Chapter 1. Working With Data 26.19
Working with Data: HOW DO WE KNOW? Fig. 26.19
Fig. 26.19 describes studies showing the abundance of Archaea in the oceans and their metabolisms. Answer the questions after the figure to practice interpreting data and understanding experimental design. Some of these questions refer to concepts that are explained in the following two brief data analysis primers from a set of four available on LaunchPad:
- Experimental Design
- Scale and Approximation
You can find these primers by clicking on the button labeled “Resources” in the menu at the upper right on your main LaunchPad page. Within the following questions, click on “Primer Section” to read the relevant section from these primers. Click on “Key Terms” to see pop-up definitions.
HOW DO WE KNOW?
FIG. 26.19: How abundant are archaeons in the oceans?
BACKGROUND Early exploration of microbial diversity in the oceans revealed that archaeons are tremendously abundant. Just how abundant are they, and what metabolisms do they employ to obtain energy and carbon?
METHOD Scientists sampled seawater throughout the depth of the ocean at a test site in the northern Pacific Ocean. To the samples, they added molecular tags bound to fluorescent molecules that are visible under the microscope. The tags were RNA sequences that bind to small-subunit ribosomal RNA genes known to be useful in identifying different types of bacteria and archaeons. In this way, separate fluorescent markers were attached to thaumarchaeote archaeons, euryarchaeote archaeons, and bacteria.
RESULTS By counting the cells marked by different fluorescent tags in seawater samples, the biologists showed that bacteria dominate microbial communities in near-surface seawater, but that thaumarchaeotes are as numerous as bacteria in deeper waters (Fig. 26.19a).
Surveys of cell abundance through a depth profile of the Pacific Ocean show that bacteria dominate cell numbers near the surface, but thaumarchaeotes make up about 40% of all cells in deeper waters.
CONCLUSION Marine thaumarchaeota, unknown in 1990, are now known to be among the most abundant organisms in the oceans.
FOLLOW-UP WORK Archaea are among the most abundant of all cells in the world’s oceans. How do these cells live? Microbial samples from water known to be sites of nitrification (that is, the conversion of ammonia to nitrite or nitrate) contained an abundance of thaumarchaeote cells. The high numbers of thaumarchaeotes from these communities supported the hypothesis that they are nitrifiers—the higher the abundance of thaumarchaeote cells (Fig. 26.19b, dark blue line), the higher the amount of nitrite (Fig. 26.19b, light blue line). Thaumarchaeotes were grown in pure culture and shown to grow by consuming ammonia (Fig. 26.19b, red line). In other words, they oxidize ammonia to provide the ATP and reducing power needed to incorporate CO2 into organic molecules. Therefore, they play a major role in the marine nitrogen cycle, thriving where sources of carbon and energy for other types of metabolism are scarce.
Growth of a marine thaumarchaeote in a medium containing ammonium chloride and bicarbonate as the only sources of energy and carbon, respectively. As cell number increased, ammonia (the ammonium ion NH4+) was increasingly converted to nitrite (NO2−), supporting the hypothesis that these cells are ammonia-oxidizing chemoautotrophs.
SOURCE Karner, M. B., E. F. DeLong, and D. M. Karl. 2001. “Archaeal Dominance in the Mesopelagic Zone of the Pacific Ocean.”Nature 409:507–510; Könneke, K., A. E. Bernhard, J. R. de la Torres, C. B. Walter, J. B. Waterbury, and D. A. Stahl. 2005. “Isolation of an Autotrophic Ammonia-Oxidizing Marine Archaeon.” Nature 437:543–546.
Question
f60pU/DH2Z9UwuwI8dQYHt/IfvfOrGORnGeF7FV/R7tP8HQ6IrUX3iA/lxkj4NpiMIZwB8rV3kBQabFNt2gHeq3X0X75FsihzQH8fcvSiiPmYE1g7SQZ+cMbnOydAnmdBLi7ZaCVi5h55T68n154ztmi54FEzkxdvPMM9MgeocslQg52bzb8eQKMbt3TNqRCV9KwJPzzq5HbqljP8UG7h5DG/75EgkBRqKiDfcanljkyveCw06f89UDVsiHDrxjsXiC1DBC3HoV8F+Cysh88TezuLfWW6YG4Kak46iPgF5u13ywstZvHOwTBS0JumhSNp6OxH6OVHHJI+2aOGSFGufrBROcK4YnA0ai+BAeuR1zmephm8jerSz1R6zFOvkAnai+5mif+4P7SYzQpHYYiAA5DyHJytfrQJMiLFmXe7XbfMPNXFytnQ/Tha1tNes6v5lGVgbFREFddHAuZ5RfB4yvxY4TaF2CuKQQV/Vs5tv79Lt22XKyN1X2qaoNoW3ZA6a+k7BcYTWIRsy3d/g/t4LYf0dpaUy4TfrYZJPZE9agWIvXK+okXttHpcuMifIkRvS9LGXFBtt+R58IUflJQ1X2+UJfgE0RZ42p+Rx3UDDJJLpehJANmY9TrSWFKjzGD+GRqQDJSHj4rhFqY/tPj/Bd9Unag5gvoSjQbi7JSnMuAbrjw5q8j6H0A8PLAa06kyTU9YnpVmsl7S0bYK36/cSWIoGKg4SAM//rJ/2Bvw0TMzZXKfCLBXmGDYh4A5GK0f8lb4kYY3xuTkd4hLg3bRW4N3oPLbhV6A0nGb97AiPfkuDe0Sf0ShTltwOdtjrBnj+3Z5XBSP7QicvmMt+lsRnfTg2EVdDmpQuestion
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Question
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| hypothesis | A tentative explanation for one or more observations that makes predictions that can be tested by experiments or additional observations. |
Experimental Design
Types of hypotheses
A hypothesis, as we saw in Chapter 1, is a tentative answer to the question, an expectation of what the results might be. This might at first seem counterintuitive. Science, after all, is supposed to be unbiased, so why should you expect any particular result at all? The answer is that it helps to organize the experimental setup and interpretation of the data.
Let’s consider a simple example. We design a new medicine and hypothesize that it can be used to treat headaches. This hypothesis is not just a hunch—it is based on previous observations or experiments. For example, we might observe that the chemical structure of the medicine is similar to other drugs that we already know are used to treat headaches. If we went into the experiment with no expectation at all, it would be unclear what to measure.
A hypothesis is considered tentative because we don’t know what the answer is. The answer has to wait until we conduct the experiment and look at the data. When an experiment predicts a specific effect, as in the case of the new medicine, it is typical to also state a null hypothesis, which predicts no effect. Hypotheses are never proven, but it is possible based on statistical analysis to reject a hypothesis. When a null hypothesis is rejected, the hypothesis gains support.
Sometimes, we formulate several alternative hypotheses to answer a single question. This may be the case when researchers consider different explanations of their data. Let’s say for example that we discover a protein that represses the expression of a gene. Our question might be: How does the protein repress the expression of the gene? In this case, we might come up with several models—the protein might block transcription, it might block translation, or it might interfere with the function of the protein product of the gene. Each of these models is an alternative hypothesis, one or more of which might be correct.
Question
UAp2pyca+9H3xJ5LbQR0bJ45+VUnVNuHBhTsZ2grJ2XtBH5mADzCx9RqWSql4Oqm5aBGfwJkpdYy/nSlVM9tqzzwfsBl6dXIZBchgmUuyQg92ij5Um3RdJ0ajtI0pw1TGSry8hUxDFrjrGdQ7ZQXzyQ/oZ/TTxY4b/jqcECTedFl8mDU47S61JYgyDHUcBoNZ7xt0HpnAqsdwh5MtfJdcgZ276mc2VSGjyv4Pfs0orVmJ+V+FKVM+UkFVeGq03oPec0u+w==| order of magnitude | The number of times you must multiply a single digit number by ten to obtain the value in question. |
Scale and Approximation
Introduction
When a biologist does an experiment or completes a calculation, she often strives for quantitative precision. Commonly, however, it is just as important to be able to approximate – to get a ball park sense of the right answer that will rapidly help her to determine the next step in her research. One way to approximate is to make an order of magnitude comparison. Order of magnitude is commonly discussed in terms of the powers of ten – how many times you must multiply a single digit number by ten to obtain the value in question. 1492, for example, is equal to 1.492 x 1000, or 1.492 x10 x 10 x 10. The order of magnitude of 1492, then, is 3. 1620 also has an order of magnitude of 3, but the order of magnitude of 16,200 is 4:
16,200 = 1.62 × 10 × 10 × 10 × 10
Question
DRAf32wwa8i8TZzx871RZXNT5nVrzd/mMrVBlKq9jI104N0cK+Y+lguvcxkdLxJmDtltuoEokKUlbbHlRN1myMHcr6z9zTVZCwxTmYumN9NPVmyD7zVT0aHm/Uv+cry1YI1ml74DOxDLSB3v8kJpABMLqtznIk5CL2Jz40HXERMMQX44t2VmugDs1puDq+pjQuestion
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| control | Operations or observations that are set up in such a way that the researcher knows in advance what result should be expected if everything in the study is working properly. |
Experimental Design
Testing Hypotheses: Controls
Hypotheses can be tested in various ways. One way is through additional observations. There are a large number of endemic species on the Galápagos Islands. We might ask why and hypothesize that it has something to do with the location of the islands relative to the mainland. To test our hypothesis, we might make additional observations. We could count the number of endemic species on many different islands, calculate the size of each of these islands, and measure the distance from the nearest mainland. From these observations, we can understand the conditions that lead to endemic species on islands.
Hypotheses can also be tested through controlled experiments. In a controlled experiment, several different groups are tested simultaneously, keeping as many variables the same among them. In one group, a single variable is changed, allowing the researcher to see if that variable has an effect on the results of the experiment. This is called the test group. In another group, the variable is not changed and no effect is expected. This group is called the negative control. Finally, in a third group, a variable is introduced that has a known effect to be sure that the experiment is working properly. This group is called the positive control.
Controls such as negative and positive control groups are operations or observations that are set up in such a way that the researcher knows in advance what result should be expected if everything in the study is working properly. Controls are performed at the same time and under the same conditions as an experiment to verify the reliability of the components of the experiment, the methods, and analysis.
For example, going back to our example of a new medicine that might be effective against headaches, you could design an experiment in which there are three groups of patients—one group receives the medicine (the test group), one group receives no medicine (the negative control group), and one group receives a medicine that is already known to be effective against headaches (the positive control group). All of the other variables, such as age, gender, and socioeconomic background, would be similar among the three groups.
These three groups help the researchers to make sense of the data. Imagine for a moment that there was just the test group with no control groups, and the headaches went away after treatment. You might conclude that the medicine alleviates headaches. But perhaps the headaches just went away on their own. The negative control group helps you to see what would happen without the medicine so you can determine which effects in the test group are due solely to the medicine.
In some cases, researchers control not just for the medicine (one group receives medicine and one does not), but also for the act of giving a medicine. In this case, one negative control involves giving no medicine, and another involves giving a placebo, which is a sugar pill with no physiological effect. In this way, the researchers control for the potential variable of taking medication. In general, for a controlled experiment, it is important to be sure that there is only one difference between the test and control groups.