World Library  

Add to Book Shelf
Flag as Inappropriate
Email this Book

Prevalence, Statistical Thresholds, and Accuracy Assessment for Species Distribution Models : Volume 13, Issue 1 (13/05/2013)

By Hanberry, B. B.

Click here to view

Book Id: WPLBN0004023308
Format Type: PDF Article :
File Size: Pages 7
Reproduction Date: 2015

Title: Prevalence, Statistical Thresholds, and Accuracy Assessment for Species Distribution Models : Volume 13, Issue 1 (13/05/2013)  
Author: Hanberry, B. B.
Volume: Vol. 13, Issue 1
Language: English
Subject: Science, Ecology
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


APA MLA Chicago

He, H. S., & Hanberry, B. B. (2013). Prevalence, Statistical Thresholds, and Accuracy Assessment for Species Distribution Models : Volume 13, Issue 1 (13/05/2013). Retrieved from

Description: School of Natural Resources, Univ. of Missouri, 203 Natural Resources Building, Columbia, MO 65211, USA. For species distribution models, species frequency is termed prevalence and prevalence in samples should be similar to natural species prevalence, for unbiased samples. However, modelers commonly adjust sampling prevalence, producing a modeling prevalence that has a different frequency of occurrences than sampling prevalence. The separate effects of (1) use of sampling prevalence compared to adjusted modeling prevalence and (2) modifications necessary in thresholds, which convert continuous probabilities to discrete presence or absence predictions, to account for prevalence, are unresolved issues. We examined effects of prevalence and thresholds and two types of pseudoabsences on model accuracy. Use of sampling prevalence produced similar models compared to use of adjusted modeling prevalences. Mean correlation between predicted probabilities of the least (0.33) and greatest modeling prevalence (0.83) was 0.86. Mean predicted probability values increased with increasing prevalence; therefore, unlike constant thresholds, varying threshold to match prevalence values was effective in holding true positive rate, true negative rate, and species prediction areas relatively constant for every modeling prevalence. The area under the curve (AUC) values appeared to be as informative as sensitivity and specificity, when using surveyed pseudoabsences as absent cases, but when the entire study area was coded, AUC values reflected the area of predicted presence as absent. Less frequent species had greater AUC values when pseudoabsences represented the study background. Modeling prevalence had a mild impact on species distribution models and accuracy assessment metrics when threshold varied with prevalence. Misinterpretation of AUC values is possible when AUC values are based on background absences, which correlate with frequency of species.

Prevalence, statistical thresholds, and accuracy assessment for species distribution models

Albert, C. H. and Thuiller, W.: Favourability functions versus probability of presence: advantages and misuses, Ecography, 31, 417–422, 2008.; Beers, T. W., Dress, P. E., and Wensel, L. C.: Aspect transformation in site productivity research, J. Forest., 64, 691–692, 1966.; Breiman, L.: Random Forests, Mach. Learn., 40, 5–32, 2001.; ECOMAP: National hierarchical framework of ecological units, USDA Forest Service, Washington, DC, 1993.; Cutler, D. R., Edwards Jr., T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., and Lawler, J. J.: Random forests for classification in ecology,. Ecology, 88, 2783–2792, 2007.; Engler, R., Guisan, A., and Rechsteiner, L.: An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data, J. Appl. Ecol., 41, 263–274, 2004.; Elith, J., Graham, C. H., Anderson, R. P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R. J., Huettmann, F., Leathwick, J. R., Lehmann, A., Li, J., Lohmann, L. G., Loiselle, B. A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J. M., Peterson, A. T., Phillips, S. J., Richardson, K., Scachetti-Pereira, R., Schapire, R. E., Soberón, J., Williams, S., Wisz, M. S., and Zimmermann, N. E.: Novel methods improve prediction of species' distributions from occurrence data, Ecography, 29, 129–151, 2006.; Fielding, A. H. and Bell, J. F.: A review of methods for assessment of prediction errors in conservation presence/absence models, Environ. Conserv., 24, 38–49, 1997.; Franklin, J.: Mapping species distributions: Spatial inference and predictions, Cambridge University Press, New York, 2010.; Freeman, E. A. and Moisen, G. G.: A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa, Ecol.l Model., 217, 48–58, 2008.; Hanberry, B. B., He, H. S., and Dey, D. C.: Sample sizes and model comparison metrics for species distribution models, Ecol. Model., 227, 29–33, 2012a.; Hanberry, B. B., He, H. S., and Palik, B. J.: Pseudoabsence generation for species distribution models, PLoS ONE, 7, e44486, doi:10.1371/journal.pone.0044486, 2012b.; Hernandez, P. A., Graham, C. H., Master, L. L., and Albert, D. L.: The effect of sample size and species characteristics on performance of different species distribution modeling methods, Ecography, 29, 773–785, 2006.; Jiménez-Valverde, A.: Insights into the area under the receiver operating characteristic curve AUC as a discrimination measure in species distribution modeling, Global Ecol. Biogeogr., 21, 498–507, 2011.; Jiménez-Valverde, A. and Lobo, J. M.: The ghost of unbalanced species distribution data in geographical model predictions, Divers. Distrib., 12, 521–524, 2006.; Jiménez-Valverde, A., Lobo, J. M., and Hortal, J.: Not as good as they seem: the importance of concepts in species distribution modelling, Divers. Distrib., 14, 885–890, 2008.; Jiménez-Valverde, A., Lobo, J. M., and Hortal, J.: The effect of prevalence and its interaction with sample size on the reliability of species distribution models, Community Ecol., 10, 196–205, 2009.; Li, W., Guo, Q., and Elkan, C.: Can we model the probability of presence of species without absence data?, Ecography, 34, 1096–1105, 2011.; Liaw, A. and Wiener, M.: Classification and regression by random Forest, R News, 2, 18–22, 2002.; Liu, C., Berry, P. M., Dawson, T. P., and Pearson, R. G.: Selecting thresholds of occurrence in the prediction of species distributions, Ecography, 28, 385–393, 2005.; Lobo, J. M. and Tognelli, M. F.: Exploring the effects of quantity and location of pseudo-absences and sampling bias on the performance of distribution models with limited point occurrence data, J. Nat. Conserv., 19, 1–7, 2011.; Lobo, J. M., Jiménez-Valverde, A., and Real, R.: AUC: a misleading measure of the performance of predictive distribution models, Global Ecol. Biogeogr., 17, 145–151, 2007.; Manel


Click To View

Additional Books

  • Sea Surface Height and Mixed Layer Depth... (by )
  • Science; Numbered Serries Volume: 32 Jul... (by )
  • Det Kongelige Danske Videnskabernes Sels... Volume: ser.8 v.4 1918-1923 (by )
  • Frontal Structures in the West Spitsberg... (by )
  • Jiang Xi Nong Ye Bing Chong Hai Zhi Bing... (by )
  • Science (by )
  • The Philippine Journal of Science Volume: v. 3 pt. A 1908 (by )
  • The M3A Multi-sensor Buoy Network of the... (by )
  • Large-scale Temperature and Salinity Cha... (by )
  • Morphology as a Key to Behavioural Flexi... (by )
  • Science; Numbered Serries Volume: 41 Jan... (by )
  • Problems of life and mind : first series... (by )
Scroll Left
Scroll Right


Copyright © World Library Foundation. All rights reserved. eBooks from World eBook Library are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.