28208be4-0163-45e6-912c-2db205126925
English
ISO/IEC 8859-1 (also known as Latin 1)
dataset
dataset
Environmental Information Data Centre
Lancaster Environment Centre, Library Avenue, Bailrigg
Lancaster
LA1 4AP
UK
info@eidc.ac.uk
pointOfContact
2021-06-25T18:40:48
UK GEMINI
2.3
WGS 84 / Pseudo-Mercator
Abundance of airborne pollen for nine grass species, measured by qPCR, UK, 2016-2017
2020-12-15
publication
https://catalogue.ceh.ac.uk/id/28208be4-0163-45e6-912c-2db205126925
10.5285/28208be4-0163-45e6-912c-2db205126925
doi:
Brennan, G., Creer, S., Griffith, G. (2020). Abundance of airborne pollen for nine grass species, measured by qPCR, UK, 2016-2017. NERC Environmental Information Data Centre 10.5285/28208be4-0163-45e6-912c-2db205126925
The dataset contains abundance data of airborne pollen (including Anthoxanthum odoratum (sweet vernal-grass), Arrhenatherum elatius (false oat-grass), Cynosurus cristatus (crested dog’s-tail), Dactylis glomerata (cock’s-foot), Lolium perenne (perennial ryegrass), Phleum pratense (Timothy), Poa pratensis (smooth meadow-grass), grass species within the genera Alopecurus/Agrostis, and one probe that was found to be degenerate and unable to discriminate grass species. Here we used qPCR to track the seasonal progression of airborne grass pollen, in time and space. To do this we collected aerial samples from thirteen sites across the UK during the pollen seasons (May to September) of 2016 and 2017. Full details about this dataset can be found at https://doi.org/10.5285/28208be4-0163-45e6-912c-2db205126925
Brennan, G.
Bangor University
g.l.brennan@bangor.ac.uk
author
Creer, S.
Bangor University
s.creer@bangor.ac.uk
author
Griffith, G.
Aberystwyth University
Gwg@aber.ac.uk
author
Dr. Georgina Brennan
Bangor University
g.l.brennan@bangor.ac.uk
pointOfContact
NERC EDS Environmental Information Data Centre
info@eidc.ac.uk
custodian
NERC Environmental Information Data Centre
info@eidc.ac.uk
publisher
Bangor University
copyright@bangor.ac.uk
owner
Environmental Monitoring Facilities
theme
GEMET - INSPIRE themes, version 1.0
2008-06-01
publication
Biodiversity
Airborne pollen
environmental DNA
quantitative PCR (qPCR)
UK
grass pollen season
otherRestrictions
no limitations
otherRestrictions
This resource is available under the terms of the Open Government Licence
otherRestrictions
If you reuse this data, you should cite: Brennan, G., Creer, S., Griffith, G. (2020). Abundance of airborne pollen for nine grass species, measured by qPCR, UK, 2016-2017. NERC Environmental Information Data Centre https://doi.org/10.5285/28208be4-0163-45e6-912c-2db205126925
textTable
1
English
utf8
biota
2016-05-01
2017-09-30
-8.648
1.768
49.864
60.861
Comma-separated values (CSV)
NERC EDS Environmental Information Data Centre
info@eidc.ac.uk
distributor
https://data-package.ceh.ac.uk/data/28208be4-0163-45e6-912c-2db205126925
Download the data
Download a copy of this data
download
https://data-package.ceh.ac.uk/sd/28208be4-0163-45e6-912c-2db205126925.zip
Supporting information
Supporting information available to assist in re-use of this dataset
information
dataset
dataset
Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services
2010-12-08
The qPCR data was collected using a QuantStudio 6 Flex Real‐Time qPCR machine (ThermoFisher Scientific). Species-specific primers were designed to target the ITS2 region of abundant grass species in the UK including Anthoxanthum odoratum (sweet vernal-grass), Arrhenatherum elatius (false oat-grass), Cynosurus cristatus (crested dog’s-tail), Dactylis glomerata (cock’s-foot), Lolium perenne (perennial ryegrass), Phleum pratense (Timothy), Poa pratensis (smooth meadow-grass), grass species within the genera Alopecurus/Agrostis, and one probe that was found to be degenerate and unable to discriminate grass species.
Of 1,400 daily aerial samples, 1,210 were selected for downstream molecular analysis. Samples were excluded if pollen could not be reliably extracted due to large volumes of rainwater in collection tubes.
Quantitative PCR runs with PCR efficiencies less than 85% and greater than 115% were not used for further analysis (efficiency of qPCR data used in downstream analysis ranged between 88.5% and 106%). Data points with a large standard deviation between three technical replicates (>6.95, based on the upper quartile range of the data) were removed. In addition, samples which amplified before 10 cycles and after 38 cycles were removed to reduce the chance of detecting false positive or false negative amplification respectively. The reliability of the data was evaluated based on the positive and negative controls.