Releases: ear-team/HAUPERT_MEE_2022
Releases · ear-team/HAUPERT_MEE_2022
Physics-based model to predict the acoustic detection distance of terrestrial autonomous recording units over the diel cycle and across seasons: insights from an Alpine and a Neotropical forest
Physics-based model to predict the acoustic detection distance of terrestrial autonomous recording units over the diel cycle and across seasons: insights from an Alpine and a Neotropical forest
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This repository contains data and functions in 'R' that were used to process the data and create figures of the publication :
Haupert et. al, Physics-based model to predict the acoustic detection distance of terrestrial autonomous recording units over the diel cycle and across seasons: insights from an Alpine and a Neotropical forest in Methods in Ecology and Evolution
The authors are: Sylvain Haupert, Frédéric Sèbe & Jérôme Sueur
Physics-based model to predict the acoustic detection distance of terrestrial autonomous recording units over the diel cycle and across seasons: insights from an Alpine and a Neotropical forest
This repository contains all the functions in 'R' that were used to process the data and create figures of the publication Haupert et. al. 2022, Methods in Ecology and Evolution