Box 1 Key terminology needed to use this review as a guide

Tracking data Locations of uniquely identified individuals that are linked through time

Occurrence data Locations that cannot be identified to specific individuals but can be labeled as belonging to a particular population, species, or taxonomic group. Occurrence records at a location may be measured as presence, count, or density values

Population The group of individual observations to which one wants to make inference. For example, a subset of individuals within the same or multiple species, a subpopulation within a defined geographic area, a meta-population considered as a whole across a region, or even a whole species spread across a region or continent. This definition differs somewhat from a biological population, and is more similar to a statistical population, which is defined by the set of observations of interest for a specific question

Individual-level movement A movement path generated by linking locations of the same individual through time

Population-level movement Population redistribution over time, which can be summarized by an aggregate metric such as center or boundary, and quantified by its rate of change in direction or magnitude. Movement at the population level can result from individual behavior, demographic processes, external factors, or their combined effects

Crowdsourced data Data collected with or without strict protocols by volunteers distributed across many locations, and placed into a repository for review and inclusion in an overall database. Advanced internet technologies are often used to harness these efforts. May also be referred to as citizen science, civic science, community science, or public monitoring data

Structured to unstructured data continuum Structured data are typically stored in tabular or relational database formats, machine readable and could be readily used in an analysis. In contrast, unstructured data are typically found in audio, image, video, or unstructured text formats, are not readily machine readable and require further specialized processing to be ready for analysis. For example, conversion and translation are needed to interpret the raw data (i.e., a target that is visible in an image or audible in a recorded sound) to an identification of the presence of an individual of a particular species. Semi-structured data fall somewhere in between, for example in xml formats where user-defined tags may be used [52]

Structured to unstructured project continuum Structured project or network designs collect data with rigorously prescribed protocols and tightly controlled measurement error, ideally with randomization to ensure representation of the overall population, and are implemented for a specific purpose or planned data analysis with clear objectives. In contrast, unstructured projects or network designs collect data by open recruitment, with few rigorous protocols, and with typically large variation in data quality and quantity within the network. Semi-structured projects fall somewhere in between, for example, by collecting information on potential covariates or biases that can be accounted for in later analysis (sensu [53, 54])

Movement Ecology Paradigm Nathan et al. [42] proposed this paradigm to organize individual movement research, based on four mechanistic components of organismal movement: (1) internal state, (2) motion, or (3) navigation capacities of an individual, and (4) external factors affecting movement