Objective: The point of this part of the lab is to show you that male traits in natural populations are subject to sexual selection.
Your TAs will have done the analysis for you. They will show you figures in class and you will discuss how to interpret them. These figures (Lab 5 Figure 3, Lab 5 Figure 4) will be posted to Blackboard as you will need to refer to them for your assignment.
Part 3 – Using simulation models to examine sexual selection
Modelling is an important part of doing biology. Models are useful if they help us to examine and identify processes that might be important in nature. In this part of the lab we will use a simple simulation model to examine the processes that drive sexual selection. To complete the main part of the lab exercise you will use an interactive web app that was built for this lab by Dr. Marcel Dorken, a Biology Professor at Trent. The app can be accessed at this link: https://marceldorken.shinyapps.io/lab4_shiny/
or
https://madorken.shinyapps.io/Lab4_Shiny/
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 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.
Objective: The point of this part of the lab is to understand that the opportunity for selection to operate depends strongly on the amount of variance in fitness. If individuals all have the same fitness, there is no scope for selection to operate. If some individuals have high fitness, and others have low fitness, selection can operate, and if the trait is heritable we would expect the trait to evolve.
Procedure:
Open the ‘Simulating Sexual Selection’ web app at: https://marceldorken.shinyapps.io/lab4_shiny/ or https://madorken.shinyapps.io/Lab4_Shiny/
Look at the default starting conditions in the four slide bars on the left.
Click ‘Run’ to see each of the figures detailed above (in ‘What do the plots show?’). Re-read ‘What do the plots show?’ and make sure you understand what each plot is showing.
For your assignment, you will change each of the starting conditions in turn to see how each affects the strength of selection in your simulated population (top and middle plots) and populations (bottom plot). Which conditions to change and by how much will be stated in the assignment. For now, you can change the conditions as you wish and see how it changes the figures.
Lab 5 assignment (44 marks; 7% of your final grade) DUE two weeks after your Lab 5
There are two parts to this assignment – answering questions in an assignment in Blackboard and a communicating science component.
Blackboard component (34 marks)
You will answer questions using the posted figures, the figures you generate in RStudio, and the Simulating Sexual Selection app in an assignment within Blackboard. The assignment will appear as a test within Blackboard, but there is no time limit. You may enter and exit the test as many times as needed, and take as much time as you require (before the due date/time, of course). Be sure to click the submit button on your assignment before the due date/time.
Communicating science component (10 marks)
As you have learned, communicating your findings to others is an important part of the scientific method. For the second part of this assignment, you will communicate the finding(s) from ONE of the three parts of this lab activity in ANY way you want. You can write a short blog post, a news release, a social media post, an elevator pitch – even write a song or a poem. Get creative! See some examples below (you can use a medium other than these examples).
You will pick one of the three parts of this Lab and communicate the findings. While you can use any medium you want to communicate your message, you should gear the message to an audience of university undergrad students.
