Aphids are an excellent indicator of local environmental conditions and scientists from JHI have collated data from Scottish insect suction traps going back to 1967. Statistical analysis has identified annual trends and changes in seasonal patterns of aphid biodiversity, community composition and abundance. Trends in aphid abundance vary by species and analysis of between year correlation has identified potential links with life-history patterns responsive to changing winter temperatures and management interventions. This lays the ground for further work investigating the human and environmental drivers behind the changes.

Aphids are, in some cases, a vector of crop disease, but they are also a widespread and often abundant component of insect communities. Due to their biology and life cycle, they provide an excellent indicator of local environmental conditions. In collaboration with scientists from JHI and SASA, BioSS has analysed insect suction trap data from Ayr, Dundee, and Edinburgh which respectively comprise landscapes dominated by livestock, by arable and soft-fruit production, and a more suburban landscape. Analyses integrating multivariate approaches with times-series analytic techniques have identified: changes in community composition; trends in diversity patterns across years and in how diversity changes within a season; and contrasting trends in species-level abundance. There has been a shift to an earlier within-year increase in aphid diversity, with average late-spring/early-summer diversity increasing over the past 20 years. The balance between dominant species has varied on a decadal timescale, and aphid species that are new to the site are being caught in the suction traps; this may be related to local changes in land use and management. Explicit modelling of changes in year-to-year correlation in population levels of key species, using time-varying autocorrelation (TV-AR) models, has shown that many species which are increasing in abundance exhibit positive dependence on the previous year’s population suggesting improved over-winter survival. Some species show negative dependence on previous years, which may relate to management interventions. 

BioSS is developing methodology integrating TV-AR and distributed lag generalised linear modelling approaches to better investigate the potential environmental and anthropogenic drivers underlying the changes and to explore whether there are links to specific life-history patterns.

The study was performed in collaboration with Dr. Ali Karley (James Hutton Institute), Fiona Highet (SASA), Katharine Preedy (BioSS), and Dave Miller (BioSS). This work was funded under the Underpinning National Capacity element of the Scottish Government's Strategic Research Programme for environment, agriculture and food.

Photo credit: Cameron Mitchell