The advent of miniaturized biologging devices has provided ecologists with unparalleled opportunities to record animal movement across scales, and led to the collection of ever-increasing quantities of tracking data. In parallel, sophisticated tools  to process, visualize and analyze tracking data have been developed in abundance. Within the R software alone, we listed 57 focused on these tasks, called here tracking packages. Here, we reviewed these tracking packages, as an introduction to this set of packages for researchers, and to provide feedback and recommendations to package developers, from a user perspective. We described each package based on a workflow centered around tracking data (i.e.  (x,y,t)), broken down in three stages: pre-processing, post-processing , and analysis( data visualization, track description, path reconstruction, behavioral pattern identification, space use characterization, trajectory simulation and others ) . Supporting documentation is key to the accessibility of a package for users. Based on a user survey, we reviewed the quality of packages' documentation, and identified 12 packages with good or excellent documentation. Links between packages were assessed through a network graph analysis. Although a large group of packages shows some degree of connectivity (either depending on functions or suggesting the use of another tracking package), a third of tracking packages work on isolation, reflecting a fragmentation in the R Movement-Ecology programming community. Finally, we provide recommendations for users to choose packages, and for developers to maximize  usefulness of their contribution and strengthen the links between the programming community.
The advent of miniaturized biologging devices has provided ecologists with unprecedented opportunities to record animal movement across scales, and led to the collection of ever-increasing quantities of tracking data. In parallel, sophisticated tools have been developed to process, visualize and analyze tracking data , however many of these tools have proliferated in isolation, making it challenging for users to select the most appropriate method for the question in hand. Indeed, within the R software alone, we listed 59 packages created to deal with tracking data or 'tracking packages'. Here we reviewed and described each tracking package based on a workflow centered around tracking data (i.e. spatio-temporal locations (x,y,t)), broken down into three stages: pre-processing, post-processing and analysis, the latter consisting of data visualization, track description, path reconstruction, behavioral pattern identification, space use characterization, trajectory simulation and others . Supporting documentation is key to render a package accessible for users. Based on a user survey, we reviewed the quality of packages' documentation, and identified 11 packages with good or excellent documentation. Links between packages were assessed through a network graph analysis. Although a large group of packages showed some degree of connectivity (either depending on functions or suggesting the use of another tracking package), one third of the packages worked in isolation, reflecting a fragmentation in the R movement-ecology programming community. Finally, we provide recommendations for users when choosing packages, and for developers to maximize the usefulness of their contribution and strengthen the links within the programming community.
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57 focused on these tasks, called here tracking packages. Here, we reviewed these tracking packages, as an introduction to this set of packages for researchers, and to provide feedback and recommendations to package developers, from a user perspective. We described each
59 packages created to deal with tracking data or 'tracking packages'. Here we reviewed and described each tracking
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