AI for Biodiversity Monitoring and Conservation

INTERNATIONAL TRAINING ON AI FOR BIODIVERSITY MONITORING AND CONSERVATION

COURSE BACKGROUND

Biodiversity loss is accelerating at an alarming rate, posing a critical threat to ecosystem stability and human well-being. Effective conservation strategies depend on accurate, timely, and scalable data on species populations, habitat health, and threats like poaching or deforestation. Traditional monitoring methods, while valuable, often struggle with the sheer volume and complexity of data required across vast and often inaccessible landscapes. Artificial Intelligence (AI) and Machine Learning (ML) are rapidly emerging as game-changers in this field, offering unparalleled capabilities to process massive datasets from diverse sources—such as camera traps, acoustic sensors, satellite imagery, drones, and even eDNA—to identify species, track movements, detect anomalies, and predict environmental changes.

ECAS Institute offers this “AI for Biodiversity Monitoring and Conservation” course to equip conservation professionals, ecologists, data scientists, and environmental managers with the specialized knowledge and practical skills needed to leverage AI and ML for impactful biodiversity initiatives. This program will delve into the specific AI/ML techniques most relevant to conservation, providing hands-on experience with tools and real-world case studies to demonstrate how these technologies can enhance monitoring efficiency, improve conservation planning, and strengthen anti-poaching efforts.

COURSE OBJECTIVES OF THE TRAINING

Upon successful completion of this course, participants will be able to:

Understand the specific applications and benefits of AI and ML in biodiversity monitoring and conservation.
Identify and utilize key AI/ML techniques relevant to ecological data, including image recognition, acoustic analysis, and predictive modeling.
Apply basic AI/ML workflows for processing and analyzing biodiversity data from various sources (e.g., camera traps, acoustic sensors, remote sensing).
Evaluate the performance and limitations of AI/ML models in conservation contexts, including considerations of bias and data quality.
Explore real-world case studies demonstrating successful AI/ML implementation in species identification, habitat mapping, anti-poaching, and conservation planning.
Understand the ethical implications and responsible deployment of AI technologies in sensitive ecological environments.
Gain familiarity with relevant software tools and platforms used for AI-driven biodiversity initiatives.

WHAT YOU WILL LEARN

This specialized course will provide you with the technical knowledge and practical skills to apply AI and Machine Learning directly to the challenges of biodiversity conservation. You will learn to:

Identify the types of biodiversity data that are best suited for AI/ML analysis and the sources from which they originate (e.g., visual, acoustic, telemetry, spatial).
Apply AI for automated species identification from images (camera traps) and sounds (bioacoustics), dramatically increasing monitoring efficiency.
Leverage Machine Learning for habitat mapping and change detection using satellite and drone imagery, crucial for understanding ecosystem health and fragmentation.
Understand predictive modeling techniques for species distribution, movement patterns, and forecasting threats like disease outbreaks or invasive species spread.
Explore AI’s role in anti-poaching efforts, including anomaly detection from surveillance data and optimizing patrol routes.
Work with relevant programming libraries and software for processing, analyzing, and visualizing large biodiversity datasets.
Critically assess the accuracy and biases inherent in AI models applied to ecological data.
Develop a strategic mindset for integrating AI solutions into existing conservation programs and projects.

DURATION AND PROGRAM

This is a structured training course designed to provide a focused and practical understanding of AI and ML applications in biodiversity monitoring and conservation. The program will combine theoretical concepts with extensive hands-on exercises, real-world case studies from conservation projects, and practical application using relevant AI/ML tools and programming environments (e.g., Python with relevant libraries). The detailed program schedule, including specific session timings and learning activities, will be communicated upon registration.

TARGET PARTICIPANTS

This course is specifically tailored for individuals working or studying in fields related to biodiversity, conservation, and ecology who wish to incorporate advanced AI/ML techniques into their work. It is particularly beneficial for:

Conservation Biologists and Ecologists
Wildlife Managers and Rangers
Environmental Data Scientists and Analysts with an interest in conservation
GIS and Remote Sensing Specialists in conservation organizations
Researchers and Academics in ecology and conservation science
NGO staff involved in conservation program design and implementation
Graduate Students in related environmental disciplines
Policymakers involved in biodiversity protection and natural resource management.

TRAINING MODULES

The course is structured to provide a comprehensive and practical journey through the application of AI/ML in biodiversity monitoring and conservation:

No	Module	Details
1.	Introduction to AI/ML in Biodiversity Conservation	Topics: The biodiversity crisis and the data challenge How AI/ML can transform conservation efforts Overview of AI/ML paradigms (supervised, unsupervised, deep learning) relevant to conservation Types of data in biodiversity monitoring: images, audio, GPS, environmental data
2.	AI for Automated Species Identification (Visual Data)	Topics: Computer Vision fundamentals for image analysis Applications of Convolutional Neural Networks (CNNs) for species recognition Automated analysis of camera trap data for population monitoring and behavior Tools and platforms for image-based species identification (e.g., Wildlife Insights, custom models) Challenges: varying conditions, low light, camouflage, rare species
3.	AI for Bioacoustics and Acoustic Monitoring	Topics: Introduction to bioacoustics and soundscapes Machine Learning for identifying species from audio recordings (e.g., bird calls, frog choruses) Techniques for processing and analyzing large acoustic datasets Applications in passive acoustic monitoring for elusive species and ecosystem health Tools for acoustic analysis (e.g., BirdNET, Arbimon)
4.	AI/ML for Habitat Mapping and Change Detection	Topics: Remote Sensing data sources (satellite, drone, aerial imagery) for habitat analysis Machine Learning for land cover classification and habitat mapping Detecting deforestation, habitat fragmentation, and degradation using AI Predictive modeling of habitat suitability and connectivity Integration with GIS for spatial analysis and visualization
5.	AI for Wildlife Tracking, Movement & Behavior	Key Topics: GPS telemetry data analysis with ML Predicting animal movement patterns and migration routes Identifying critical habitats and corridors Anomaly detection for unusual behavior (e.g., poaching, disease) Human-wildlife conflict mitigation using predictive AI
6.	AI/ML in Anti-Poaching and Threat Detection	Key Topics: AI for real-time surveillance and anomaly detection (e.g., using drones, ground sensors) Predictive analytics for identifying poaching hotspots Optimizing patrol routes for rangers (e.g., PAWS, SMART Patrols) Using AI for illegal wildlife trade detection (e.g., at ports, online markets)
7.	Advanced Topics, Ethics, and Future Trends	Key Topics: Introduction to Environmental DNA (eDNA) analysis with AI Challenges and limitations of AI in conservation (data bias, interpretability, resource intensity) Ethical considerations in deploying AI for conservation (privacy, impact on local communities) The role of explainable AI (XAI) in conservation decision-making Emerging trends: AI for ecosystem restoration, synthetic data generation, democratizing AI tools
8.	Practical Application Project (Hands-on)	Topics: Guided project applying learned AI/ML techniques to a specific biodiversity monitoring or conservation challenge. Data acquisition, preprocessing, model training, and evaluation. Interpretation of results and presentation of findings. Discussion of real-world applicability and scalability.

TRAINING STYLE

The modules will be taught through PowerPoint presentations, and lectures and will include a case study/field visit, breakout sessions, case studies and other interactive discussion components.

The course will also include a few guest speakers, both in person and via Zoom and other online learning platforms for overseas speakers. This provides useful real-world insights alongside the more theoretical aspects of the course.

The conference faculty shall consist of experienced decision makers, as well as practitioners and representatives from established educational and research institutions active around climate change, engineering and international development. Throughout the course, theoretical presentation of concepts will be moderated and more group discussions and plenary engagements will be optimized. PowerPoint presentations will be made by facilitators and resource persons, to highlight key concepts before embarking on group work.

GENERAL NOTES

Training manuals and additional reference materials are provided to the participants.
Upon successful completion of this course, participants will be issued with a certificate.
We can also do this as a tailor-made course to meet organization-wide needs. Contact us to find out more: info@ecasiafrica.org.
Payment should be sent to our bank account before the start of training and proof of payment sent to: info@ecasiafrica.org.

ABOUT ECAS INSTITUTE

The ECAS Institute designs and delivers independent and targeted training, research, and consulting services. Our work focusses on climate change and resilience building, carbon markets, renewable energy, nature-based solution, biodiversity conservation, agriculture and food systems, We are located in Nairobi Kenya and work across the African region. We have implemented training and research assignments in Kenya, Tanzania, Uganda, South Sudan, Somalia, Malawi, Rwanda, Congo, and South Africa. Globally, we have supported our partners from the UK, Denmark, Italy, Sweden, Germany, and USA.