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.