Browsing by Subject "Random encounter model"

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    Generating wildlife density data across Europe in the framework of the European Observatory of Wildlife (EOW)
    (Wiley, 2024-10-30) Martínez-Carrasco Pleite, Carlos; ENETWILD-consortium "et.al."; Sanidad Animal; Facultad de Veterinaria
    The European Observatory of Wildlife EOW, as part of the ENETWILD project, represents a collaborative network that has been operating since 2021 to develop and implement standardized protocols to obtain harmonized data on distribution and density of target mammal species. In so doing, the EOW aims at contributing to improving the quality of data that are available for wildlife management and risk assessment on a European scale. This report describes the activities carried out during the 2023 EOW campaign, which was joined by a total of 30 organizations who committed to collect data in 44 sites across 22 different countries. We present data on the distribution and density of three species – wild boar (Sus scrofa), European roe deer (Capreolus capreolus), and red fox (Vulpes vulpes) – obtained by implementing a camera trapping protocol and by fitting the random encounter model (REM) for density estimation. Camera-trap images were processed using the Agouti platform and some of its tools specifically designed for the management of camera trapping projects. This includes the use of photogrammetry to obtain parameters for the REM directly from the sequences of images. A total of 24 EOW sites were monitored in past years as well, providing multiannual density estimates and population trends and highlighting an improvement in the precision of the estimates, related to the improved study design and protocol implementation. We also describe the activities of the 2024 campaign, carried out as part of ENETWILD 2.0, where big efforts were made to expand the network, focusing on sites at risk of African Swine Fever, with wild boar/pig interactions and containing wetlands, as potential hubs for Avian Influenza. This effort resulted in the engagement of 40 participants monitoring 64 study sites (27 countries), including 28 study sites located either in infected areas or <100km from the ASF frontline, and 25 sites with wetland habitats. Furthermore, in at least 20 sites pig farming is practised either intensively, extensively or as backyard farming. Finally, synergies were established with other international initiatives related to wildlife monitoring and disease prevention, with the aim of sharing experiences and sustaining a transnational data collection and harmonization.
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    Open Access
    Harmonization of the use of hunting statistics for wild boar density estimation in different study areas: report based on comparison of case studies in different wild boar populations representative of the different management and habitat conditions across Europe.
    (Wiley, 2019-08-29) ENETWILD-consortium; Vicente, Joaquín; Palencia, Pablo; Plhal, Radim; Blanco Aguiar, José Antonio; Laguna, Eduardo; Soriguer, Ramón; Fernández López, Javier; Podgórski, Tomasz; Petrović, Karolina; Apollonio, Marco; Scandura, Massimo; Ferroglio, Ezio; Zanet, Stefania; Brivio, Fracesca; Keuling, Oliver; Smith, Graham C.; Guibert, Miguel; Villanua, Diego; Rosell, Carme; Colomer, Joana; Armenteros, Jose Ángel; González Quirós, Pablo; Hernández Palacios, Orencio; Ferreres, Javier; Torres, José Antonio; Pareja, Pablo; Martínez-Carrasco Pleite, Carlos; Fafián, José Antonio; Escribano, Fernando; Esteve, Carles; Acevedo Lavandera, Pelayo; Sanidad Animal; Facultades de la UMU::Facultad de Veterinaria
    Hunting statistics can be suitable to determine wild boar density estimates if a calibration with an accepted rigorous method is performed. Here, densities calculated from drive counts during collective drive hunting activities are compared against density values calculated by camera trapping using the random encounter method. For this purpose, we selected 10 study sites in Spain, from North to South representing a diversity of habitats, management and hunting traditions without artificial feeding, plus one study site in Czech Republic where artificial feeding was practiced. Density values estimated from both drive counts and camera trapping were strongly positively correlated (R2=0.84 and 0.87 for linear and non-linear models, respectively) and showed a good agreement. Drive counts data might be therefore used as a density estimate to calibrate models for estimating density in large areas and potentially, to compare densities among areas. For these purposes, there is still the need to harmonise hunting data collection across Europe to make them usable at a large scale. Our results need to be confirmed across a wider number of European populations to provide valid geographical wild boar density predictions across Europe.