Cite this dataset as
Harrower, C.A., Bell, J.R., Blumgart, D., Botham, M.S., Fox, R., Isaac, N.J.B., Roy, D.B., Shortall, C.R. (2020). Moth trends for Britain and Ireland from the Rothamsted Insect Survey light-trap network (1968 to 2016). NERC Environmental Information Data Centre. (Dataset). https://doi.org/10.5285/0a7d65e8-8bc8-46e5-ab72-ee64ed851583
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This dataset is available under the terms of the Open Government Licence 
Moth trends for Britain and Ireland from the Rothamsted Insect Survey light-trap network (1968 to 2016)
This is the most recent version of this dataset (view other versions )
The dataset contains abundance trends for 432 species of moths (mostly macro-moths) estimated using the data collected by Rothamsted Insect Survey (RIS) from their light-trap network between the years 1968 and 2016. The trends were calculated using a Generalized Abundance Index (GAI) model. The trends are presented as year coefficients from the statistical model, Annual Growth Rates (AGR), and the total percentage changes over the time series for each species. For each trend metric 95% and 90% confidence intervals are provided. Two versions of the trends are presented: one using data from all traps in the Britain & Ireland over the period of 1968-2016 and a second dataset restricted to traps in Great Britain over the period of 1970-2016.
Data acquisition was partially funded by the Natural Environment Research Council (NERC) under research programme NE/N018125/1 Achieving Sustainable Agricultural Systems (ASSIST). ASSIST is an initiative jointly supported by NERC and the Biotechnology and Biological Sciences Research Council (BBSRC). The Rothamsted Insect Survey is funded by the BBSRC under the Core Capability Grant BBS/E/C/000J0200. The research builds upon model development supported by NERC award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.
Data acquisition was partially funded by the Natural Environment Research Council (NERC) under research programme NE/N018125/1 Achieving Sustainable Agricultural Systems (ASSIST). ASSIST is an initiative jointly supported by NERC and the Biotechnology and Biological Sciences Research Council (BBSRC). The Rothamsted Insect Survey is funded by the BBSRC under the Core Capability Grant BBS/E/C/000J0200. The research builds upon model development supported by NERC award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.
Publication date: 2020-04-20
View record as
Format
Comma-separated values (CSV)
Spatial information
- Study area
-
- Spatial representation type
- Tabular (text)
- Spatial reference systems
-
OSGB 1936 / British National Grid
TM75 / Irish Grid
Temporal information
- Temporal extent
-
1968-01-01 to 2016-12-31
Provenance & quality
The Rothamsted light-trap network is a network of standardised light-traps that are operated throughout the year by volunteers. Using standardised traps means that the data from different sites and/or times are directly comparable. Although for the majority of species the time period of the data used in the analysis was 1968-2016, there was a handful of species of species where the time period had to be modified due taxonomic or data issues. For example the time period used for many pug species in the analysis was 1986 onwards due to inconsistency in the identification of these species in earlier years through a lack of suitable identification resources such as field guides. A few other species had initial years excluded due to data issues in those earlier years.
The GAI methodology is a two-step statistical method designed to analyse count data for taxa showing seasonal patterns in abundance, such as Butterflies and Moths. The first step uses statistical models, in this case generalised additive models (GAMs) with a smoothing spline, to estimate annual flight curves for each species. The second stage takes these annual flight curves and uses them to correct for seasonal gaps in recording, producing estimated annual abundance indices for each site (site indices), to which a Poisson general linear model (GLM) is fitted allowing to the year and sites effects upon the site. The year coefficient from these GLMs are one of the measures of the temporal abundance trend presented in this data. In addition to the coefficient itself two additional metrics of temporal trend were estimated and are presented here, specifically the Annual Growth Rate (AGR) and the total percentage change over the time series for each species. Confidence intervals on the trend metrics were determined by bootstrapping (1000 replicates).
The GAI methodology is a two-step statistical method designed to analyse count data for taxa showing seasonal patterns in abundance, such as Butterflies and Moths. The first step uses statistical models, in this case generalised additive models (GAMs) with a smoothing spline, to estimate annual flight curves for each species. The second stage takes these annual flight curves and uses them to correct for seasonal gaps in recording, producing estimated annual abundance indices for each site (site indices), to which a Poisson general linear model (GLM) is fitted allowing to the year and sites effects upon the site. The year coefficient from these GLMs are one of the measures of the temporal abundance trend presented in this data. In addition to the coefficient itself two additional metrics of temporal trend were estimated and are presented here, specifically the Annual Growth Rate (AGR) and the total percentage change over the time series for each species. Confidence intervals on the trend metrics were determined by bootstrapping (1000 replicates).
Related
This dataset is included in the following collections
Data from the Achieving Sustainable Agricultural Systems programme (ASSIST)
Citations
Mitchell, L.J., Horsburgh, G.J., Dawson, D.A., Maher, K.H., & Arnold, K.E. (2021). Metabarcoding reveals selective dietary responses to environmental availability in the diet of a nocturnal, aerial insectivore, the European Nightjar ( Caprimulgus europaeus ). In Ibis (Vol. 164, Issue 1, pp. 60–73). Wiley. https://doi.org/10.1111/ibi.13010
Blumgart, D., Botham, M.S., Menéndez, R., & Bell, J.R. (2022). Moth declines are most severe in broadleaf woodlands despite a net gain in habitat availability. Insect Conservation and Diversity, 15(5), 496-509. https://doi.org/10.1111/icad.12578
Tordoff, G.M., Dennis, E.B., Fox, R., Cook, P.M., Davis, T.M., Blumgart, D., & Bourn, N.A.D. (2022). Inconsistent results from trait-based analyses of moth trends point to complex drivers of change. Biodiversity and Conservation, 31(12), 2999-3018. https://doi.org/10.1007/s10531-022-02469-8
Supplemental information
The Insect Survey is host to a nationwide network of light-traps and suction-traps that collect invaluable data on the migration of moths and aphids.
Correspondence/contact details
Harrower, C.
UK Centre for Ecology & Hydrology
Maclean Building, Benson Lane, Crowmarsh Gifford
Wallingford
Oxfordshire
OX10 8BB
UNITED KINGDOM
enquiries@ceh.ac.uk
Wallingford
Oxfordshire
OX10 8BB
UNITED KINGDOM
Authors
https://orcid.org/0000-0001-5070-5293
Blumgart, D.
Rothamsted Research
Other contacts
- Custodian
-
NERC EDS Environmental Information Data Centreinfo@eidc.ac.uk
- Publisher
-
NERC Environmental Information Data Centreinfo@eidc.ac.uk
Additional metadata
- Topic categories
- biota
- INSPIRE theme
- Environmental Monitoring Facilities
- Keywords
- Achieving Sustainable Agricultural Systems , ASSIST , Biodiversity , Environmental survey , UK-SCAPE
- Funding
- Natural Environment Research Council Award: NE/N018125/1
Biotechnology and Biological Sciences Research Council Award: BBS/E/C/000J0200 - Last updated
- 27 February 2024 16:14