2023/08/08 by Matěj Man, Vojtěch Kalčík, Martin Macek +4 · 1 voice · 7 citations
Computer Science · Environmental Science · #Data Analysis with R #Plant Water Relations and Carbon Dynamics #Species Distribution and Climate Change
paper · pdf · doi:10.1111/2041-210x.14192
openalex publication_date 2023/08/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
Abstract Microclimates have been recognised as one of the key drivers in global change biology. Durable microclimate loggers, detailed in‐situ measurements and sophisticated modelling tools are increasingly available, but a lack of standardised workflows for microclimate data handling hinders synthesis across the studies and thus progress in the global change biology. To overcome these limitations, we developed an R package myClim for microclimate data processing, storage and analyses. The myClim package supports complete workflow for microclimate data handling, including reading raw logger data files, their preprocessing and cleaning, time‐series' aggregation, calculation of ecologically relevant microclimatic variables, data export and storage. The myClim package stores data in a size‐efficient, hierarchical structure which respects the hierarchy of field microclimate measurement (locality > loggers > sensors). For imported microclimatic data, myClim provides an informative summary and automatically detects and corrects common issues like duplicated and wrongly ordered measurements. The myClim package also provides advanced functions for microclimate data aggregation to various timescales (e.g. days, months, years or growing seasons) as well as tools for sensor calibration, data conversion and joining of multiple microclimatic time series. The myClim package provides advanced functions for standardised calculation of ecologically relevant microclimatic variables like freezing and growing degree days, snow cover period, soil volumetric water content and atmospheric vapour pressure deficit. Calculated microclimatic variables are stored efficiently in myClim data format and can be easily exported to long or wide tables for further analyses and visualisations. Adopting myClim can facilitate large‐scale syntheses, boost data sharing and increase the comparability and reproducibility of microclimatic studies. The stable version of myClim is available on CRAN ( https://cran.r‐project.org/web/packages/myClim ) and the development version is available on GitHub ( https://github.com/ibot‐geoecology/myClim ).