Adaptive niche‐based sampling to improve ability to find rare and elusive species: Simulations and field tests

Abstract

Abstract Sampling efficiency is crucial to overcome the data crisis in biodiversity and to understand what drives the distribution of rare species. 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. 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. Overall, ANBS is an effective and flexible strategy that can contribute to a better understanding of distribution drivers in rare species.