Biodiversity conservation faces a methodological conundrum: Biodiversity measurement often relies on species, most of which are rare at various scales, especially prone to extinction under global change, but also the most challenging to sample and model. Predicting the distribution change of rare species using conventional species distribution models is challenging because rare species are hardly captured by most survey systems. When enough data are available, predictions are usually spatially biased towards locations where the species is most likely to occur, violating the assumptions of many modelling frameworks. Workflows to predict and eventually map rare species distributions imply important trade-offs between data quantity, quality, representativeness and model complexity that need to be considered prior to survey and analysis. Our opinion is that study designs need to carefully integrate the different steps, from species sampling to modelling, in accordance with the different types of rarity and available data in order to improve our capacity for sound assessment and prediction of rare species distribution. In this article, we summarize and comment on how different categories of species rarity lead to different types of occurrence and distribution data depending on choices made during the survey process, namely the spatial distribution of samples (where to sample) and the sampling protocol in each selected location (how to sample). We then clarify which species distribution models are suitable depending on the different types of distribution data (how to model). Among others, for most rarity forms, we highlight the insights from systematic species-targeted sampling coupled with hierarchical models that allow correcting for overdispersion and spatial and sampling sources of bias. Our article provides scientists and practitioners with a much-needed guide through the ever-increasing diversity of methodological developments to improve the prediction of rare species distribution depending on rarity type and available data.

BIOME Lab Department of Biological Geological and Environmental Sciences Alma Mater Studiorum University of Bologna Bologna Italy

Department of Conservation Biology and Social Ecological Systems UFZ Helmholtz Centre for Environmental Research Leipzig Germany

Department of Ecology and Evolution and Yale Center for Biodiversity and Global Change Yale University New Haven Connecticut USA

Department of Life Sciences Natural History Museum London UK

Department of Plant Sciences University of Oxford Oxford UK

Department of River Ecology and Conservation Senckenberg Research Institute and Natural History Museum Frankfurt Gelnhausen Germany

Department of Spatial Sciences Faculty of Environmental Sciences Czech University of Life Sciences Prague Praha Suchdol Czech Republic

Faculty of Biology University of Duisburg Essen Essen Germany

Institute of Plant Sciences University of Bern Bern Switzerland

School of Biology Faculty of Biological Sciences University of Leeds Leeds UK

University of Leeds Leeds UK

University of Paris Saclay INRAE UR HYCAR Antony France

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A quixotic view of spatial bias in modelling the distribution of species and their diversity