GPS tracking data of Eurasian oystercatchers (Haematopusostralegus) from the Netherlands and Belgium

We describe six datasets that contain GPS and accelerometer data of 202 Eurasian oystercatchers (Haematopusostralegus) spanning the period 2008-2021. Birds were equipped with GPS trackers in breeding and wintering areas in the Netherlands and Belgium. We used GPS trackers from the University of Amsterdam Bird Tracking System (UvA-BiTS) for several study purposes, including the study of space use during the breeding season, habitat use and foraging behaviour in the winter season, and impacts of human disturbance. To enable broader usage, all data have now been made open access. Combined, the datasets contain 6.0 million GPS positions, 164 million acceleration measurements and 7.0 million classified behaviour events (i.e., flying, walking, foraging, preening, and inactive). The datasets are deposited on the research repository Zenodo, but are also accessible on Movebank and as down-sampled occurrence datasets on the Global Biodiversity Information Facility (GBIF) and Ocean Biodiversity Information System (OBIS).

GPS tracking data of Western marsh harriers breeding in Belgium and the Netherlands

In this data paper three datasets are described containing GPS tracking and acceleration data of Western marsh harriers (Circusaeruginosus) breeding in Belgium and the Netherlands. The Western marsh harrier is included as a threatened bird species in Annex I of the European Bird Directive due to the steep decline in population densities. In order to collect data of habitat use and migration behaviour, Western marsh harriers were equipped with light-weight solar powered GPS trackers developed by the Institute for Biodiversity and Ecosystem Dynamics (IBED) at the University of Amsterdam (University of Amsterdam Bird Tracking System, UvA-BiTS). These trackers automatically collect and store data on the bird’s activity and 3D position in time and transmit these data to ground stations.

The datasets were collected by the Research Institute for Nature and Forest (INBO) and the Dutch Montagu’s Harrier Foundation. Tracked Western marsh harriers were breeding in the northeast of the Dutch province of Groningen and on the opposite side of the river Ems in Germany (H_GRONINGEN), in the region of Waterland-Oudeman near the Belgian-Dutch border (MH_WATERLAND), and at the left bank of the Scheldt estuary, close to the Belgian-Dutch border and north of the city of Antwerp (MH_ANTWERPEN). Most individuals remained within 10 km from their nesting sites during the breeding season and wintered in West Africa. H_GRONINGEN contains 987,493 GPS fixes and 3,853,859 acceleration records of four individuals since 2012. MH_WATERLAND contains 377,910 GPS fixes of seven individuals. Sampling in this region began in 2013. Three more Western marsh harriers were tagged in the Scheldt estuary near Antwerp more recently in 2018 (one individual) and 2019 (two individuals) for the MH_ANTWERPEN study, which contains 47,917 GPS fixes and 227,746 acceleration records.

The three Western marsh harrier datasets were published as separate studies in Movebank (https://www.movebank.org) and archived as data packages in Zenodo (https://www.zenodo.org) to ensure long-term preservation and versioning of the data.

Light-level geolocator analyses: A user's guide

Light-level geolocator tags use ambient light recordings to estimate the whereabouts of an individual over the time it carried the device. Over the past decade, these tags have emerged as an important tool and have been used extensively for tracking animal migrations, most commonly small birds. Analysing geolocator data can be daunting to new and experienced scientists alike. Over the past decades, several methods with fundamental differences in the analytical approach have been developed to cope with the various caveats and the often complicated data. Here, we explain the concepts behind the analyses of geolocator data and provide a practical guide for the common steps encompassing most analyses - annotation of twilights, calibration, estimating and refining locations, and extraction of movement patterns - describing good practices and common pitfalls for each step. We discuss criteria for deciding whether or not geolocators can answer proposed research questions, provide guidance in choosing an appropriate analysis method and introduce key features of the newest open-source analysis tools. We provide advice for how to interpret and report results, highlighting parameters that should be reported in publications and included in data archiving. Finally, we introduce a comprehensive supplementary online manual that applies the concepts to several datasets, demonstrates the use of open-source analysis tools with step-by-step instructions and code and details our recommendations for interpreting, reporting and archiving.

Is pre-breeding prospecting behaviour affected by snow cover in the irruptive snowy owl? A test using state-space modelling and environmental data annotated via Movebank

BACKGROUND

Tracking individual animals using satellite telemetry has improved our understanding of animal movements considerably. Nonetheless, thorough statistical treatment of Argos datasets is often jeopardized by their coarse temporal resolution. State-space modelling can circumvent some of the inherent limitations of Argos datasets, such as the limited temporal resolution of locations and the lack of information pertaining to the behavioural state of the tracked individuals at each location. We coupled state-space modelling with environmental characterisation of modelled locations on a 3-year Argos dataset of 9 breeding snowy owls to assess whether searching behaviour for breeding sites was affected by snow cover and depth in an arctic predator that shows a lack of breeding site fidelity.

RESULTS

The state-space modelling approach allowed the discrimination of two behavioural states (searching and moving) during pre-breeding movements. Tracked snowy owls constantly switched from moving to searching behaviour during pre-breeding movements from mid-March to early June. Searching events were more likely where snow cover and depth was low. This suggests that snowy owls adapt their searching effort to environmental conditions encountered along their path.

CONCLUSIONS

This modelling technique increases our understanding of movement ecology and behavioural decisions of individual animals both locally and globally according to environmental variables.

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

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.