Lab 2

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Feb 20, 2024

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Ying-Quinn Liu ANT 001 Human Evolutionary Biology Week 2 Lab 1. Start off with the following settings for this simulation: a. Natural selection = off (unchecked). This means that only drift is at work. b. Carrying capacity = a “very-low”. This means the environment can only sustain a small population of E-coli. c. Number of types = 8. This means there are 8 different variants of E-coli (colors) d. Max initial population = 16. This means that on average there will be 2 individuals of each color variant. e. This is a fairly slow simulation so I recommend setting the model speed to it’s maximum. 2. Does drift produce the same result in different lineages? a. Click “go” to run the simulation. b. How many time steps does it take for at least 1 variant to go extinct? Which color variant went extinct first? (Check out bottom left graph. Note: you can click the name of the color to toggle between it being graphed or not). c. After about 5k timesteps pause the simulation by clicking “go”. How many variants have gone extinct? Which variant is most common? (Hover over the lines in the bottom graph to see the name of the color) Run # Colors extinct at 5k timesteps Most common color 1 3 Cyan d. Run the simulation a couple more times from scratch with the same settings by pressing “setup.” Note which color goes extinct and which becomes more common each time. Is it the same each time? Why or why not? It is not the same each time because it is up to chance. Run First extinct color Most common color 2 Orange Yellow Run Time to extinction First extinct color 1 93 Magenta

3 Orange Cyan e. What does this tell us about the effect of drift on variation within and between groups? (Hint: we can think of each simulation we run with the same settings as representing different groups of E-coli.) Within a group, variation always decreases due to genetic drift. genetic drift tends to decrease genetic diversity within individual groups (simulations) of E. coli over time. However, it doesn't address the variation between different groups or populations, as genetic drift is a random process, and outcomes can vary. 3. How can we reduce the strength of drift? a. How do you think you can change 1 parameter setting in the model to reduce the strength of drift? Why? To reduce the strength of genetic drift in your model, you should increase the population size parameter. This change should result in a more stable genetic diversity within the population and less drastic changes in allele frequencies due to random chance events. b. Run the experiment to test your prediction. That is, change the parameter and see if the extinctions are less severe (e.g., time to first extinction is longer, fewer variants have gone extinct at 5k timesteps). You may want to run the study several times to check. c. Are your predictions supported? Why or why not? Yes, it is taking a very long time for a type of bacteria to go extinct. When there was a smaller population, it only took 93 time steps for one type to fo extinct. Now that there is a larger population, it will take many more time steps. DRIFT AND NATURAL SELECTION EXPERIMENTS 4. Now let’s turn on natural selection (check the box) a. Set the selective advantage to 1. b. Make sure the brown E-coli have the selective advantage and that you can see them on your screen once you click setup. The variants with the selective advantage can eat sugar more efficiently and are outlined on the visual space with a blue halo. c. Set number of types = 4 d. Max initial population = to maximum (40) e. Carrying capacity = very high 5. Run the simulation to about 5k timesteps about 4 times and compare with your neighbors. a. What fraction of the time did the brown E-coli become the most common one? Most common color: Orange brown red brown 2/4= 1/2= 0.5 b. Change the selective advantage to .2 and run a couple times to 5k timesteps. c. Compare how often the brown E-coli becomes the most popular one.

Most common color: Red yellow orange brown 1/4= 0.25 d. What can you conclude about the extent to which natural selection and drift play a role in evolution? The roles of natural selection and genetic drift in evolution are both significant, but they operate in different ways. Natural selection is thel driving force of evolution, favoring traits that provide survival advantages and leading to the adaptation of populations to their environments. It is a non-random process that results in the accumulation of adaptive traits. On the other hand, genetic drift is a random process that can lead to changes in allele frequencies within populations due to chance events. It is more pronounced in smaller populations and can lead to the random fixation or loss of alleles, reducing genetic diversity. 1. Natural Selection Predictive Claim: The breakdown of the water treatment system leading to fecal contamination in a community already infected with E. coli increases the force of natural selection. Evidence: Natural selection operates by favoring traits that enhance an organism's fitness in a specific environment. In this case, the environment consists of the human gut, and certain E. coli variants may be better suited for survival and replication in this environment. Explanation: The increased exposure to new E. coli strains due to the contaminated water source subjects them to selective pressures within each individual's gut. Over time, natural selection may favor E. coli variants that are better adapted to thrive in the human gut environment. This could result in a decrease in genetic variation within populations,within each individual human, as these advantageous variants become dominant. 2. Migration Predictive Claim: The introduction of E. coli infections to a neighboring community through contaminated runoff increases the force of migration. Evidence: Migration occurs when individuals from different populations intermingle, leading to the mixing of genetic variants. In this scenario, new individuals from the neighboring community are exposed to E. coli from the contaminated water source. Explanation: Migration can increase genetic variation within populations of each human as new genetic variants are introduced through infection. Between populations, it can lead to a mixing of genetic diversity as E. coli strains from different sources intermingle. However, this mixing may not completely homogenize the populations, as selective pressures within each human's gut may still act to maintain some distinct genetic characteristics. 3. Mutatiom Predictive Claim: The use of a humanitarian shipment of antibiotics increases the force of mutation on E. coli. Evidence: Antibiotics exert selective pressures on bacteria, often leading to mutations of resistance. These mutations are a source of genetic variation. Explanation: The use of antibiotics will select for antibiotic-resistant E. coli variants within each individual. This can increase genetic variation within certain populations as new resistant variants emerge. However, the overall genetic diversity between everyone may not change significantly unless resistant strains are transmitted and establish in new individuals.

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4. Genetic Drift: Predictive Claim: Genetic drift is a continuous evolutionary force acting on E. coli populations. Evidence: Genetic drift acts on E. coli populations due to random selection of alleles, and its influence is ongoing within populations of E. coli residing in different human hosts. Explanation: Genetic drift is a random process that can lead to allele frequency changes over time due to chance events. It operates continuously in all populations, influencing genetic variation within and between populations. In smaller populations or isolated groups of individuals, genetic drift may have a more pronounced impact, potentially leading to greater divergence in E. coli populations between different humans. In larger populations, its effect may be less significant, but it still contributes to ongoing genetic changes. Genetic drift is a constant force that can operate alongside other evolutionary mechanisms.