data_files_manuscript.zip

In biodiversity monitoring, large datasets are becoming more and more
widely available and are increasingly used globally to estimate species
trends and conservation status. these large-scale datasets challenge
existing statistical analysis mlethods, many of which are not adapted to
their size, incompltness and heterogeneity. The development of scalable
methodsto impute missing data in incomplete large-scale monitoring
datasets is crucial to balance sampling in time or space and thus better
inform conservation policies. To address this, we developed a new
method based on penalized Poisson models to impute and analyse
incomplete monitoring data in a large-scale framework. The method allows
parameterization of (a) space and time factors, (b) the main effects of
predictor covariates, (c) space-time interactions. This new method
benefits from robust statistical and computational capability in
large-scale setting.