Published June 10, 2024 | Version v1
Dataset Open

Data and scripts for Wind is a primary driver of fungal dispersal across a mainland-island system

Authors/Creators

  • 1. ROR icon University of Jyväskylä
  • 2. ROR icon University of Helsinki
  • 3. ROR icon University of Guelph

Description

Data and scripts for reproducing the analyses of Naranjo-Orrico et al Wind is a primary driver of fungal dispersal across a mainland-island system. 

The file allData.RData contains the data in R format. The data include three matrices: the metadata (md), the sample x OTU table (otu.table) and the taxonomic information of the identified OTUs (taxonomy). These three files need to be loaded using the function load in R for reproducing the statistical analyses performed in the study.

The statistical analyses consist of joint species distribution modelling with the package Hmsc. To perform the analyses the three scripts need to be run consecutively from S1 to S3. S1 defines and fits the 12 models fitted in the study (which include both presence-absence and abundance models, with different variants with a different set of explanatory variables). S2 shows the parameter estimates from the fitted models, in particular the beta parameters and the variance partitioning across environmental covariates. S3 builds the predictions on species richness and community weighted spore traits in relation to the focal environmental predictors. 

Files

Files (64.8 kB)

Name Size Download all
md5:892a6831bc831450dc509125c30d7a46
51.8 kB Download
md5:07ad13ec0d8a04249ff76d2b9f583581
4.3 kB Download
md5:643699c278fd148de9dc53a2ab555854
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md5:645c457bdeb69b665f7f0090e45bba82
6.7 kB Download

Additional details

Funding

Research Council of Finland
Bringing an evolutionary perspective to community ecology using plant-fungal symbiotic networks as a study system 342374
Research Council of Finland
Predictive Understanding of Global Biodiversity Dynamics 345110
European Commission
LIFEPLAN - A Planetary Inventory of Life – a New Synthesis Built on Big Data Combined with Novel Statistical Methods 856506