A practical method to speed up the discovery of unknown populations using distribution models

Species Distribution Models (SDMs) could be an important tool to limit search efforts by selecting theareas where field surveys are to be carried out; due to the constant decrease of financial funds, thischallenging purpose is particularly necessary. In particular, these methods are useful when applied toendangered and/or rare species with a poor known distribution area, especially due to difficulties in plantdetection and in reaching the study areas.We hereby describe the development of maximum-entropy (Maxent) models for the endangered yel-low gentian Gentiana lutea L. in Sardinia with the aims of (i) guiding survey efforts; (ii) estimating SDMsutility by post-test species current/extinct localities through the Observed Positive Predictive Power(OPPP) values; and (iii) evaluating the influence of sample data addition. Besides the Area Under Curve(AUC) values, we used the OPPP (observed/modelled positive localities ratio) to compare results fromeight, 24 and 58 presence-only data points. Even with the initial small and biased sample data, we foundthat surveys could be effectively guided using such methods, whereby the focus of our research wason 48% of our initial 721 km2study area. The high OPPPs values additionally proved the reliability ofour results in discovering 16 new localities of G. lutea. Nevertheless, the predictive models should beconsidered as a complementary tool rather than a replacement for expert knowledge.