The dataset contains national trends for 432 species of moths (mostly macro-moths) estimated using the moth data collected by Rothamsted Insect Survey (RIS) from their national light trap network between the years 1968 and 2016. The trends were calculated using a Generalised 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 whole time series. For each trend metric 95% and 90% confidence intervals are also provided. The dataset additionally contains a related set of trends produced specifically for the Atlas of Britain & Ireland’s Larger Moths which used a restricted subset of the data from UK analysis, specifically only using data from traps in Great Britain and only data from 1970 and 2016.
Publication date: 2019-07-23
This dataset is part of the following
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 research was primarily funded by Natural Environment Research Council (NERC) and the Biotechnology and Biological Sciences Research Council (BBSRC) under research programme NE/N018125/1 LTS-M ASSIST – Achieving Sustainable Agricultural Systems. The Rothamsted Insect Survey, a National Capability, 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.