Many countries are concerned by and wish to arrest or reverse what is termed a biodiversity crisis in invertebrates. To understand the issues facing riverine invertebrates in England, a fully integrated dataset where macroinvertebrate monitoring sites were aligned in space and time with physical, geographic, habitat, and chemical factors from 2003 to 2018 (quantitative abundance data being universally available from 2003) was brought together for statistical analysis. Over this period the median abundance either did not change or for some groups actually increased. The aim was to identify what the principal factors were that influenced Ephemeroptera (Mayflies), Plecoptera (Stoneflies), Trichoptera (Caddisflies), Odonata (Dragonflies and Damselflies), Diptera (True Flies), Coleoptera (Beetles), Hemiptera (True Bugs), and Gastropoda (Snails) abundance over this 16-year period. The dataset was examined using an ensemble framework within two modelling approaches: generalised linear mixed-effects models with permutation-based variable importance, as well as non-linear generalised additive mixed models to assess the percentage of deviance explained by each variable. The range of approaches aimed to offer different perspectives on variable importance, providing a more comprehensive understanding of the data and highlighting how model selection can influence ecological data interpretation. For most groups, physical factors, such as altitude, distance from source, slope, bed substrate and flow discharge, were strong predictors of abundance, likely reflecting natural habitat preferences shaped by evolutionary history. Land cover was also influential, with seminatural areas generally supporting higher abundances and urban land cover associated with lower abundances. Some chemical and ecological factors – such as wastewater and nutrient content, were particularly important for Ephemeroptera, Plecoptera, and Trichoptera abundance. For Coleoptera, Hemiptera, Trichoptera, Diptera and Gastropoda, metal levels played a role in their abundance, whilst for Odonata, mean temperature appeared to be important. Diptera appeared to be relatively insensitive to the factors examined. This statistical examination of large monitoring datasets, with no a priori assumptions, is vital in resolving a key challenge in bioassessment: identifying what influences invertebrate abundance when data are sparse. The results can provide policy options to improve ecological conditions, and the approach is transferable to other regions.