The microsporidian fungus Nosema ceranae represents one of the primary bee infection threats worldwide and the antibiotic fumagillin is the only registered product for nosemosis disease control, while few alternatives are, at present, available. Natural bioactive compounds deriving from the glucosinolate–myrosinase system (GSL–MYR) in Brassicaceae plants, mainly isothiocyanates (ITCs), are known for their antimicrobial activity against numerous pathogens and for their health-protective effects in humans. This work explored the use of Brassica nigra and Eruca sativa defatted seed meal (DSM) GSL-containing diets against natural Nosema infection in Apis mellifera colonies. DSM patties from each plant species were obtained by adding DSMs to sugar candy at the concentration of 4% (w/w). The feeding was administered in May to mildly N. ceranae-infected honey bee colonies for four weeks at the dose of 250 g/week. In the treated groups, no significant effects on colony development and bee mortality were observed compared to the negative controls. The N. ceranae abundance showed a slight but significant decrease. Furthermore, the GSL metabolism in bees was investigated, and MYR hydrolytic activity was qualitatively searched in isolated bee midgut and hindgut. Interestingly, MYR activity was detected both in the bees fed DSMs and in the control group where the bees did not receive DSMs. In parallel, ITCs were found in gut tissues from the bees treated with DSMs, corroborating the presence of a MYR-like enzyme capable of hydrolyzing ingested GSLs. On the other hand, GSLs and other GSL hydrolysis products other than ITCs, such as nitriles, were found in honey produced by the treated bees, potentially increasing the health value of the final product for human consumption. The results are indicative of a specific effect on the N. ceranae infection in managed honey bee colonies depending on the GSL activation within the target organ.

The pollination ecology in agroecosystems tackles a landscape in which plants and pollinators need to adjust, or be adjusted, to human intervention. A valid, widely applied approach is to regard pollination as a link between specific plants and their pollinators. However, recent evidence has added landscape features for a wider ecological perspective. Are we going in the right direction? Are existing methods providing pollinator monitoring tools suitable for understanding agroecosystems? In Italy, we needed to address these questions to respond to government pressure to implement pollinator monitoring in agroecosystems. We therefore surveyed the literature, grouped methods and findings, and evaluated approaches. We selected studies that may contain directions and tools directly linked to pollinators and agroecosystems. Our analysis revealed four main paths that must come together at some point: (i) the research question perspective, (ii) the advances of landscape analysis, (iii) the role of vegetation, and (iv) the gaps in our knowledge of pollinators taxonomy and behavior. An important conclusion is that the pollinator scale is alarmingly disregarded. Debate continues about what features to include in pollinator monitoring and the appropriate level of detail: we suggest that the pollinator scale should be the main driver.

The phenomenon of antibiotic resistance stands as a paramount health challenge in the contemporary era. Within a One Health approach, it becomes crucial to effectively track the dissemination of antibiotic resistance, not only within humans and animals but also within the environment. To investigate the environment, the honey bee (Apis mellifera) has emerged as a prominent environmental bioindicator due to its social, behavioral, and morphological features. The objective of this study was to describe the antimicrobial resistance (AMR) patterns of bacterial isolates from the body surface and the gut of honey bees sampled from 33 colonies throughout the Emilia-Romagna region (Italy). A total of 608 strains were examined for 19 distinct antimicrobial compounds from various classes, and the results showed that more than 50% of the isolates for eight out of nine provinces showed characteristics of nonsusceptibility toward amoxicillin and penicillin, and, generally, 98.19% of isolated strains were considered AMR and 74.67% exhibited multidrug resistance (MDR) characteristics, more frequent in Gram-negative strains (87.74%) than in Gram-positive ones (60.34%). Additionally, a significant correlation with a lower prevalence of MDR bacteria was demonstrated for one province (Ferrara, odds ratio (OR) = 3.33, (1.67; 6.64), $p=0.0006$ ). In conclusion, this study provides evidence for the utility of A. mellifera colonies as bioindicators for MDR bacteria, enabling their characterization and distribution at a geographical level. Additional investigations are required to further explore the potential role of honey bees as bioindicators for antimicrobial-resistant bacteria, particularly in terms of their association with environmental characteristics.

Beekeeping is a crucial agricultural practice that significantly enhances environmental health and food production through effective pollination by honey bees. However, honey bees face numerous threats, including exotic parasites, large-scale transportation, and common agricultural practices that may increase the risk of parasite and pathogen transmission. A major threat is the Varroa destructor mite, which feeds on honey bee fat bodies and transmits viruses, leading to significant colony losses. Detecting the parasite and defining the intervention thresholds for effective treatment is a difficult and time-consuming task; different detection methods exist, but they are mainly based on human eye observations, resulting in low accuracy. This study introduces a digital portable scanner coupled with an AI algorithm (BeeVS) used to detect Varroa mites. The device works through image analysis of a sticky sheet previously placed under the beehive for some days, intercepting the Varroa mites that naturally fall. In this study, the scanner was tested for 17 weeks, receiving sheets from 5 beehives every week, and checking the accuracy, reliability, and speed of the method compared to conventional human visual inspection. The results highlighted the high repeatability of the measurements (R² ≥ 0.998) and the high accuracy of the BeeVS device; when at least 10 mites per sheet were present, the device showed a cumulative percentage error below 1%, compared to approximately 20% for human visual observation. Given its repeatability and reliability, the device can be considered a valid tool for beekeepers and scientists, offering the opportunity to monitor many beehives in a short time, unlike visual counting, which is done on a sample basis.