TY - JOUR T1 - Unlocking the potential of historical abundance datasets to study biomass change in flying insects JF - bioRxiv DO - 10.1101/695635 SP - 695635 AU - Rebecca S. Kinsella AU - Chris D. Thomas AU - Terry J. Crawford AU - Jane K. Hill AU - Peter J. Mayhew AU - Callum J. Macgregor Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/07/08/695635.abstract N2 - Insect abundance changes are well-established in some datasets, but far less is known about how this translates into biomass changes. Moths (Lepidoptera) provide particularly good opportunities to study trends and drivers of biomass change at large spatial and temporal scales, given the existence of long-term abundance datasets for moths. This requires estimation of the body mass of moths sampled over time, but such data do not currently exist.We collected empirical data in 2018 on the forewing length and dry mass of sampled moths, and used these to train and test a statistical model that predicts the body mass of moth species from their forewing lengths (with refined parameters for Crambidae, Erebidae, Geometridae and Noctuidae). We tested the relationships between biomass, abundance and species richness of samples of moths for our 2018 samples, and over a 16-year period using long-term historical moth data (with model-estimated biomass) from a single site.Modelled biomass was positively correlated with measured biomass of moth species (R2 = 0.910) and mixed-species samples of moths (R2 = 0.915), showing that it is possible to predict biomass accurately. Biomass correlated with moth abundance and species richness in our 2018 data and in the historical dataset, revealing biomass declined by 65.9 % over a 16-year period.By allowing biomass to be estimated for historical moth abundance datasets, our approach creates opportunities to investigate trends and drivers of insect biomass change over long timescales and broad geographic regions. ER -