Abstract Safeguarding biodiversity requires a detailed understanding of the biosphere, especially regarding the distribution and state of habitats. Citizen science data offer a rich source of useful information, but they usually only contain partial records of species assemblages, as predominantly opportunistic observations of individual species are reported. We explored the capacity of recommender systems—a class of algorithms originally developed for tasks like recommending movies to users on streaming platforms—to fill gaps in citizen science data and assign unreported plant species to partial assemblages, using opportunistic presence‐only observations as training data. We employed a hierarchical Poisson factorization (HPF) model to learn distributions exclusively from pixel‐wise pooled observations, which were synthesized to a site‐by‐species matrix of a dimension of c . 80,000 × 2000 covering Switzerland. Based on about 1500 independent surveys, we assessed HPF's capacity to predict held‐out species and compared these to predictions from traditional species distribution models (SDMs). Moreover, we investigated HPF's latent features, that is, its low‐dimensional summaries of the similarity in species' occurrence across sites and the similarity in site suitability for species. Indicator species of most broad habitat categories distinguished in Switzerland clustered significantly in species‐related latent feature space, indicating a high capacity of HPF to approximate co‐occurrence. Patterns in site‐related latent features were primarily explained by climate, soil, and topography. However, about 40% of the signal could not be attributed to gridded predictors and likely mirrors small‐scale differences, for example, due to disturbances like those experienced in ruderal habitats. Predictive performance of HPF was close to that of SDMs if 20 (or 49% by median) species were observed at a site. However, when ensembling predictions of HPF and SDMs, performance could be improved for sites with ≥6 species observed. HPF predictions performed comparably well in disturbed habitats like roadside vegetation and comparably poorly in habitats experiencing elevated environmental filtering, such as subalpine forests. Given their ability to efficiently learn the distribution of entire floras based on presence‐only data, their unique capacity to obtain generalized co‐occurrence proxies from partial inventories, and their potential to boost distributional predictions, recommender systems can valuably contribute to improving our understanding of the biosphere.
Brun et al. (Sun,) studied this question.