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Building skills in remote wildlife monitoring techniques
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Resumo(s)
Camera 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.
Descrição
Relatório de estágio de mestrado, Bioestatística, 2022, Universidade de Lisboa, Faculdade de Ciências
Palavras-chave
armadilhagem-fotográfica comparação de métodos random encounter model densidade texugo Relatórios de estágio de mestrado - 2023