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

Abstract

Background: The movement of animals is strongly influenced by external factors in their surrounding environment such as weather, habitat types, and human land use. With advances in positioning and sensor technologies, it is now possible to capture animal locations at high spatial and temporal granularities. Likewise, scientists have an increasing access to large volumes of environmental data. Environmental data are heterogeneous in source and format, and are usually obtained at different spatiotemporal scales than movement data. Indeed, there remain scientific and technical challenges in developing linkages between the growing collections of animal movement data and the large repositories of heterogeneous remote sensing observations, as well as in the developments of new statistical and computational methods for the analysis of movement in its environmental context. These challenges include retrieval, indexing, efficient storage, data integration, and analytical techniques.

Results: This paper contributes to movement ecology research by presenting a new publicly available system, Environmental-Data Automated Track Annotation (Env-DATA), that automates annotation of movement trajectories with ambient atmospheric observations and underlying landscape information. Env-DATA provides a free and easy-to-use platform that eliminates technical difficulties of the annotation processes and relieves end users of a ton of tedious and time-consuming tasks associated with annotation, including data acquisition, data transformation and integration, resampling, and interpolation. The system is illustrated with a case study of Galapagos Albatross (Phoebastria irrorata) tracks and their relationship to wind, ocean productivity and chlorophyll concentration. Our case study illustrates why adult albatrosses make long-range trips to preferred, productive areas and how wind assistance facilitates their return flights while their outbound flights are hampered by head winds.

Conclusions: The new Env-DATA system enhances Movebank, an open portal of animal tracking data, by automating access to environmental variables from global remote sensing, weather, and ecosystem products from open web resources. The system provides several interpolation methods from the native grid resolution and structure to a global regular grid linked with the movement tracks in space and time. The aim is to facilitate new understanding and predictive capabilities of spatiotemporal patterns of animal movement in response to dynamic and changing environments from local to global scales.

Keywords: Animal movement; Migration; Movebank; Movement ecology; Remote sensing; Track annotation; Weather.

Figure 1

Movebank Env-DATA System. RZG: Computing Center Garching, Germany; OSU: The Ohio State University Supercomputer Center. The gray box highlights the Env-DATA system components within Movebank.

Figure 2

Env-DATA Track Annotation Service Components. The figure illustrates the workflow of an annotation request through the different servers and components of the system. Steps shown indicate the following: (a) selection and submission of a data annotation request by the User, (b) read annotation request information, process Track Annotation in the Env-DATA application cluster, storage of annotation results in the Env-DATA storage system, and delivery of results to User through the Env-DATA web server, and (c) environmental data acquisition and storage in the Env-DATA storage system through the Env-DATA application cluster. RZG: Computing Center Garching, Germany; OSU: The Ohio State University Supercomputer Center.

Figure 3

An example for the graphical user interface (GUI) that serves the annotation system users. The figure illustrates an annotation request for the data in the variable “surface wind (10m above ground, U component)” from the global weather reanalysis dataset ECMWF (see Table 1 for more details), and selection of interpolation methods for each requested variable.

Figure 4

Interpolation in space and time. (a) The variable data for track-point P _i is first interpolated in space (using one of several interpolation methods) based on the data from the available points in the environmental dataset native grid around P _i. (b) Similar spatial interpolations are conducted at the two nearest available points in time, the nearest before and nearest after the timestamp of the track-point P _i. Then, the two interpolated spatial values are interpolated in time to the timestamp of P _i.

Figure 5

Nine annotated albatross trajectories. The tracks of nine adult albatrosses, overall containing 8286 GPS locations, during the breeding season in June to September 2008, (a) color coded with annotated values of 8-day ocean NPP (see Table 1 for more information on this variable), (b) the same tracks (yellow lines) plotted on the geographic area annotation using the monthly MODIS-ocean chlorophyll-a variable (Table 1) for the month of July 2008. We used the KML data format and combined the annotated area with a Google-Earth satellite image of the region using the program Matlab and its “Google Earth Toolbox”.

Figure 6

Probability density histograms and 3D surface plot of Ocean NPP. Available net primary ocean production (NPP, mg C/m²/day) compared to NPP along the tracks of nine Galapagos albatrosses during June to September 2008. Red lines fitted on NPP histograms (left) highlight probability density distributions of NPP use versus NPP availability. Red points connected with gray lines on a 3D surface (right) illustrate the annotated albatross tracks overlaid on the averaged ocean NPP during June to September 2008.

Figure 7

Space-time-cube illustration of an albatross' flights annotated by tail-wind support. The track contains 1326 GPS locations of one individual albatross from 23 June to 15 September 2008. The albatross’ outbound flights towards the Peruvian coast are hampered by head winds while the return flights are facilitated by tail-wind assistance.

Figure 8

Map (top) and histogram (bottom) illustration of an albatross’ flights annotated by tail-wind support and side-wind (cross wind).The track contains 1326 GPS locations of one individual albatross from 23 June to 15 September 2008.