1. Long distance dispersal (LDD), or movements far beyond the occupied habitat borders, maintains the integrity of metapopulations in fragmented landscapes. Recent studies on butterflies increasingly reveal that LDD exists even in species that were long regarded as sedentary. Mark-recapture (MR) studies covering larger study areas typically reveal movements among distant colonies.2. We studied dispersal of the EU-protected, regionally endangered Euphydryas aurinia Rottemburg butterfly in the Czech Republic, using two complementary MR approaches. The single system study was carried out for eight seasons within 30 habitat patches covering 28 ha. The multiple populations study was carried out for a single season, but covering almost all Czech colonies of the species (82 colonies, 110 distinct patches, total area 324 ha within ca 1500 km 2 ). 3. Single system mean lifetime movements were consistently higher for males, but slopes of dispersal kernel power functions were shallower for females, implying that higher proportions of females crossed distances of several kilometres.4. The multiple populations study allowed detection of 51 lifetime movements exceeding 5 km (41 males, 10 females) and 14 movements exceeding 10 km (13 males, 1 female). Both mean lifetime movements and slopes of the dispersal kernels varied among systems, with no consistent pattern between sexes. All Czech Republic populations are within 0.1% movement probability of both sexes, whereas 1% movement probability delimits three separate management units. 5. Dispersal predictions from local data underestimate total mobility, warning against the use of local MR data for extrapolating long-distance movements. Local dispersal data, however, remain useful for analysing finer details of insect mobility.

We present 1 a personalized ingredient-based Deep Learning recommender on the food domain that exploits ingredients and nutrition information to create recipe representations and propose to every user a more personalized and healthier meal. The recommender will be a critical component in our Meal Prediction Tool (MPT) designed with a focus on the personalization of services, increasing business efficiency and sustainability in the hospitality, restaurant and catering (HoReCa) industry.

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