Data In The Wild
  1. Module 5
  2. Overview
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  • Module 1
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    • 1.1: Introduction to R and RStudio
    • 1.2: Intro to Coding in R
    • 1.3: Introduction to the tidyverse
    • Assignment 2
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  • Module 2
    • Overview
    • 2.1: Good Food Gone Bad
    • 2.2: Plotting with ggplot2
    • 2.3: Data Visualization
    • 2.4: Exploring geom Functions
    • 2.5: Module 2 Wrap-Up
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  • Module 3
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    • 3.1: Leopard Seals
    • 3.2: T-Tests
    • 3.3: Comparing (Multiple) Means
    • Assignment 1
    • Assignment 2
  • Module 4
    • Overview
    • 4.1: Combining Datasets (Joins & Binds)
    • 4.2: K-Nearest Neighbor
    • 4.3: Roads and Regressions
    • 4.4: Multiple Regression
    • 4.5: Writing Functions
    • Assignment 1
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    • Assignment 4
  • Module 5
    • Overview
    • 5.1: Population Growth
    • 5.2: Sustainable Fishing
    • 5.3: Comparing Populations
    • Assignment 1
    • Assignment 2
    • Assignment 3
  • Final Project

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  1. Module 5
  2. Overview

Module 5: Sustainable Fishing

Overview

With fishing bays selected and roads planned, the team must now figure out how many fish they can sustainably harvest without depleting the population. Students are introduced to population growth models - exponential and logistic - and use them to estimate carrying capacities and maximum sustainable yield for Antarctic toothfish populations. They then learn to fit logistic growth curves to real data and compare populations statistically. Along the way, students also step outside of R to practice finding and citing scientific literature and writing a scientific abstract in preparation for the final project.

Note: Module 5 is an example of how Data in the Wild can be extended or adapted to reflect a specific disciplinary focus. This version was developed by Dr. Ellen Bledsoe and draws on her background in ecology. The course can be taught with or without this module, and instructors are encouraged to develop alternatives that reflect their own expertise or research context.

Learning Objectives

By the end of this module, students will be able to:

  • explain the difference between exponential and logistic population growth and identify each from a graph
  • define carrying capacity (K) and intrinsic rate of increase (r) and use them in population growth calculations
  • calculate maximum sustainable yield from population growth parameters
  • fit a logistic growth model to population data using the drc package
  • overlay a fitted logistic growth curve onto a ggplot2 scatter plot
  • compare multiple populations statistically using tools from previous modules
  • find and evaluate scientific literature using AI-assisted search tools
  • summarize scientific articles and integrate them into written work
  • write a scientific abstract for a research question using supporting literature

Lessons & Assignments

Lessons

Lesson QMD Data Files
5.1: Population Growth Download fish_pops.csv
5.2: Sustainable Fishing Download fish_pops.csv
5.3: Comparing Populations Download comparing_populations.csv

Assignments

Assignment QMD Data Files Outputs
M5A1: Literature Search and Abstract Writing Download None None
M5A2: Choosing a Bay to Fish Download fish_abund_2010-2024.csv Download
M5A3: Comparing Populations Download toothfish.csv Download

GitHub

To clone the entire course, visit the course GitHub repository.

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