Abstract: Measurements obtained at ground-based observatories are crucial to understanding the geomagnetic field and its secular variation (SV). However, current data processing methods rely on piecemeal closed-source codes or are performed on an ad hoc basis, hampering efforts to reproduce data sets underlying published results. We present MagPySV, an open-source Python package designed to provide a consistent and automated means of generating high-resolution SV data sets from hourly means distributed by the Edinburgh World Data Centre. It applies corrections for documented baseline changes, and optionally, data may be excluded using the ap index, which removes effects from documented high solar activity periods such as geomagnetic storms. Robust statistics are used to identify and remove outliers. Developing existing denoising methods, we use principal component analysis of the covariance matrix of residuals between observed SV and that predicted by a global field model to remove a proxy for external field contamination from observations. This method creates a single covariance matrix for all observatories of interest combined and applies the denoising to all locations simultaneously, resulting in cleaner time series of the internally generated SV. In our case studies, we present cleaned data in two geographic regions: monthly first differences are used to investigate geomagnetic jerk morphology in Europe, an area previously well-studied at lower resolution, and annual differences are investigated for northern high latitude regions, which are often neglected due to their high noise content. MagPySV may be run on the command line or within an interactive Jupyter notebook; two notebooks reproducing the case studies are supplied.

Abstract: Seabirds are well known to be attracted by fishing boats to forage on offal and baits. We used recently developed loggers that record accurate GPS position and detect the presence of boats through their radar emissions to examine how albatrosses use Area Restricted Search (ARS) and if so, have specific ARS behaviours, when attending boats. As much as 78.5% of locations with a radar detection (contact with boat) during a trip occurred within ARS: 36.8% of all large-scale ARS (n = 212) and 14.7% of all small-scale ARS (n = 1476) were associated with the presence of a boat. During small-scale ARS, birds spent more time and had greater sinuosity during boat-associated ARS compared with other ARS that we considered natural. For, small-scale ARS associated with boats, those performed over shelves were longer in duration, had greater sinuosity, and birds spent more time sitting on water compared with oceanic ARS associated with boats. We also found that the proportion of small-scale ARS tend to be more frequently nested in larger-scale ARS was higher for birds associated with boats and that ARS behaviour differed between oceanic (tuna fisheries) and shelf-edge (mainly Patagonian toothfish fisheries) habitats. We suggest that, in seabird species attracted by boats, a significant amount of ARS behaviours are associated with boats, and that it is important to be able to separate ARS behaviours associated to boats from natural searching behaviours. Our study suggest that studying ARS characteristics should help attribute specific behaviours associated to the presence of boats and understand associated risks between fisheries.