of the Oregon Trail. Unfortunately, this innovation can be more annoying than helpful, especially on the Apple II, which has no volume control.

Taken together, these 21 innovations resulted in a very different experience compared to the earlier versions of the game. The new game was longer, with a much richer set of opportunities for decision making. It also allowed players to succeed in more than one way — by using various possible strategies and gameplay techniques. This was essential for my goal of making the game just as appealing to girls as it was to boys, as well as appealing to a broader range of tastes. In the end, the new game proved to be immensely popular with an exceedingly wide audience.

Of course, another big difference is that the original OREGON was a text-based game, while the 1985 design was full of color graphics. The decision to include graphics was an obvious necessity, not an innovation. On the other hand, some of our decisions about how to include the graphics were indeed innovative. For the 1985 game, we incorporated as many graphical details as we could fit on the Apple II disk, including the animated travel screen, animated river crossings, the hunting game, the rafting game, and a full-screen graphic for each of the landmarks.

Every now and then, I run across an online article that suggests that the 1985 Apple II version of The Oregon Trail was essentially identical to the original 1971 game, except for the addition of color graphics. This assertion is so ridiculously inaccurate that it completely baffles me. In fact, most of the features and details that people associate with the game were first invented for the 1985 product. On the other hand, my new design would never have been created if not for the great success of the original. Furthermore, the original version introduced seven key concepts upon which all later versions of the product were based. But after my 1985 design came out, every subsequent version treated the 21 major new concepts from that design as part of the essential core original design. Likewise, many of the lesser details from the 1985 design also become incorporated into later versions.

Why the Process Succeeded

In the end, we met and exceeded all the goals for this project — except that the timeline and budget ran somewhat longer than we had anticipated. However, the product was completed in time for the autumn 1985 release to the home market, as intended. True to the goals, the product became a massive hit — not only in the home market, but also in the school market.

We certainly ran into some bumps and detours as we designed, built, and tested The Oregon Trail. And yet, overall, the process was quite successful. Looking back at the details of the project, I see several lessons regarding how to plan and start a new project:

1. Seek lots of input, but don’t succumb to design-by-committee.

I wanted all members of the product team to express their thoughts and contribute ideas. However, a committee will seldom produce a good design, nor can a committee make decisions quickly. Therefore, for the initial stages of design, I started with a divergent phase, when ideas are generated, followed by a convergent phase, when the ideas are narrowed down. Lots of people can and should participate in the divergent phase, but no more than three people should participate in the convergent phase for any particular module. After the winning concepts are selected, most of the design work still lies ahead, and most of that work will be done by individuals. But throughout the project, team members provide regular feedback to one another. And at key points in the project, feedback is solicited from people outside of the team, and user testing is conducted. In this way we continue the balance between getting ideas from many people, yet making decisions with very few people.

2. Create the design in stages of successive refinement.

Before attempting to design The Oregon Trail, I felt it important to define the high-level ideas, starting with the goals: Why are we creating this product? Who is the target audience? What will be our criteria for success? Then I proposed a general framework for the product, along with the key modules and features. After that, we defined each of the modules and features to a medium level of detail — enough to communicate how each would work. Finally, I fleshed out the full details of every module and feature. Meanwhile, as the product took form — first as prototypes and then as actual code modules — I continued to make design improvements. Each screen went through successive waves of scrutiny to improve the layout, text, and usability, and the underlying mathematical equations went through rounds of tuning to improve the gameplay. I doubt that it would have been possible to design everything first, in a single huge document, before starting work on building the product. Attempting to create one huge document at the outset would have been wasteful at best — and catastrophic at worst.

3. Create prototypes.

While it is helpful to draw sketches of the proposed screens, a prototype provides additional information. The first objective of prototyping is “visualization” — allowing people to see a vision of the finished product, even if only in rough form. As a second stage, the prototype can be made partially interactive, allowing people to get a taste of playing the finished game. Both of these stages provide useful information for adjusting the design relatively early in the project — certainly much earlier than if we had waited for a beta version before showing it to anyone.

4. Test the prototypes and early draft software with users.

It is certainly helpful when teammates and other colleagues evaluate the prototypes — but this alone is not adequate. It is also crucial to investigate how real users react to the product — as soon in the development cycle as possible. No matter how experienced and insightful the designers are, there will always be surprises when real users try out the product. These surprises range from small details to much larger issues. For The Oregon Trail, one of the small surprises was that the kids did not understand the word “outfits” — which I then changed to “sets of clothing”. A medium-level surprise was that many players were confused by the screens where you name the members of the party, so I revised those screens. And the most significant issue we found was that the travel screen in the alpha testing did not hold the interest of the players. And so we redesigned this screen, which then became the most famous screen in the product.

5. Constantly prioritize what needs to be done next.

On nearly every project I have worked on, we had to scale back the plans at some point, either subtly or dramatically. This is a key reason to prioritize the various components. Always put an early emphasis on the modules and features that are most likely to be in the finished product.

Risks are another factor to consider. Any project has risks, which are tied to unknowns. To reduce the risks, it is crucial to explore the unknowns early in the project. Some risks are technology based — Will this module be fast enough? Can we get everything to fit on the disk? Some risks are user based — Will users be able to complete this complicated task successfully? Will users understand the meaning and details of this crucial screen? The priorities early in the project are to design and build prototypes or proof-of-concept modules that address the principal unknowns, thereby increasing the odds of success.

This is exactly what we did with The Oregon Trail. In the earliest stages we decided what was most important to illustrate with the internal prototype. Then we decided what features were most important to test with kids, and we included those in the Alpha version. Immediately after that, we prioritized all the modules and features that were planned for the product, on the assumption that not everything would make it into the finished product. From a fairly early stage, we also drew up technology plans and conducted technology tests to address the technical risks.

Summary and Thanks

In the course of my career, I have worked on countless software projects. For the vast majority of these projects, I have every reason to be proud of the results — while also being aware of certain details that could have been further improved. However, except for The Oregon Trail — and to a lesser degree Number Munchers — none of these projects resulted in a product that became truly famous — even though some of them (especially the web-based applications that I designed for corporations) did indeed get a lot of use.

The upshot is that I feel thankful for whatever fame my products have been able to achieve. I am particularly thankful for having had the opportunity to work on The Oregon Trail — and for the combination of circumstances that allowed the product to become such an outstanding success. I am thankful for having been able to work with highly talented and dedicated colleagues — whose collaboration played a huge role in the successful outcome. (My principal collaborators were Charolyn Kapplinger, John Krenz, Shirley Keran, Bob Granvin, and Roger Shimada.) I am thankful to MECC for assigning me to this project as the lead designer and project leader — and for giving me the latitude to steer the project as I saw fit (most of the time). I am thankful that Don Rawitsch, Bill Heinemann, and Paul Dillenberger invented the first brilliant version of the game, which spawned all subsequent versions. And I am thankful that millions of people have been able to play and enjoy this game — and that so many of them fondly remember the game after all of these years.

• For the complete story of how the hunting activity was designed, see Designing the Hunting Game for The Oregon Trail.
• For the complete story of how the travel screen was designed, see Designing the Travel Screen for The Oregon Trail.
• To see some of the funniest memes about The Oregon Trail, visit The Oregon Trail Memes.

How I Managed to Design the Most Successful Educational Computer Game of All Time was originally published in The Philipendium on Medium, where people are continuing the conversation by highlighting and responding to this story.

I’ve recently been approached by a publicist representing the Plant Kingdom, and on behalf of all the plants in the world, I have been requested to assist in setting the record straight. The plants of the world feel strongly that they have not been getting enough credit. It seems that very few people have a full appreciation of the roles that plants have played — and continue to play — in making the world what it is today. The issue is not just that plants are the ultimate source of everything we eat — which means that without plants we would not be here. It’s also that plants have played an absolutely essential role in so many other aspects of our development, and we seldom talk about most of these other roles.

Honey

But first, before we get into the big stuff, I’ve been asked by the publicist to air a pet peeve of the angiosperms (also known as flowering plants). These guys are sick and tired of hearing that “bees make honey”. According to the official representative of the Flowering Plants Union, it is actually flowering plants that make honey, although they prefer to call it “nectar”. The bees simply collect the nectar, and then concentrate it (slightly) by evaporating some of the remaining water. The bees do this by fanning the collected nectar with their wings. The plants go to considerable effort to provide nectar that is already quite concentrated — otherwise it would not be energy-efficient for the bees to collect: The bees would spend more energy buzzing their wings than they would get from the honey. In contrast, butterflies and hummingbirds are perfectly happy to accept watery nectar that contains a lower concentration of sugars. This demonstrates that flowering plants go out of their way to meet the finicky demands of bees.

Imagine if a plant were to ask you what maple syrup is. I don’t think you’d say “Maple syrup is a concentrated sugar-water solution made by humans.” No, you would say that maple syrup is the concentrated sap of maple trees. During the process of converting that sap into maple syrup, humans have to evaporate a lot more water than bees do when “making honey”. (Maple sap is typically around 5% sugar and 95% water, while the nectar that flowers produce for bees is typically more than 50% sugar.) And yet we have the good sense to give credit to the effort of the maple trees. The flowers involved in the honey trade — such as clover, orange blossoms, blueberries, buckwheat, sourwood, alfalfa, tupelo, and many, many others — simply request the same due respect and credit that maple trees already receive.

(For more details on bees, see What Does “Save the Bees” Really Mean?)

Oxygen

You are undoubtedly aware that people — and virtually all other animals — require oxygen in the air in order to survive. If our atmosphere did not contain oxygen, then we humans would not exist. But how did the oxygen get into the air in the first place? Plants put it there! Before plants began their tremendous task of transforming the earth’s atmosphere, there was no oxygen in the air. But thanks to plants, our atmosphere is now 21% oxygen — a very comfortable level for humans and other animals.

I should mention that the Greater Brotherhood of Green Plants allows any living thing that contains chlorophyll to become a member — and therefore to be counted as a plant. This includes not only the things that we normally think of as being plants, but also microscopic creatures, even one-celled creatures such as blue-green algae — which are actually related to bacteria. What all of these organisms have in common is that they contain chlorophyll, which allows them to capture the energy of sunlight. The captured energy is stored in the form of sugar molecules — as we saw in the case of flower nectar and maple sap. What may not be obvious is that all green plants produce sugars. They do this by using the energy of sunlight to convert molecules of water and carbon dioxide into molecules of sugar — a process that requires a lot of energy. Later on, whenever a plant needs any energy, it simply converts some of that sugar back into water and carbon dioxide, releasing the energy for whatever purpose it is currently needed.

Every time a plant creates a molecule of sugar, six molecules of carbon dioxide (CO2) are removed from the air. Simultaneously, six molecules of oxygen (O2) are released into the air as a byproduct. Early in the history of life on earth — when every living creature in the world was still microscopic — there were so many busy little plants on earth that they gradually changed the atmosphere from completely anoxic (without oxygen) into the atmosphere that we have today. The process took a very long time — more than a billion years — but the little plants kept at it until the job was done. And now the Greater Brotherhood of Green Plants simply wants a little credit for what they have done — an acknowledgement that their efforts produced a world in which animals can successfully live and breathe.

(For more details on why blue-green algae — the little guys that produced so much of that oxygen — might or might not be considered a plant, see How Many Kinds of Living Things Are There?)

Fossil Fuels

Imagine how many molecules of sugar were produced in order to fill the earth’s atmosphere with 21% oxygen. Keep in mind that each time a sugar molecule is turned back into carbon dioxide and water — thereby releasing the stored energy — six oxygen molecules from the atmosphere get used up. Even if the plant itself does not use the energy, because an animal ate the plant, oxygen is used up when the animal uses the energy. Even if no animal eats the plant, but the plant simply dies and decomposes, then oxygen is used up (and energy released) when the plant decays. Therefore all of those oxygen molecules in the atmosphere correspond to sugar energy that was never used. Where did all of that sugar energy go?

Sugar is an example of what is called an organic compound. A “compound” is any substance in which all of the molecules are identical — in other words, the substance consists of only one kind of molecule, instead of a mix of molecules. The word “organic” can be a bit confusing, because it can have very different meanings in different contexts. An “organic compound” is any compound in which the molecules contain atoms of carbon. In most organic compounds, the carbon atoms are linked together into a chain — which can be a straight chain, a branched chain, or even a ring. The simple sugars produced by photosynthesis are chains of six carbons each, attached to some additional atoms of oxygen and hydrogen. (Therefore sugars are carbohydrates.) Plants and animals can convert these sugars into other organic molecules without releasing the energy that is stored in the bonds. As a result, organic molecules of all kinds contain stored energy.

The bodies of most plants and animals consist primarily of organic compounds and water. All of this mass is sometimes called biomass. The term “biomass” refers not only to the mass of all the living creatures in the world, but also to the mass of anything that has died but not yet decomposed, such as the leaf litter on a forest floor, or dead tree trunks. The biomass on land surfaces is more than 99% plant material, and less than 1% animal material. (There is also biomass in the oceans.) If you consider all of the biomass currently on the surface of the earth (including the oceans), then you can account for part of the sugar energy that has been stored away by plants over the eons. All of this biomass corresponds to carbon dioxide that has been removed from the atmosphere, and to oxygen that has been released into the atmosphere. If all of this biomass were to burn up or decompose, then part of the oxygen in the atmosphere would be replaced with carbon dioxide.

And yet, even considering all of the biomass on the surface of the earth, we still have not accounted for all of the organic compounds stored away by billions of generations of plants. To account for the rest, we must consider fossil fuels — coal, petroleum, and natural gas. Fossil fuels are the remains of biomass that got buried in the earth without decomposing. Therefore these fuels consist of organic molecules that never turned back into carbon dioxide and water. These molecules never released their stored energy, and they never used up the oxygen that was released when the energy was first captured.

And now we come to another pet peeve of the Plant Kingdom. Nothing drives those guys up the wall more than hearing the often-repeated notion that petroleum comes from the remains of dinosaurs. This really infuriates many of the green plants, especially the lycopsids — club mosses and their relatives. The ancient biomass deposits accumulated in soggy areas where a lack of oxygen prevented decomposition. Huge, thick layers of biomass accumulated over time. But back then, just like today, more than 99% of the biomass was of plant origin, and less than 1% was of animal origin. As this plant biomass became buried and compressed, most of the organic molecules never decomposed, although the carbohydrates gradually changed into hydrocarbons. Thus we owe these energy-rich buried treasures to plants, not to animals.

Our modern civilization is quite dependent upon fossil fuels. Even though we are trying to reduce this dependence — primarily because of the consequences of increased carbon dioxide in the atmosphere — we still generate a majority of our electric power using fossil fuels. We still rely on fossil fuels for the vast majority of the energy used in transportation — to drive our cars, trucks, trains, ships, and airplanes. Imagine what life would be like today if plants had never created this vast store of chemical energy. Our modern civilization simply would not exist.

Fire

How did civilization first begin? What was the first step in the long transformation of humankind from just another smart animal into a species that can shape and control the entire earth? Most of us would consider that first step to be when humans learned to control fire. By mastering the use of fire, we were able to warm ourselves and to cook our food. Eventually we learned to use fire to produce metals and durable ceramics — and these skills allowed us to create the tools to advance ourselves even more. Fire has been an essential partner in every stage of the advancement of civilization.

And yet without plants, we would have nothing to burn. At first we depended entirely upon wood and other plant materials as our fuels. Eventually we came to rely more and more on fossil fuels — but these also come from plants. Without plants, humans would never have had fire, and therefore we would never have had civilization.

Of course, fire sometimes causes problems too. Wildfires are a natural and even essential part of most terrestrial ecosystems, and yet we justifiably fear such fires, because they can kill us or destroy our property. But consider this: In a wildfire, it is plant material that burns — primarily trees, shrubs, and grasses. If there were no plants, then there would be no wildfires.

If a house should catch fire, then what parts of the house are actually flammable? It is not the bricks or stone, nor the metals or concrete or mortar. In many cases, a large percentage of the flammable material is the wooden framing. Inside the house we have materials made from wood, paper, textiles, and plastics — all of which quickly burn. The wood and paper are obviously made from plants. The textiles and the flooring are most likely made from either plant materials or plastics. But the plastics are made from petroleum products, which come from fossil fuels. In short, all of the flammable materials in a house consist of organic compounds that can be traced back to plants.

In essence, all fires are the indirect result of plants having captured the energy of sunlight in the form of sugars. These sugars ultimately get converted into a wide range of organic compounds — some natural and some man-made — without losing their stored energy. But when these compounds catch fire, they are converted back into carbon dioxide and water (assuming complete combustion), releasing all the stored energy and consuming oxygen. Therefore nearly all fires — whether controlled or wild — are ultimately due to plants. If plants had never existed, then there would be no fire in this world, because there would be no fuel to burn.

Of course, if plants had never existed, then there would be no oxygen in the atmosphere, which also means that there would be no fire. In capturing the energy of sunlight, plants flooded the world with organic compounds and filled the atmosphere with free oxygen. Fire is simply a rapid reversal of the process.

(For more details on fire, see Fire Is Not What You Think It Is.)

Steel and Chemicals

Two of the hallmarks of recent civilization are the production of steel and chemicals. Of course we use a lot of energy to create steel and chemicals — and that energy usually comes from fossil fuels. But there is another dimension to how plants are involved in the process.

The production of steel not only requires a lot of heat (usually produced by coal), but it also requires carbon — because steel is an iron alloy that contains around 1% to 2% carbon. That carbon comes from metallurgical grade coal — which in turn comes from ancient plants. So without plants, we would not have steel.

Human industry produces a bewildering variety of chemicals. Some of the chemicals with the longest history — such as turpentine — are extracted directly from plants. We have also learned to synthesize chemicals — converting one set of chemicals (serving as the raw ingredients) into a different set of chemicals. Certain ingredients come from minerals, such as salt. But much of our chemical industry is built around the production of organic compounds — including all of our plastics. Most of the raw ingredients are themselves organic compounds, which are sometimes extracted from plants, but are often extracted from petroleum — which ultimately comes from plants. The upshot is that most of our chemical industry relies on raw materials that can be traced back to plants. Without plants, we would not have plastics, nor would we have most of the other man-made organic compounds that we churn out in great quantities.

Food

And now we come to the elephant in the room — although I have been asked to refer to it as the elephant grass in the room. Many of us know that we ultimately rely on plants for all of our food — but a surprising number of people have not fully picked up on this message. Even if you eat a lot of meat, you probably realize that the animals you eat are primarily fed with plants. And even if you love to eat fish that primarily eat other fish, you can always trace the food chain back to plants.

The irony is that carnivores are even more dependent upon plants than herbivores, although indirectly. All of the food energy in an ecosystem depends upon the capturing of sunlight by plants (with a few very rare exceptions). But as you go up the food chain, from herbivores to carnivores to top carnivores, you lose at least 90% of the available energy at each level. Therefore it requires at least ten times as much plant material to sustain an herbivore-eating carnivore as it does to sustain an herbivore of the same size. And it requires at least one hundred times as much plant material to sustain a top carnivore as it does to sustain a similar sized herbivore.

All animals need food for two different reasons — for the energy stored in the food, and for an assortment of organic compounds that are needed to build bodies and control the functions of life. In particular, we need sugars from carbohydrates, fatty acids from fats, and amino acids from proteins. Starting with these basic building blocks, our bodies (and the bodies of other animals) can synthesize most of the thousands of organic compounds that our bodies need to survive. But we are unable to build any of these compounds from scratch — as plants do — starting with carbon dioxide and water as the two principal raw ingredients. We require plants to assemble the carbon into chains, producing the sugars, fatty acids, and amino acids that we can then repurpose and convert into other organic molecules. Without plants, we would have no source for the energy we need to survive, and we would have no source for the organic molecules we need to survive.

(For more details on food molecules, see Digesting Food Is Like Playing with Lego Blocks.)

Evolution of Animals

It is not just human civilization that required the presence of plants in order to evolve. The entire evolutionary history of the animal kingdom depended upon the continued presence of plants. Moreover, each new species of animal evolved in a context in which certain kinds of plants were available, presenting opportunities for food, nesting, and other uses — but also presenting challenges to overcome, such as thorns or toxic compounds. Furthermore, throughout evolutionary history, the mix of plants in any given spot kept changing — evolving to be better adapted to current local conditions.

This led to a constant evolutionary spiral, in which evolutionary changes in the plants led to evolutionary changes in the animals, and vice versa. For example, the rapid emergence and spread of angiosperms (flowering plants) in the mid-Cretaceous period provided lots of new food sources for many species of dinosaurs, helping to fuel a boom in dinosaur evolution. However, the heavy predation on the plants led many of them on an evolutionary path to improve their defenses, resulting in new species that were unpalatable or difficult to consume. As a result, new animals evolved that could overcome these defenses.

(For more details on plant and animal evolution, see Snapshots of Prehistory: A Vacation in the Geologic Past.)

Interactions between plants and animals are not limited to predator-prey relationships. Mutually beneficial relationships exist as well. The best-known examples involve flowering plants and their pollinators, especially bees. There are countless examples of co-evolution among flowers and pollinators, where the species evolve together to become ever more specialized and dependent upon one another. For example, if you should find a trumpet-shaped wildflower with a narrow 3-inch corolla tube, then you can be sure that some sort of pollinator with a 3-inch tongue has evolved to make use of that flower. Furthermore, the wildflower is almost certainly dependent upon that pollinator in order to get pollinated.

(For more details on pollination, see How Plants Solve Their Transportation Needs.)

The upshot is that the entire history of animal evolution has been heavily shaped by the details of the plants with which each species of animal interacts. Of course, not all animals eat plants, but nearly all of them must interact with plants — hiding in tall grass, climbing into trees, building nests from plant material, and so on. Therefore the animals evolve to become better at all of these tasks, based on the plants that are locally available.

Summary

I must emphasize that I am not a paid representative of the Plant Kingdom. Not a single species of plant has paid me a single cent as an incentive to write this essay. After their publicist (who admittedly receives a stipend) brought this issue to my attention — that plants fail to receive adequate credit for what they have done and continue to do — I felt that it was my duty to speak out to the world, calling attention to the injustice of this situation.

I hope that you now realize that plants have played a huge role in making the world what it is today. There are so many ways in which plants have made Earth habitable for animals — and especially for humans. We can thank plants for the food we eat, the oxygen we breathe, and the fuels that we burn. We can thank plants for the ingredients that made civilization possible — such as fire, agriculture, and steel. We can thank plants for the environments that led to the evolution of all of our favorite species of animals, including ourselves. And I haven’t even gotten to some of the topics that I could have covered. For example, the rich topsoil in which we grow our crops is due to the incorporation of organic matter from plants. Our clothes are all made from materials that can be traced back to plants. The list goes on and on. It is high time that we finally give plants the credit that they deserve.

And don’t forget that plants are the ones that actually make honey. Bees just collect it.

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Plants Don’t Get Enough Credit was originally published in The Philipendium on Medium, where people are continuing the conversation by highlighting and responding to this story.