Spatial-temporal interpolation of satellite geomagnetic data to study long-distance animal migration

Abstract

IntroductionIncreased access to remote sensing datasets presents opportunities to model an animal's in-situ experience of the landscape to study behavior and test hypotheses such as geomagnetic map navigation. MagGeo is an open-source tool that combines high spatiotemporal resolution geomagnetic data with animal tracking data. Unlike gridded remote sensing data, satellite geomagnetic data are point-based measurements of the magnetic field at the location of each satellite. MagGeo converts these measurements into geomagnetic values at an animal's location and time. The objective of this paper is to evaluate different interpolation methods and data frameworks within the MagGeo software and quantify how accurately MagGeo can model geomagnetic values and patterns as experienced by animals. MethodWe tested MagGeo outputs against data from 109 terrestrial geomagnetic observatories across 7 years. Unlike satellite data, ground-based data are more likely to represent how animals near the Earth's surface experience geomagnetic field dynamics. Within the MagGeo framework, we compared an inverse-distance weighting interpolation with three different nearest-neighbour interpolation methods. We also compared model geomagnetic data with combined model and satellite data in their ability to capture geomagnetic fluctuations. Finally, we fit a linear mixed-effect model to understand how error is influenced by factors like geomagnetic activity and distance in space and time between satellite and point of interest. Results and conclusionsThe overall absolute difference between MagGeo outputs and observatory values was <1% of the total possible range of values for geomagnetic components. Satellite data measurements closest in time to the point of interest consistently had lowest error which likely reflects the ability of the nearest neighbour in time interpolation method to capture small continuous daily fluctuations and larger discrete events like geomagnetic storms. Combined model and satellite data also capture geomagnetic fluctuations better than model data alone across most geomagnetic activity levels. Our linear mixed-effect models suggest that most of the variation in error can be explained by location-specific effects originating largely from local crustal biases, and that high geomagnetic activity usually predicts higher error though ultimately remaining within the 1% error range. Our results indicate that MagGeo can help researchers explore how animals may use the geomagnetic field to navigate long distances by providing access to data and methods that accurately model how animals moving near the Earth's surface experience the geomagnetic field.