Standardized data has a mean of_ and a standard deviation of_ . (Use numbers, n

Standardized data has a mean of_ and a standard deviation of_ . (Use numbers, not letters / words when filling in the blanks)
Is there variation in claw size among male crayfish that was observed in your lab section (see Part 1, Figure 1 – raw data)? Does this mean that there is or that there is not potential for selection to operate in the “population” of crayfish? Why is variation in a trait relevant to whether there is potential for selection to operate?
Part 1: Did the ‘population’ of male and female crayfish in your lab show sexual dimorphism? How can you tell (refer to the p-value in your answer)? Be sure to also include the definition of sexual dimorphism in your answer.
What is the slope of the line in Results Figure 4?
In Part 2, what does the slope mean? That is, what does the slope repesent?
What would a negative slope in Part 2 mean?
a.
smaller lizards sire more offspring than larger lizards
b.
the same thing as a positive slope
c.
a negative slope is not possible
d.
body size is not subject to selection
Run the Simulating Sexual Selection app with values below and answer the question that follows.
Conditions:
Population size: 1000
Number of males per female: 5
Sex ratio: 0.2
Number of mating events: 2
Which statement is true of the topmost plot?
There is variation in the size of the sexually-selected trait among males in the simulated population.
This plot doesn’t show whether there is variation in the size of the sexually-selected trait among males in the simulated population
There is not variation in the size of the sexually-selected trait among males in the simulated population.
Males with larger trait values sire more offspring
Using the plots you generated in Question 9, compare the data in the middle plot with those from Results Figure 4 from Part 2 of the lab. In which population was the magnitude of directional selection on the male trait larger, the natural population (corresponding with Results Fig. 4) or the simulated population (corresponding with the middle plot you generated for question 9)?
The magnitude of directional selection was substantially larger in the natural population of lizards examined in Part 2 of the lab than in the simulated population.
The magnitude of directional selection was substantially larger in the simulated population examined in Part 3 of the lab than in the lizard population from Part 2.
The magnitude of directional selection was about the same in the natural (lizard) and the simulated populations.
There is no selection operating in either population.
One can’t tell from these plots what the strength of selection is.
Using the plots you generated for Question 9, what does the bottom plot tell you about the impact of variance in mating success on the strength of selection? HINT: Using the conditons from Question 9, re-run the simulation a few times (i.e., click Run a few times) and look for a fairly consistent overall trend in what the bottom plot shows.
Using the plots you generated in question 9 (i.e., with the conditions of Population size 1000; Number of males per female: 5; Sex ratio: 0.2; Number of mating events: 2), fill in the blanks below:
The magnitude of selection in our simulated population is_ and the average selection gradient across all simulated populations is_ . Use 2 decimal places in your answers. E.g., change 5 to 5.00 or 5.1 to 5.10
For the next 3 questions you will be changing one parameter at a time to see which parameter has the greatest effect on the magnitude of selection. You will compare findings from these questions to those from question 12 to answer question 17.
Use the appropriate slide bar in the Simulating Sexual Selection app to set new starting conditions for your simulated population and answer the question below.
Population size: 2000 (changed from 1000; this means there are twice as many individuals in the population)
Number of males per female: 5
Sex Ratio: 0.2
Number of mating events: 2
Press ‘Run’. New plots will be generated. The magnitude of selection in our simulated population is now and the average selection gradient across all simulated populations is now . Use 2 decimal places in your answers. E.g., change 5 to 5.00 or 5.1 to 5.10
Use the appropriate slide bar in the Simulating Sexual Selection app to set new starting conditions for your simulated population and answer the question below.
Population size: 1000
Number of males per female: 10 (changed from 5; this means that there are twice as many males competing for each female)
Sex Ratio: 0.2
Number of mating events: 2
Press ‘Run’. New plots will be generated. The magnitude of selection in our simulated population is now and the average selection gradient across all simulated populations is now . Use 2 decimal places in your answers. E.g., change 5 to 5.00 or 5.1 to 5.10
Use the appropriate slide bar in the Simulating Sexual Selection app to set new starting conditions for your simulated population and answer the question below.
Population size: 1000
Number of males per female: 5
Sex Ratio: 0.4 (changed from 0.2; this means that 40% rather than 20% of the population is male)
Number of mating events: 2
Press ‘Run’. New plots will be generated. The magnitude of selection in our simulated population is now and the average selection gradient across all simulated populations is now . Use 2 decimal places in your answers. E.g., change 5 to 5.00 or 5.1 to 5.10
Use the appropriate slide bar in the Simulating Sexual Selection app to set new starting conditions for your simulated population and answer the question below.
Population size: 1000
Number of males per female: 5
Sex Ratio: 0.2
Number of mating events: 4 (changed from 2; this means that each female will mate 4 times rather than 2)
Press ‘Run’. New plots will be generated. The magnitude of selection in our simulated population is now and the average selection gradient across all simulated populations is now . Use 2 decimal places in your answers. E.g., change 5 to 5.00 or 5.1 to 5.10
Which parameter change from questions 13-16 (i.e., your changes to the population size, the number of males competing per female, the sex ratio, or the number of mating events in turn) increased the strength of sexual selection the most compared to starting conditions (i.e., compared to in question 12)? How do you know? Which change had the least effect on the strength of sexual selection? How do you know?
Now that you have completed the lab (HINT: and part 3 in particular), what can you conclude about the impact of the variance in mating success within populations on the strength of sexual selection? Just one or two sentences is sufficient for your answer.
Open the ‘Simulating Sexual Selection’ web app at:
https://madorken.shinyapps.io/Lab4_Shiny/ (students in D108) Essentially this app allows you to set different starting conditions for a simulated population and then to see how those conditions affect selection in your population. All you need to know is: (1) the program (app) creates a virtual population of females and males; (2) males are defined by the size of a trait used by females for mate selection; (3) variation in the trait is of the same kind as we examined in the previous sections – the trait is normally distributed with a mean of ~ 0 and a standard deviation of ~ 1; (4) each female randomly chooses a mate, one mate for each egg she produces; (5) if the trait possessed by a male satisfies her threshold for accepting a mate, that male becomes the father of the offspring. If not, the female chooses another male. Because we know from the previous part of the lab that males with larger traits are more likely to be chosen as mating partners, we have assumed (and built into the program) that, on average males with larger traits should have higher siring success.
The app has four scroll bars that will allow you to set different starting conditions from which to run the app (that is, the starting conditions for your simulated population). You will be able to change the following:
The population size
The number of males that could compete for females
The sex ratio (i.e., the proportion of males in the population)
The number of mating events per female
What do the plots show?
After you set the starting conditions, you click ‘Run’ and the app will generate three figures. The first is a frequency histogram of the size of the sexually-selected trait for the males in one of the virtual populations. This plot is of the same kind that we generated in Part 1 of this lab when we measured the size of the male crayfish claws. The second plot lets you check whether our assumption that males with larger traits have higher reproductive success is valid for this simulated population. Note that this plot is of the same kind that we produced in Part 2 (results figure 4). How does this plot compare to the one from the natural population of lizards?
The third plot is of a new kind and represents the purpose of this particular part of the lab. This plot shows the relationship between the variance in male mating success in each of the simulated populations and the value of the slope of the line between the size of the male trait and his mating success, also known as the “selection gradient”. That’s a lot to digest at once so let’s go through that piece by piece:
Variance in male mating success: In each population, some males will have had high reproductive success, others will have low reproductive success. The variance in male reproductive success is a measure of just how different males were at attaining mates and therefore siring offspring. If all males had the same reproductive success, the variance would be 0. If males vary a lot, the variance will be substantial. You will find that the variance is non-zero in all of your simulated populations, in part because we have assumed that there is a random element to female mate choice, but also because, on average, males with larger traits will have higher siring success than males with smaller traits.
The slope of the relationship between the size of the male trait and mating success: This is almost exactly what we have calculated in Part 2 above. In Part 2 we plotted the size of male trait against fitness. The slope of this line is the selection gradient and reveals the magnitude of selection on the male trait.
You will see that there is a positive association between the amount of variance in male mating success and the magnitude of selection. What does this mean? Why should the magnitude of selection increase when the differences in male mating success in a population are larger? Well, if the variation in male mating success is large, that means that some males had either very low mating success (low fitness), or some males had really high mating success (high fitness), or both. By contrast, if there was little variation in male mating success, the fitness of any one male was similar to that of any other male. So, the scope for selection to operate increases with the amount of variation in fitness.
The simulations have captured a very important aspect of the biology underlying sexual selection. The sex that is subject to greater variance in mating success will be subject to selection on traits that affect the competitiveness or “attractiveness” of that sex. The sex with less variation in mating success will be “choosy” and select mates based on their “attractiveness”. You will note two things about the preceding two sentences. First, we didn’t specify which sex should evolve to become more “attractive” or which sex should evolve to be “choosy”. It depends entirely on which sex is subject to greater variance in mating success. In most species it is the males that have the greater variance in mating success. However, in some species, females have greater variance in mating success and they have evolved traits that make them more competitive in terms of attaining mates. A classic example of this is the Jacana, a bird in which dominant females control “harems” of males. The second thing to note is that “attractiveness” is in the eye of the beholder. You might not think that large claws are attractive, but then you’re not a female crayfish.