Sampling and modelling rare species: Conceptual guidelines for the neglected majority

Biodiversity conservation faces a methodological conundrum: Biodiversity measure- ment 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 distribu- tion models is challenging because rare species are hardly captured by most survey systems. When enough data are available, predictions are usually spatially biased to- wards 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, representa- tiveness and model complexity that need to be considered prior to survey and analy- sis. 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 predic- tion 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 distri- bution 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 suit- able depending on the different types of distribution data (how to model). Among oth- ers, 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 practi- tioners with a much-­needed guide through the ever-­increasing diversity of methodo- logical developments to improve the prediction of rare species distribution depending on rarity type and available data.