A practical method to speed up the discovery of unknown populations using Species Distribution Models |
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| Affiliation: | 1. Centro Conservazione Biodiversità, Dipartimento di Scienze della Vita e dell’Ambiente, Università degli Studi di Cagliari, Viale S. Ignazio da Laconi, 11-13, Cagliari 09123, Italy;2. Dipartimento di Biologia Ambientale, ‘Sapienza’ Università di Roma, P.le A. Moro 5, 00185 Roma, Italy;1. Forest Research Institute of Baden-Württemberg, Wonnhaldestrasse 4, D-79100 Freiburg, Germany;2. Conservation Biology, Institute of Ecology and Evolution, University of Bern, Baltzerstrasse 6, 3012 Bern, Switzerland;1. Departamento de Ecologia, Instituto de Biociências, Universidade de São Paulo, Rua do Matão, Travessa 14, Cidade Universitária, São Paulo, SP, 05508-090, Brazil;2. Laboratório de Ecologia da Paisagem, WWF-Brasil, SHIS QL 6/8, Conjunto E, Lago Sul, Brasília, DF, 71620-430, Brazil;1. Centre for Climate Change and Adaptation Research, Anna University, Chennai 600 025, India;2. Environmental Informatics and Spatial Modeling Lab (EISML), Department of Ecology and Environmental Sciences, School of Life sciences, Pondicherry University, Puducherry 605 014, India;1. Department of Integrative Biology, University of Guelph, Guelph, ON, Canada N1G 2W1;2. Department of Ecology and Evolutionary Biology, University of Toronto Mississauga, Mississauga, ON, Canada L5L 1C6;1. Instituto de Biologia Roberto Alcantara Gomes, Departamento de Zoologia, Laboratório de Malacologia Límnica e Terrestre, Universidade do Estado do Rio de Janeiro (UERJ), Rua São Francisco Xavier 524, PHLC, sala 525/2, CEP 20550-900, Maracanã, RJ, Brazil;2. Instituto de Biodiversidad Neotropical (IBN), CONICET-UNT, Crisóstomo Álvarez 722, 4000 San Miguel de Tucumán, Argentina;3. Facultad de Ciencias Naturales e IML, Universidad Nacional de Tucumán, Miguel Lillo 205, 4000 San Miguel de Tucumán, Argentina;4. Laboratório de Biogeografia da Conservação Departamento de Ecologia, Universidade Federal de Goiás, Goiânia, Goiás, Brazil;5. Fundação Brasileira para o Desenvolvimento Sustentável, Rio de Janeiro, RJ, Brazil |
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| Abstract: | ![]()
Species Distribution Models (SDMs) could be an important tool to limit search efforts by selecting the areas where field surveys are to be carried out; due to the constant decrease of financial funds, this challenging purpose is particularly necessary. In particular, these methods are useful when applied to endangered and/or rare species with a poor known distribution area, especially due to difficulties in plant detection and in reaching the study areas.We hereby describe the development of maximum-entropy (Maxent) models for the endangered yellow gentian Gentiana lutea L. in Sardinia with the aims of (i) guiding survey efforts; (ii) estimating SDMs utility 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 from eight, 24 and 58 presence-only data points. Even with the initial small and biased sample data, we found that surveys could be effectively guided using such methods, whereby the focus of our research was on 48% of our initial 721 km2 study area. The high OPPPs values additionally proved the reliability of our results in discovering 16 new localities of G. lutea. Nevertheless, the predictive models should be considered as a complementary tool rather than a replacement for expert knowledge. |
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| Keywords: | Field efforts Maxent Positive Predictive Power Sardinia Threatened vascular flora |
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