eEcology Annotation Tool
Visualize and annotate GPS measurements of bird movements.
Virtual laboratories for inspiration and discovery in ecology
Bird behavior varies for different species and types of birds, and even between genders. To understand bird behavior, we have to track their movement and behavior at multiple scales in space and time – no easy task. At the University of Amsterdam a team of researchers have developed a bird tracking system (UvA-BiTS) designed to answer the needs of people with diverse research aims. In the summer of 2008 the team did the first tests with their newly developed GPS trackers. Since then, tags have been deployed on many bird species including Oystercatchers, Crows, Lesser Black-backed Gulls, Montagu’s Harriers, Honey Buzzards, Common Buzzards, Storks, Brent Geese (all in the Netherlands), Crab Plovers (in Oman), Stone Curlews (in Israel), Great Skua (on the Shetland Islands), Griffon Vultures (in France and Spain), and Verreaux’s Eagle (South Africa).
Key features of the UvA Bird Tracking System include a solar powered, lightweight GPS tag with rechargeable batteries, multiple onboard sensors including a tri-axial accelerometer, two way data-communication to a ground station network, automated data processing and visualization. The combination of programmability and two-way data communication is a unique feature that enables the user to tailor the measurement scheme to the needs of the research while the tag is still on the bird. Using the Virtual Lab, an eScience research infrastructure for handling the massive amounts of data that can be obtained with the UvA-BiTS system, researchers can study migration, navigation, and foraging strategies of birds on land and at sea.
This project shows that ecology is evolving into a data and computationally intensive science with the amount of (heterogeneous) data included for analysis increasing rapidly with time. Traditionally, ecologists are not trained in coping with the massive amounts of data that result from data sharing, sensor networks and the incorporation of environmental data into ecological research. Generally, the methodologies ecologists use for management, visualization, exploration, analysis of data are often not suited to cope with large data sets. The challenge of the eEcology project is to bridge the gap between the worlds of ecology and technology. Virtual Labs (VLs) will help to bridge this gap as they support scientific collaboration through facilitating data access, data integrity and quality control, data post-processing, data storage and backup, data merging, data sharing, interactive data visualization, and data analysis.
Most effort has gone into the Bird Movement Modeling Virtual Lab. This Virtual Lab has a growing international user community, whose users are mainly field biologists using the VL to track individual birds. In the VL, the track data from the UvA-BiTS system can be combined with, for example, landscape data, weather data, and tidal data to gain new insights on the influence of the environment on the bird’s behavior.
This project page covers two projects, eScience project "eEcology" (project number 027.011.305) and its extension "LifeWatch" (project number 33013002) funded by the UvA IBED group.
Global vegetation water dynamics using radar satellite data
Overcoming the challenge of locality using a community multi-model environment
eScience infrastructure for ecological applications of LiDAR point clouds
A novel filtering method to estimate bird densities
Handling data assimilation on a large scale
Virtual laboratories for inspiration and discovery in ecology
Global water information when it matters
Visualize and annotate GPS measurements of bird movements.
If you have birds flying around carrying UvA-BiTS trackers, then this software can save the SMS messages containing the bird's last known positions which are sent by the tracker to the UvA Bird tracking system's central database.
Calendar overview with daily statistics of GPS-trackers used to track bird movements.
Automatic classification of accelerometer data using a supervised learning approach.