Search

Introduction Increased 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. Method We 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 conclusions The 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

Marine sediments are important repositories of organic matter, effectively burying organic carbon (OC) over geological timescales thus providing a climate regulation service. However, the spatial distribution of this marine sedimentary OC store is not well constrained. In this study we leverage a high resolution multibeam echosounder (MBES) survey taken at Loch Creran, a model fjordic site on the west coast of Scotland, to develop a new methodology for predicting the distribution of OC in surface sediments. Using an integrated approach, we use MBES survey, video imagery and ground-truthing data to produce a high-resolution (2 × 2 m) map of surficial carbon and calculate a 10 cm stock. We find that the backscatter survey reliably uncovers a heterogeneous seabed and that OC correlates strongly with the MBES backscatter signal as a function of sediment composition. We estimate that there are approximately 12,346 ± 2,677 t of OC held within the top 10 cm of mixed sediments across the MBES survey area (7.96 km2; 60% of the total area), upscaled to 20,577 ± 4,462 t of OC across Loch Creran (13.27 km2). Normalised by area, we find that fine sediments with small fractions of sand and gravel hold more OC than homogenous fine sediments. This initial work proposes a novel methodological approach to using high resolution MBES surveys to improve the spatial mapping of sedimentary carbon (C) and identification of C hotspots, enabling consideration of this resource in sedimentary carbon accounting, seabed management and climate mitigation strategies.