DOI: 10.5061/dryad.x95x69pxs
title: Data from: Rapid shifts in bryophyte phenology revealed by airborne eDNA

description: In our publication, “Rapid shifts in bryophyte phenology revealed by
 airborne eDNA”, we report on multi-decadal shifts in the phenology of
 spore dispersal in 16 bryophyte taxa based on the data deposited here. The
 dataset represents a subset of a unique 35-year time series of airborne
 environmental DNA (eDNA ) collected in Kiruna, northern Sweden. The
 observed phenological shifts indicate strong perturbations in bryophyte
 phenology, consistent with ongoing climate change, and demonstrate that
 the use of airborne particles analysed by eDNA methodology is a valuable
 complement to other monitoring methods. The main dataset provided here
 consists of pivot coordinates of relative abundance data for the selected
 taxa between 1974 and 2008. The time series comprises data for every week
 when mean temperatures reached 0 °C, for every second year. Environmental
 DNA was extracted from archived samples of air filters that had been
 collected weekly since the 1960s and stored in air-tight containers. The
 samples were shotgun sequenced, and the reads matched across all major
 organism groups, with a high proportion identified as bryophytes. We
 interpret the relative abundance of airborne bryophyte eDNA as a proxy for
 spore dispersal, because most mosses are wind-dispersed. We used these
 data to describe phenological change during the time series. To analyse
 relationships between the phenological shifts and climate, we used local
 weather data from the same years (weekly precipitation and temperature),
 obtained from the Swedish Meteorological Institute (SMHI, Griddad
 nederbörd- och temperaturdata — SMHI). These data are also provided here.
description: # Data from: Rapid shifts in bryophyte phenology revealed by airborne eDNA
 Dataset DOI: [10.5061/dryad.x95x69pxs](10.5061/dryad.x95x69pxs) ##
 Description of the data and file structure We provide two data sheets. The
 first contains a time-series (1974-2008) of weekly pivot coordinates
 representing eDNA based relative abundance data from 16 bryophyte taxa.
 The dataset is a subset of that used in the preprint Sullivan et al.
 (2023; [https://doi.org/10.1101/2023.12.06.569882](https://doi.org/10.1101/2023.12.06.569882)) that includes all organism groups detected in the samples. The eDNA was obtained from airborn particles captured in air filters collected in northern Sweden and subsequently sequenced. The second file contains local weather data from the site for the same period as the eDNA time series. Variables include weekly precipitation and temperature, obtained from the Swedish Meteorological and Hydrological Institute (SMHI). ### Files and variables #### File: Data_Rel_abundance_Bengtsson_et_al_2025.csv **Description:** For every second year in the time series (1974-2008) the pivot coordinates are given for each measured week (weeks with mean temperature above °C) for each of the 16 bryophyte taxa. Pivot coordinates are transformed values that express the relative abundances as independent values between taxa allowing the use of standard statistical analyses without the bias introduced when using compositional data. ##### Variables * genus: bryophyte taxon * All other columns consist of relative abundance pivot coordinates with the column name format: yyyy_week number, e.g., 1974_21, i.e., the filter data from week 21 in 1974. #### File: Data_Weekly_weather_Bengtsson_et_al_2025.csv **Description:** The precipitation (mm) and temperature (°C) data for each week during 1997-2008 from the closest weather station to the filter collection site. The data were obtained fromSMHI: PTHBV - en areellt hgupplst klimatdatabas fr hydrologiska modellberakningar: [https://www.smhi.se/kunskapsbanken/hydrologi/pthbv-en-areellt-hogupplost-klimatdatabas-for-hydrologiska-modellberakningar-1.190268 ](https://www.smhi.se/kunskapsbanken/hydrologi/pthbv-en-areellt-hogupplost-klimatdatabas-for-hydrologiska-modellberakningar-1.190268)Accessed 04 March 2024. ##### Variables * year.week: year and week number * year: year number * week: week number * temp.mean: mean temperature (°C) * temp.max: maximum temperature (°C) * temp.min: minimum temperature (°C) * precip.mean: mean precipitation (mm) * precip.sum: summed precipitation (mm) * precip.max: maximum precipitation (mm) * precip.min: minimum precipitation (mm) ## Code/software These data are in csv-format and can be read by the likes excel, any text processor and R, for example.