Theory

Week 1

Course Introduction

  • Understand course expectations and requirements

  • Discuss research interests and how they align with the course

  • Describe and discuss current perspectives on ecological modelling & forecasting

  • Introduction to the course and to the R language using Swirl

  • Introduction to basic R syntax

  • Revisting common statistical tests

Week 2

R Time Series data

Learning Objectives:

  • Discover unique challenges with working with time-date information on computers

  • Be able to properly format time-date data

  • Apply time-date knowledge to ecological data

Paleoecological Dynamics

Learning Objectives:

  • Describe and discuss past responses of organisms to climate change
  • Compare and contrast past responses to change with modern conditions
  • Apply knowledge of the past to assess challenges for forecasting

Week 3

Phenology

Learning Objectives

  • Describe phenology and how it differs from other types of long-term change

  • Describe different classess of models used in ecological forecasting

  • Compare and contrast challenges for forecasting phenology vs other types of long-term change

Week 4

Time series decomposition

Learning Objectives:

  • Describe time series decomposition and how it applies to ecological data

  • Apply R functions to plot and examine dynamics at different time-scales in data

Community dynamics

  • Describe contemporary biodiversity dynamics

  • Critically assess current biodiversity data limitations

  • Determine what challenges and opportunities both our data and observations of nature imply for ecological forecasting

Week 5

Time Series autocorrelation

Learning Objectives:

  • Describe autocorrelation in time series data and what generates it

  • Use R to examine autocorrelation structure in ecological time series data

  • Introduction to Time Series Data Learning Objectives:

  • Gain familiarity with the spatial and temporal scales of ecological data collection Explore relationships between forecasting needs and current ecological data collection

Week 6

Time Series modelling in R I

Learning Objectives:

  • Apply lessons on autocorrelation, seasonal dynamics, and time-date formatting

  • Describe what an ARIMA model is

  • Fit ARIMA models to ecological data

Time Series modelling in R II

Learning Objectives:

  • Apply lessons on autocorrelation, seasonal dynamics, and time-date formatting

  • Describe what an ARIMA model is

  • Fit ARIMA models to ecological data

Week 7

Time Series modelling in R III

Learning Objectives:

  • Describe what a TSLM model is

  • Fit TSLM models to ecological data

  • Describe how to integrate linear models and ARIMA

  • Fit linear models with ARIMA errors

Introduction to Forecasting

Learning Objectives:

  • Read and communicate using forecasting terminology

  • Describe general approaches to making forecasts

  • Describe and discuss current perspectives on ecological forecasting

  • Describe uncertainty and the implications of uncertainty for ecological forecasting

Week 8

Uncertainty

Learning Objectives

  • Define the different types and sources of uncertainty and variation associated with ecological forecasts

  • Describe graphical models of forecast uncertainty in simple population dynamics model

  • Describe how uncertainty and variation influence ecological forecasts

Week 9

Evaluating Forecasts in R I

Learning Objectives:

  • Evaluate point forecast accuracy in R Evaluate forecast uncertainty in R

Evaluating Forecasts in R II

Learning Objectives:

  • Evaluate point forecast accuracy in R

Week 10

Species Distribution models I

Learning Objectives:

  • Describe how species distribution models are built

  • Define different approaches to species distribution modeling and explain how they relate to explanation vs. prediction

  • Describe the challenges associated with distribution modeling based forecasts

Species Distribution models II

Learning Objectives:

  • Describe how species distribution models are built

  • Define different approaches to species distribution modeling and explain how they relate to explanation vs. prediction

  • Describe the challenges associated with distribution modeling based forecasts

Week 11

Election Forecasts

Learning Objectives:

  • Describe data issues and modelling approaches used in election forecasting

  • Compare and constrast data issues and modelling approaches to those used in ecological forecasting

  • Assess which aspects of election forecasting may be useful for ecological forecasting

Hurricane Forecasts

Learning Objectives:

  • Describe different model types used to hurricane forecasting

  • Compare and contrast issues with hurricane forecasting with issues for ecological forecasting

  • Assess whether hurricane forecasting is a good model for ecological forecasting

Week 12

Complex Time Series models

Learning Objectives:

  • Fit generalized dynamic linear models in R using mvgam

  • Interpret complex time-series models from mvgam Make forecasts using mvgam models in R

State-Space Models

Learning Objectives:

  • Define state space models

  • Describe why state space models are useful in ecology

  • Explain how some non-linear state space models capture population dynamics

Week 13

Complex Time Series Models II

Learning Objectives:

  • Fit generalized dynamic linear models in R using mvgam

  • Interpret complex time-series models from mvgam

  • Make forecasts using mvgam models in R

Week 14

Ethics

Learning Objectives:

  • Describe the different types of ethical concerns related to forecasting Scenarios

Learning Objectives:

  • Define and describe scenario based forecasting methods and their uses

  • Distinguish between scenario planning, predictions, and forecasting

  • Describe approaches to multi-group decision making using scenarios

Week 15

Course Wrap-Up

Learning Objectives:

  • Synthetic perspective on the strengths and weakness of ecological forecasting