BIOL1020H Writing and Graphing Assignment

BIOL1020H Writing and Graphing Assignment

DEPARTMENT OF BIOLOGY TRENT UNIVERSITY BIOL1020H – FOUNDATIONS OF BIODIVERSITY

BIOL1020H Writing and Graphing Assignment (10% of your final grade) Introduction:

The ability to graph and interpret data is an essential skill for biologists. This exercise is designed to: 1) give you some familiarity with graphing and statistical techniques used by contemporary biologists, and 2) give you a chance to provide a written interpretation biological data.

Not that long ago biologists did all of their statistics and graphing in programs like Excel, or other specialized programs (e.g., SigmaPlot, SPSS, Statistica, JMP) – some of which were very expensive. While Excel is still used by many biologists to organize their data, conduct exploratory analyses, or to do some simple graphing, the majority of scientists now rely on a free program called R. This new software is very powerful and gives the user more control over their analyses and figures. It is also continuously updated with new cutting-edge graphical or statistical tools and supported by a large online community.

One potential drawback to R is that it uses a ‘command line’ interface which requires that you know how to tell the program what you want it to do. At first this can be a bit intimidating, but once you start using it and see how the code works you will see that it gets much easier. A companion program called RStudio makes using the R software much easier. A goal of this exercise is to introduce you to R and RStudio, and show you how these programs can be used to graph and analyze biological data.

Once you have made these figures using RStudio, you will make two of the same figures in Excel. The goal of this is to give you some familiarity with the graphing applications of Excel, and to illustrate the distinction from an R based approach.

Sexual Dimophism:

In many animals, males and females of the same species differ substantially in their appearance or behaviour. These differences have fascinated biologists for well over a century. In fact, after publishing ‘On the Origin of Species’ Darwin himself wrote that “The sight of a feather in a peacock’s tail, whenever I gaze at it, makes me sick!”. How could elaborate traits like a male peacock’s tail ever evolve by natural selection?

Evolutionary biologists have now identified a number of different mechanisms which can produce significant differences in male and female traits. Darwin would go on to write another book called ‘The Decent of Man, and Selection in Relation to Sex’ where he outlined one important solution to this problem – something we now call sexual selection. Sexual selection favours traits that increase an individual’s access to reproductive opportunities, and sometimes works in the opposite direction of natural selection. For example, a male peacock’s showy tail increases his chance of impressing a reproductive female, but also could put him at an increased risk of being killed by a predator.

Two ways that sexual selection can act on a population is through intrasexual competition, and intersexual competition. In intrasexual competition individuals of the same sex (usually males) compete for access to the other sex (usually females). This typically produces traits like large body size or ‘weaponry’ (e.g., antlers) that help an individual win access to the other sex. In intersexual competition one sex (usually the male) competes for the attention of the other sex (usually the female). This is where we see the evolution of traits in one sex that impress the other sex (e.g., the showy tail of a male peacock impresses the female peahens). See also Chapter 21 pp 437-438 in your textbook.

Interestingly, natural selection can also produce differences in the traits of males and females. For example, natural selection may favour the evolution of a relatively large body size in females as a way to increase the number of offspring she can produce. This is called fecundity selection and helps us understand why females are larger than males in many species of reptile and amphibian. Another way that natural selection can cause males and females to evolve different traits is when selection acts to minimize resource competition between males and females of the same species (recall SimBio Lab 2). This results in males and females of the same species occupying a different ecological niche.

Biologists continue to discover new ways that males and females differ, and we will be testing whether or not there is evidence of sexual dimorphism in breeding Spotted salamanders (Ambytoma maculatum).

Salamander Biology & Breeding Ecology:

The Spotted salamander (Ambytoma maculatum) is a large-bodied salamander which is predominantly grey or black and is covered in bright yellow spots (Figure 1). In the early spring these amazing animals migrate to temporary ponds that fill with the water from snow-melt and the spring rains. Whereas males attempt to breed every year, reproduction is very energetically costly for the female (active breeders lose 24-38% of their mass in the breeding season, Sexton et al. 1986, Windmiller 1996) so generally they skip one or more years between breeding attempts. This results in a strong male- biased sex ratio in the breeding ponds and intense completion for females.

Males walk along the bottom of the pond searching for females which they court by nudging them with their head. Females may be courted by as many as 50 males at a time, and reproduction can appear chaotic as a swarm of frenzied males encircle the few gravid females in the pond. Males then begin to place small packages of sperm on the pond bottom called ‘spermatophores’. Arnold (1976) observed that males will even deposit spermatophores on top of the spermatophores of other males as a way to increase their chances of fertilizing a female (and interfere with the chances of other males reproducing)! Females pick up these spermatophores using their reproductive opening (cloaca). In fact females typically pick up several spermatophores, often from multiple males. Fertilization is internal and they will deposit one or more egg masses on submerged sticks in the next 24-48 hours.

In collaboration with the Murray Lab, we have been studying a Spotted salamander population in Buckhorn, Ontario each year since 2015. These animals are photographed, measured, and individually marked as part of a study on salamander population dynamics. Key measurements include mass (g), snout-vent length (a measure of body size), head length, and tail length (see Figure 1).

Previous research has found that the congeneric Tiger salamander (Ambystoma tigrinum) is sexually dimorphic in tail length. Specifically, for a given body size, males tend to have longer tails than females (Arnold 1976, Howard 2009). We wanted to know if the same pattern is observed in Spotted salamanders, and whether males and females differ in other traits as well (e.g., head size). I have compiled a data set that includes data from 100 males and 100 female Spotted salamander collected as part of our research for you to analyse, graph, and test for sexual dimorphism.

Figure 1: A dorsal view of a male Spotted salamander (Ambystoma maculatum) collected from Buckhorn, Ontario as part of a long-term population study. This figure depicts the morphological measurements used in the lab exercise. Note the swollen cloaca at the base of the tail (seen only during the breeding season), which indicates that this is a reproductive male.

Before you start these exercises, you will have to install R and RStudio. Both programs are free to use and install. Follow the instructions below, making sure your computer has the minimum requirements for installation.

How to install R and RStudio for Windows users:

• Download R from http://cran.us.r-project.org/ (click on “Download R for Windows” > “base” > “Download R-4.3.2 for Windows”)
• Install R by running R-4.3.2-win.exe. Leave all default settings in the installation options (i.e., keep clicking “Next”).
• Download the free version of RStudio from https://posit.co/download/rstudio- desktop/#download using the installer for Windows (scroll to near the bottom of the page). Install by running RStudio-2023-.2.0-369.exe. Leave all default settings in the installation options.
• Open

(NOTE that you do not have to open R at all, but RStudio needs it downloaded to run. We will do all our R work in RStudio).

How to install R and RStudio for Mac users:

• Download R from http://cran.us.r-project.org/. Click on “Download R for macOS” > “R- 4.3.2-arm64.pkg” if you are running macOS 11 (Big Sur) and higher. If you are running earlier Mac OS versions scroll down and download “R-4.3.2-x86_64.pkg” OR “R-

4.2.3.pkg” as appropriate to your OS.

• Install Leave all the default settings (i.e., keep clicking “Continue”)
• Download the free (personal license) version of RStudio from https://posit.co/download/rstudio-desktop/
• Install RStudio by double-clicking the ‘RSTUDIO-2023.12.0-369.DMG’ file that downloads. In the new window, drag the application icon to your Applications
• Open (NOTE that you do not have to open R, but RStudio needs it downloaded to run. We will do all our R work in RStudio).

If you run into problems during the installation look for help online. There is a large community of R and RStudio users that provide support for R users, including the installation process.

Assignment Procedure:

• Download “BIOL 1020 Web – Writing and R” and “Data_SalamanderDimorphism.csv” from Blackboard.
• Open RStudio and then open “BIOL 1020 Web – Writing and R”.
• Set the “working directory” so that RStudio knows where to find the data that will be analysed and where to put the figures you are going to generate.
• Select “Set Working Directory” under the “Session” tab in
• Select “Choose Directory”.
• Navigate to the folder in which you have saved “csv”.
• Highlight a few lines of text in the upper-left hand window of RStudio and click on the “Run” tab in the upper-right hand part of that window.
• Proceed by running lines of code and seeing what they
• As you work through the R code, you will learn what each line of code does. Toward the end you will be asked to interpret the results, and then to make slight modifications to the code to test slightly different questions and make a slightly different
• In a separate Word document (*.doc, or *.docx), provide answers to Questions 1-6 below. Save this file and submit it on Blackboard.

Questions:

1. Provide a copy of the SVL Tail Length figure made in RStudio. Call this Figure 1 and provide an informative figure caption. Based on the graphs, which Sex has the longer tail? Approximately how much does tail length differ between the sexes?
2. Which of the two variables has a significant effect on Tail Length?
3. Provide a figure that compares the relationship between SVL and Head Length between the two sexes (this will be Figure 2). Provide an informative figure
4. Is there statistical support for the hypothesis that salamanders are sexually dimorphic in their Head Length? Provide your statistical output to justify your answer
5. Write a paragraph that answers the following:
   1. In your own words, explain what is meant by “sexual dimorphism”.
   2. What are a few reasons why males and females of the same species sometimes differ in body size, shape, or colouration
   3. Do Spotted salamanders show signs of sexual dimorphism? Outline why you think this might be the case. Use at least one reference from the primary literature to back up your ideas.
   4. Provide 2 different examples of sexual dimorphism in the natural world, and include citations from the primary literature (i.e., research articles from the peer reviewed literature) to back up your statements.
   5. In 2-3 sentences provide an explanation for why understanding sexual dimorphism is interesting, useful, or valuable.

6. Use Excel to re-create Figure 1 (Snout-Vent Length vs. Tail Length for males and females) and Figure 2 (Snout-Vent Length Head Length for males and females). Try to make them as similar as possible to the figures you created in RStudio. Which approach did you prefer and why?

• Here are some useful resources to get you started in Excel:
  • https://www.youtube.com/watch?v=gBbGBrHTMrM
  • https://www.youtube.com/watch?v=kLROcLFzH8o
  • https://support.office.com/en-us/article/creating-charts-from-start-to-finish- 36f8a2b7-0f74-4d40-942d-bc2e42436d73
  • https://www.dummies.com/software/microsoft-office/excel/how-to-create- a-scatter-plot-in-excel/

Just a reminder that all Trent students have free access to MS Office 365, including Excel!

Submission Checklist:

Include the following in a Word (*.doc or *.docx) document:

• Answer to Question 1. Use full sentences. Include a copy of Figure 1 (a figure of Tail Length Snout-vent Length (with lines of bet fit) made in RStudio using the ggplot2 package. Include an informative figure caption.
• Answer to Question
• Question 3 (Figure 2). Provide a figure of Tail Length Snout-vent Length (with lines of bet fit) made in RStudio using the ggplot2 package. Include an informative figure caption.
• Answer to Question 4, and the corresponding ANOVA table statistical
• Question 5: Use full sentences and appropriate paragraph form to answer a-

Include in-text citations and a list of your references formatted appropriately. Include an appropriately formatted reference list.

• Figures 1 and 2 re-created in Excel, and a written answer to which approach you preferred (R vs. Excel) and why.
• Make sure your name and student number are on your

Tips:

This assignment is not a formal lab report. That said, the graphing and referencing aspects of this assignment follow the same guidelines as formal lab reports. Look at the additional materials provided on Blackboard in the Writing & Graphing section to help you:

• Determine how to write an appropriate figure caption
• How to use in-text
• How to correctly format the references in your reference
• Make scatterplots in Excel

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Literature Cited:

Arnold, S.J. 1976. Sexual behaviour, sexual interference, and sexual defence in the salamanders Ambystoma maculatum and Plethodon jordani. Zeitschrift für Tierpsychologie 42: 247-300.

Howard, R.D. 2009. Ontogeny of a sexual dimorphism in tiger salamanders. Canadian Journal of Zoology 87: 573-580.

Sexton, O.J., Bizer, J., Gayou, D.C., Freiling, P., Moutseous, M. 1986. Field studies of breeding spotted salamanders, Ambystoma maculatum, in eastern Missouri USA, Milwaukee Public Museum, Milwaukee, Wisconsin. Contribution in Biology and Geology 67: 1-19.

Windmiller, B.S. 1996. The Pond, the Forest and the City: Spotted salamander ecology and Conservation in a human-dominated landscape. PhD dissertation. Tufts University, Medford, Massachusetts. 184 p.

R Code:

# First you need to upload your data into your R workspace.

# In the menu bar at the top click: Session > Set Working Directory > Choose Directory # Then select the folder where you have saved the data file.

# Now you are ready to run some code!

# Highlight the line of text below, then click the “Run” button in the top of this window data <- read.csv(“Data_SalamanderDimorphism.csv”)

# In the “Console” window below you will see that code has run, and it ready for more.

# The str() function shows you the “structure” of your data set.

# This is useful as a check to see if your data was brought in to R correctly.

# Highlight the line of text below, then click the “Run” button in the top of this window str(data)

# In the Console window you should see that “Sex” is a Factor, and the other # variables are “num” which means they are a numeric continuous variable.

# It also tells you how many data point there are (200), and shows us a sample of the data.

# Now, let’s make a basic plot using the plot() function.

# When you have a continuous variable on Y-axis (e.g., TailLength) and

# a categorical variable on X-axis (e.g., Sex) this code makes a Boxplot. # Boxplots are a good way to visualize the spread of your data.

# Highlight and run the next line of code. boxplot(SVL~Sex, data)

# The figure will show up in the window to the right. Click ‘Zoom’ to see a larger version. # The code below will make a basic scatterplot when both variables are continuous. plot(TailLength~SVL, data)

# Now, let’s try to make some nicer looking and more informative figures: # First, you will need to install the package “ggplot2”

# On your right you should see the “Environment” window. Select the “Packages” tab and

# and click “Install”. Then type “ggplot2” and click “Install”. Leave the default settings. # Once ggplot2 is installed you have to run the code below in order to make its

# tools available for you to use. library(ggplot2)

# We will start with the basic plotting function in the ggplot2 package:

ggplot(data, aes(x=SVL, y=TailLength, group = Sex, color = Sex)) + geom_point(shape=16) # This is already a much nicer figure than the base plot() function.

# Take a second to look at this code and see if you can figure out how it works.

# We can make improve this plot by modifying the code slightly (See below). # See if you can understand how these changes modify the figure.

# Highlight all 7 lines of code below and click ‘run’. It may take a few seconds

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ggplot(data, aes(x=SVL, y=TailLength, group = Sex, color=Sex)) + geom_point(shape=16) +

ylab(“Tail Length (mm)”) +                 # Set y-axis label

xlab(“Snout-Vent Length (mm)”) +             # Set x-axis label scale_colour_hue(l=50) + # Use a slightly darker palette than normal geom_smooth(method=lm,   # Add linear regression lines

se=FALSE)          # Don’t add shaded confidence region # You should now see the figure

# If we want to get really fancy we can add 95% confidence intervals # around our line of best fit:

ggplot(data, aes(x=SVL, y=TailLength, group = Sex, color=Sex)) + geom_point(shape=16) +

ylab(“Tail Length (mm)”) +                      # Set y-axis label xlab(“Snout-Vent Length (mm)”) +   # Set x-axis label scale_colour_hue(l=50) + # Use a slightly darker palette than normal geom_smooth(method=lm, # Add linear regression lines

se=TRUE)     # Add shaded 95% confidence region

# Save a copy of this figure and include it with your assignment! # To do this click Export > Save as Image

# Questions 1-5 are provided in here for convenience, but should be answered

# in a separate Word document (*.doc or *.docx) which will be submitted online via Blackboard.

#####################################################################

# Question 1:                                                       #

# Based on the graphs, which Sex has the longer tail?               # # Approximately how much does tail length differ between the sexes? # #####################################################################

# Now, let’s actually test the hypothesis that the sexes differ in relative tail length. # We suspect that Sex (a categorical predictor variable) and SVL (a continuous predictor variable)

# might influence TailLength (a continuous response variable).

# We can use the code below to run a statistical test called a linear model. # This code runs our statistical model:

m1<-(lm(TailLength~SVL+Sex, data))

# This code gives us our ANOVA table (our statistical results): anova(m1)

# The F value is what we call our ‘test statistic’ and the Pr(>F) column gives us # our ‘P value’. When the P value is small (i.e., <0.05) it indicates that our

# predictor variable has a statistically significant effect on the response variable. # Statistically significant effects are indicated by one or more * symbols.

SCORE A+ WITH HELP FROM OUR PROFESSIONAL WRITERS – BIOL1020H Writing and Graphing Assignment

######################################################################

# Question 2:                                                        #

# Which of the two variables has a significant effect on TailLength? # ######################################################################

# Now that you have seen how the code works, make a scatterplot that

# illustrates the relationship between SVL and HeadLength for each Sex!

# Hint: copy the code we used above and change TailLength for HeadLength.

#################################################################################

# Question 3:                                                                    #

# Provide a figure that compares the relationship between SVL and HeadLength  # # between the two Sexes. Don’t forget to provide an informative figure caption. # #################################################################################

# Once you have done that, run a statistical test to evaluate whether HeadLength # is sexually dimorphic in Spotted salamanders.

#########################################################################################

# Question 4:                                                                                     #

# Is there statistical support for the hypothesis that salamanders are sexually                                                                            # # dimorphic in their HeadLength? Provide your statistical output to justify your answer # #########################################################################################

##########################################################################################

SCORE A+ WITH HELP FROM OUR PROFESSIONAL WRITERS – BIOL1020H Writing and Graphing Assignment

# Question 5:                                                                                     #

# Write a paragraph that answers the following                                                                                     #

# (a) In your own words, explain what is meant by “sexual dimorphism”.                                                                            #

# (b) What are a few reasons why males and females of the same species sometimes differ # #   in body size, shape, or colouration?                                                                                                                                                         #

# (c) Do Spotted salamanders show signs of sexual dimorphism? Outline why you think   #

#  this might be the case. Use at least one reference from the primary literature  # #   to back up your ideas.                                                                            #

# (d) Provide 2 different examples of sexual dimorphism in the natural world, and    #

#   include citations from the primary literature (i.e., research articles from    # #            the peer reviewed literature) to back up your statements.            #

# (e) In 2-3 sentences provide an explanation for why understanding sexual dimorphism  #

#   is interesting, useful, or valuable.                        #

####################################################################################

SCORE A+ WITH HELP FROM OUR PROFESSIONAL WRITERS – BIOL1020H Writing and Graphing Assignment

ADDITIONAL INSTRUCTIONS FOR THE CLASS

SCORE A+ WITH HELP FROM OUR PROFESSIONAL WRITERS – BIOL1020H Writing and Graphing Assignment

We will process your orders through multiple stages and checks to ensure that what we are delivering to you, in the end, is something that is precise as you envisioned it. All of our essay writing service products are 100% original, ensuring that there is no plagiarism in them. The sources are well-researched and cited so it is interesting. Our goal is to help as many students as possible with their assignments, i.e. our prices are affordable and services premium.

• Discussion Questions (DQ)

Initial responses to the DQ should address all components of the questions asked, including a minimum of one scholarly source, and be at least 250 words. Successful responses are substantive (i.e., add something new to the discussion, engage others in the discussion, well-developed idea) and include at least one scholarly source. One or two-sentence responses, simple statements of agreement or “good post,” and responses that are off-topic will not count as substantive. Substantive responses should be at least 150 words. I encourage you to incorporate the readings from the week (as applicable) into your responses. BIOL1020H Writing and Graphing Assignment

• Weekly Participation

Your initial responses to the mandatory DQ do not count toward participation and are graded separately. In addition to the DQ responses, you must post at least one reply to peers (or me) on three separate days, for a total of three replies. Participation posts do not require a scholarly source/citation (unless you cite someone else’s work). Part of your weekly participation includes viewing the weekly announcement and attesting to watching it in the comments. These announcements are made to ensure you understand everything that is due during the week. BIOL1020H Writing and Graphing Assignment

• APA Format and Writing Quality

Familiarize yourself with the APA format and practice using it correctly. It is used for most writing assignments for your degree. Visit the Writing Center in the Student Success Center, under the Resources tab in Loud-cloud for APA paper templates, citation examples, tips, etc. Points will be deducted for poor use of APA format or absence of APA format (if required). Cite all sources of information! When in doubt, cite the source. Paraphrasing also requires a citation. I highly recommend using the APA Publication Manual, 6th edition.

• Use of Direct Quotes

I discourage over-utilization of direct quotes in DQs and assignments at the Master’s level and deduct points accordingly. As Masters’ level students, it is important that you be able to critically analyze and interpret information from journal articles and other resources. Simply restating someone else’s words does not demonstrate an understanding of the content or critical analysis of the content. It is best to paraphrase content and cite your source. BIOL1020H Writing and Graphing Assignment

• LopesWrite Policy

For assignments that need to be submitted to Lopes Write, please be sure you have received your report and Similarity Index (SI) percentage BEFORE you do a “final submit” to me. Once you have received your report, please review it. This report will show you grammatical, punctuation, and spelling errors that can easily be fixed. Take the extra few minutes to review instead of getting counted off for these mistakes. Review your similarities. Did you forget to cite something? Did you not paraphrase well enough? Is your paper made up of someone else’s thoughts more than your own? Visit the Writing Center in the Student Success Center, under the Resources tab in Loud-cloud for tips on improving your paper and SI score. BIOL1020H Writing and Graphing Assignment

• Late Policy

The university’s policy on late assignments is a 10% penalty PER DAY LATE. This also applies to late DQ replies. Please communicate with me if you anticipate having to submit an assignment late. I am happy to be flexible, with advance notice. We may be able to work out an extension based on extenuating circumstances. If you do not communicate with me before submitting an assignment late, the GCU late policy will be in effect. I do not accept assignments that are two or more weeks late unless we have worked out an extension. As per policy, no assignments are accepted after the last day of class. Any assignment submitted after midnight on the last day of class will not be accepted for grading. BIOL1020H Writing and Graphing Assignment

• Communication

Communication is so very important. There are multiple ways to communicate with me: Questions to Instructor Forum: This is a great place to ask course content or assignment questions. If you have a question, there is a good chance one of your peers does as well. This is a public forum for the class. Individual Forum: This is a private forum to ask me questions or send me messages. This will be checked at least once every 24 hours. BIOL1020H Writing and Graphing Assignment

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