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The detection of local extinctions is often hindered by the lack of long-term monitoring schemes, and thus relies on time series of presence data. Recently, citizen science has repeatedly shown its value in documenting species occurrences. We investigated the effectiveness of unstructured citizen science records in reducing the perception of local extinctions in butterfly populations across Italian National Parks. We addressed three research questions: (i) the ability of citizen science data to supplement existing knowledge to complete time series of occurrences, (ii) the impact on data collection of three species features (species size, distribution and length of flight period) determining their appearance, and (iii) the interplay between participant effort and species appearance in the amount of diversity recorded on the iNaturalist platform. Our analysis of 98,922 records of Italian butterflies (39,929 from literature and 58,993 from iNaturalist of which 7427 from National Parks) showed that the addition of iNaturalist data filled many recent gaps in time series, thus reducing the perception of potential local extinctions. Records from more engaged users encompassed a higher fraction of local biodiversity and were more likely to reduce the perception of local extinctions. User effort strongly interacted with species features in determining the frequency of records for individual species. In particular, more engaged users were less affected by species size. We provided updated butterfly checklists for Italian National Parks and a R package to calculate potential extinction upon time series. These results offer guidance for protected areas, conservationists, policymakers, and citizen scientists to optimize monitoring of local populations.

1. Climate change is causing shifts in the distribution of many species and populations inhabiting mountain tops are particularly vulnerable to these threats because they are constrained in altitudinal shifts. Apennines are a relatively narrow and low mountain chain located in Southern Europe, which hosts many isolated populations of mountain species. The butterfly Erebia pandrose was recorded for the last time in the Apennines in 1977, on the top of a single massif (Monti della Laga). 2. We confirmed the presence of a small, isolated population of E. pandrose in the Apennines, at a distance of more than 400¿km to any other known populations. Then, we examined the cytochrome c oxidase subunit 1 mitochondrial DNA marker of this species across the Palaearctic area and estimated the potential decline over the Alps and the Apennines due to future climatic changes. 3. The Apennine population represents an endemic lineage characterised by eight mutations over the 658¿bp analysed (1.2%). In the Alps and Apennines, this species has shifted uphill more than 3 m per year since the end of the 19th century and more than 22 m per year since 1995. Species distribution models suggested that these mountain populations will experience a generalised loss of climatic suitability, which, according to our projections, could lead to the extinction of the Apennine population in a few decades. 4. Erebia pandrose has the potential to become a flagship species for advertising the risk of losing unique fractions of genetic diversity for mountain species.

Hornets and honey bees have a long history of coevolution resulting in a plethora of captivating adaptations and counteradaptations between predator and prey. From simple physiological mechanisms to complex behavioral strategies, some Vespa hornets have specialized in hunting honey bees, while the latter have put in place effective defenses to counteract their attack. Both hornets and honey bees have evolved the ability to detect the odors and the pheromones emitted by the other to locate the prey or to spot foraging predators. Hornets often rely on their bigger size, heavily armored body and destructive attacks, while honey bees differentiated collective defense responses finely coordinated to deter or kill the hornet menace. However, when new species of hornets and honey bees come into contact, the absence of coevolution can have a heavy impact on the defenseless bees. The evolutionary arms race between hornets and honey bees provides not only compelling examples of adaptations and counteradaptations between predator and prey, but could also represent a starting point for the development of effective and sustainable strategies to protect honey bees and beekeeping activities and to control invasive alien species of hornets.

Endemics co-occur because they evolved in situ and persist regionally or because they evolved ex situ and later dispersed to shared habitats, generating evolutionary or ecological endemicity centres, respectively. We investigate whether different endemicity centres can intertwine in the region ranging from Alps to Sicily, by studying their butterfly fauna. We gathered an extensive occurrence data set for butterflies of the study area (27,123 records, 269 species, in cells of 0.5 × 0.5 degrees of latitude-longitude). We applied molecular-based delimitation methods (GMYC model) to 26,557 cytochrome c oxidase subunit 1 (COI) sequences of Western Palearctic butterflies. We identified entities based on molecular delimitations and/or the checklist of European butterflies and objectively attributed occurrences to their most probable entity. We obtained a zoogeographic regionalisation based on the 69 endemics of the area. Using phylogenetic ANOVA we tested if endemics from different centres differ from each other and from nonendemics for key ecological traits and divergence time. Endemicity showed high incidence in the Alps and Southern Italy. The regionalisation separated the Alps from the Italian Peninsula and Sicily. The endemics of different centres showed a high turnover and differed in phylogenetic distances, phenology and distribution traits. Endemics are on average younger than nonendemics and the Peninsula-Sicily endemics also have lower variance in divergence than those from the Alps. The observed variation identifies Alpine endemics as paleoendemics, now occupying an ecological centre, and the Peninsula-Sicily ones as neoendemics, that diverged in the region since the Pleistocene. The results challenge the common view of the Alpine-Apennine area as a single “Italian refugium”.

The abandonment of agricultural land and the afforestation of grassland habitats represent major threats for butterflies in European and Mediterranean areas. A crucial goal for Lepidoptera conservation is to maintain and/or restore habitat quality by targeted management. Nevertheless, there are few experimental studies allowing to derive data-driven strategies to protect butterflies of open grasslands in the Mediterranean region. We developed a habitat management strategy for the conservation of the Italian endemic butterfly Zerynthia cassandra by adopting a three-step procedure: (i) characterising which environmental and host plant features influence oviposition on plants; (ii) identifying and testing the effect of a data-driven habitat management intervention; (iii) understanding which micro-habitat features promote Z. cassandra oviposition in restored places to optimise the intervention. Both patch (areas of 1 m radius hosting Aristolochia shoots) and plant features affect oviposition, with the strongest positive effects showed by high irradiation of the patch and plant quality (high number of flowers and leaves). Accordingly, the management consisted in vegetation cuts to increase irradiation, and 2 years of monitoring demonstrated that this procedure significantly increased oviposition (average increase of about 2 eggs per plant) and larval presence. Micro-habitat sampling demonstrated that the maximum oviposition differed between vegetation structures, highlighting the importance of a local fine-tuning before the intervention. We provided a data-driven, effective, and sustainable management strategy to increase habitat suitability and oviposition for an endemic and endangered Mediterranean butterfly. Our framework can drive management strategies for other species with similar ecological requirements and subjected to similar threats.

Populations inhabiting Mediterranean islands often show contrasting genetic lineages, even on islands that were connected to the mainland during glacial maxima. This pattern is generated by forces acting in historical and contemporary times. Understanding these phenomena requires comparative studies integrating genetic structure, functional traits and dispersal constraints. Using as a model the butterfly species living across the Messina strait (3 km wide) separating Sicily from the Italian Peninsula, we aimed to unravel the mechanisms limiting the dispersal of matrilines and generating genetic differentiation across a narrow sea strait. We analysed the mitochondrial COI gene of 84 butterfly species out of 90 documented in Sicily and compared them with populations from the neighbouring southern Italian Peninsula (1,398 sequences) and from the entire Palaearctic region (8,093 sequences). For each species, we regressed 13 functional traits and 2 ecological constraints to dispersal (winds experienced at the strait and climatic suitability) against genetic differentiation between Sicily and Italian Peninsula to understand the factors limiting dispersal. More than a third of the species showed different haplogroups across the strait and most of them also represented endemic haplogroups for this island. One fifth of Sicilian populations (and 32.3% of endemic lineages) had their closest relatives in distant areas, instead of the neighbouring Italian Peninsula, which suggests high relictuality. Haplotype diversity was significantly explained by the length of the flight period, an intrinsic phenology trait, while genetic differentiation was explained by both intrinsic traits (wingspan and degree of generalism) and contemporary local constraints (winds experienced at the strait and climatic suitability). A relatively narrow sea strait can produce considerable differentiation among butterfly matrilines and this phenomenon showed a largely deterministic fingerprint. Because of