This dataset contains data from a field study conducted in 2019 and described in the paper "Relationship of insect biomass and richness with land use along a climate gradient" by Uhler et al.

In this study, arthropod communities were collected by Malaisetraps along a land use and climate gradient and identified by Metabarcoding. Malaise traps are versatile passive net-traps, capable of catching a large spectrum of insect taxa and are very effective when the aim is to get a representative snapshot of the local insect community. Overall, 179 Malaise traps were set up in 2019 along a gradient of increasing land use intensity, ranging from forests, to meadows, to arable fields, and settlements. The analyzed sites were spatially distributed over 400km and covered an elevational gradient of 1000m.

Generalized additive models were fitted to test for the effects of local and landscape-level land-use categories on insect biomass and species richness.

We found the largest difference in biomass between semi-natural and urban environments (−42%), whereas  differences in total richness (−29%) and the richness of threatened species (−56%) were largest from semi-natural to agricultural environments. These results point to urbanization and agriculture as major drivers of decline. We also found that richness and biomass increase monotonously with increasing temperature, independent of habitat. The contrasting patterns of insect biomass and richness question the use of these indicators as mutual surrogates. Our study provides support for the implementation of more comprehensive measures aimed at habitat restoration in order to halt insect declines.

The data was collected during the course of a field experiment conducted in 2019. Malaisetraps were set up in 179 locations in Bavaria, Germany and emptied every two weeks. The biomass of insect samples was measured and a subset of samples were futher analyzed through meta-barcoding. For a complete summary of all methods used, see the methods section of the associated manuscript.

The ReadMe file contains an explanation of all variables of the main dataset used in the analyses and their measurement units. For easy access to all analyses and script outputs, see the attached html file.