FW840: Landscape Ecology
Spring 2025
1 Welcome to FW840!
Welcome to FW840 Landscape Ecology, a course where we will explore the dynamic interactions between spatial patterns and ecological processes. Landscape ecology bridges scales and disciplines, offering powerful tools to understand ecosystems and address real-world challenges such as habitat fragmentation, biodiversity conservation, and land-use change. Our approach emphasizes not only theoretical foundations but also hands-on methods to analyze and interpret spatial data.
1.1 Course Information
1.1.1 Instructor Information
Instructor: Dr. Patrick Hanly
Office: Natural Resources 334 (by appointment only outside office hours)
Class Days & Times:
Tuesdays + Thursdays 11:30AM-12:20PM - Natural Resources 19
Thursdays 3:00PM-4:50PM - Anthony Hall 1210
Office Hours: Thursday 12:30PM-3:00PM in NR334 Conference Area
E-mail: hanlypat@msu.edu
Final Exam: NO FINAL
Zoom info:
https://msu.zoom.us/j/93822941064 Meeting ID: 938 2294 1064 Passcode: odonata
1.1.2 Other Standard Syllabus Material
Technical Assistance If you need technical assistance at any time during the course or to report a problem you can: • Visit the MSU Libraries Discovery Services Site (https://lib.msu.edu/dls/) • Visit the Desire2Learn Help Site (https://help.d2l.msu.edu/) • Visit the MSU IT Help & Support Site (https://tech.msu.edu/support/help/), call (517) 432-6200 or toll free (844) 678-6200, or email ithelp@msu.edu
Resource Center for Persons with Disabilities (RCPD) To make an appointment with a specialist, contact (517) 353-9642 or TTY: (517) 355-1293 • RCPD Get Started Info: https://www.rcpd.msu.edu/get-started
Diversity Equity and Inclusiveness Diversity, Equity and Inclusion are important, interdependent components of everyday life in the College of Agriculture and Natural Resources (CANR) and are critical to our pursuit of academic excellence. Our aim is to foster a culture where every member of CANR feels valued, supported and inspired to achieve individual and common goals with an uncommon will. This includes providing opportunity and access for all people across differences of race, age, color, ethnicity, gender, sexual orientation, gender identity, gender expression, religion, national origin, migratory status, disability / abilities, political affiliation, veteran status and socioeconomic background. (See the full CANR statement: https://www.canr.msu.edu/news/canr-statement-on-diversity-equity-and-inclusion) If you ever feel that this course (or FW department or MSU) is marginalizing you or other students, or if principles of diversity, equity and inclusion are not being supported, please tell someone. You can speak to your instructor, or a trusted mentor of yours, or Mary Tate Bremigan (Associate Chair for Academic Programs in FW) or Jim Schneider (FW Undergraduate Advisor).
Commit to Integrity: Academic Honesty Article 2.3.3 of the Academic Freedom Report states that “The student shares with the faculty the responsibility for maintaining the integrity of scholarship, grades, and professional standards.” In addition, the (insert name of unit offering course) adheres to the policies on academic honesty as specified in General Student Regulations 1.0, Protection of Scholarship and Grades; the all-University Policy on Integrity of Scholarship and Grades; and Ordinance 17.00, Examinations. (See Spartan Life: Student Handbook and Resource Guide and/or the MSU Web site: www.msu.edu.)
Therefore, unless authorized by your instructor, you are expected to complete all course assignments, including homework, lab work, quizzes, tests and exams, without assistance from any source. You are expected to develop original work for this course; therefore, you may not submit course work you completed for another course to satisfy the requirements for this course. Also, you are not authorized to use the www.allmsu.com Web site to complete any course work in this course. Students who violate MSU academic integrity rules may receive a penalty grade, including a failing grade on the assignment or in the course. Contact your instructor if you are unsure about the appropriateness of your course work. (See also the Academic Integrity webpage.)
Inform Your Instructor of Any Accommodations Needed From the Resource Center for Persons with Disabilities (RCPD): Michigan State University is committed to providing equal opportunity for participation in all programs, services and activities. Requests for accommodations by persons with disabilities may be made by contacting the Resource Center for Persons with Disabilities at 517-884-RCPD or on the web at rcpd.msu.edu. Once your eligibility for an accommodation has been determined, you will be issued a Verified Individual Services Accommodation (“VISA”) form. Please present this form to me at the start of the term and/or two weeks prior to the accommodation date (test, project, etc.). Requests received after this date may not be honored.
1.2 Class Format
Tuesday classes are in Natural Resources 19 11:30AM-12:20PM and will be traditional lectures covering key landscape ecology background, important classic and contemporary papers, and general “textbook” knowledge.
Thursday morning classes are in Natural Resources 19 11:30AM-12:20PM will cover material related to landscape ecology methods, introducing both methods background and other information needed to complete labs. The key component of this course is the Thursday methods learning labs, where you will gain practical experience in ecological data analysis using R, a powerful statistical and programming language widely used in ecological research. In these labs, we will focus on reproducible workflows, employing RMarkdown to create dynamic documents that integrate code, results, and interpretations seamlessly. By learning to generate reproducible and transparent analyses, you’ll develop essential skills for ecological research, including efficient data management, spatial analysis, and creating professional-quality reports. You’ll publish your RMarkdown documents to PDFs or HTML files, creating a portfolio of reproducible code that demonstrates your expertise in landscape ecology (and for me to grade!). This process mirrors modern ecological research practices, preparing you to contribute effectively to academic and professional projects, and generating the reproducible code increasingly required for publication. Please bring your laptop to this class if you plan on using one.
IMPORTANT: Thursday Lab. Our class is scheduled for a computer lab period on Thursday in Anthony Hall 1210 from 3:00PM-4:50PM. This class period is optional but will be necessary if you would like assistance from me on your computer labs and do not have a desktop. I am making this period optional since, as a graduate student, coming and going to the same class on one day may not be efficient or productive for you. Following the lab introduction on Thursday you are welcome to start then and/or go directly to the collaborative work space I have reserved in Natural Resources 334 that also doubles as my office hours time.
D2L: The course will be administered through D2L with all submissions and grading managed there. Please check D2L relatively frequently for class announcements, assignment due dates, and lab feedback.
1.3 Graded Course Activities
Graded material will be a total of A) 8 computer labs, which will also include some brief writing are part of the published reports that relate to concepts from lecture, and B) a final project that analyzes your own data or other data of interest (ideally related to your thesis) and a 5 minute presentation with a minimum of 3 figures or results tables. Note that the presentation is not graded but is required for completion of the course.
| Graded.Material | Description | Points.Each | Total.Points |
|---|---|---|---|
| Computer Labs (8 total, 1 dropped) | Hands-on learning labs using R | 100 | 700 |
| Draft Final Project Code | Initial submission of reproducible code | 100 | 100 |
| Final Project Submission | Complete project with figures and analysis | 200 | 200 |
| Total | 1000 |
Viewing Grades: Grades will be managed through D2L. Generally, Dr. Hanly will post grades and feedback within 2 weeks of submission deadlines. Feel free to gently remind me if this timeline is not kept.
Grading Scale: This course uses the standard grading scale for the department. There is no curving or rounding in this course but small extra points are provided for class surveys, feedback, etc.
| Grade.Point | Percentage | Performance |
|---|---|---|
| 4.0 | ≥90% | Excellent Work |
| 3.5 | 85% to 89% | Above Average |
| 3.0 | 80% to 84% | Good Work |
| 2.5 | 75% to 79% | Mostly Good Work |
| 2.0 | 70% to 74% | Average Work |
| 1.5 | 65% to 69% | Below Average Work |
| 1.0 | 60% to 64% | Poor Work |
| 0.0 | ≤59% | Failing Work |
1.4 Class Policies
Assignment Submission and Due Dates. All assignments will have an affiliated due date that will be visible to you on D2L. I strongly encourage you to stick to the due dates so that you do not get behind. However, the class is small enough that you can submit material other than the final project components late without penalty. I would prefer you to work with me to learn the material rather than submit garbage on time. No regrades or resubmissions are allowed excepting technical difficulties (this does not include not being able to knit your code).
Participation and Engagement. In-person attendance of the lectures is expected but not enforced. Unless you have previously made arrangements with me, I am only available for coding assistance during the collaborative office hours and scheduled lab session. The bulk of engagement with myself and your classmates will be during the labs, and I find it highly beneficial if everyone is able to share knowledge with each other during these sessions.
Collaboration. I strongly encourage students to collaborate with each other on the lab coding assignments. I only ask that you ensure you actually understand the code and are not just copying blindly. i.e., two students may share the same code but not the same annotations or writing. When there is a choice in an assignment such as choosing a species or country or metric, please make a different choice than anyone you collaborate with. You may also use AI tools for aiding coding (not written responses), but I recommend these be used to improve/troubleshoot your code rather than starting with AI. Each student is responsible for their own written responses, code annotations, and final project without outside help.
1.5 Course Schedule
Key Dates: • First Tuesday class: January 14, 2025 • No class on Thursday, February 13, 2025 • Spring Break: March 3–7, 2025 (no classes on March 4 & 6) • April 30, 2025 (Course end date. Note that this is listed as a Final Exam but there is no exam associated with this class.However, all work must be completed and turned in by this date at 11:59PM).
| Lab | Topic | Start | Due |
|---|---|---|---|
| Lab 1 | Intro to Rstudio and APIs | 16-Jan | 24-Jan |
| Lab 2 | Point Process Modeling and Landscape Metrics | 23-Jan | 31-Jan |
| Lab 3 | Least-Cost Path Analysis and Graph Theory | 30-Jan | 7-Feb |
| Lab 4 | Species Distribution Modeling | 6-Feb | 21-Feb |
| Lab 5 | Home Range and Movement Analysis | 20-Feb | 28-Feb |
| Lab 6 | Remote Sensing and Temporal Change | 27-Feb | 14-Mar |
| Lab 7 | Classification using Machine Learning | 13-Mar | 21-Mar |
| Lab 8 | Systematic Conservation Planning | 20-Mar | 28-Mar |
Week 1 (Jan 14 & Jan 16) Tuesday (Lecture) – Topic: Introduction to Landscape Ecology • Defining landscape ecology and its interdisciplinary roots • Pattern-process relationships as a core principle • Scale, heterogeneity, and the mosaic concept • Historical development and key foundational texts
Thursday (Lab Exercise) – Topic: Introduction to Reproducible Coding • Introduction to RMarkdown • Accessing APIs for data • Access species observation data from the Global Biodiversity Information Facility (GBIF) using the API in R • Preprocess and filter data • Plot data with a method that addresses the issue of overplotting
Week 2 (Jan 21 & Jan 23) Tuesday (Lecture) – Topic: Spatial Scale and Hierarchy • Understanding grain and extent • Hierarchical organization of landscapes • Scale dependence of ecological patterns and processes • Choosing appropriate scales for analysis
Week 3 (Jan 28 & Jan 30) Tuesday (Lecture) – Topic: Quantifying Landscape Pattern • Common landscape metrics (patch size, shape, edge, diversity) • Interpreting landscape metrics ecologically • Limitations and proper use of metrics
Week 4 (Feb 4 & Feb 6) Tuesday (Lecture) – Topic: Remote Sensing and Data Sources • Remotely sensed data products (e.g., Landsat, Sentinel) • Metrics from remote sensing • Preprocessing and classification for landscape analysis
Week 5 (Feb 11 & Feb 13) Tuesday (Lecture) – Topic: Neutral Landscape Models & Spatial Autocorrelation • Concept and purpose of neutral models • Generating random landscapes to test hypotheses • Assessing spatial patterns via autocorrelation
Thursday – Class not held.
Week 6 (Feb 18 & Feb 20) Tuesday (Guest Lecture) – Topic: Disturbance Regimes & Temporal Dynamics • Disturbance as discrete events shaping landscapes • Temporal changes in pattern and succession • Landscape trajectories and legacy effects
Thursday (Lab) – TBD: Guest Lab Content
Week 7 (Feb 25 & Feb 27) Tuesday (Lecture) – Topic: Fragmentation & Connectivity • Defining fragmentation and its ecological implications • Patch isolation and the importance of connectivity • Landscape structure influence on species movement
(March 3–7: Spring Break, no classes on March 4 & 6)
Week 8 (Mar 11 & Mar 13) Tuesday (Lecture) – Topic: Corridors & Least-Cost Paths • Concept of corridors and their role in facilitating movement • Least-cost path analysis to identify functional connectivity • Circuit theory as an alternative connectivity model
Week 9 (Mar 18 & Mar 20) Tuesday (Lecture) – Topic: Species Distributions & Climate Change • Linking species distribution models (SDMs) with landscape patterns • Climate change implications on species ranges • Incorporating environmental layers into SDMs
Week 10 (Mar 25 & Mar 27) Tuesday (Lecture) – Topic: Ecosystem Services and Landscape Pattern • Linking pattern to ecosystem function and services • Identifying hotspots of ecosystem services • Trade-offs and synergies among services
Week 11 (Apr 1 & Apr 3) Tuesday (Lecture) – Topic: Conservation Planning • Systematic conservation planning principles • Reserve design and prioritization tools • Balancing biodiversity, services, and human needs
Week 12 (Apr 8 & Apr 10) Tuesday (Lecture) – Topic: Integration, Synthesis & Scenario Modeling • Integrating pattern, process, and scale in dynamic models • Scenario building for future landscapes • Using models to inform policy and management
Remaining time will be spent on final projects and presentations.
1.6 Textbook
This HTML book will be continuously updated throughout the semester, with lecture notes and lab introductions added to it. Each week will have a set of organized notes summarizing the Tuesday lecture and intro methods content including example code that will be sufficient to complete the lab exercise independently if you so choose. Generally, I will keep images/figures to a minimum in the notes section here and you can refer to the lecture slides since it is very time consuming for me to add those to the HTML book.