Ecological Dynamics and Forecasting: A graduate course on the fundamentals of time series and forecasting in ecology

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Authors

S.K. Morgan Ernest, Hao Ye, Ethan White 

Abstract

Ecological Dynamics and Forecasting is a semester-long course to introduce students to the fundamentals of ecological dynamics and forecasting. This course implements a combination of paper-based discussion to introduce students to concepts and ideas and R-based tutorials for hands-on application and training. The course material includes a reading list with prompting questions for discussions, teachers notes for guiding discussions, lecture notes for live coding demonstrations, and video presentations of all R tutorials. The course is structured around two sessions per week - with most weeks consisting of a one hour paper discussion session and a 1-2 hour session focused on applications in R. R tutorials use publicly available ecological datasets to provide realistic applications. This course material can be used either as self-directed learning or as all or part of a college or university course. Individual learners have access to all of the necessary material - including discussion questions and instructor notes - on the website. The course does currently assume users have access to some closed-access papers, though open-access versions and links are provided when available and suitable open-access papers are recommended. Because the material is organized around content themes, university courses can modify and remix materials based on their course goals and student levels of background knowledge. Course videos have been viewed by thousands of users and course materials has been taught for several years at the authors' university.

DOI

https://doi.org/10.32942/X2B88H

Subjects

Ecology and Evolutionary Biology, Life Sciences

Keywords

Forecasting, ecological dynamics, time series, Programming, models, uncertainty

Dates

Published: 2022-12-05 22:17

Last Updated: 2022-12-06 05:17

License

CC BY Attribution 4.0 International

Additional Metadata

Data and Code Availability Statement:
https://doi.org/10.5281/zenodo.6993466

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