Data wrangling for article "The role of long-distance dispersal for community dynamics of fungi"

This repository shows the data wrangling for SILAM footprints and various land cover data.

Installation

conda env create -f env.yml installs the required dependencies. For jupyter, install additionally either notebook, lab or ipykernel, whichever you like.

Data used

• Sampler locations are the same than used in Global Spore Sampling Project: A global, standardized dataset of airborne fungal DNA (Ovaskainen et al, 2024)
• Footprints for the samplers were created with SILAM, and distributed as .nc4 files with 0.5x0.5° resolution
• Land cover data used is The land cover data used in this study is Copernicus Global Land Service: Land Cover 100m: collection 3: epoch: 2019 (Buchhorn et al, 2019)
• For data above 80°N and below 60°S, land-ocean classification was done with dissolved OpenStreetMap data. Raw data available here: https://osmdata.openstreetmap.de/data/land-polygons.html. Simplified land polygons is accurate enough considering the resolution of SILAM footprints. Download and dissolve the data, or use the data available in data.

Citation

@unpublished{makinen2026dispersal,
    title={The role of long-distance dispersal for community dynamics of fungi},
    author={Mäkinen, Jussi and Mäyrä, Janne and Fatahi, Yalda, and Sofiev, Mikhail and Ovaskainen, Otso and Abrego, Nerea and Norros, Veera}
    year=2026
}

Mäkinen, J., Mäyrä, J., Fatahi, Y., Sofiev, M., Ovaskainen, O., Abrego, N. & Norros, V. (2026) The role of long-distance dispersal for community dynamics of fungi [Unpublished manuscript]