Disentangling observation biases to monitor spatio-temporal shifts in species distributions

The accelerated pace of environmental change due to anthropogenic activities makes it more important than ever to understand current and future ecosystem dynamics at a global scale. Species observations stemming from citizen science platforms are increasingly leveraged to gather information about the geographic distributions of many species. However, their usability is limited by the strong biases inherent to these community-driven efforts. These biases in the sampling effort are often treated as noise that has to be compensated for. In this project, we posit that better modelling the sampling effort (including the usage of the different platforms across countries, local accessibility, attractiveness of the location for platform users, affinity of different user groups for different species, etc.) is the key towards improving Species Distribution Models (SDM) using observations from citizen science platforms, thus opening up the possibility of leveraging them to monitor changes in species distributions and population densities.