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Update on the development of the Agouti platform for collaborative science with camera traps and a tool for wildlife abundance estimation

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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.


Camera traps (CT) provide an easy and non‐invasive way to study wildlife. It is also possible to estimate densities if accurate protocols are followed in a standardised way and additional parameters are estimated. However, the processing and storage of the thousands of images that a typical CT study generates has become a major challenge for CT users. Also, project management can get complex, especially if many CT and multiple people are involved. To facilitate collaborative science among professionals and semi‐professionals, the ENETWILD consortium is developing the existing Agouti platform for the management of camera‐trapping projects, and the processing and storage of images. Moreover, the consortium is extending Agouti with tools for doing the measurements needed for acquiring the additional estimates and is building an R package to estimate actual density. These developments will significantly further the completion of the Agouti ecosystem. A network of CT‐based abundance estimations is consolidated by providing analytical tools and by promoting collaborative science. Specifically, we have worked to (1) harmonize dataset generation by means of Agouti and (2) develop an interface for running CT abundance models (REM, REST, distance sampling). After completion of this work, users should be able to easily export their camera trap records into a format (camtrap‐dp) that can subsequently be used to easily run models and determine density using an interface in R, following the methods recommended by ENETWILD. The progress that has been made to date in relation to data generation and analysis is detailed; interactive maps and institutional portals; data recording for abundance estimation; distance and speed estimation, making distances part of camtrap‐dp; and finally, R package for distance, speed, and density calculation.