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Implementing practical methods to estimate population density of wild boar and other wild mammals: field trials and development of automatic identification

on the Wiley Online Library

Metadata

Disclaimer: The 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.

Abstract

The main aims of this report are (i) to implement camera trap practical methods to estimate wild boar density by means of field trials, which is also of application to other medium and big sized mammals and (ii) to report on progress of the development of automatic identification for density estimation based on camera trapping. As for (i), we specifically investigated if the local environment conditions influence the two key parameters that are used to estimate the detection zone, radius and angle, for the Random Encounter method (REM). We hypothesized that, after controlling for intrinsic factors (species, camera brand), the detection zone of a camera is influenced by the local vegetation and environmental conditions predominant in the field of view of the camera. If our hypothesis was rejected, we can provide reference values of radius and angle for species and camera brand that can alleviate the efforts requested to process the data to estimate densities using REM. As for (ii), participants of the European Observatory of Wildlife (EOW) are currently applying a new protocol in over 30 study areas across Europe, which incorporated the necessary steps to calibrate camera trap deployments in the field for further use of artificial intelligence to estimate density (using REM). In conclusion, regarding the camera trap field trial, we found that the significance of both the radio and angle difference in the different regions can be explained through the additive effect of several factors that contribute to diminishing performance of the camera trap. These results emphasise the importance of project design and the standardisation of the method to reduce bias and facilitate comparison between different projects. In relation to the development of automatic identification, our progress indicates that the completion of the work (i.e. the Agouti platform suitable for the management of project‐wise camera‐trap surveys of wildlife in each of the partner countries, including the measurement for density estimation in a separate R package) will be completed in time for the Europe‐wide surveys.

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