Revista Chapingo Serie Ciencias Forestales y del Ambiente
Universidad Autónoma Chapingo
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Revista Chapingo Serie Ciencias Forestales y del Ambiente
Volume XX, issue 2, May - August 2014
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SELECCIÓN DE PREDICTORES AMBIENTALES PARA EL MODELADO DE LA DISTRIBUCIÓN DE ESPECIES EN MAXENT
SELECTION OF ENVIRONMENTAL PREDICTORS FOR SPECIES DISTRIBUTION MODELING IN MAXENT

Gustavo Cruz-Cárdenas; José Luis Villaseñor; Lauro López-Mata; Enrique Martínez-Meyer; Enrique Ortiz

http://dx.doi.org/10.5154/r.rchscfa.2013.09.034

Received: 14-09-2013

Accepted: 07-05-2014

Available online: / pages.188-201

 

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  • descriptionAbstract

    Prior to conducting the modeling of the potential distribution of a species, it is advised to make a pre-selection of covariables because redundancy or irrelevant variables may induce errors in most modeling systems. In this study, we propose an automated method for a priori selection of covariables used in modeling. We used five typical species of the Mexican flora (Catopheria chiapensis, Liquidambar styraciflua, Quercus martinezii, Telanthopora grandifolia and Viburnum acutifolium) and 56 environmental covariables. Presence-absence matrices were generated for each species and were analyzed using logistic regression, and the resulting model of each species was evaluated via a bootstrap resampling. We modeled the distribution of five species using maximum entropy and employed three sets of environmental covariables. The precision of the models generated was evaluated with the confidence intervals for each receiver operating characteristic (ROC) curve. The confidence intervals of the resulting ROC curves showed no significant difference between (P < 0.05) the three predictive models generated; nevertheless, the most parsimonious model was obtained with the proposed method.

    Keyworks: Remote sensing data, soil properties, automated selection of covariables.
  • beenhereReferences
    • Austin, P. C., & Tu, J. V. (2004a). Bootstrap methods for developing predictive models. The American Statistician, 58(2), 131–   137.

    • Austin, P. C., & Tu J. V. (2004b). Automated variable selection methods for logistic regression produced unstable models for predicting acute myocardial infarction mortality. Journal of Clinical Epidemiology, 57(11), 1138–1146.

    • Bruijzeel, L. A., Waterloo, M. J., Proctor, J., Kuiters, A. T., & Kotterink, B. (1993). Hydrological observations in montane rain forest on Gunung Silam, Sabah, Malasya, with special reference to the ‘Massenerhebung’ effect. Journal of Ecology,   81(1), 141–167.

    • Challenger, A., & Caballero, J. (1998). Utilizaci.n y conservaci.n de los ecosistemas terrestres de M.xico: Pasado, presente y futuro. México: Comisión Nacional para el Conocimiento y Uso de la Biodiversidad.

    • Cimmery, V. (2010). SAGA User Guide, updated for SAGA version 2.0.5. USA: Geosystem Analysis.

    • Cruz-Cárdenas, G., López-Mata, L., Ortiz-Solorio, C. A., Villaseñor, J. L., Ortiz, E., Silva, J. T., & Estrada-Godoy, F. (2014). Interpolation of Mexican soil properties at a scale of 1: 1,000,000. Geoderma, 213, 29–35.

    • Der, G., & Everitt, B. S. (2002). Handbook of statistical analyses using   SAS. USA: CRC Press.

    • D’heygere, T., Goethals, P. L., & De Pauw, N. (2003). Use of genetic algorithms to select input variables in decision tree models for the prediction of benthic macroinvertebrates.   Ecological Modelling, 160(3), 291–300.

    • D’heygere, T., Goethals, P. L., & De Pauw, N. (2006). Genetic algorithms for optimization of predictive ecosystems models based on decision trees and neural networks. Ecological Modelling, 195(1-2), 20–29.

    • DiCiccio, T., & Efron, B. (1996). Bootstrap confidence intervals. Statatical Science, 11, 189–228.

    • Dimitris, R. (2009). Bootstrap stepAIC. R package version 1.2-0. Vienna, Austria: R Foundation for Statistical Computing.

    • Elith, J., & Leathwick, J. R. (2009). Species distribution models: Ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics, 40(1), 677–697.

    • Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1), 43–57.

    • Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.

    • Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., & Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology,   25(15), 1965–1978.

    • Instituto Nacional de Estadística y Geografía (INEGI). (2005). Mapa de uso de suelo y vegetación 1:250000.

    • Kumar, S., & Stohlgren, T. J. (2009). Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. Journal of   Ecology and natural Environment, 1(4), 94–98.

    • Luna-Vega, I., Alcantara-Ayala, O., Ruíz-Pérez, C. A., & Contreras- Medina, R. (2006). Composition and structure of humid montane oak forests at different sites in central and eastern Mexico. In Kapelle, M. (Ed.), Ecology and conservation of neotropical montane oak forests (pp. 101–112). New York, USA: Springer-Verlag.

    • Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3), 231–259.

    • Phillips, S. J., & Dudik, M. (2008). Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography, 31(2), 161–175.

    • R Development Core Team (2010). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.

    • Ramıírez-Marcial, N., González-Espinosa, M., & Williams-Linera, G. (2001). Anthropogenic disturbance and tree diversity in montane rain forests in Chiapas, Mexico. Forest Ecology and Management, 154(1), 311–326.

    • Riley, S. J., DeGloria, S. D., & Elliot, R. (1999). A terrain ruggedness that quantifies topographic heterogeneity. Intermountain Journal of Sciences, 5(1-4), 23–27.

    • Rzedowski, J. (1996). Análisis preliminar de la flora vascular de los bosques mesófilos de montaña de México. Acta Bot.nica de M.xico, 35, 25–44.

    • Sappington, J., Longshore, K. M., & Thompson, D. B. (2007). Quantifying landscape ruggedness for animal habitat analysis: A case study using bighorn sheep in the Mojave Desert. Journal of Wildlife Management, 71(5), 1419–1426.

    • Stockwell, D. R., & Peterson, A. T. (2002). Effects of sample size on accuracy of species distribution models. Ecological Modelling 148(1), 1–13.

    • Turc, L. (1954). Le bilan d’eau des sols: Relations entre les precipitation, l’évaporation et l’écoulement. Annales Agronomiques, 5, 491–596.

    • United State Geological Survey (USGS). (2010). Global 30 Arc-   Second Elevation (GTOPO30).

    • United State Geological Survey (USGS). (2010). The USGS global visualization viewer.

    • Vázquez-García, J. A. (1995). Cloud forests archipelagos: Preservation of fragmented montane ecosystems in tropical America. In L. S. Hamilton, J. O. Juvik, & and F. N. Scatena (Eds.), Tropical montane cloud forests (pp. 315– 332). London: Springer.

    • Venables, W. N., & Ripley, B.D. (2010). stepAIC: MASS. R package version 7.3-9. Vienna, Austria: R Foundation for Statistical Computing.

    • Villaseñor, J. L. (2010). El bosque h.medo de monta.a en M.xico y sus plantas vasculares: Cat.logo flor.stico-taxon.mico. México: CONABIO-UNAM.

    • Vogelman, H. M. (1973). Fog precipitation in the cloud forest of Eastern Mexico. BioScience, 23(2), 96–100.

  • starCite article

    Cruz-Cárdenas, G., Villaseñor, J. L.,  López-Mata, L., Martínez-Meyer, E., &  Ortiz, E. (2014).  SELECTION OF ENVIRONMENTAL PREDICTORS FOR SPECIES DISTRIBUTION MODELING IN MAXENT. Revista Chapingo Serie Ciencias Forestales y del Ambiente, XX(2), 188-201. http://dx.doi.org/10.5154/r.rchscfa.2013.09.034