African swine fever (ASF) is one of the most severe diseases of pigs and has a drastic impact on pig industry. Wild boar populations play the role of ASF genotype II virus epidemiological reservoir. Disease surveillance in wild boar is carried out either by testing all the wild boar found sick or dead for virus detection (passive surveillance) or by testing for virus (and antibodies) all hunted wild boar (active surveillance). When virus prevalence and wild boar density are low as it happens close to eradication, the question on which kind of surveillance is more efficient in detecting the virus is still open. We built a simulation model to mimic the evolution of the host-parasite interaction in the European wild boar and to assess the efficiency of different surveillance strategies. We constructed a deterministic SIR model, which estimated the probability to detect the virus during the 8 years following its introduction, using both passive and active surveillance. Overall, passive surveillance provided a much larger number of ASF detections than active surveillance during the first year. During subsequent years, both active and passive surveillance exhibited a decrease in their probability to detect ASF. Such decrease, though, was more pronounced for passive surveillance. Under the assumption of 50% of carcasses detection, active surveillance became the best detection method when the endemic disease prevalence was lower than 1.5%, when hunting rate was >60% and when population density was lower than 0.1 individuals/km². In such a situation, though, the absolute probability to detect the disease was very low with both methods, and finding almost every carcass is the only way to ensure virus detection. The sensitivity analysis shows that carcass search effort is the sole parameter that increases proportionally the chance of ASF virus detection. Therefore, an effort should be made to promote active search of dead wild boar also in endemic areas, since reporting wild boar carcasses is crucial to understand the epidemiological situation in any of the different phases of ASF infection at any wild boar density.

An increase of nocturnal activity of ungulate species may represent a compensatory opportunity for energy intake, when activity in daylight is hindered by some disturbance events (e.g. hunting or predation). Therefore, mostly-diurnal and crepuscular species may be active in bright moonlight nights whereas others may shift their diurnal activity towards darkest nights to limit their exposure to predators. In natural and undisturbed conditions, the wild boar may be active both during the day and the night, with alternating periods of activity and resting. In this work, we tested whether activity patterns of wild boar, a species with poor visive abilities, were dependent on moon phases and environmental lightening. We aimed to assess if nocturnal activity could be better explained by variations of the lunar cycle or by the variations of environmental lightening conditions, evaluated by means of different measures of night brightness. Data were collected through camera-trapping in Central Italy in 2019–2020. Despite the poor visive abilities of the wild boar, we observed that this ungulate significantly reduced their activity by avoiding the brightest nights. In our study area, the wild boar has to cope with both human pressure (i.e. mostly hunters and poachers) and predation by the grey wolf. Furthermore, the nocturnal activity of wild boar peaked in mid-Autumn, i.e. when hunting pressure is the highest and when leaf fall may bring wild boar to range for long distances to find suitable resting sites for diurnal hours.

The impact of predation on prey populations has long been a focus of ecologists, but a firm understanding of the factors influencing prey selection, a key predictor of that impact, remains elusive. High levels of variability observed in prey selection may reflect true differences in the ecology of different communities but might also reflect a failure to deal adequately with uncertainties in the underlying data. Indeed, our review showed that less than 10% of studies of European wolf predation accounted for sampling uncertainty. Here, we relate annual variability in wolf diet to prey availability and examine temporal patterns in prey selection; in particular, we identify how considering uncertainty alters conclusions regarding prey selection. Over nine years, we collected 1,974 wolf scats and conducted drive censuses of ungulates in Alpe di Catenaia, Italy. We bootstrapped scat and census data within years to construct confidence intervals around estimates of prey use, availability and selection. Wolf diet was dominated by boar (61.5±3.90 [SE] % of biomass eaten) and roe deer (33.7±3.61%). Temporal patterns of prey densities revealed that the proportion of roe deer in wolf diet peaked when boar densities were low, not when roe deer densities were highest. Considering only the two dominant prey types, Manly's standardized selection index using all data across years indicated selection for boar (mean = 0.73±0.023). However, sampling error resulted in wide confidence intervals around estimates of prey selection. Thus, despite considerable variation in yearly estimates, confidence intervals for all years overlapped. Failing to consider such uncertainty could lead erroneously to the assumption of differences in prey selection among years. This study highlights the importance of considering temporal variation in relative prey availability and accounting for sampling uncertainty when interpreting the results of dietary studies.

African swine fever (ASF) is a contagious haemorrhagic fever that affects both domesticated and wild pigs. Since ASF reached Europe wild boar populations have been a reservoir for the virus. Collecting reliable data on infected individuals in wild populations is challenging, and this makes it difficult to deploy an effective eradication strategy. However, for diseases with high lethality rate, infected carcasses can be used as a proxy for the number of infected individuals at a certain time. Then R₀ parameter can be used to estimate the time distribution of the number of newly infected individuals for the outbreak. We estimated R₀ for two ASF outbreaks in wild boar, in Czech Republic and Belgium, using the exponential growth method. This allowed us to estimate both R₀ and the doubling time (T_d) for those infections. The results are R₀ = 1.95, T_d = 4.39 for Czech Republic and R₀ = 1.65, T_d = 6.43 for Belgium. We suggest that, if estimated as early as possible, R₀ and T_d can provide an expected course for the infection against which to compare the actual data collected in the field. This would help to assess if passive surveillance is properly implemented and hence to verify the efficacy of the applied control measures.