The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data

Area of Science:

• Movement ecology
• Environmental data science
• Bioinformatics

Background:

• Animal movement is influenced by environmental factors like weather and habitat.
• Integrating diverse environmental data with animal tracking data presents significant challenges.
• Advances in sensor technology provide high-resolution animal location data alongside complex environmental datasets.

Purpose of the Study:

• To present a new, publicly available system, Environmental-Data Automated Track Annotation (Env-DATA), for automating the annotation of animal movement trajectories with environmental data.
• To overcome technical difficulties in data acquisition, transformation, integration, resampling, and interpolation for movement ecology research.
• To facilitate a deeper understanding of animal movement patterns in response to environmental dynamics.

Main Methods:

• Developed the Environmental-Data Automated Track Annotation (Env-DATA) system.
• Integrated data from global remote sensing, weather, and ecosystem products.
• Implemented various interpolation methods to match environmental data to animal track spatiotemporal scales.
• Utilized a case study involving Galapagos Albatross tracks and environmental variables.

Main Results:

• Env-DATA automates the annotation of animal movement trajectories with atmospheric and landscape information.
• The system simplifies complex data integration processes, saving researchers time.
• A case study demonstrated how albatrosses utilize environmental conditions for long-range foraging and efficient flight.

Conclusions:

• The Env-DATA system enhances existing platforms like Movebank by automating environmental data access.
• It enables researchers to link animal movements with environmental variables at various scales.
• Facilitates new insights into spatiotemporal animal movement patterns and predictive modeling in response to environmental change.