To represent JTM’s climatic niche, we used four climatic variables: average maximum temperature, coefficient of variation in average maximum temperature, total precipitation, and coefficient of variation in total precipitation. These four variables have been found to be dominant factors in the range shift and migration of other lepidoptera24,25. Climatic data were all calculated per year for the flying time of JTM (July – September)22. Climatic data were gathered from WorldClim at a 2.5 minute spatial resolution (~21 km2)26,27. We note that the CHELSA dataset is an improvement on WorldClim, but the differences are very small within Europe, and within the non-mountainous habitat that JTM largely occupies. Our goal was to understand the impact of social media data and recorder effort on biomonitoring of range-shifts, rather than make the most accurate range-shift prediction possible, and it is highly unlikely the small, unsystematic, differences between CHELSA and WorldClim in our study region would affect these results. Other climatic layers were not included to avoid overfitting HSMs, under-predicting potential distributions and tolerances under climatic conditions where species may be underreported28. A fifth environmental layer, night light, was used to capture the degree of urbanisation29. Night light data were collected from December of every year (data from summer months may not be an accurate representation due to the lighter summers in the northernmost parts of the study region). Data were collected from the National Centers for Environmental Information30 and converted to a 2.5 minute spatial resolution (~21 km2) by averaging. Stray light, lightning, lunar illumination, and cloud cover are all removed from the average measure of illumination prior to calculation of averages for each layer. Only data from 2012 onwards were comparable between years, so for all years prior to 2012, the night light dataset from 2012 was used.