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Presence-only species distribution models are sensitive to sample prevalence: Evaluating models using spatial prediction stability and accuracy metrics

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  • Grimmett, Liam
  • Whitsed, Rachel
  • Horta, Ana

Abstract

Species distribution modelling (SDM) is an important tool for ecologists, but different algorithms and different sampling strategies produce different results. Using virtual species with differing characteristics, this study investigated the effect of sampling strategy choices on SDM predictions across multiple algorithms and species, including the impacts of different sample size and prevalence choices, and the effects of validating models using presence and background data as opposed to true absences. We also assessed the consistency of predictions between algorithms, and investigated the effectiveness of using stability assessment of spatial predictions in geographic space to evaluate SDM predictions. Maxent performed most consistently under all scenarios both in regards to performance metrics and spatial prediction stability, and should be considered for most scenarios either on its own or as part of a model ensemble, in particular when true absences are not available. A key recommendation of this study is the use of metrics to assess agreement between replicate predictions as a measure of spatial stability, rather than relying solely on performance metrics such as area under the curve (AUC).

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  • Grimmett, Liam & Whitsed, Rachel & Horta, Ana, 2020. "Presence-only species distribution models are sensitive to sample prevalence: Evaluating models using spatial prediction stability and accuracy metrics," Ecological Modelling, Elsevier, vol. 431(C).
    • Handle: RePEc:eee:ecomod:v:431:y:2020:i:c:s0304380020302659
    DOI: 10.1016/j.ecolmodel.2020.109194
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    References listed on IDEAS

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    1. Giovanelli, João G.R. & de Siqueira, Marinez Ferreira & Haddad, Célio F.B. & Alexandrino, João, 2010. "Modeling a spatially restricted distribution in the Neotropics: How the size of calibration area affects the performance of five presence-only methods," Ecological Modelling, Elsevier, vol. 221(2), pages 215-224.
    2. Jesús Aguirre-Gutiérrez & Luísa G Carvalheiro & Chiara Polce & E Emiel van Loon & Niels Raes & Menno Reemer & Jacobus C Biesmeijer, 2013. "Fit-for-Purpose: Species Distribution Model Performance Depends on Evaluation Criteria – Dutch Hoverflies as a Case Study," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-11, May.
    3. Iturbide, Maialen & Bedia, Joaquín & Herrera, Sixto & del Hierro, Oscar & Pinto, Miriam & Gutiérrez, Jose Manuel, 2015. "A framework for species distribution modelling with improved pseudo-absence generation," Ecological Modelling, Elsevier, vol. 312(C), pages 166-174.
    4. Vincenzi, Simone & Zucchetta, Matteo & Franzoi, Piero & Pellizzato, Michele & Pranovi, Fabio & De Leo, Giulio A. & Torricelli, Patrizia, 2011. "Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy," Ecological Modelling, Elsevier, vol. 222(8), pages 1471-1478.
    5. VanDerWal, Jeremy & Shoo, Luke P. & Graham, Catherine & Williams, Stephen E., 2009. "Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know?," Ecological Modelling, Elsevier, vol. 220(4), pages 589-594.
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    1. Barker, Justin R. & MacIsaac, Hugh J., 2022. "Species distribution models: Administrative boundary centroid occurrences require careful interpretation," Ecological Modelling, Elsevier, vol. 472(C).
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    3. Yeeun Shin & Eunseo Shin & Sang-Woo Lee & Kyungjin An, 2024. "Predicting Changes in and Future Distributions of Plant Habitats of Climate-Sensitive Biological Indicator Species in South Korea," Sustainability, MDPI, vol. 16(3), pages 1-17, January.

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