Syllabus#

Fall 2026

Synopsis#

Application of quantitative methods to support conservation decisions:

  • Ecosystem value mapping

  • Systematic conservation planning

  • Policy instrument design

  • Rigorous impact evaluation

  • Decision theory

  • Data visualization

Implementation in state-of-the-art open-source software.

Real-life case studies from the U.S. and abroad.

Where & when#

Tue + Thu, 2pm – 3:15pm

Schedule

CGS 323, College of General Studies, 871 Commonwealth Ave

Instructor#

Christoph Nolte

Associate professor, Earth & Environment (EE)
Affiliate professor, Computing & Data Sciences (CDS)
chrnolte@bu.edu
placeslab.org

Office hours#

Hours
Mon 3 - 4pm (Zoom)
Wed 1:30 - 2:30pm (Zoom or CAS 445)
Fri 11 - 12pm (Zoom or CAS 445)

Office

CAS 445 (685 Commonwealth Ave)

Zoom link

https://bostonu.zoom.us/j/9191896749

  • If you would like to meet with me, please let me know 2 hours in advance by reserving a 15-min slot with the Google Appointment Schedule. It’s quick and convenient.

  • Office hours are open to all. Once a 15-min slot is scheduled (i.e., isn’t available when you’re trying to schedule it), everyone else is welcome to join the Zoom call / come to the office and listen in. However, the student who scheduled the appointment gets to ask their questions first.

  • If we do not find time for your questions during office hours, you can schedule a separate 15-min Zoom session with me. Just send me a quick email (chrnolte@bu.edu) with 2-3 time slots that work for you within the next few days.

Motivation#

Growing demand for food, fiber, housing, and energy affects global ecosystems in ways that reduce their ability to generate public environmental benefits.

Conservation interventions can protect and restore ecosystems and the benefits they provide, but usually face budget constraints / limited political will, and low willingness of landowners to forgo productive uses.

Conservation decisions thus need to be “smart” to maximize environmental benefits under those constraints. Decision makers regularly face questions such as:

  • How can we quantify changes to the spatial distribution of environmental values across a given landscape?

  • Given limited budgets, where should conservation occur to maximize benefits (species representation, carbon sequestration, water protection)?

  • How can policy instruments, such as ecosystem markets and payments, be configured in ways that deliver the greatest environmental benefits?

  • How much of a difference do different intervention types make?

Conservation scientists have developed a range of methods and computational tools to answer these questions. Some are widely applied to support decisions in governments, non-profits, and academia. We will use a selection of the most important ones and apply them to real-life cases.

All labs are implemented in open-source, cross-platform programming languages and software packages. We will predominantly use Python packages, but also include QGIS, R, and Marxan.

Objectives#

After taking this course, you will be able to:

  • Identify questions that are of interest to conservation decision makers and that can be solved using computational methods.

  • Design analyses to answer these questions, including characterization of the problem, data acquisition, processing, analysis, and presentation of results.

  • Implement such analyses in state-of-the-art open-source software tools that work on all major operating systems (OSx, Windows, and Linux), and verify that your code does what you think it does.

  • Evaluate the strengths and shortcomings of different methods to find solutions, including a critical assessment of underlying data and theoretical frameworks.

  • Build a dataset that nobody prepared for you, from public sources, in a form others can reuse.

BU Hub learning outcomes#

  • Quantitative Reasoning II: you will be trained in using analytical, statistical, and computational methods to support decisions in conservation policy, including the presentation of results, and the interpretation of findings in the light of methodological strengths & limitations.

  • Digital/Multimedia: you will learn to communicate the results of your analyses to a diverse range of decision makers, including through digital visualizations, interactive map products, reports, and presentations.

  • Research and Information Literacy: you will learn to confidently access open-source datasets, software packages, and knowledge exchange websites to conduct both prescribed and self-directed research to inform conservation.

Where such skills are in demand#

  • Carbon and biodiversity credit registries, ratings agencies, and buyers.

  • Insurers, mortgage lenders, and real estate analytics firms

  • Land trusts and conservation NGOs

  • Water and energy utilities, state land-use agencies

  • Geodata and AI-data companies

Prerequisites#

This course has two prerequisites.

Introductory programming skills#

This is a class for students who already have:

  • a clear grasp of basic data types (e.g., int, float, str, list, set, dictionary),

  • applied knowledge of control structures (if conditions, for loops, functions),

  • experience with developing small coding projects (~1000 lines), and

  • experience with troubleshooting (e.g., how to search errors for clues).

There are many ways to obtain these skills, and most introductory coding classes (in any programming language) have you covered.

  • Earth & Environment students who have taken EE 375 will be well-prepared.

  • CDS students who have finished the DS-120-121-122 and/or DS-110-210 sequences will breeze through the first weeks of the course.

  • If you are uncertain, just send me a quick email or come talk to me after class.

Lab 1 consists of a series of smaller, graded coding tasks that will help you gauge whether the level of the class will work for you.

Introduction to statistics#

You need to have completed sufficient prior coursework in statistics to explain the concept of a p-value and to interpret coefficient estimates in multivariate linear regression models, as in:

\[ln(price_{ijt}) = \alpha + X_i \beta + \mu_j + \tau_t + \varepsilon_{ijt}\]

EE 270, EE 516, the data science sequences mentioned above, and many other classes at BU fulfill this requirement.

Supporting skills#

You will find this class easier if you have prior knowledge in the following two domains. These are not required, however: students with no prior knowledge usually pick it up quickly.

  • Spatial data. Working with spatial data requires a basic understanding of vector and raster data and the ability to work with geographic coordinate reference systems (CRS) / projections. EE 365, EE 505, and similar classes cover this. We will go over essentials at the beginning of the course.

  • Optimization (algebraic or computational). Experience with defining objective functions, constraints, and decision variables will come in useful when we wrap our heads around complex yet solvable decision problems. EE 545 is one of the classes that teaches this approach to think analytically.

Assignments & grading#

This is a “hands on” course designed to help you become familiar with key methods and open-source software tools used to support conservation decisions. Much of your grade is based on your ability to understand the underlying theory, to implement the methods correctly, and to present results in a form understandable to non-experts. In your final project, you will bring those skills together to support a spatial policy design decision of your choice.

Labs (55%)#

Four hands-on labs will develop and test your capacity to tackle computational questions in the following areas:

Tests (25%)#

There will be four short written tests, one after each lab. They will be mixing multiple-choice and open-ended answers to coding challenges that should be familiar from the labs. I will design them to be on the easy side for students that have done their own thinking in the labs.

Project (15%)#

Identify and help solve a real-life conservation problem using one or more of the methods covered in class. You generate the idea for a simple project that interests you. We work on this through a prospectus (5%), project plan (5%) and final paper (5%).

Class participation (5%)#

You will get extra credit for answering your classmates’ questions on Piazza.

Materials#

All class materials will be made available through this course website. There is no required textbook.

Policies#

Attendance & participation#

Class participation is beneficial to group learning. Attendance is generally expected. More than two unexcused absences will affect the participation component of your grade. Please come to class familiar with the day’s readings, ready to engage by asking and answering technical questions, and critically discussing the readings’ contents. Please review the labs before we discuss them in class and start implementing them as you read, until you get stuck somewhere.

Assignment completion#

Lab assignments are submitted on Blackboard. First submissions of labs can achieve up to 100%. Then, there are second chances: if you figure out how to correct your results, you get back two thirds of the points you missed.

Late policy#

Assignments need to be submitted by 11:59pm on the day they are due. You have a time bank of two days for the term that you may withdraw from and apply to the submission of any of your assignments (labs, project). This eliminates the need to request extensions and allows you some flexibility in managing your workflow. After you empty your time bank, graded assignments will be penalized by one-third of a letter grade for each day of lateness. If you anticipate difficulties due to documentable extenuating circumstances, please notify me as soon as possible.

AI policy#

Large Language Models (LLMs) can be incredibly valuable tools for coding, troubleshooting, exploring ideas, and discovering new ones.

As an instructor and coder, I’m enthusiastic about the productivity leaps that LLMs make possible for me and my students.

Yes, their overuse of certain sentence structures and words can be infuriating. They create occasional hallucinations and make their own share of stubborn errors: I once spent 10 minutes trying to get ChatGPT to recognize that it was serving me the wrong statistical formula for a pooled weighted standard deviation.

However, in combination with your own thinking and watchfulness, they are extremely useful, and they’re clearly here to stay.

I therefore do not put formal restrictions on AI use in EE 508 labs and projects.

The one thing I ask for is a one-line AI-use statement at the top of every lab submission and of your final report: “AI use: none” or “AI use: [tool] for [what]”. It won’t affect how I grade the quality of your lab. However, it helps me keep track of how student progress is associated with AI use and helps me improve the course.

Written lab tests are there to check that you have done your own thinking, absorbed the material, and can work independently without AI support if needed. Treat your homework on labs as your opportunity to practice the skills you will need on your own!

How I use AI#

This course and its assignments were created before AI came along. I assume that >98% of the words on this website are my own. I use AI occasionally to keep the material fresh when new functions and packages come around. I use AI almost daily in my own coding projects and research. I will never use AI to design or grade your tests or in my communications with you.

Academic conduct#

All Boston University students are expected to maintain high standards of academic honesty and integrity. It is your responsibility to be familiar with the Academic Conduct Code, which describes the ethical standards to which BU students are expected to adhere and students’ rights and responsibilities as members of BU’s learning community. All instances of cheating, plagiarism, and other forms of academic misconduct will be addressed in accordance with this policy. Penalties for academic misconduct can range from failing an assignment or course to suspension or expulsion from the university http://www.bu.edu/academics/policies/academic-conduct-code/.

Religious observances#

Campus policy regarding religious observances requires that faculty make every effort to reasonably and fairly deal with all students who, because of religious obligations, have conflicts with scheduled tests, assignments or required attendance. Please notify me as soon as possible so that the proper arrangements can be made. For details, consult http://www.bu.edu/chapel/religion and http://www.interfaithcalendar.org.

Diversity and inclusion#

Diversity enriches all research and education, and is realized only with all voices, views, and perspectives operating within a supportive and respectful community. For this reason, the Department of Earth & Environment, including myself and the students in this course, are committed to fostering diverse, inclusive, and equitable living, learning, and working environments that are supportive and free from violence, harassment, disruption, and intimidation. Further, the Department of Earth & Environment recognizes that creating a safe environment and a culture of respect is the shared responsibility of all members of our community. To ensure an equitable environment that values and respects the unique experiences and perspectives of our community, the Department, including myself, are dedicated to promoting diversity, inclusion, and equity among all members of our departmental community and encouraging open, honest, and compassionate communication. See also: https://www.bu.edu/earth/diversity-and-inclusion.