1 Introduction

This guide is a team effort curating insights and solutions for implementing integrated step selection analyses (iSSAs) by synthesizing past experiences, best practices, and key resources. Given how difficult it is to start an integrated step selection analysis and all the choices that need to be made, we created it to be an approachable gateway to the world of iSSAs. We hope that putting everything together will help others. Please get in touch if you have any questions or comments, and if you have any suggested edits.

This guide integrates functions and outputs from the targets-iSSA workflow. targets manages the relationships between data, variables and intermediate outputs, as well as the functions that generate them, and ensures that outdated objects are regenerated. It also provides helpers for flexibly batching steps across individuals, study periods or regions. Using a targets workflow like this one has helped us running iSSAs by caching data processing, saving computation time, and programatically identifying and dropping individuals or time periods that cause us headaches by breaking runs outside this type of workflow. Starting from this workflow, users can swap in their own data, variables, and questions without having to reinvent the wheel.

1.1 Acknowledgements

Thank you to iSSA club alumni Katrien Kingdon, Levi Newediuk, Quinn Webber, Emilie Debedan, Eric Vander Wal for the motivation to solve problems and seek answers. Extra thank you to Katrien for reviewing this document.

Note: Everyone learns differently and reading a document may not be the most helpful. Working with a group or discussing issues with a peer is immensely helpful, even for the most experienced practitioners. Every new analysis is an opportunity to learn and rethink our current ideas.

Thanks to all developers of R, R packages, and related software that make this book and related analyses possible. Specifically developers of targets, renv, bookdown, R, RStudio, amt, data.table, R-lib, and R-spatial.