Module #1 Introduction to Biodiversity Protection Overview of the importance of biodiversity, threats to biodiversity, and the role of predictive models in conservation efforts
Module #2 Foundations of Predictive Modeling Introduction to machine learning and statistical modeling concepts, including supervised and unsupervised learning
Module #3 Biodiversity Data Sources and Collection Overview of data sources for biodiversity research, including field observations, remote sensing, and citizen science
Module #4 Data Preprocessing for Biodiversity Modeling Techniques for data cleaning, feature engineering, and data transformation for biodiversity modeling
Module #5 Introduction to Species Distribution Modeling Overview of species distribution modeling concepts, including correlative and mechanistic models
Module #6 SDM Methods:MaxEnt and Presence-Only Models Introduction to Maximum Entropy (MaxEnt) and presence-only modeling methods for species distribution modeling
Module #7 SDM Methods:Ensemble and Machine Learning Approaches Introduction to ensemble and machine learning approaches for species distribution modeling, including random forests and gradient boosting
Module #8 Model Evaluation and Validation Techniques for evaluating and validating predictive models, including metrics for model performance and uncertainty
Module #9 Case Study:Species Distribution Modeling for Conservation Real-world example of species distribution modeling for conservation, including data collection, model development, and application
Module #10 Introduction to Community Ecology Modeling Overview of community ecology modeling concepts, including community assembly and species co-occurrence
Module #11 Community Ecology Modeling:Co-occurrence Networks Introduction to co-occurrence network models for community ecology, including null model testing and network analysis
Module #12 Community Ecology Modeling:Machine Learning Approaches Introduction to machine learning approaches for community ecology modeling, including clustering and dimensionality reduction
Module #13 Case Study:Community Ecology Modeling for Conservation Planning Real-world example of community ecology modeling for conservation planning, including data collection, model development, and application
Module #14 Introduction to Ecosystem Modeling Overview of ecosystem modeling concepts, including ecosystem processes and dynamics
Module #15 Ecosystem Modeling:Dynamic Models Introduction to dynamic ecosystem models, including compartment models and system dynamics
Module #16 Ecosystem Modeling:Spatial and Landscape Models Introduction to spatial and landscape ecosystem models, including metapopulation and landscape ecology approaches
Module #17 Case Study:Ecosystem Modeling for Conservation Management Real-world example of ecosystem modeling for conservation management, including data collection, model development, and application
Module #18 Uncertainty and Sensitivity Analysis in Biodiversity Modeling Techniques for quantifying uncertainty and sensitivity in biodiversity models, including probabilistic and scenario-based approaches
Module #19 Predictive Modeling for Invasive Species Management Applications of predictive modeling for invasive species management, including risk assessment and habitat suitability modeling
Module #20 Predictive Modeling for Climate Change Impacts on Biodiversity Applications of predictive modeling for understanding climate change impacts on biodiversity, including species migration and extinction risk
Module #21 Collaborative Conservation and Stakeholder Engagement Importance of collaborative conservation and stakeholder engagement in biodiversity modeling and conservation efforts
Module #22 Ethical Considerations in Biodiversity Modeling Ethical considerations in biodiversity modeling, including model transparency, reproducibility, and fairness
Module #23 Case Study:Integrating Predictive Models into Conservation Policy Real-world example of integrating predictive models into conservation policy and decision-making
Module #24 Future Directions in Predictive Modeling for Biodiversity Emerging trends and future directions in predictive modeling for biodiversity conservation, including machine learning and AI applications
Module #25 Hands-on Exercise:Species Distribution Modeling Hands-on exercise in species distribution modeling using a case study dataset
Module #26 Hands-on Exercise:Community Ecology Modeling Hands-on exercise in community ecology modeling using a case study dataset
Module #27 Hands-on Exercise:Ecosystem Modeling Hands-on exercise in ecosystem modeling using a case study dataset
Module #28 Group Project:Applying Predictive Models to a Conservation Problem Group project applying predictive models to a real-world conservation problem, including model development and presentation
Module #29 Interactive Discussion:Challenges and Opportunities in Biodiversity Modeling Interactive discussion on challenges and opportunities in biodiversity modeling, including ethics, stakeholder engagement, and future directions
Module #30 Course Wrap-Up & Conclusion Planning next steps in Predictive Models for Biodiversity Protection career
