Welcome to Introduction to Quantitative Ecology at Umass Amherst!

About the course

Logistics

Instructor: Michael France Nelson

The course meets Tu/Th 10:00 - 11:15 AM in Flint Lab, room 201.

Office Hours:

  • Mondays 3:00 - 4:30
  • Other times by appointment (email me for scheduling)

Mike’s office hours are in person (Holdsworth Hall, room 311) and via Zoom. You’ll find the office hours zoom link in Moodle.

Course Description

This introductory statistics course aims to provide students interested in ecology with a supportive, encouraging and comfortable environment for developing a sound knowledge of core statistical concepts in ecology.

Ecology, the study of the relationships between organisms to one another and their environment, is a discipline concerned with quantifying the relationships we observe in nature. The objective of the course is to demystify statistics and help develop the basic level of understanding that all future ecologists should possess.

In this course, you will develop a detailed understanding of why and how to apply the great variety of statistical tools available for answering important ecological questions.

This course fulfills a R2 General Education Requirement at UMass.

Syllabus

You can view the syllabus as a webpage or download a copy in pdf format.

Late Assignment Policy

Assignments will be penalized by 25% if submitted up to one week after the due date. After one week, late assignments will not be accepted unless arrangements have been made.

If you need extra time on an assignment, let us know right away. Life happens and we’re happy to extend deadlines when needed.

Course Software

In this course, we’ll be using R, RStudio, and Microsoft Excel.

In addition, you’ll set up your UMass Azure Virtual Desktop and OneDrive accounts.

The Set-up Course Software assignment description contains detailed instructions for setting up the course software.

R and RStudio

R and RStudio are available for Windows, Mac OSX, and Linux. The Set-Up Course Software assignment contains detailed instructions.

Help, I can’t install R and RStudio!

Occasionally there are problems with installing R and RStudio on your personal computer. In that case there are several workarounds we can discuss on an individual basis.

Azure Virtual Desktop (AVD)

UMass students are eligible for a Microsoft Azure Virtual Desktop account:

  • AVD allows you to run windows through your browser, or via a desktop client.
  • Windows runs on a UMass computer and you control the virtual desktop as if you were running windows on your machine.
  • Check out the UMass Azure Virtual Desktop page for info on how to get started.

If you are also using AVD for a different course, make sure you select any specialized software you need when you first request access.

For example, if you are taking Introduction to GIS, or another GIS course, you must request access to ArcGIS when you first sign up for AVD.

  • If you forget, it’s possible to get access to the software later, but the process is tedious and can take some time.

Excel

We’ll be using Microsoft Excel for some of the exercises as well as some data management tasks.

You can run Excel either as a stand-alone program, or in a browser-based interface using OneDrive (see below)

UMass students can get a free copy of Excel via the Microsoft Office 365 education licensing system (see below)

NOTE: The Gardener text refers to the Analysis Toolpak, a plugin for Excel with functionality for basic statistical tests.

  • We will not be using Analysis Toolpak in this course since it is not available for the online version of Excel.

Microsoft OneDrive

If you haven’t already done so, you should navigate to the Online File Storage & Collaboration page to set up your account.

You can use all of the Office 365 applications with OneDrive, and it’s very convenient for working on documents simultaneously with collaborators or in small groups for class.

OneDrive [mostly] seamlessly integrates with Azure Desktop.

Microsoft Office 365

All UMass students are eligible to obtain a license for Microsoft Office 365.

Visit the Microsoft Office 365 Education page for info and installation instructions.

Weekly Schedule

Note: This schedule is subject to change as needed

Week 1: Introductions and Software Setup. Feb 7, 9

Week 1 Topics and learning objectives:

  • Familiarize yourself with the course structure, requirements, grades, expectations, etc.
  • Set up the course software.
  • What is quantitative ecology?
  • Appreciate the value of quantitative skills in ecology, environmental, and conservation science.

Readings

Tuesday

Thursday

Assignments Due Next Week

Week 2 Quantitative Ecology and the scientific process. Feb 14, 16

Week 2 Topics and Learning Objectives

  • How the skills and topics we will cover in this class will benefit you in your future as a student and a professional.
    • The Barraquand et al. (2014) paper will help you with this.
  • Practice R and RStudio
  • Appreciate the value of quantitative reasoning in ecology (and beyond).
  • Basics of data collection.

Readings

  • Gardner Chapter 1: Planning
  • Barraquand et al. 2014
  • Slide Deck 2

Tuesday

  • Lecture: Scientific Process
    • Steps in the scientific process, planning
    • Hypotheses
    • Data types and scales
    • Vectors, matrices, and data frames in R
  • In-class groups: working with vectors and data frames

Thursday

  • Q&A from Tuesday’s class.
  • Lecture: Data and Sampling
  • In-class: Begin Desert Shrubs group assignment

Assignments Due Next Week

You can check out this page and this video for a guide on how to use here() to read csv files. –>

Week 3: Data Management And The Row Data Paradigm. Feb 21, 23

Week 3 Topics and Learning Objectives

  • Work with raw data
  • Sampling units and variables
  • Arrange biological data electronically in row-format: csv files
  • Data management in Excel

Readings

  • Gardener Ch. 2: Data Recording
  • Gardener Ch. 3: Beginning Data Exploration
  • Slide Deck 3

Tuesday

  • Lecture: The row-data paradigm and data management.
  • Group time for Desert Shrubs assignment.

Thursday

  • No class - adverse weather conditions.
    • Extra office hours on class zoom.

Assignments due next week

Week 4: Data Exploration 1 - Numerical Data Exploration. Feb 28, Mar 2

Week 4 Topics and learning objectives:

  • Measures of center
  • Measures of spread
  • 5-number summaries
  • Summarizing numerical data in R

Readings

Tuesday

  • Finish Deck 3
    • Variables: predictors and responses
    • Data file formats
    • Data cleaning and management
  • Deck 4: Numerical Data Exploration
  • In-Class data import

Thursday

Assignments due next week

Week 5: Data Exploration - Graphical Data Exploration. Mar 7, 9

Week 5 Topics and learning objectives:

  • Graphical data exploration:
    • Plot types
    • How to interpret plots

Readings

  • Gardener Chapter 6: Exploring data - using graphs
  • Slide Deck 5

Tuesday

Thursday

Assignments due next week (after spring break)

Week 6: Inferential Statistics And Testing For Differences Between Two Groups Mar 21, 23

Week 6 Topics and learning objectives:

  • Descriptive and inferential statistics
  • Tests for differences
  • Plots for describing differences
  • Parametric and non-parametric tests
  • Assumptions

Readings

  • Gardener Chapter 5: Exploring data - which test is right?
  • Gardener Chapter 7: Tests for differences
  • Slide Deck 6

Tuesday

  • Lecture: Lecture today is a free zoom period to catch up on group work.

Thursday

  • Lecture: R formulas and plotting limitations of 2-sample tests.

Week 7: Testing For Differences Among More Than Two Groups. Mar 28, 30

Week 7 Topics and learning objectives:

  • Testing for differences

Readings

  • Gardener Chapter 10: Differences between more than two samples
  • Slide Deck 7

Tuesday

  • Lecture: Differences among 3 or more groups, ANOVA, assumptions, etc.
  • In-class t-tests

Assignments due next week

Week 8: Testing For Correlations And Associations Apr 4, 6

  • Categorical and numerical data
  • Correlations
  • Contingency tables

Week 8 Topics and learning objectives:

Readings

  • Gardener Chapter 8: Tests for linking data – correlations
  • Gardener Chapter 9: Tests for linking data – associations
  • Slide Deck 8

Tuesday

  • Lecture: Continuous quantities, correlations, associations, tests
  • In-class chi-square tests

Thursday

Week 9: Regression Modeling - Simple and Not-So-Simple Apr 11, 13

Week 9 Topics and learning objectives:

  • What is a linear regression?
  • Predictors and response.
  • Model assumptions and diagnostics.

Readings

  • Gardener Chapter 11: Tests for linking several factors
  • Deck 9

Thursday

  • Begin Group Assignment: Introduction to National Ecological Network Bird Data
    • Before class:
      • Read through the assignment description.
      • Download the data files to your ‘data’ subdirectory.
      • Download and read the data description file (pdf)

Assignments due next week

Week 10: Putting It Together - ANOVA, Regression, and ANCOVA Apr 20

Week 10 Topics and learning objectives:

  • Understanding model coefficient and ANOVA tables
  • Choosing the right model

Readings

Tuesday

  • NO Class

Thursday

  • In-Class: work on group NEON assignment.

Week 11 Model Selection, Reporting, and Communicating Apr 25, 27

Week 11 Topics and learning objectives:

  • Interpreting and communicating models

Readings

Tuesday

  • In-Class: R Tips + Tricks + Cheetsheet

Assignments due next week

Week 12 Interactions And Intro To Final Projects May 2, 4

Topics and learning objectives:

  • Interactions
  • Your questions

Tuesday

Thursday

Week 13 Ecological Community Similarity And Diversity, Spatial Ecology May 9 - 10

Topics and learning objectives:

  • Measures of species diversity
  • Complete spatial randomness

Readings

Tuesday

  • Community and Spatial Ecology

Thursday

  • Course Concept Recap

Week 14 Final Projects May 16

Course Materials

Required Readings

You can access the reading materials through UMass Amherst libraries. The Gardener text is available for reading online. You may also purchase a physical copy.

Additional readings may be included as needed throughout the semester.

  • Gardener, M. (2017). Statistics for ecologists using R and Excel: Data collection, exploration, analysis and presentation (Second Edition). Pelagic Publishing.

  • Barraquand et al. (2014), Lack of quantitative training among early-career ecologists: a survey of the problem and potential solutions. PeerJ 2:e285

  • DOI: https://doi.org/10.7717/peerj.285

Lecture Notes

Note: Weekly slide decks will be posted on the Monday of the corresponding week.

DataCamp

We’ll use some of DataCamp’s materials to get acquainted with R.

Enrolled students can find an invite link to create a free account in the course Moodle site.

https://datacamp.com

This class is supported by DataCamp, the most intuitive learning platform for data science and analytics. Learn any time, anywhere and become an expert in R, Python, SQL, and more. DataCamp’s learn-by-doing methodology combines short expert videos and hands-on-the-keyboard exercises to help learners retain knowledge. DataCamp offers 325+ courses by expert instructors on topics such as importing data, data visualization, and machine learning. They’re constantly expanding their curriculum to keep up with the latest technology trends and to provide the best learning experience for all skill levels. Join over 5 million learners around the world and close your skills gap.

In-Class Activity Schedule

You’ll find links to all the in-class assignments for the semester in the following table:

Week 1 Tue Rarefaction Thu Introductions/R Markdown
Week 2 Tue Vectors and Data Frames Thu Begin Group Desert Shrubs Assignment
Week 3 Tue Group Time For Desert Shrubs Thu Class cancelled - inclement weather
Week 4 Tue File Import and Logical Subset Exercise Thu Random Numbers/Exploration and Begin Group Salamander Data Exploration Assignment
Week 5 Tue Histograms Thu Multi-Panel Plots and Ogranize Group Penguin Tests for Differences
Week 6 Tue None Thu None
Week 7 Tue T-tests Thu In-Class ANOVA
Week 8 Tue In-Class Chi-square tests Thu Group Time for Salanamder Correlation/Association
Week 9 Tue In-Class NEON Bird Data Thu Organize NEON Bird Data
Week 10 Tue No Class Thu Group Time for NEON Bird Data
Week 11 Tue TBD Thu In-Class Confidence/Significance
Week 12 Tue Start Individual Assignment: R Markdown 2 Thu Organize Group Penguin ANCOVA
Week 13 Tue TBD Thu TBD
Week 14 Tue TBD Thu No Class