| Summary: | 1. Animal tracking data are indispensable for understanding the ecology, behaviour and physiology of mobile or cryptic species. Meaningful signals in these data can be obscured by noise due to imperfect measurement technologies, requiring rigorous quality control as part of any comprehensive analysis. 2. State-space models are powerful tools that separate signal from noise. These tools are ideal for quality control of error-prone location data and for inferring where animals are and what they are doing when they record or transmit other information. However, these statistical models can be challenging and time-consuming to fit to diverse animal tracking data sets. 3. The R package aniMotum eases the tasks of conducting quality control on and inference of changes in movement from animal tracking data. This is achieved via: 1) a simple but extensible workflow that accommodates both novice and experienced users; 2) automated processes that alleviate complexity from data processing and model specification/fitting steps; 3) simple movement models coupled with a powerful numerical optimization approach for rapid and reliable model fitting. 4. We highlight aniMotum 's capabilities through three applications to real animal tracking data. Full R code for these and additional applications are included as Supporting Information so users can gain a deeper understanding of how to use aniMotum for their own analyses. This datafile contains the data used by Jonsen et al. (in Applications 3.2 & 3.3) to highlight the capabilities of the aniMotum R package for analysis of animal tracking data. The lipe_gps_ex32.csv file contains GPS-derived locations for 4 little penguins, recorded by AxyTrek data loggers. The lipe_pc_ex32.csv file contains processed prey capture events inferred from 3-D accelerometry data, recorded by AxyTrek data loggers. The sese_extra.csv file contains Argos-derived locations for 4 southern elephant seals (Jonsen et al. Appendix 3), transmitted by SMRU CTD satellite relay data loggers. The hase_ex33.csv ...
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