Dr Michael F. Nelson
Holdsworth Hall, Room 311
Email: michaelnelso@umass.edu
Phone: 651-308-5430
It’s unlikely, but if you need to send me physical mail, you can send it to the Environmental Conservation Department office:
University of Massachusetts 160 Holdsworth Way Amherst, MA 01003-9285 1 (413) 545-2665
The Covid 19 situation is rapidly changing in sometimes unpredictable ways.
The current UMass mask policy (as of August 2022) is that mask use is not required for instructors and students during class. We’ll be mindful of changing conditions and policies as they occur. Let’s respect everyone’s individual choices about masking.
You can always find the latest UMass COVID 19 information here at the UMass Amherst COVID-19 Information page. Information about masking is available in this FAQ
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
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.
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.
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.
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.
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.
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 know this seem like an inconvenience, but it is crucial that we support the UMass Library system as a critical resource.
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.
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.
This is a citation list of other resources that may be relevant to 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.
Setup instructions for all of the course software is covered in the Software Setup assignment.
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, 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.
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/
We’ll use DataCamp materials for R training. You’ll find an invite link in the course Moodle.
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 additional information.
You may also use browser-based software such as Google Docs.
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.
The sections below describe each theme in greater detail.
“All models are wrong, but some are useful” - George Box
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
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:
The Dual Model Paradigm is a way to think about modeling natural systems in which we specify two models:
A mathmatical model, usually defined by a formula, can help us describe the average behavior.
A deterministic model describes expected, or average behavior.
Variation is everywhere! The power of statistical models lies in understanding and dealing with noise.
A stochastic model describes variability.
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!
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.
Most of the course will consist of a mix of the following elements:
Weekly Course Events
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 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.
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.
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 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.
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!
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.
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.
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 % |
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.
For additional details please visit: http://www.umass.edu/dean_students/codeofconduct/acadhonesty/
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.
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