Impacts of agricultural management practices on the receiving environment are seldom suitably assessed because environmental monitoring is costly. In this regard, data generated by already existing environmental survey networks (ESNs) may have sufficient capacity to detect effects. Here, we study the capacity of the Catalan butterfly monitoring scheme (CBMS) to detect differences in butterfly abundance due to changes in agricultural practices. As a model, we compared butterfly abundance across two landscape types according to agricultural intensification. A 2 km diameter buffer area was centered on the CBMS transect, the control group were transects located in areas where intensive agriculture represented <20% of the area; a treated group was simulated by selecting transects located in areas where intensive agriculture occupied an area over 40%. The Welch t‐test (α = 0.05 and 80% power) was used to compare butterfly abundance per section across landscape types. The capacity of the t‐test to detect changes in mean butterfly abundance, of 12 butterfly indicators relevant to farmland, was calculated annually and for 5‐, 10‐, and 15‐yr periods. Detection capacity of the t‐test depended mainly on butterfly data sample size and variability; difference in butterfly abundance was less important. The t‐test would be capable of detecting acceptably small population changes across years and sites. For instance, considering a 15‐yr period, it would be possible to detect a change in abundance below 10% of the multispecies indicators (all butterfly species, open habitat species, mobile species, and grassland indicators) and two single species (Lasiommata megera and Lycaena phlaeas). When comparisons were carried out within each year, the t‐test would only be capable of detecting a change below 30% for all butterfly species, mobile species, and L. megera. However, detection capacity rapidly improved with the addition of further years, and with 5 yr of monitoring, all indicators but Thymelicus acteon had a detection capacity below 30%. We therefore conclude that, from a statistical point of view, the CBMS data “as is” are sensitive enough for monitoring effects of changes in agricultural practices. It could be used, for instance, for the general surveillance of genetically modified crops.This work was partially supported by the Spanish Government‐funded project AGL2011‐23996 and a FI‐DGR scholarship to M. Lee from the Catalan Government
