Summary The White-winged Snowfinch Montifringilla nivalis nivalis is assumed to be highly threatened by climate change, but this high elevation species has been little studied and the current breeding distribution is accurately known only for a minor portion of its range. Here, we provide a detailed and spatially explicit identification of the potentially suitable breeding areas for the Snowfinch. We modelled suitable areas in Europe and compared them with the currently known distribution. We built a distribution model using 14,574 records obtained during the breeding period that integrated climatic, topographic and land-cover variables, working at a 2-km spatial resolution with MaxEnt. The model performed well and was very robust; average annual temperature was the most important occurrence predictor (optimum between c.-3°C and 0°; unsuitable conditions below -10° and above 5°). The current European breeding range estimated by BirdLife International was almost three times greater than that classified as potentially suitable by our model. Discrepancies between our model and the distribution estimated by BirdLife International were particularly evident in eastern Europe, where the species is poorly monitored. Southern populations are likely more isolated and at major risk because of global warming. These differences have important implications for the supposed national responsibility for conservation of the species and highlight the need for new investigations on the species in the eastern part of its European range.

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Sampling efficiency is crucial in order to overcome the data crisis in biodiversity and to understand what drives the distribution of rare species. 2Adaptive niche-based sampling (ANBS) is an iterative sampling strategy that relies on the predictions of species distribution models (SDMs). By predicting highly suitable areas to guide prospection, ANBS could improve the efficiency of sampling effort in terms of finding new locations for rare species. Its iterative quality could potentially mitigate the effect of small and initially biased samples on SDMs.3In this study, we compared ANBS with random sampling by assessing the gain in terms of new locations found per unit of effort. The comparison was based on both simulations and two field surveys of mountain birds. 4We found an increasing probability of contacting the species through iterations if the focal species showed specialization in the environmental gradients used for modelling.We also identified a gain when using pseudo-absences during first iterations, and a general tendency of ANBS to increase the omission rate in the spatial prediction of the species' niche or habitat. 5Overall, ANBS is an effective and flexible strategy that can contribute to a better understanding of distribution drivers in rare species.

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