Predictive Modeling in Wildlife Population Dynamics
( 30 Modules )
Module #1 Introduction to Wildlife Population Dynamics Overview of wildlife population dynamics, importance of predictive modeling, and course objectives
Module #2 Fundamentals of Population Ecology Key concepts in population ecology, including population growth, density dependence, and species interactions
Module #3 Types of Wildlife Data Overview of different types of data used in wildlife population dynamics, including abundance, presence/absence, and demographic data
Module #4 Basic Statistical Concepts for Predictive Modeling Review of statistical concepts, including regression, hypothesis testing, and confidence intervals
Module #5 Introduction to Predictive Modeling Overview of predictive modeling, including types of models, model evaluation, and common challenges
Module #6 Linear Regression for Wildlife Data Application of linear regression to wildlife data, including simple and multiple regression
Module #7 Generalized Linear Models (GLMs) for Wildlife Data Application of GLMs to wildlife data, including logistic regression and Poisson regression
Module #8 Mixed Effects Models for Wildlife Data Application of mixed effects models to wildlife data, including accounting for spatial and temporal autocorrelation
Module #9 Introduction to Machine Learning for Wildlife Data Overview of machine learning algorithms, including decision trees, random forests, and neural networks
Module #10 Random Forests for Wildlife Data Application of random forests to wildlife data, including feature importance and partial dependence plots
Module #11 Species Distribution Models (SDMs) Overview of SDMs, including correlative and mechanistic approaches
Module #12 SDMs in Practice Hands-on exercise applying SDMs to real-world wildlife data
Module #13 Population Viability Analysis (PVA) Overview of PVA, including deterministic and stochastic models
Module #14 PVA in Practice Hands-on exercise applying PVA to real-world wildlife data
Module #15 Structured Population Models Overview of structured population models, including matrix models and integral projection models
Module #16 Structured Population Models in Practice Hands-on exercise applying structured population models to real-world wildlife data
Module #17 Model Selection and Model Averaging Strategies for selecting and averaging multiple models for predictive modeling
Module #18 Uncertainty and Sensitivity Analysis Methods for quantifying uncertainty and performing sensitivity analysis in predictive models
Module #19 Case Study:Predictive Modeling for Conservation Real-world example of predictive modeling for conservation, including data collection, model development, and management implications
Module #20 Computational Tools for Predictive Modeling Overview of computational tools, including R, Python, and Julia, for predictive modeling in wildlife population dynamics
Module #21 Data Visualization for Wildlife Data Strategies for effective data visualization for wildlife data, including visualization best practices and tools
Module #22 Communication and Collaboration in Predictive Modeling Strategies for effective communication and collaboration in predictive modeling, including stakeholder engagement and model interpretation
Module #23 Challenges and Limitations of Predictive Modeling Discussion of common challenges and limitations of predictive modeling in wildlife population dynamics
Module #24 Future Directions in Predictive Modeling Overview of emerging trends and future directions in predictive modeling for wildlife population dynamics
Module #25 Special Topics in Predictive Modeling In-depth exploration of special topics, including spatially-explicit models, Bayesian networks, and machine learning for wildlife data
Module #26 Project Development and Presentation Guided project development and presentation, applying predictive modeling techniques to a real-world wildlife population dynamics problem
Module #27 Peer Review and Feedback Peer review and feedback on project presentations, including discussion of strengths, weaknesses, and areas for improvement
Module #28 Course Wrap-Up and Next Steps Course wrap-up, including review of key concepts, discussion of next steps, and resources for continued learning
Module #29 Appendix:R and Python Code for Predictive Modeling Supplementary materials, including R and Python code for predictive modeling techniques covered in the course
Module #30 Course Wrap-Up & Conclusion Planning next steps in Predictive Modeling in Wildlife Population Dynamics career
