Course Information

Instructor

Dr Michael F. Nelson

Holdsworth Hall, Room 311

Email:

Phone: 651-308-5430

It’s unlikely, but if you need to send me physical email, you can send it to the Environmental Conservation Department office:

University of Massachusetts 160 Holdsworth Way Amherst, MA 01003-9285 1 (413) 545-2665

Teaching Assistant

Anastasia Ivanova

Email:

Covid 19 and Mask Policies

The Covid 19 situation is rapidly changing in sometimes unpredictable ways.

The current UMass mask policy is that instructors and students shall wear masks during class. We’ll be mindful of changing conditions and policies as they occur.

Course Meetings

Live course meetings: Tuesdays and Thursdays 8:30 to 9:45 AM in Holdsworth Hall room 308.

Office Hours: Tuesdays and Thursdays 1:00 - 2:00 PM and by appointment

  • Live office hours will be held in my office (Holdsworth 308).
  • You may also attend office hours virtually via Zoom. You’ll find the zoom link in the course Moodle page.

Office hours may need to be adjusted based on students’ schedule needs. We’ll discuss the timing of office hours in the first two weeks of class.

UMass Syllabus Guidelines

The following sections are to be included in course syllabi per the UMass faculty senate guidelines.

This syllabus includes information on each of topics in the, indicated syllabus sections, as well as additional sections specific to this course in the body of this syllabus document.

  • Course objectives are in the Learning Objectives and Course Themes sections.

  • Expectations and requirements such as papers, lab reports or exams are in the Course Structure and sections.

  • Attendance policies in the Course Structure section.

  • Grading criteria and the approximate weight of each course requirement in the final grade are in the Grading section.

  • Examination schedule and any make up or rescheduling policies. Information on these topics is found in the Grading, Final Take-home Exam, and Course Structure sections.

  • Policies on academic honesty. Please see the the Accommodation and Academic Honesty section.

  • Office, phone and, mailbox numbers of instructor (instructors should include preferred online contact information (i.e. instructor or course email or use of Blackboard/Moodle communication tools) are in the Course Information section.

Course Description

This course provides students with an introduction to basic concepts critical to the proper use and understanding of statistics in environmental conservation and prepares students for subsequent ECo courses in statistical modeling.

Learning Objectives

The overall goal of this course is to provide students with a gentle introduction and overview of the range of statistical techniques widely used in ecology and conservation. The specific objectives are for students to:

  • Gain a broad understanding of the role of statistics in ecology and conservation.

  • Recognize and critically assess study designs commonly used in ecology and conservation.

  • Assess which types of analyses are appropriate for different study designs and data types.

  • Build a broad conceptual and applied understanding of the range of frequently used analyses.

  • Refine written and oral communication skills.

Course Materials

Electronic versions of all required texts are available from UMass Libraries unless otherwise noted.

Any materials not available in electronic form from UMass Libraries will be made available on the course Moodle.

NOTE: Additional items may be added as needed throughout the semester.

UMass Libraries and Course Materials

It is important for you to individually access the course reading materials through the UMass libraries. The library uses uses student and faculty access volume to justify budgets and and allocate funding for subscriptions to journals and other resources.

In order to support the UMass Libraries’ mission:

  • I will not be posting electronic copies of materials you can access through the library.
  • I ask that each student independently search for and access the readings via the UMass Libraries website.
  • Please do not share downloaded pdfs of books or articles among students.

I know this seem like an inconvenience, but it is crucial that we support the UMass Library system as a critical resource.

Textbooks

  • Bolker, B.M. (2008). Ecological models and data in R (Princeton University Press). [Electronic version available at UMass Libraries]
    • eISBN: 978-1-4008-4090-8
  • Zuur, A.F. (2007). Analyzing ecological data (New York; London: Springer). [Electronic version available at UMass Libraries]
    • ISBN: (online) 978-0-387-45972-1
  • Kevin McGarigal’s course readings for previous versions ECo 602/634. Available on course Moodle

Additional Required Readings

Students can access these resources through the UMass libraries website.

  • Jorge Luis Borges, The Library of Babel (Original title in Spanish: La Biblioteca de Babel. Don’t worry, you can read the English translation!). libraryofbabel.info

  • Epstein, J.M. (2008). Why Model? Journal of Artificial Societies and Social Simulation 11, 12.

    • ISSN 1460-7425
  • Bang, Megan, Ananda Marin, and Douglas Medin. If Indigenous Peoples Stand with the Sciences, Will Scientists Stand with Us? Daedalus 147, no. 2 (March 1, 2018): 148–59.

Additional readings may be added throughout the course to reinforce topics as needed.

Supplementary Readings

This is a citation list of other resources used in the lectures, class discussions, etc. These are optional, but may be of interest to you.

  • Beckmann, J.P., and Berger, J. (2003). Using Black Bears to Test Ideal-Free Distribution Models Experimentally. Journal of Mammalogy 84, 594–606.

  • Davis, S.M., Childers, D.L., Lorenz, J.J., Wanless, H.R., and Hopkins, T.E. (2005). A conceptual model of ecological interactions in the mangrove estuaries of the Florida Everglades. Wetlands 25, 832.

  • Fischer, J., Lindenmayer, D.B., and Fazey, I. (2004). Appreciating Ecological Complexity: Habitat Contours as a Conceptual Landscape Model. Conservation Biology 18, 1245–1253.

  • Ray, C., and Collinge, S.K. (2014). Quantifying the dominance of local control and the sources of regional control in the assembly of a metacommunity. Ecology 95, 2096–2108.

Computer Resources

Setup instructions for all of the course software is covered in the Software Setup assignment.

Microsoft OneDrive

Microsoft OneDrive is one of the preferred online storage platforms for UMass. Students taking UMass courses have access to a university OneDrive account.

With your OneDrive account, you’ll have browser-based access to all the Office 365 applications with OneDrive. This is a very convenient format for working on documents simultaneously with collaborators or in small groups for class.

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

Azure Virtual Desktop

Azure Virtual Desktop (AVD)

Azure Virtual Desktop, formerly Windows Virtual Desktop (WVD), allows you to run a virtual Windows session within a web browser from any computer. This is a great way to run the course software if your usual computer is unavailable, or if you can’t install the course software directly on your machine.

AVD also communicates with Microsoft OneDrive, the cloud storage service we’ll use for this course.

Students enrolled in courses at UMass Amherst can find more information and obtain access at the UMass AVD page

NOTE: The access form may be one or more semesters out of date (i.e. it may still say Spring 2021). Don’t panic, you’re still at the right page. Just fill out the form as usual.

NOTE: If you are using AVD for any other courses this semester (such as Intro to GIS), check to see if those courses require any specialty software. When you sign up for AVD access, you’ll be prompted to select specialty software that you need. It is easy to select the software you need at this stage. It is difficult, but not impossible, to request packages after you already have AVD access.

R and RStudio

We’ll use the free and open-source R programming language. You can download an appropriate version of R here: https://cran.r-project.org/

We’ll use the RStudio Integrated Development Environment (IDE) to edit and run our R code. Download RStudio here: https://rstudio.com/

GitHub and GitHub Desktop

  • You will need to create a free account on github.com.
  • You can also find links to install GitHub Desktop on your computer.

DataCamp

We’ll use DataCamp materials for R training. You’ll find an invite link in the course Moodle.

Other Software

In addition, you’ll need word processing software, as well as software that can easily display comma separated values (CSV) files.

All UMass students are eligible for a free license of Microsoft Office 365, a suite of programs including Word, Excel, and PowerPoint. Visit the Information Technology page for more info: https://www.umass.edu/it/software/microsoft-office-365-education

You may also use browser-based software such as Google Docs. Your UMass Box account gives you access to browser-based versions of Word, Excel, and PowerPoint.

Course Themes

These themes form a useful framework of how to think about modeling and working with data. We will emphasize them throughout the course. These themes form a useful framework of how to think about modeling, working with data, and science in general. We will emphasize them throughout the course.

  • Model Thinking
  • The Dual Model Paradigm
  • Uncertainty
  • Working with Mathematical and Statistical Models

The sections below describe each theme in greater detail.

1: Model Thinking

“All models are wrong, but some are useful” - George Box

What is a model?

  • In this course, we’ll think of a model as a simplified representation of reality.
  • We use models all of the time, even though we may not be conscious of it!

What is model thinking?

  • Model thinking is a paradigm in which we learn to intentionally see the world through the lens of models.
  • When a model thinker encounters a new situation, they implicitly identify key components, hypothesize relationships, and start thinking about how they could simplify the system to to understand how it works!

Modeling is a broad concept. For most of this course we’ll focus on mathematical and statistical models.

“Everything should be made as simple as possible, but no simpler.” - Albert Einstein

2: The Dual Model Paradigm

The world is a complicated noisy place. To understand it, our models need to address expected or typical behavior as well as the variation.

In any of the systems we are interested in modeling, we can imagine there exist:

  • An expected typical or average behavior.
  • A range of natural variation that we expect to see around the average behavior.

The Dual Model Paradigm is a way to think about modeling natural systems in which we specify two models:

Determinsitic Models

A mathmatical model, usually defined by a formula, can help us describe the average behavior.

A deterministic model describes expected, or average behavior.

  • The deterministic model describes a relationship between a response and one or more predictors.
  • It is often known as a model of the means.
  • We commonly use a deterministic model to address our primary questions, such as:
    • How does % forest cover correspond with salamander populations?
    • How do predator populations and winter temperatures correlate with prey populations?

Stochastic Models

Variation is everywhere! The power of statistical models lies in understanding and dealing with noise.

A stochastic model describes variability.

  • Stochastic models can help understand the possible range of behaviors in our model.
  • We use the stochastic model to assess the significance of our statistical inferences.

3: Uncertainty

Uncertainty is a mysterious, ubiquitous, and sometimes scary, concept, but we can learn to embrace, quantify, and understand how to usit it to our advantage!

We’ll develop an intuitive understanding of what uncertainty means in the context of environmental data. To accomplish this we’ll think about ways to identify sources of and ways to quantify uncertainty.

We’ll also learn to embrace uncertainty, not as something to be feared or eliminated, but as something we can understand and learn from!

4: Mathematical and Statistical Modeling

We’ll learn how to recognize patterns and understand when mathematical and statistical models can help us understand a problem.

Mathematical and statistical models are powerful scientific tools we can use to propose, and test, hypotheses about how a natural system behaves.

We use mathematical objects, such as functions and probability distributions, to build these models. Symbolic representations can seem overly complicated, opaque, and hard to understand. We’ll develop tools to recognize and understand the important components of these mathematical objects.

To learn to apply appropriate mathematical and statistical models, we’ll focus on intuition-building to help us choose and fit an appropriate model to our data.

Course Structure

Most of the course will consist of a mix of the following elements:

Weekly Course Events

  • Required readings
  • Live lectures
  • Occasional pre-recorded mini lectures
  • Reading and lecture questions
  • Individual and small group assignments

In addition, we will have a learning outcomes assessment at the beginning of the course and a final, take-home learning outcomes assessment and exam at the end.

Check out the Grading section to see how each course component contributes to your individual grade.

Course Component Descriptions

Readings and mini-recorded Lectures

Course readings and video mini-lectures will provide the content and background information needed to actively participate in the synchronous sessions.

The video mini-lectures will reinforce, complement, and supplement the concepts from the readings. They will also be used to address specific technical issues (such as software setup questions) as well as commonly asked questions.

You can watch the videos at your own pace, however to maximize the value of the the concurrent sessions, I’ll expect you to complete the readings, videos, and associated assignments prior to the live sessions.

I’ll post lecture notes to accompany each mini-lecture.

There will be short question sets on Moodle covering the material in the readings and videos (see Readings and mini-lecture questions below for details).

You may also be asked to submit questions or topics for discussion (via Moodle) based on the readings/videos prior to the live meetings.

Reading and mini-lecture questions

These are brief multiple choice or short answer question sets that reinforce topics in the readings and/or mini lectures. You must complete these before the corresponding live session. If you have completed the question sets prior to the live session, you may revise incorrect answers after the live session.

You may receive a maximum of 25% credit for question sets submitted after the corresponding live sessions.

Synchrous meetings

Our synchronous meetings are a chance for you to discuss the materials and ask questions of me and your peers. The structure of each live meeting will be tailored to the needs of the week’s topics and assignments.

  • The live sessions will consist of lecture segments, small-group breakout sessions for discussions and collaborations, and full class Q+A sessions.

Individual Assignments

The individual assignments provide an opportunity to engage more deeply with selected, important course concepts than the reading and lecture questions.

Every student will submit original answers, but you are strongly encouraged to work together with your classmates on the individual assignments. When you work collaboratively, you will be asked to list the students with whom you worked.

In-Class Small Group Activities

We will have small group activities and discussions during many of our live meetings.

Some of these activities will have questions for your group to submit via Moodle. These are meant to be completed and submitted by the group during our live session. It will be essential to be up-to-date on the readings and mini-lectures so that you can be a positive contributor to the in-class activities!

Final Take Home Exam

The final exam covers a range of the most important take-home ideas from the course.

The exam and objectives assessment is meant to be a tool to help you reinforce the key concepts from the course that you can use in your future careers.

It helps both you and me to assess your success achieving the learning outcomes, as described in the learning objectives section of the syllabus.

As students, When we work our way through a course it’s easy to underestimate how much we have learned. My aim is for the final and outcomes assessment to remind you of how hard you worked throughout the semester and to be aware of your intellectual growth and the suite of tools you have learned.

The exam will emphasize concepts in the framework of the course themes.

Specific details about the final exam will be discussed communicated in the later weeks of the semester.

Attendance

You are required to attend all real-time sessions, i.e. the Tuesday and Thursday sessions from 8:30 to 9:45. If you cannot attend due to illness, conference attendance, or other extenuating circumstances, please contact me at least 1 day before the session so that we can make arrangements to make up any missed in-class materials.

You must watch all of the required pre-recorded mini-lectures prior to the associated real-time sessions. After the first week, the mini-lectures will be made available at least one week before the associated live session. Since these are asynchronous, you can watch them any time that fits your own schedule.

I understand that life events often happen when we least expect, so please keep an open line of communication with me as needed when you need additional time, or must miss class.

Grading

Your grade for the course is composed of the following elements:

Item Weight
Learning Objectives Assessment 5 %
Reading and Lecture questions 20 %
Individual Assignments 25 %
In-class Group Assignments and Activities 30 %
Take-home final and Learning Outcomes Assessment 20 %

Weekly Concepts Overview

This is a brief overview of the topics covered each Week .

Note that the schedule is subject to change as needed.

Click on Week to expand or hide.

Week 1 - Introductions, Why Model?
  • Introduction to course
  • Course themes
  • Model thinking
  • Computing for scientists
Week 2 - Environmental Data and Model Thinking
  • Data scales
  • The row-format paradigm
  • Data recording, cleaning, and metadata
  • Model thinking: scientific and cultural contexts
Week 3 - Data Exploration and Deterministic Functions
  • Numerical and graphical data exploration
  • Functions
Week 4 - The Dual-Model Paradigm, Uncertainty and Probability
  • Essential probability theory basics
  • Uncertainty and stochastic processes
Week 5 - Frequentist Statistics
  • Conceptual framework of the frequentist paradigm
  • Sample and population
  • Description and inference
Week 6 - The Sampling Distribution and Confidence Intervals
  • Describing a sample vs. describing the sampling process
  • Frequentist interpretation of confidence intervals
Week 7 - Least Squares: Modeling Categorical and Continuous Data
  • Model fitting
  • Intro to t-tests, ANOVA, and simple linear models
Week 8 - Linear Models 1: Concepts
  • ANOVA
  • simple and multiple linear regression.
Week 9 – Linear Models 2: Assumptions, Diagnostics, and Selection
  • Using models with real-life, messy data
Week 10 – Linear Models 3: Extending the Linear Model
  • Survey of extended linear techniques:
  • generalized linear models
  • mixed models
  • generalized least squares
  • other extensions of the linear model
Week 11 – Frameworks: Frequentist, Bayesian, and Maximum Likelihood
  • Contrasts among inferential paradigms
Week 12 – Course Concepts Revisited
  • Review of course concepts in the context of the course themes
Week 13 – Where to go from here?
  • Spatial Analysis
  • Multivariate statistics
  • Simulation
  • Machine learning techniques

Academic Honesty and Accommodations

For additional details please visit: http://www.umass.edu/dean_students/codeofconduct/acadhonesty/

Accommodation Statement

The University of Massachusetts Amherst is committed to providing an equal educational opportunity for all students. If you have a documented physical, psychological, or learning disability on file with Disability Services (DS), you may be eligible for reasonable academic accommodations to help you succeed in this course. If you have a documented disability that requires an accommodation, please notify me within the first two weeks of the semester so that we may make appropriate arrangements.

Academic Honesty Statement

Since the integrity of the academic enterprise of any institution of higher education requires honesty in scholarship and research, academic honesty is required of all students at the University of Massachusetts Amherst. Academic dishonesty is prohibited in all programs of the University. Academic dishonesty includes but is not limited to: cheating, fabrication, plagiarism, and facilitating dishonesty. Appropriate sanctions may be imposed on any student who has committed an act of academic dishonesty. Instructors should take reasonable steps to address academic misconduct. Any person who has reason to believe that a student has committed academic dishonesty should bring such information to the attention of the appropriate course instructor as soon as possible. Instances of academic dishonesty not related to a specific course should be brought to the attention of the appropriate department Head or Chair. Since students are expected to be familiar with this policy and the commonly accepted standards of academic integrity, ignorance of such standards is not normally sufficient evidence of lack of intent