Rethinking receiver operating characteristic analysis applications in ecological niche modeling

By A. Townsend Peterson, Monica Papes, Jorge Soberón. 2008

1,602 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/c626d9e53138e9c4035035b21bb0b671ea521d2c

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Predicting potential distribution of the jaguar (Panthera onca) in Mexico: identification of priority areas for conservation (2011, methodology): “Maxent, according to the AUC value that we measured, showed the best performance in this study, a result that has already been obtained before (Elith et al., 2006; but see Peterson et al., 2008 and Roura-Pascual et al., 2008).”
Niches and distributional areas: Concepts, methods, and assumptions (2009, background): “The resulting explosion of work focused on applying these methods to a large number of species and problems and addressing important, but mostly methodological, issues, like sensitivity to number of occurrence records (12), ratio of presences to absences (13), grain of the environmental layers (14),”
Predicting the Potential Worldwide Distribution of the Red Palm Weevil Rhynchophorus ferrugineus (Olivier) (Coleoptera: Curculionidae) using Ecological Niche Modeling (2012, methodology): “We filtered these replicates based on their error characteristics to emphasize the overriding importance of omission Downloaded From: https://bioone.org/journals/Florida-Entomologist on 06 Feb 2020 Terms of Use: https://bioone.org/terms-of-use 664 Florida Entomologist 95(3) September 2012 error (as…”
Invasive and flexible: niche shift in the drosophilid Zaprionus indianus (Insecta, Diptera) (2010, methodology): “Despite recent discussion on the validity of AUC and ROC curves (e.g., Peterson et al. 2008; Lobo et al. 2008), they were used here only as a description of a single model fit and not in a comparative fashion among species.”
Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling (2012, methodology): “…a key challenge in SDM (Vaughan & Ormerod, 2005) and despite the wide acceptance of some standard methods/statistics, there is currently a strong ongoing debate as to their use (e.g.Allouche et al., 2006; JiménezValverde et al., 2008; e.g. Lobo et al., 2008; Peterson et al., 2008; Santika, 2011).”
Pest Risk Maps for Invasive Alien Species: A Roadmap for Improvement (2010, neutral): “Generally, statistical tests of model performance are preferred to qualitative comparisons of model output and known occurrences, but there are limitations to many goodness-of-fit measures (Lobo et al. 2008, Peterson et al. 2008).”
Consensual predictions of potential distributional areas for invasive species: a case study of Argentine ants in the Iberian Peninsula (2009, background): “However, Peterson et al. (2008) evidenced that these results are extremely biased by the evaluation technique, which favours algorithms that predict across the whole range of predicted thresholds (such as Maxent) in detriment of those models that make only predictions at the end of the spectrum…”
MaxEnt versus MaxLike: empirical comparisons with ant species distributions (2013, methodology): “However, given the documented issues with AUC and pseudoabsence data (Lobo et al. 2008, Peterson et al. 2008, Jiménez-Valverde 2012), the interpretation of AUC is problematic.”
Modeling Species Distribution and Change Using Random Forest (2011, methodology): “Caution should be used when interpreting the ROC/AUC because; (1) error components are equally weighted (Peterson et al. 2008), (2) models can over-value models of rare species (Manel et al. 2001), and (3) certain models do not predict across the spectrum of probabilities violating the assumption…”