Fallow deer is one of the most common and widespread cervid species in Europe. To make informed management decisions on any scale, it is essential to have long-term data on the abundance of populations and their harvest. We provide missing information on the changes and status of fallow deer populations in Europe and analyse the relationships between population and harvest changes using a numerical approach. To conduct our analyses, we collected national statistical data on population sizes and hunting bags for all European countries for four periods: 1984, the mid-2000s, mid-2010s, and early 2020s. The fallow deer population increased five-fold from 1984 to the early 2020s and the harvest increased six-fold in the same period. Although the correlations between the population growth rate and harvest growth rate are not strong, removing outliers strengthened the correlation. This indicates that the hunting effort increases as the population increases. Overall, the lack of some data shows that consistent, reliable data collection (monitoring programs) is needed to efficiently manage the increasing fallow deer populations as a renewable natural resource and mitigate the potential negative impacts in a holistic and responsible manner.

The golden jackal has rapidly expanded across Europe in recent decades and was one of the first to reappear in Hungary. Using hunting bag data from the National Game Management Database from 1995 to 2021, we examined the spatial expansion of the species and its population dynamics. Our findings reveal an exponential increase in the golden jackal population since the mid-1990s, with an average annual growth rate of 40% in the hunting bag and an occupancy of 86% of the country. The average speed of the range expansion was 536.9 km² year^-1 until 2007 and increased to an average of 5,289.1 km² year^-1 during the second part of the expansion process. The density of shot golden jackals also shows an increasing trend with a substantial presence of outliers, indicating that nearly 10% of the hunting bag demonstrates exceptionally high numbers, particularly in southwestern Hungary. The spread originated in the southwest and extended towards the northeast and northwest. However, the increase of the hunting bag slowed down and the expansion rate has decreased in the previous two years, leading us to anticipate that the golden jackal population will stabilize soon, as the expansion has reached its maximum in Hungary. This indicates that from the initial settlement to the stabilization of the population, the time span can be two to three decades.

Sustainable wildlife management requires reliable data on population, habitat, and the interaction between them. Since 1993, Hungary has maintained the National Game Management Database, which collects data on game populations and hunting bags annually. During the last three decades, populations of medium-sized predators have remarkably increased in the country. Our study analyzed changes in hunting bag data and spatial distribution of two invasive alien species, the raccoon (Procyon lotor) and the raccoon dog (Nyctereutes procyonoides), utilizing annual game management reports from Game Management Units from 1997 to 2021. For spatial analysis, we employed a UTM grid system (10 × 10 km) covering the country. Our findings confirmed increasing trends in the hunting bag and area (number of grid cells) containing hunting bags for both species. From the first (1997–2001) to the last (2017–2021) period, the average annual hunting bag increased (raccoon: 0.40 → 11.00 ind/year; raccoon dog: 3.40 → 8.00 ind/year), and the average number of cells with hunting bags also expanded significantly (raccoon: 2.00 → 9.60 UTM cells/year; raccoon dog: 5.80 → 15.20 UTM cells/year). These trends and the size of potentially suitable habitats suggest a further spread and population increase, posing challenges for nature conservationists and wildlife managers.

Canidae is a species-rich, abundant, and widespread family. Several wild canid species, in particular, have shown a significant range expansion and increased abundance in the last few decades or even in the last century. The grey wolf (Canis lupus), coyote (Canis latrans), and the red fox (Vulpes vulpes) are resident on whole continents or even on multiple continents. Although canids share common behavioural and ecological characteristics, the formula of species-specific elements contributes to their success. This review investigated which factors have contributed mainly to the expansion of the grey wolf, coyote, and red fox. Analysis of the literature review shows that the grey wolf has dramatically benefitted from legal protection, reintroduction programs, and the ability to colonise areas naturally because of its particular social system, early reproduction, high fecundity, and rapid physical development. As a meso-carnivore, the coyote has shown a rapid spread after the extermination of apex predators in several regions in North America. Along with changes in land use, their high adaptability and hybridisation with wolves have all contributed to their prolonged success. The red fox has shown the largest expansion among canids even though it is a solitary species. Their morphological, reproductive and behavioural traits have facilitated their expansion to all corners of the world. Moreover, the species benefitted from human-caused changes like land conversion and the almost complete eradication of rabies in Europe. Overall, it is crucial to change management policies for grey wolves and increase control measures to regulate the three species and mitigate (potential) human-carnivore conflicts.

Porcine Reproductive and Respiratory Syndrome (PRRS) significantly impacts the pig farming industry globally, leading to economic losses due to reduced productivity. This study focuses on assessing the presence and impact of PRRS within Hungarian wild boar populations amidst efforts to eradicate the virus from domestic pig herds. We used a combination of serological and virological tests on samples collected from wild boars across Hungary to evaluate the prevalence of PRRS virus and its potential transmission risks to domestic pigs. Our findings reveal a low seropositivity rate in wild boars, suggesting a minimal role of wild boars in the transmission of PRRS to domestic pig populations. Moreover, no relationship was found between domestic pig and wild boar densities, emphasizing the limited interaction and consequent risk of disease spread between these populations. We confirm the effectiveness of Hungary’s PRRS eradication measures among domestic herds and highlight the negligible risk posed by wild boars in re-introducing the PRRS virus.

Camera trapping has become an important tool in wildlife research in the past few decades. However, one of its main limiting factors is the processing of data, which is labour-intensive and time-consuming. Consequently, to aid this process, the use of machine learning has increased. A summary is provided on the use of both camera traps and machine learning and the main challenges that come with it by performing a general literature review. Remote cameras can be used in a variety of field applications, including investigating species distribution, disease transmission and vaccination, population estimation, nest predation, animal activity patterns, wildlife crossings, and diet analysis. Camera trapping has many benefits, including being less invasive, allowing for consistent monitoring and simultaneous observation (especially of secretive or aggressive animals even in dangerous or remote areas), providing photo/video evidence, reducing observer bias, and being cost effective. The main issues are that they are subject to their environment, dependent on human placements, can disrupt animal behaviour, need maintenance and repair, have limitations on photographic data, and are sensitive to theft and vandalism. When it comes to machine learning, the main aim is to identify species in camera (trap) images, although emerging technologies can provide individual recognition as well. The downsides in- clude the large amount of annotated data, computer power, and programming and machine learning expertise needed. Nonetheless, camera trapping and machine learning can greatly assist ecologists and conservationists in wildlife research, even more so as technology further develops.