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.

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

FOIS, MAURO;FENU, GIUSEPPE;CUENA LOMBRANA, ALBA;COGONI, DONATELLA;BACCHETTA, GIANLUIGI
2015-01-01

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/65483
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