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.
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.
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.
The European Observatory of Wildlife (EOW) as part of the ENETWILD project, aims to improve the European capacity for monitoring wildlife populations, implementing international standards for data collection, providing guidance on wildlife density estimation, and finally, to promote collaborative, open data networks to develop wildlife monitoring, initially focusing on terrestrial wild mammals. This report presents density estimates for species that are widely distributed (wild boar (Sus scrofa), European roe deer (Capreolus capreolus), red deer (Cervus elaphus)) by following a standardised camera trapping (CT) protocol, in 48 areas from 28 different countries in Europe, during 2022. Density values are provided for 37 areas from 20 countries, while an additional 9 locations from 8 countries are currently completing the data analysis. The EOW involved different stakeholders over most European countries, which resulted for the first time in a number of reliable (known precision) wild ungulate density estimates, from areas representing different European bioregions. These estimates are the result of a collaborative effort from the network to apply practical systematic and rigorous protocols. The results presented from the first pilot campaign of the EOW cannot be used to accurately describe wildlife population gradients and trends at European level but can be used as first baseline data for future trend analyses. Our 1 www.enetwild.com www.efsa.europa.eu/publications 2 EFSA Supporting publication 2023:EN-7892The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.results show data gaps, but also provide relevant insights into some of the main drivers of demographic evolution of wild ungulate populations in Europe. We will expand and improve the EOW in the future to include more representative sites. The Agouti app, including photogrammetry methods to estimate CT detection zone size and animal speed of movement using a computer vision process proved useful to reduce the workload and to improve objectivity of measurements for REM method. We discuss the results obtained by the 2022 campaign in relation to the specific objectives of the EOW and propose the next steps.
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