In Christopher-Peterson/Bio373L-Book: Does not matter.

Homework: Mark-Release-Recapture {#hw-mrr}

Required Reading

This homework will prepare you to analyze the data from the MRR lab. You should read over that the material to properly understand the data you'll be working with here. I will assume that you are familiar with the material used in the Succession homework. In addition, you should read Section \@ref(r-stats-lm).

Please attempt this to the best of your ability.
Once you've read the above chapters, I will be happy to meet with you (individually or in groups) to provide guidance and support on this material.

Objectives (and what's due)

There are four primary questions in this lab, along with the associated results you'll be submitting:

• How does sampling intensity improve mark-release-recapture (MRR) population size estimates?

  • Submit: Lincoln-Petersen population estimate at maximum sample size $(\hat{N})$
  • Submit: A figure that shows how the Lincoln-Peterson population size estimate changes with sample size.

• Does separating animals by sex alter our estimate of population size? What's the sex ratio?

  • Submit: Separate estimates of population size for males $(\hat{N}_m)$ and females $(\hat{N}_f)$.
  • Submit: Does $\hat{N} = \hat{N}_m + \hat{N}_f$?
  • Submit: A chi-squared test to see if the sex ratio is 50:50.

• How does the Lincoln-Petersen model compare with alternate MRR methods?

  • Submit: A 3-panel figure showing the total number of recaptures as a function of the daily recapture rate for Dr. Gilbert's Trinidad dataset (split over each of the three months).
  • Submit: The Lincoln-Petersen estimate for each month.
  • Submit: A regression-based estimate for each month.

• Are the Heliconius populations in Hardy-Weinberg equilibrium for the Optix gene?

  • Submit: The observed genotype frequencies and the expected frequencies under HWE.
  • Submit: A chi-square test for whether the observed frequencies deviate from HWE.

Getting started

There are several datasets you'll need to download for this; they are all located on Canvas under the MRR module.

• F19_recapture_data.zip: This contains a bunch of csv files. Extract it as a subfolder of data (so you should have a file named data/F19_recapture_data/1_Teams_A.csv).
• Trinidad Data: Download & Save this file under data.
• F19_genotype_consensus.csv: Put this in data.

You should begin your script with the following:

# MRR Lab Analysis
#### Setup ####
library(tidyverse)
library(readxl) # used to read the excel file; this is installed with Tidyverse
theme_set(theme_classic()) # Removes gridlines from ggplot

# Data file locations: Change these when analyzing the current dataset
recap_folder = "data/F19_recapture_data"
genotype_file = "data/F19_genotype_consensus.csv"
trinidad_file = "data/MRR GilbertTrinidad data.xlsx" # This won't change

Sampling Intensity and Lincoln-Petersen

The data

To simulate different sample sizes, I've pooled together the data collected by different groupings of teams. This includes all the data collected by each team on its own, by all pairs of teams (e.g., A&B, A&C, A&D, etc), all triplets, etc., all the way to all teams combined. Each of these data are in a separate csv file in the recapture data folder. You're going to load them all into a single data frame.

First, list all the csv files in the directory, then import them with the read_csv() function from the readr package. Note that you need readr version 2.0 or higher; earlier ones can't handle multiple files. Additionally, make sure you use read_csv(), not read.csv(); these are different functions and they work slightly differently.

#### Sample Size and LP Method ####

# List the names of all csv files in the recap folder
recap_files <- dir(recap_folder, pattern =  ".csv", full.names = TRUE)
recap_files # take a look at it
recap_data <- read_csv(recap_files) # Read all the recap files and combine as a single data frame
# You could add the argument id = 'file_name' if you wanted to keep the original file name as an argument,
# but that isn't necessary with these data

The columns of this data frame are:

• Period: The first or second sampling period (as described in Section \@ref(lab-mrr-field-methods) ).
• Mark_Number: The id of the butterfly that was marked.
• Sex: The sex, "M" or "F". Some sex data may be missing (NA).
• Capture_Status: Is this the first time the butterfly was observed ("Mark") or has it been seen before ("Recap").
• Team_combo: What combinations of teams were used to create this dataset? This relates to sample size.

Calculating Lincoln-Petersen numbers {#hw-mrr-lp-calc}

For the LP method, we need the following numbers:

• $M_1$: Number of individuals marked in first sample period (This will be all of the first period individuals).
• $S_2$: Total number of individuals in second sample period.
• $R_2$: Number of individuals that were recaptured in the second period.

Once you have these

We want to calculate these for eachTeam_combo.\ The easiest way to do this with a grouped summarize command. The basic outline should be as follows:

lp_estimates <- recap_data  |>  
  group_by(        ) |> # Finish this
  summarize( # We're dropping the subscripts because they aren't necessary
    M =     ,
    S =     ,
    R =     ,
    sample_size = n() ) |> 
  mutate(N_hat = ) # Calculate N-hat from S, M, and R

To calculate M, S, and R, you'll need to combine logical statements (like you'd use in filter(), Section \@ref(r-dplyr-filter)) with the sum() function. This will give you a count of the number of cases where the logical condition is TRUE. For example, you can count the number of Male individuals in each Period and Capture Status with this:

recap_data |> 
  group_by(Period, Capture_Status) |> 
  summarize(N_males = sum(Sex == "M"))

You may need to combine multiple logical conditions for some of these.

You can use an arrange() function (Section \@ref(r-dplyr-arrange)) to figure out what the $hat{N}$ corresponds with the highest sample size.

Visualizing the effect of sample size on \$\hat{N}

Create a plot from lp_estimates with sample_size on the x axis and N_hat on the y axis. Be sure to caption your figure.

Estimates by Sex and Sex Ratios

We want to estimate the number of males and females separately. Sampling intensity isn't important for this, so let's only include the data from the whole-class.

#### Sex Ratios ####
recap_data_full <- recap_data |> 
  filter(Team_combo == "ABCDE") # You may need to change this if there are more than 5 teams, e.g., "ABCDEFG"

Now, you'll want to use the same approach you used in Section \@ref(hw-mrr-lp-calc) to estimate the number of males and females from this dataset. This should result in a two-row data frame (one row for males, one for females). Be sure to inspect the data; if you have a row where sex is NA, you'll need to filter it out. You can do this by adding |> filter(!is.na(Sex)) to your chain of data transformations. Save this dataframe as the variable lp_estimate_by_sex.

Is the Sex Ratio 50:50?

Next, we want to run a one-sample chi-squared test to see if the sex ratio is unbalanced. First, we define the observed counts (the LP estimates) and expected frequencies. Then, use a chi-squared test with the following arguments

sr_observed_counts <- lp_estimates_by_sex$N_hat
sr_expected_freq   <- c(0.5, 0.5) # 50:50
chisq.test(x = sr_observed_counts,
           p = sr_expected_freq) #  You MUST name the p argument

Alternate Methods for Population Size Estimates

In this section, we'll be comparing the LP method with a continuous-sampling approach that uses regression analysis. For background, see Section \@ref(lab-mrr-trinidad).

The data

This data is saved as an excel file; to import it, we'll be using the read_excel() function, from the readxl package.

#### Regression vs LP analysis (Trinidad Data) ####

trinidad_data <- read_excel(trinidad_file, sheet = "Sheet1")

The columns we care about

• Sample_Period: These data were collected over Late April/ Early May, July, and August.
• Date: the date. Not used in this analysis.
• new_captures: Number of new individuals captured on that date.
• recaptures: Number of recaptures on that date.
• total_captured: Total number of individuals captured on that date.
• cumulative_M: Total number of unique individuals marked since the beginning of the sample period.
• prop_recapture: Proportion of individuals captured on date that are recaptures.
• The two columns beginning with Collect_ can be ignored

Lincoln-Petersen estimate

Use a grouped filter() command to create a data frame containing the highest proportion of recaptures in each sampling period (3 rows total). Calculate $\hat{N}$ based on these data. Note that some of the column names you'll need to use will be different, and you may need to calculate M_1, R_2, or S_2 from a combination of existing columns.

Regression estimate

Filter your data to only include the first sample period and run a linear regression (Section \@ref(r-stats-lm)), with prop_recapture as the predictor/x/independent variable and cumulative_M as the response/y/dependent variable. Save the result as trinidad_lm_apr. View the results with print(trinidad_lm_apr) and take a look at the coefficients. These are the slope and intercept for the regression line. From these, you could calculate the expected value of cumulative_M for any value of prop_recapture. Therefore, calculating the value of cumulative_M when prop_recapture = 1 could be another way to estimate the total population size.

While it's pretty straightforward to do this by hand, we're going to use R's predict() function, which predicts the y values associated with new x values. Versions of predict have been designed for a wide variety of different statistical models in R, so understanding it can help with a variety of tasks.
First, define a new data frame that contains the new predictor variables (in this case, prop_recapture = 1). Then, use predict with the regression object and new data:

trinidad_predict_df = data.frame(prop_recapture = 1) 
trinidad_nhat_lm_apr = predict.lm(trinidad_lm_apr, newdata = trinidad_predict_df)

Do this for all three sample periods (you don't need to make multiple trinidad_predict_df's, they're all the same) and compare these values with the Lincoln-Peterson estimates.

Regression figure

Create a figure in ggplot for your regressions (Section \@ref(r-ggplot-contx-conty)). You should use geom_smooth(method = 'lm') and geom_point() to display both the raw data and the regression line. Facet your data by sample period with facet_wrap() and make sure all three periods are stacked on top of each other in a single column (see ?facet_wrap for details).

Hardy-Weinberg Equilibrium & Optix

For this, we'll be doing some quick population genetics.

The data

Import the data as:

#### HWE Analysis ####

genotype_data <- read_csv(genotype_file)

The only column we care about for this is Genotype, which will be one of FF, FH or HH.

Calculating HWE ratios

First, we want to get the count and frequency of each genotype; do this with a grouped summarize(), followed by a mutate(), and name your new columns count and frequency. Your genotype frequencies should sum to 1. Save the new data frame as genotype_observed.

To make the frequencies easier to work with, we're going to re-shape the data (this is similar to what is covered in Chapter \@ref(r-tidyr), but it isn't covered there yet). Do this:

gt_freq_observed <- genotype_observed |> 
  select(Genotype, frequency) |> 
  pivot_wider(names_from = Genotype, values_from = frequency)

Next, use summarize() on gt_freq_observed to calculate your allele frequencie for F and H from the genotype frequencies as described in Section \@ref(lab-mrr-hwe-theory). Save the new data frame as allele_freq. Then, calculate your expected HWE genotype frequencies from allele_freq with another summarize() function and save it as gt_hwe. Make sure that the columns of gt_hwe are in the same order as gt_freq_observed. Finally, add a column for HWE frequencies to genotype_observed:

genotype_obs_hwe <- genotype_observed |> 
  mutate(HWE_freq = as.numeric(gt_hwe)) # as.numeric converts gt_hwe from a data frame to a vector

You'll want to report this table.

Testing for HWE

Run a one-sample chi-squared test (as you did earlier with the sex ratios); you'll want to use two columns from genotype_obs_hwe for x and p.

Christopher-Peterson/Bio373L-Book documentation built on Oct. 26, 2022, 8:36 a.m.