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Koelzsch, A.

Publications and source records attributed to Koelzsch, A..

3 recordsLinked to original sources

MoveTraits - A database for integrating animal behaviour into trait-based ecology

Trait-based approaches are key to understanding eco-evolutionary processes but rarely account for animal behaviour despite its central role in ecosystem dynamics. We propose integrating behaviour into trait-based ecology through movement traits - standardised and comparable measures of animal movement derived from biologging data, such as daily displacements or range sizes. Accounting for animal behaviour will advance trait-based research on species interactions, community structure, and ecosystem functioning. Importantly, movement traits allow for quantification of behavioural reaction norms, offering insights into species acclimation and adaptive capacity to environmental change. We outline a vision for a living global movement trait database that enhances trait data curation by (1) continuously growing alongside shared biologging data, (2) calculating traits directly from individual-level data using standardised, consistent methodology, and (3) providing information on multi-level (species, individual, within-individual) trait variation. We present a proof-of-concept MoveTraits database with 55 mammal and 108 bird species, demonstrating calculation workflows for 5 traits across multiple time scales. Movement traits have significant potential to improve trait-based global change predictions and contribute to global biodiversity assessments as Essential Biodiversity Variables. By making animal movement data more accessible and interpretable, this database could bridge the gap between movement ecology and biodiversity policy, facilitating evidence-based conservation.

ecology↗

Nesting flight statistics for wind turbine planning: a MoveApps workflow

As green, renewable energy is increasing by the installation of more and more wind turbines, the assessment of their impact on protected species has to be improved by more automatized, data-driven risk analyses. We have developed as set of two workflows to extract simple parameters for collision risk models from GPS tracks of sensitive bird species during nesting. The workflows have been integrated into the free MoveApps platform and are available there. The analysis code of all components of the workflows is openly available on GitHub, and improvement and adaption to other, similar requirements is encouraged. With three example data sets of white storks (WS), red kites (RK) and marsh harriers (MH), we illustrate how the workflows are used. The first workflow identifies nesting sites and time of nesting from the GPS tracks, the second workflow calculates flight speeds, flight duration, flight height and distance from the nest. Estimated flight speeds show low within species variability, with averages of 4.1 m/s (MH), 6.9 m/s (RK) and 10.9 m/s (WS). Extracted times of nesting are widely spread through the season and flight height and distance to the nest when in flight show large differences between individuals and years. Similar to the central evaluation distances around the nest required by national legislation, the 50% in-flight usage thresholds are 700 m (MH), 1100 m (RK) and 1400 m (WS). Flight height during nesting is rather low, on average 16 m (MH), 75 m (RK) and 193 m (WS) above the ground. These values can help to estimate collision risk with wind turbines for large birds in Central Europe during their nesting period. Finally, the developed MoveApps workflows (an possible adaptions) can be used to extract required parameters from tracking studies of any other vulnerable species or populations in an unbiased, automated manner to improve wind turbine placement in relation to nesting sites.

ecology↗

MoveApps - a serverless no-code analysis platform for animal tracking data

BackgroundBio-logging and animal tracking datasets continuously grow in volume and complexity, documenting animal behaviour and ecology in unprecedented extent and detail, but greatly increasing the challenge of extracting knowledge from the data obtained. A large variety of analysis methods are being developed, many of which in effect are inaccessible to potential users, because they remain unpublished, depend on proprietary software or require significant coding skills. ResultsWe developed MoveApps, an open analysis platform for animal tracking data, to make sophisticated analytical tools accessible to a global community of movement ecologists and wildlife managers. As part of the Movebank ecosystem, MoveApps allows users to design and share workflows composed of analysis modules (Apps) that access and analyse tracking data. Users browse Apps, build workflows, customise parameters, execute analyses and access results through an intuitive web-based interface. Apps, coded in R or other programming languages, have been developed by the MoveApps team and can be contributed by anyone developing analysis code. They become available to all user of the platform. To allow long-term and cross-system reproducibility, Apps have public source code and are compiled and run in Docker containers that form the basis of a serverless cloud computing system. To support reproducible science and help contributors document and benefit from their efforts, workflows of Apps can be shared, published and archived with DOIs in the Movebank Data Repository. The platform was beta launched in spring 2021 and currently contains 44 Apps that are used by 156 registered users. We illustrate its use through two workflows that (1) provide a daily report on active tag deployments and (2) segment and map migratory movements. ConclusionsThe MoveApps platform is meant to empower the community to supply, exchange and use analysis code in an intuitive environment that allows fast and traceable results and feedback. By bringing together analytical experts developing movement analysis methods and code with those in need of tools to explore, answer questions and inform decisions based on data they collect, we intend to increase the pace of knowledge generation and integration to match the huge growth rate in bio-logging data acquisition.

ecology↗