Adaptive 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. In 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.We found an increasing probability of contacting the species through iterations if the focal species showed specialization in the environmental gradients used for modelling. Overall, ANBS is an effective and flexible strategy that can contribute to a better understanding of distribution drivers in rare species. The datasets stored in this repository were used to implement and assess ANBS using 2 mountain bird species as biological models, complementarily to simulations.

Opportunistic and standardized counts data on rock thrush Monticola saxatilis and snowfinch Montifringilla nivalis, Pyrenees, 2014 - 2016.

Dataset used for species distribution modelling, method comparison, and model checking.

Initial datasets ("Opportunistic data and point counts.zip") are opportunistic data and standardized point count data, extracted from local citizen science databases (GOPA and Pyrenees National Park). We recommend to open those files with R cran for data exploration and analysis. Both opportunistic and point counts data were used for initial species distribution modelling, and point counts data were also used for method comparison.

Transects trajectories ("TRANSECTS_2015-2017.zip") and counts ("DATA_TRANSECTS.zip"), are compressed QGIS shapefile (1 shapefile is made of 5 files with same name but different extensions). They were used to implement ANBS,  species distribution modelling iterations 1 and 2, and finally, for method comparison, and finally for field-based assessment of models performed previously. We recommand to use Quantum GIS for data exploration (opening .shp file), or to use R cran -rgeos package for data analysis (use .shp file again to get all information at once).