1 Introduction

1.1 Research purpose

Migratory animals play significant roles in shaping ecosystems through a variety of transport and trophic effects that also represent services and disservices to human infrastructure, agriculture and welfare. Their aerial and terrestrial habitats have changed dramatically over the past decades and are expected to change further, particularly due to rapid climate change, increased urbanization, wind energy installations, and habitat fragmentation.

Within GloBAM: Towards monitoring, understanding and forecasting global biomass flows of aerial migrants, we aim to use weather radar data to quantify the biomass flows of aerial migrants (birds, insects and bats) from regional to continental scales across Europe and North America, over time-scales from days to years. We are particularly interested in identifying the drivers of migrant movements and abundances and will relate the timing and intensity of movements to a suite of atmospheric, climatic and landscape variables, exploring the implications for aerial migrants in a changing world.

For more information on GloBAM, see the project website.

1.2 Data manager

Peter Desmet is responsible for data management and DMP maintenance in GloBAM. He is also leading WP1 - Data infrastructure. Peter works as open data coordinator for the Open science lab for biodiversity at the Research Institute for Nature and Forest (INBO). He and his team have extensive expertise and experience with data management and open data publication meeting FAIR principles.

1.3 How this DMP is maintained

  1. This DMP is maintained and versioned on GitHub at https://github.com/enram/globam-dmp/.
  2. Each chapter is an R Markdown file (Rmd) in the src directory of that GitHub repository. You can access it directly by clicking the pencil icon in the top navigation of the website version of this DMP.
  3. Changes to the R Markdown files can be made by contributors to the GitHub repository or suggested by anyone as pull requests. Textual changes can be done directly on GitHub, code changes are better tested in RStudio first.
  4. Accepted changes (i.e. changes to the master branch) will trigger an automatic build procedure that will generate a new version of the DMP using the R package bookdown. The date of the build is used as the version number.