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

WIZAPE

ONLINE RESOURCES

ABOUT WIZAPE

ONLINE PORTAL

Our priority is to cultivate a vibrant community before considering the release of a token. By focusing on engagement and support, we can create a solid foundation for sustainable growth. Let’s build this together!

We're giving our website a fresh new look and feel! 🎉 Stay tuned as we work behind the scenes to enhance your experience.
Get ready for a revamped site that’s sleeker, and packed with new features. Thank you for your patience. Great things are coming!

CONTACT-US PRIVACY POLICY

Predictive Modeling in Wildlife Population Dynamics ( 30 Modules )

Predictive Modeling in Wildlife Population Dynamics
( 30 Modules )