Fusion of wildlife tracking and satellite geomagnetic data for the study of animal migration

Abstract

Background: Migratory animals use information from the Earth's magnetic field on their journeys. Geomagnetic navigation has been observed across many taxa, but how animals use geomagnetic information to find their way is still relatively unknown. Most migration studies use a static representation of geomagnetic field and do not consider its temporal variation. However, short-term temporal perturbations may affect how animals respond - to understand this phenomenon, we need to obtain fine resolution accurate geomagnetic measurements at the location and time of the animal. Satellite geomagnetic measurements provide a potential to create such accurate measurements, yet have not been used yet for exploration of animal migration.

Methods: We develop a new tool for data fusion of satellite geomagnetic data (from the European Space Agency's Swarm constellation) with animal tracking data using a spatio-temporal interpolation approach. We assess accuracy of the fusion through a comparison with calibrated terrestrial measurements from the International Real-time Magnetic Observatory Network (INTERMAGNET). We fit a generalized linear model (GLM) to assess how the absolute error of annotated geomagnetic intensity varies with interpolation parameters and with the local geomagnetic disturbance.

Results: We find that the average absolute error of intensity is - 21.6 nT (95% CI [- 22.26555, - 20.96664]), which is at the lower range of the intensity that animals can sense. The main predictor of error is the level of geomagnetic disturbance, given by the Kp index (indicating the presence of a geomagnetic storm). Since storm level disturbances are rare, this means that our tool is suitable for studies of animal geomagnetic navigation. Caution should be taken with data obtained during geomagnetically disturbed days due to rapid and localised changes of the field which may not be adequately captured.

Conclusions: By using our new tool, ecologists will be able to, for the first time, access accurate real-time satellite geomagnetic data at the location and time of each tracked animal, without having to start new tracking studies with specialised magnetic sensors. This opens a new and exciting possibility for large multi-species studies that will search for general migratory responses to geomagnetic cues. The tool therefore has a potential to uncover new knowledge about geomagnetic navigation and help resolve long-standing debates.

Keywords: Animal migration; Data fusion; Earth’s magnetic field; GPS tracking; Swarm satellite constellation.

Fig. 1

Components of the Earth’s magnetic field. a Orientation of the dipole field with respect to Earth’s rotation axis. b Measuring the field in the NEC coordinate system. B is the field vector, H its horizontal component, I the inclination and D the declination. c Earth’s magnetosphere is dynamically distorted by the solar wind carrying the Interplanetary Magnetic Field (IMF), which depresses the magnetosphere on the day side and extends its shape on the night side. Magnetosphere is the region of space around the Earth that is affected by its magnetic field. Bow shock marks its outermost boundary, where the speed of solar wind decreases. In magnetosheath, the Earth’s magnetic field is affected by the shocked solar wind and becomes weak and irregular. In magnetopause, the pressure from the Earth’s magnetic field and the solar wind are in balance - the size and the shape of magnetopause therefore constantly change in response to temporal variability in the speed, direction and strength of the solar wind. Magnetotail is the extended anti-sunward part of the magnetosphere: in reality the sphere is not a sphere (as in panel a) but has a large extended tail, created through the pressure of the solar wind

Fig. 2

A general outline of our magnetic annotation method. Green boxes show data inputs, blue boxes calculation steps and yellow boxes outputs

Fig. 3

Orbits of the three Swarm satellites over a 24 h period (15 October 2014), A shown in 3D and B projected on the surface of the Earth. Measurements points are coloured according to the magnetic intensity F. (These images were created with the VirES web client https://vires.services/)

Fig. 4

Using Swarm residuals to calculate real-time magnetic field at the altitude of migrating animal. We take the measured field at the satellite height (Z₁) and subtract modelled contributions of the core, crust and magnetosphere fields at this same height (orange/brown), to obtain the real-time solar-wind induced variability, represented as residuals (yellow). This variability varies at a much higher temporal scale than the modelled contributions and can only be measured in situ. We then obtain modelled field values at the elevation Z₂ of the migrating animal (values in blue/teal) and add residuals from height Z₁, which gives us real-time field values at height Z₂. All modelled values are from the CHAOS-7 model [50, 51]. Charts are not to scale: the contribution of the core field typically represents over 98% of the total field

Fig. 5

Selection of Swarm points. A The spatial extent of the spatio-temporal kernel varies with latitude, with larger circles on the Equator and smaller towards the Poles. B Spatio-temporal kernels shown in a space-time cylinder (note that in this display, the third dimension is time), demonstrating the calculation of the spatio-temporal weights (details in Supplementary Information 1). C The spatio-temporal kernel allows us to select the nearest Swarm points to the tracking point

Fig. 6

Map of INTERMAGNET observatories showing locations of the three that we selected for accuracy assessment (given with their observatory codes, Lerwick – LER, Hartland – HAR, Pedeli – PEG)

Fig. 7

Error in magnetic intensity (F). A shows the distribution of the error for all three observatories and the black dashed line indicates the mean error = − 21.61, with a 95% CI [− 22.26555, − 20.96664]. The two red dashed lines show the 2.5 and 97.5 percentile of the distribution. B shows the probability density and distribution of error values per station. C shows a scatterplot of the absolute error against the K index and the curve of best fit for each observatory (Lerwick, Hartland, Pedeli)

Fig. 8

Map showing migration tracks of 22 great white-fronted geese during 2017 autumn migration. Locations where individuals encountered geomagnetic storm conditions (local Kp > =5) during flight are shown in red. Map was created using the Albers equal area projection

Fig. 9

Distribution of movement parameters during and outside of geomagnetic storms. Panels A and B show the distribution of segment durations for storm (A) and no storm (B) – the most common duration in both cases is 5 min (300 s). Panels C and D show distribution of speed during stormy conditions (C) and during quiet conditions (D). Panels E and F show distributions of turning angle values during stormy (E) and quiet conditions (F). In these two panels, the 0 reference is the bearing obtained from the previous and current GPS points