Marques, Tiago A.Rowcliffe, MarcusAurélio, Joana André2023-04-122023-04-1220232022http://hdl.handle.net/10451/57089Relatório de estágio de mestrado, Bioestatística, 2022, Universidade de Lisboa, Faculdade de CiênciasCamera traps have become a standard tool in wildlife management and conservation as they enable the monitoring of unmarked populations. Methods that allow estimating animal density, such as the Random Encounter Model (REM), require the estimation of three parameters i) encounter rate (between moving animals and static cameras), ii) day range (average daily distance travelled), and iii) detection zone (effective area in which the cameras detect animals). To estimate the animal’s speed and detection zone, we rely on the animal’s position data measured using a computer vision model that maps image pixel position to position on the ground. The model’s accuracy depends on the camera’s fixed position and the acquisition of calibration images from its initial position. If the camera shifts, it may change the detection zone, which breaks the model and makes animal positions in subsequent images unreliable. On the other hand, excluding images after the first camera movement may result in a significant data loss in the analysis. There is a lack of information about how to proceed in this situation. In addition, data processing pipelines and camera trap imagery software used in these tasks are under active development, raising questions about the most effective way to apply them. In this context, this report compares three methods used to deal with data when cameras move during deployments and focuses on questions about the sensitivity of estimates in terms of accuracy and precision. It documents all the steps of generating, processing, and analysis of camera trap data for REM. Our findings did not reveal significant differences concerning the density values estimated by the three methods. The results presented in this report provide insights for future REM applications and encourage users to share how they process their imagery and data.engarmadilhagem-fotográficacomparação de métodosrandom encounter modeldensidadetexugoRelatórios de estágio de mestrado - 2023master thesis203491688