We live in a world where environmental conditions are constantly changing due to various human pressures. Ecological connectivity—the degree to which the landscape facilitates the movement of organisms, gametes, and ecological processes—is essential for ecosystem functioning. However, it is threatened by habitat loss, fragmentation, and degradation. Analyzing and assessing ecological connectivity are crucial for conservation planning and spatial prioritization, ensuring species persistence and ecosystem resilience. Today, ecological connectivity is a key component of international conservation and ecosystem restoration goals, such as the Sustainable Development Goals (SDGs) and the Kunming-Montreal Global Biodiversity Framework, including its "30x30" target.

This course introduces the theoretical foundations of ecological connectivity studies and their spatial analysis, with a primary focus on the R package “Makurhini" (Godínez & Correa, 2020). This package is specifically designed to calculate landscape fragmentation and connectivity indices, which are widely used in conservation planning. Makurhini provides a set of functions to assess the connectivity of protected area networks and the importance of different landscape elements in maintaining connectivity. Additionally, it allows the analysis of scenarios under landscape connectivity changes and incorporates territorial heterogeneity as a limiting factor. The course is aimed at individuals interested in connectivity analysis, taking advantage of the processing capabilities and innovative metric calculations implemented in Makurhini. n this course, we will use the R programming language and its development environment, RStudio, both of which are open-source alternatives, making them legally and easily accessible for governmental, educational, and research institutions.

• Starts on Wednesday, July 9, and ends on August 27, 2025.
• Classes will be held every Wednesday from 18:00 to 20:00 (GMT-6) for 8 weeks.

• Modality: Virtual.
• Theoretical/Practical: To complete the program, participants must attend more than 75% of virtual synchronous classes and achieve an average greater than or equal to 70 in evaluations.
• Cost: Free.

• Availability of at least 16 hours during the entire program to attend eight virtual synchronous sessions. (2 hrs/class)
• Availability of at least 32 hours during the entire the program for completing short assignments, labs, and a final project. (4 hrs/week)
• Have basic knowledge of R, RStudio and GIS (in R or any other program).
• Fill out the form to Participate in Redbioma activities (previously circulated, please fill out only once).

Strengthen participants' skills in the theoretical and methodological foundations of ecological connectivity analysis, using open-source software tools, particularly the R package Makurhini, to assess and plan landscape connectivity in conservation and restoration contexts.

• Explain the fundamental concepts of ecological connectivity and its importance for conservation and ecosystem resilience.
• Introduce the main approaches and metrics used in landscape connectivity analysis.
• Teach the use of the Makurhini package to calculate fragmentation and connectivity indices.
• Facilitate the interpretation of results and their application in conservation and ecological restoration planning.

The course combines a theoretical and practical approach, with a stronger emphasis on developing applied skills. The first part of the course will cover the theoretical foundations of ecological connectivity and its implications for conservation and ecosystem restoration. However, the majority of the course will focus on the practical application of these concepts through the use of the Makurhini package and other spatial analysis tools. Active learning will be encouraged through case study analyses presented by the instructor and potential guest speakers, as well as group discussions to reflect on challenges and applications in different contexts. Additionally, participants will develop a research project where they will apply the methodologies learned to address a real ecological connectivity issue, promoting experiential learning and the development of applied solutions.

• All synchronous sessions will be recorded and published on the redbioma website.
• Final research projects will be published on the redbioma website.

1. Introduction to ecological connectivity (I)
1. What is connectivity? Definitions and approaches.
2. Ecological foundations and their importance in conservation.
3. Habitat fragmentation and its implications for biodiversity.
4. Ecological connectivity in global policies (SDGs, Kunming-Montreal, "30x30").
2. Introduction to ecological connectivity (II)
1. Principles of landscape ecology: structure, composition, and configuration.
2. What should we connect? Spatial and ecological scales.
3. Introduction to habitat and resistance models.
3. Connectivity analysis (I): Fundamentals and first steps
1. Installing the Makurhini package in RStudio.
2. Landscape fragmentation statistics.
3. Introduction to graph theory and connectivity indices.
4. Centrality indices.
4. Connectivity analysis (II): Evaluation and prioritization of connectivity
1. Integral Connectivity Index (IIC).
2. Probability of Connectivity Index (PC).
5. Connectivity analysis (III): Evaluation and prioritization of connectivity
1. Composite Connectivity Index (ICC_f).
2. Route prioritization: Link Removal and Link Change.
6. Connectivity analysis (IV): Evaluation of changes in connectivity
1. Equivalent Connected Area Index (ECA).
2. Connectivity monitoring: dECA.
7. Connectivity analysis (V): Applications in protected areas
1. Assessment of protected area connectivity: ProtConn.
8. Closure and final project presentation
1. Discussion of results and key learnings.
2. Presentation and feedback on final projects.

Students will complete assignments and a final project. Evaluation items are as follows:

Assignments must be submitted by 12:00 PM (GMT-6) on the day before the next class.

The final project consists of applying the knowledge acquired throughout the course to a topic of interest chosen by each student. The instructor will provide spatial data from a set of landscapes with different contexts where the acquired concepts can be applied. However, students are also welcome to use their own data for their final project presentation.