Skip to main content

Advertisement

Log in

Modeling and Prediction of Habitat Suitability for Ferula gummosa Medicinal Plant in a Mountainous Area

  • Original Paper
  • Published:
Natural Resources Research Aims and scope Submit manuscript

Abstract

Habitat suitability modeling and mapping are important aspects of long-term strategies for sustaining plant ecosystems. In this study, seven state-of-the-art machine learning models including boosted regression tree (BRT), functional discriminant analysis (FDA), generalized linear model (GLM), multivariate adaptive regression splines (MARS), mixture discriminant analysis (MDA), random forest (RF), and support vector machine (SVM) were applied to model habitat suitability for Ferula gummosa medicinal plant in the Firozkuh County of Tehran. Different factors that affect the habitat of this plant were prepared for modeling, including slope angle, silt percentage, sand percentage, aspect, annual mean rainfall, clay percentage, topographic wetness index, elevation, distance from rivers, drainage density, annual mean temperature, plan curvature, profile curvature, land use, lithological units, and organic carbon. After running the models in R software, their evaluation using various measures (area under the curve, accuracy, precision, F-measure, fallout, true skill statistics, and corrected classify instances) indicated that the RF model was the best one for assessing Ferula gummosa habitat suitability. The SVM, MARS, MDA, GLM, FDA, and BRT models also displayed acceptable performances. The results of our study contribute to the understanding of the stability of the medicinal plant Ferula gummosa and to help avoid its extinction in the future.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10

Similar content being viewed by others

References

  • Abedi, D., Jalali, M., Asghari, G., & Sadeghi, N. (2008). Composition and antimicrobial activity of oleogumresin of Ferula gumosa Bioss. Essential oil using Alamar BlueTM

  • Ada, M., & San, B. T. (2018). Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir catchment area, Antalya Turkey. Natural Hazards, 90(1), 237–263.

    Google Scholar 

  • Adnan, R. M., Liang, Z., Heddam, S., Zounemat-Kermani, M., Kisi, O., & Li, B. (2019). Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. Journal of Hydrology, 586, 124371.

    Google Scholar 

  • Ai, N. X. M., Bun, S. S., Ollivier, E., & Thao, D. T. P. (2019). Ethnobotanical study of medicinal plants used by K’Ho-Cil people for treatment of diarrhea IN Lam Dong province Vietnam. Journal of Herbal Medicine, 19, 100320.

    Google Scholar 

  • Albajes-Eizagirre, A., Solanes, A., Vieta, E., & Radua, J. (2019). Voxel-based meta-analysis via permutation of subject images (PSI): Theory and implementation for SDM. NeuroImage, 186, 174–184.

    Google Scholar 

  • Albanese, M. A., Mejicano, G., Mullan, P., Kokotailo, P., & Gruppen, L. (2008). Defining characteristics of educational competencies. Medical Education, 42(3), 248–255.

    Google Scholar 

  • Alfaro-Sánchez, R., Jump, A. S., Pino, J., Díez-Nogales, O., & Espelta, J. M. (2019). Land use legacies drive higher growth, lower wood density and enhanced climatic sensitivity in recently established forests. Agricultural and Forest Meteorology, 276, 107630.

    Google Scholar 

  • Al-Tabini, R., Al-Khalidi, K., & Al-Shudiefat, M. (2012). Livestock medicinal plants and rangeland viability in Jordan’s Badia: through the lens of traditional and local knowledge. Pastoralism: Research, Policy and Practice, 2(1), 4.

    Google Scholar 

  • Anselmo, C. A., Dias, R., & Garcia, N. L. (2005). Adaptive basis selection for functional data analysis via stochastic penalization. Computational and Applied Mathematics, 24(2), 209–229.

    Google Scholar 

  • Anywar, G., Kakudidi, E., Byamukama, R., Mukonzo, J., Schubert, A., & Oryem-Origa, H. (2019). Medicinal plants used by traditional medicine practitioners to boost the immune system in people living with HIV/AIDS in Uganda. European Journal of Integrative Medicine, 35, 101011.

    Google Scholar 

  • Ao, Y., Li, H., Zhu, L., Ali, S., & Yang, Z. (2019). The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling. Journal of Petroleum Science and Engineering, 174, 776–789.

    Google Scholar 

  • Araújo, M. B., & New, M. (2007). Ensemble forecasting of species distributions. Trends in Ecology and Evolution, 22, 42–47.

    Google Scholar 

  • Balashi, M. S., McGuirez, A. D., Duffy, P., Flannigan, M., Walsh, J., & Melillo, J. (2009). Assessing the response of area burned to changing climate in western boreal North America using a multivariate adaptive regression splines (MARS) approach. Global Change Biology, 15, 578–600.

    Google Scholar 

  • Bar Massada, A. B., Syphard, A. D., Stewart, S. I., & Radeloff, V. C. (2013). Wildfire ignition-distribution modelling: A comparative study in the Huron-Manistee National Forest, Michigan, USA. International Journal of Wildland Fire, 22(2), 174–183.

    Google Scholar 

  • Bashir, S., & Carter, E. (2005). High breakdown mixture discriminant analysis. Journal of Multivariate Analysis, 93(1), 102–111.

    Google Scholar 

  • Beedy, T. L., Snapp, S. S., Akinnifesi, F. K., & Sileshi, G. W. (2010). Impact of Gliricidia sepium intercropping on soil organic matter fractions in a maize-based cropping system. Agriculture, Ecosystems & Environment, 138(3–4), 139–146.

    Google Scholar 

  • Behpour, M., Ghoreishi, S. M., Kashani, M. K., & Soltani, N. (2009). Inhibition of 304 stainless steel corrosion in acidic solution by Ferula gumosa (galbanum) extract. Materials and Corrosion, 60(11), 895–898.

    Google Scholar 

  • Brédoire, F., Kayler, Z. E., Dupouey, J. L., Derrien, D., Zeller, B., Barsukov, P. A., & Legout, A. (2020). Limiting factors of aspen radial growth along a climatic and soil water budget gradient in south-western Siberia. Agricultural and Forest Meteorology, 282, 107870.

    Google Scholar 

  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

    Google Scholar 

  • Brenning, A., Grasser, M., & Friend, D. A. (2007). Statistical estimation and generalized additive modeling of rock glacier distribution in the San Juan Mountains, Colorado, United States. Journal of Geophysical Research Atmosphere, 112, F2.

    Google Scholar 

  • Brus, D. J., & Saby, N. P. (2016). Approximating the variance of estimated means for systematic random sampling, illustrated with data of the French soil monitoring network. Geoderma, 279, 77–86.

    Google Scholar 

  • Burfield, R., Neumann, C., & Saunders, C. P. (2015). Review and application of functional data analysis to chemical data—the example of the comparison, classification, and database search of forensic ink chromatograms. Chemometrics and Intelligent Laboratory Systems, 149, 97–106.

    Google Scholar 

  • Cardinale, B. J., Duffy, J. E., Hooper, D. U., Perrings, C., Venail, P., & Kinzig, A. P. (2012). Biodiversity loss and its impact on humanity. Nature, 486(7401), 59.

    Google Scholar 

  • Catry, F. X., Rego, F. C., Bação, F. L., & Moreira, F. (2010). Modeling and mapping wildfire ignition risk in Portugal. International Journal of Wildland Fire, 18(8), 921–931.

    Google Scholar 

  • Chamroukhi, F., Glotin, H., & Samé, A. (2013). Model-based functional mixture discriminant analysis with hidden process regression for curve classification. Neurocomputing, 112, 153–163.

    Google Scholar 

  • Chamroukhi, F., Samé, A., Govaert, G., & Aknin, P. (2010). A hidden process regression model for functional data description. Application to curve discrimination. Neurocomputing, 73(7–9), 1210–1221.

    Google Scholar 

  • Cleasby, I. R., Owen, E., Wilson, L., Wakefield, E. D., O’Connell, P., & Bolton, M. (2020). Identifying important at-sea areas for seabirds using species distribution models and hotspot mapping. Biological Conservation, 241, 108375.

    Google Scholar 

  • Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press.

    Google Scholar 

  • Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007). Random forests for classification in ecology. Ecology, 88, 2783–2792.

    Google Scholar 

  • De Veaux, R. D., Gordon, A. L., Comiso, J. C., & Bacherer, N. E. (1993). Modeling of topographic effects on Antarctic sea ice using multivariate adaptive regression splines. Journal of Geophysical Research: Oceans, 98(C11), 20307–20319.

    Google Scholar 

  • De’Ath, G. (2007). Boosted trees for ecological modeling and prediction. Ecology, 88(1), 243251.

    Google Scholar 

  • Deichmann, J., Eshghi, A., Haughton, D., Sayek, S., & Teebagy, N. (2002). Application of multiple adaptive regression splines (MARS) in direct response modeling. Journal of Interactive Marketing, 16(4), 15–27.

    Google Scholar 

  • Digby, P. G. N., & Kempton, R. A. (1987). Multivariate analysis of ecological communities. Chapman and Hall.

    Google Scholar 

  • Donaldson, L., Bennie, J. J., Wilson, R. J., & Maclean, I. M. (2021). Designing effective protected area networks for multiple species. Biological Conservation, 258, 109125.

    Google Scholar 

  • Donati, L., & Turrini, M. C. (2002). An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: Application to an area of the Apennines (Valnerina; Perugia, Italy). Engineering Geology, 63(3–4), 277–289.

    Google Scholar 

  • Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., &... Lautenbach, S. (2013). Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1), 27–46.

    Google Scholar 

  • D’Orso, G., & Migliore, M. (2020). A GIS-based method for evaluating the walkability of a pedestrian environment and prioritized investments. Journal of Transport Geography, 82, 102555.

    Google Scholar 

  • Duan, R. Y., Kong, X. Q., Huang, M. Y., Fan, W. Y., & Wang, Z. G. (2014). The predictive performance and stability of six species distribution models. PloS one, 9(11), e112764.

    Google Scholar 

  • Dumbser, M., Fambri, F., Gaburro, E., & Reinarz, A. (2019). On GLM curl cleaning for a first order reduction of the CCZ4 formulation of the Einstein field equations. Journal of Computational Physics, 25, 109088.

    Google Scholar 

  • Eftekhar, F., Yousefzadi, M., & Borhani, K. (2004). Antibacterial activity of the essential oil from Ferula gummosa seed. Fitoterapia, 75(7–8), 758–759.

    Google Scholar 

  • El Houby, E. M. (2018). A survey on applying machine learning techniques for management of diseases. Journal of Applied Biomedicine, 16(3), 165–174.

    Google Scholar 

  • Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77(4), 802–813.

    Google Scholar 

  • Elith, J., & Graham, C. H. (2009). Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. Ecography, 32(1), 66–77.

    Google Scholar 

  • Epifanio, I., & Ventura-Campos, N. (2011). Functional data analysis in shape analysis. Computational Statistics and Data Analysis, 55(9), 2758–2773.

    Google Scholar 

  • Evans, J.S., & Murphy, M.A. (2019). Random forests model selection and performance evaluation. https://cran.r-project.org/web/packages/rfUtilities/rfUtilities.pdf

  • Fattahi, M., Nazeri, V., Sefidkon, F., Zamani, Z., & Palazon, J. (2011). The effect of pre-sowing treatments and light on seed germination of Dracocephalum kotschyi Boiss: An endangered medicinal plant in Iran. Horticulture, Environment, and Biotechnology, 52(6), 559–566.

    Google Scholar 

  • Febrianto, H., Fariza, A., & Hasim, J. A. N. (2016). Urban flood risk mapping using analytic hierarchy process and natural break classification (Case study: Surabaya, East Java, Indonesia). In 2016 International Conference on Knowledge Creation and Intelligent Computing (KCIC) (pp. 148–154). IEEE.

  • Federici, P. R., Puccinelli, A., Cantarelli, E., Casarosa, N., Avanzi, G. D. A., Falaschi, F., & Salvati, N. (2007). Multidisciplinary investigations in evaluating landslide susceptibility—an example in the Serchio River valley (Italy). Quaternary International, 171, 52–63.

    Google Scholar 

  • Fernández, V., & Brown, P. H. (2013). From plant surface to plant metabolism: The uncertain fate of foliar-applied nutrients. Frontiers in Plant Science, 4, 289.

    Google Scholar 

  • Fielding, A. H., & Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24(1), 38–49.

    Google Scholar 

  • Fiori, S. (2002). Hybrid independent component analysis by adaptive LUT activation function neurons. Neural Networks, 15(1), 85–94.

    Google Scholar 

  • Friedman, J. H. (1991). Multivariate adaptive regression splines. Annals of Statistics, 19(1), 1–67.

    Google Scholar 

  • Ghasemi, Y., Faridi, P., Mehregan, I., & Mohagheghzadeh, A. (2005). Ferula gummosa fruits: An aromatic antimicrobial agent. Chemistry of Natural Compounds, 41(3), 311–314.

    Google Scholar 

  • Gill, J., & Torres, M. (2019). Generalized Linear Models. Understand the Foundations of Research Methods. https://doi.org/10.4135/9781526421036

    Article  Google Scholar 

  • Gobeyn, S., & Goethals, P. L. (2019). Multi-objective optimization of species distribution models for river management. Water Research, 163, 114863.

    Google Scholar 

  • Goedecke, F., Marcenò, C., Guarino, R., Jahn, R., & Bergmeier, E. (2020). Reciprocal extrapolation of species distribution models between two islands–Specialists perform better than generalists and geological data reduces prediction accuracy. Ecological Indicators, 108, 105652.

    Google Scholar 

  • Grenié, M., Violle, C., & Munoz, F. (2020). Is prediction of species richness from stacked species distribution models biased by habitat saturation? Ecological Indicators, 111, 105970.

    Google Scholar 

  • Grömping, U. (2009). Variable importance assessment in regression: Linear regression versus random forest. The American Statistician, 63(4), 308–319.

    Google Scholar 

  • Gui, J., & Li, H. (2003). Mixture functional discriminant analysis for gene function classification based on time course gene expression data. In Proceeding Joint Statitics Meeting (Biometric Section)

  • Guisan, A., Weiss, S. B., & Weiss, A. D. (1999). GLM versus CCA spatial modeling of plant species distribution. Plant Ecology, 143(1), 107–122.

    Google Scholar 

  • Guisan, A., & Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. Ecological Modelling, 135(2–3), 147–186.

    Google Scholar 

  • Gutiérrez, Á. G., Schnabel, S., & Contador, J. F. L. (2009). Using and comparing two nonparametric methods (CART and MARS) to model the potential distribution of gullies. Ecological Modelling, 220(24), 3630–3637.

    Google Scholar 

  • Halbe, Z., & Aladjem, M. (2005). Model-based mixture discriminant analysis—an experimental study. Pattern Recognition, 38(3), 437–440.

    Google Scholar 

  • Halbe, Z., & Aladjem, M. (2007). Regularized mixture discriminant analysis. Pattern Recognition Letter, 28(15), 2104–2115.

    Google Scholar 

  • Halberstein, R. A. (2005). Medicinal plants: Historical and cross-cultural usage patterns. Annals of Epidemiology, 15(9), 686–699.

    Google Scholar 

  • Hastie, M.T. (2017). Package ‘mda’. http://cran.dcc.fc.up.pt/web/packages/mda/mda.pdf.

  • Hastie, T., & Tibshirani, R. (1996). Discriminant analysis by Gaussian mixtures. Journal of the Royal Statistical Society . Series B (Methodological), 58(1), 155–176.

    Google Scholar 

  • Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning: Data mining, inference, and prediction. Springer-Verlag.

    Google Scholar 

  • Hernandez, P. A., Franke, I., Herzog, S. K., Pacheco, V., Paniagua, L., Quintana, H. L., Soto, A., Swenson, J. J., Tovar, C., Valqui, T. H., Vargas, J., & Young, B. E. (2008). Predicting species distributions in poorly-studied landscapes. Biodiversity and Conservation, 17, 1353–1366.

    Google Scholar 

  • Herrera-Añazco, P., Taype-Rondan, A., Ortiz, P. J., Málaga, G., del Carpio-Toia, A. M., Alvarez-Valdivia, M. G., & Perez-Rafael, E. (2019). Use of medicinal plants in patients with chronic kidney disease from Peru. Complementary Therapies in Medicine, 47, 102215.

    Google Scholar 

  • Hjort, J., & Luoto, M. (2013). Statistical methods for geomorphic distribution modeling (pp. 59–73). Academic Press.

    Google Scholar 

  • Holmes, E. E., Ward, E. J., & Wills, K. (2012). MARSS: Multivariate autoregressive state-space models for analyzing time-series data. The R Journal, 4(1), 11–19.

    Google Scholar 

  • Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression. New York: Wiley.

    Google Scholar 

  • Hou, X., Li, R., He, W., & Ma, K. (2020). Effects of planting density on potato growth, yield, and water use efficiency during years with variable rainfall on the Loess Plateau China. Agricultural Water Management, 230, 105982.

    Google Scholar 

  • Huo, Y., Xin, L., Kang, C., Wang, M., Ma, Q., & Yu, B. (2019). SGL-SVM: A novel method for tumor classification via support vector machine with sparse group Lasso. Journal of Theoretical Biology, 56, 110098.

    Google Scholar 

  • Isaac, N. J., Jarzyna, M. A., Keil, P., Dambly, L. I., Boersch-Supan, P. H., Browning, E., & Jarvis, S. (2019). Data integration for large-scale models of species distributions. Trends in Ecology and Evolution, 35(1), 56–67.

    Google Scholar 

  • James, G. M., & Hastie, T. J. (2001). Functional linear discriminant analysis for irregularly sampled curves. Journal of the Royal Statistical Society: Series B (methodology), 63(3), 533–550.

    Google Scholar 

  • Jamshidi-Kia, F., Lorigooini, Z., & Amini-Khoei, H. (2018). Medicinal plants: Past history and future perspective. Journal of Herbmed Pharmacology, 7(1), 1–7.

    Google Scholar 

  • Jiménez, M. N., Navarro, F. B., Sánchez-Miranda, A., & Ripoll, M. A. (2019). Using stem diameter variations to detect and quantify growth and relationships with climatic variables on a gradient of thinned Aleppo pines. Forest Ecology and Management, 442, 53–62.

    Google Scholar 

  • Ju, J., Kolaczyk, E. D., & Gopal, S. (2003). Gaussian mixture discriminant analysis and sub-pixel land cover characterization in remote sensing. Remote Sensing of Environment, 84(4), 550–560.

    Google Scholar 

  • Kaky, E., Nolan, V., Alatawi, A., & Gilbert, F. (2020). A comparison between ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants. Ecological Informatics, 60, 101150.

    Google Scholar 

  • Karimian, V., Vahabi, M. R., Roustakhiz, J., & Nodehi, N. (2017). Identification of some ecological factors affecting essential oil of Verbascum songaricum Schrenk Shoots (Case study: Rangelands of Isfahan and Kohgiluyeh and Buyerahmad Provinces, Iran). Journal of Rangeland Science, 7(2), 183–194.

    Google Scholar 

  • Kent, M. (2011). Vegetation description and data analysis: a practical approach (2nd ed.). Boston: Wiley Blackwell.

    Google Scholar 

  • Kenyhercz, M. W., & Berg, G. E. (2018). Evaluating mixture discriminant analysis to classify human mandibles with (hu) MANid, a free, R-based GUI.in new perspectives in Forensic Human Skeletal Identification, 35–43

  • Khanum, R., Mumtaz, A. S., & Kumar, S. (2013). Predicting impacts of climate change on medicinal asclepiads of Pakistan using Maxent modeling. Acta Oecologica, 49, 23–31.

    Google Scholar 

  • Khwarahm, N. R., Ararat, K., Qader, S., & Sabir, D. K. (2021). Modeling the distribution of the Near Eastern fire salamander (Salamandra infraimmaculata) and Kurdistan newt (Neurergus derjugini) under current and future climate conditions in Iraq. Ecological Informatics, 63, 101309.

    Google Scholar 

  • Kim, W. S., Song, H. Y., Han, J. M., & Byun, E. B. (2019). GLM, a novel luteolin derivative, attenuates inflammatory responses in dendritic cells: Therapeutic potential against ulcerative colitis. Biochemical and Biophysical Research Communications, 518(1), 87–93.

    Google Scholar 

  • Komori, O., Eguchi, S., Saigusa, Y., Kusumoto, B., & Kubota, Y. (2020). Sampling bias correction in species distribution models by quasi-linear Poisson point process. Ecological Informatics, 55, 101015.

    Google Scholar 

  • Krzemień, A. (2019). Fire risk prevention in underground coal gasification (UCG) within active mines: Temperature forecast by means of MARS models. Energy, 170, 777–790.

    Google Scholar 

  • Labarrere, C. A., Woods, J. R., Hardin, J. W., Campana, G. L., Ortiz, M. A., Jaeger, B. R., & Pitts, D. E. (2011). Early prediction of cardiac allograft vasculopathy and heart transplant failure. American Journal of Transplantation, 11(3), 528–535.

    Google Scholar 

  • Lai, W., & Khan, A. A. (2012). Modeling dam-break flood over natural rivers using discontinuous Galerkin method. Journal of Hydrodynamics, 24(4), 467–478.

    Google Scholar 

  • Lee, S., Hwang, J., & Park, I. (2013). Application of data-driven evidential belief functions to landslide susceptibility mapping in Jinbu, Korea. CATENA, 100, 15–30.

    Google Scholar 

  • Leuenberger, M., Kanevski, M., & Orozco, C.D.V. (2013). Forest fires in a random forest. Austria: EGU General Assembly.

  • Li, D. H., Chen, W., Li, S., & Lou, S. (2019). Estimation of hourly global solar radiation using multivariate adaptive regression spline (MARS)–A case study of Hong Kong. Energy, 186, 115857.

    Google Scholar 

  • Li, H., Zhang, C., Zhang, S., & Atkinson, P. M. (2020). Crop classification from full-year fully polarimetric L-band UAVSAR time-series using the random forest algorithm. International Journal of Applied Earth Observation, 87, 102032.

    Google Scholar 

  • Li, J., Fan, G., & He, Y. (2020). Predicting the current and future distribution of three Coptis herbs in China under climate change conditions, using the MaxEnt model and chemical analysis. Science of the Total Environment, 698, 134141.

    Google Scholar 

  • Liu, X. W., & Lu, D. G. (2018). Survival analysis of fatigue data: Application of generalized linear models and hierarchical Bayesian model. International Journal of Fatigue, 117, 39–46.

    Google Scholar 

  • Liu, Y., Li, N., Zhang, Z., Huang, C., Chen, X., & Wang, F. (2020). The central trend in crop yields under climate change in China: A systematic review. Science of the Total Environment, 704, 135355.

    Google Scholar 

  • Long, J. T., Neogi, S., Caldwell, C. M., & DeLange, M. P. (2018). Variation inflation factorbased regression modeling of anthropometric measures and temporal-spatial performance: Modeling approach and implications for clinical utility. Clinical Biomechanics, 51, 51–57.

    Google Scholar 

  • Lu, L., Xing, D., & Ren, N. (2012). Pyrosequencing reveals highly diverse microbial communities in microbial electrolysis cells involved in enhanced H2 production from waste activated sludge. Water Research, 46(7), 2425–2434.

    Google Scholar 

  • Luoto, M., & Hjort, J. (2008). Downscaling of course grained geomorphological data earth surface processes and landforms. The Journal of the British Geomorphological Research Group, 33(1), 75–89.

    Google Scholar 

  • Marmion, M., Hjort, J., Thuiller, W., & Luoto, M. (2008). A comparison of predictive methods in modelling the distribution of periglacial landforms in Finnish Lapland. Earth Surface Processes and Landforms, 33(14), 2241–2254.

    Google Scholar 

  • Marmion, M., Hjort, J., Thuiller, W., & Luoto, M. (2009). Statistical consensus methods for improving predictive geomorphology maps. Computers & Geosciences, 35(3), 615625.

    Google Scholar 

  • Mazel, F., Guilhaumon, F., Mouquet, N., Devictor, V., Gravel, D., Renaud, J., & Thuiller, W. (2014). Multifaceted diversity–area relationships reveal global hotspots of mammalian species, trait and lineage diversity. Global Ecology and Biogeography, 23(8), 836847.

    Google Scholar 

  • Mendes, P., Velazco, S. J. E., de Andrade, A. F. A., & Júnior, P. D. M. (2020). Dealing with overprediction in species distribution models: How adding distance constraints can improve model accuracy. Ecological Modelling, 431, 109180.

    Google Scholar 

  • Méndez-Vázquez, L. J., Lira-Noriega, A., Lasa-Covarrubias, R., & Cerdeira-Estrada, S. (2019). Delineation of site-specific management zones for pest control purposes: Exploring precision agriculture and species distribution modeling approaches. Computers and Electronics in Agriculture, 167, 105101.

    Google Scholar 

  • Merow, C., Smith, M. J., & Silander, J. J. A. (2013). A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography, 36(10), 1058–1069.

    Google Scholar 

  • Micheletti, N., Foresti, L., Kanevski, M., Pedrazzini, A., & Jaboyedoff, M. (2011). Landslide susceptibility mapping using adaptive support vector machines and feature selection (Master Thesis submitted to University of Lausanne Faculty of Geosciences and Environment for the Degree of Master of Science in Environmental Geosciences, 99p.

  • Miles, J. (2014). Tolerance and variance inflation factor. Wiley StatsRef: Statistics Reference Online

  • Mohabatkar, H., Ebrahimi, S., & Moradi, M. (2021). Using Chou’s five-steps rule to classify and predict glutathione S-Transferases with different machine learning algorithms and pseudo amino acid composition. International Journal of Peptide Research and Therapeutics, 27(1), 309–316.

    Google Scholar 

  • Mohammadzadeh, M. J., Emam, J. Z., Safari, M., Mousavi, M., Ghanbarzadeh, B., & Philips, G. O. (2007). Physicochemical and emulsifying properties of Barijeh (Ferula gumosa) Gum. Iranian Journal of Chemistry & Chemical Engineering-International English Edition, 26(3), 81–88.

    Google Scholar 

  • Morris, K., & McNicholas, P. D. (2016). Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures. Computational Statistics and Data Analysis, 97, 133–150.

    Google Scholar 

  • Muñoz, J. D., Steibel, J. P., Snapp, S., & Kravchenko, A. N. (2014). Cover crop effect on corn growth and yield as influenced by topography. Agriculture, Ecosystems & Environment, 189, 229–239.

    Google Scholar 

  • Nadjafi, F., Bannayan, M., Tabrizi, L., & Rastgoo, M. (2006). Seed germination and dormancy breaking techniques for Ferula gummosa and Teucrium polium. Journal of Arid Environments, 64(3), 542–547.

    Google Scholar 

  • Najafi, ASl. Z. (2018). The industrial-therapeutic impact of ferula in sustainable development: A case study in Lezoor Village (Firuzkuh, Iran). International Journal of Ayurveda Research, 9(2), 92–98.

    Google Scholar 

  • Nakatsuka, H., Karasawa, T., Ohkura, T., & Wagai, R. (2020). Soil faunal effect on plant litter decomposition in mineral soil examined by two in-situ approaches: Sequential density-size fractionation and micromorphology. Geoderma, 357, 113910.

    Google Scholar 

  • Nan, W., Liu, S., Yang, S., Dong, Z., Yang, J., & Shi, W. (2020). Changes of Sabina vulgaris growth and of soil moisture in natural stands and plantations in semi-arid northern China. Global Ecology and Conservation, 21, e00859.

    Google Scholar 

  • Nazari, M., Sadeghi, S. M. M., Van Stan, I. I., & Chaichi, M. R. (2020). Rainfall interception and redistribution by maize farmland in central Iran. Journal of Hydrology: Regional Studies, 27, 100656.

    Google Scholar 

  • Nefeslioglu, H. A., Sezer, E., Gokceoglu, C., Bozkir, A. S., & Duman, T. Y. (2010). Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul. Turkey. Mathematical Problems in Engineering, 2010, 901095.

    Google Scholar 

  • Nettel-Aguirre, A. (2008). Nuclei shape analysis, a statistical approach. Image Analysis & Stereology, 27(1), 1–10.

    Google Scholar 

  • Newbold, T., Hudson, L. N., Hill, S. L., Contu, S., Lysenko, I., Senior, R. A., & Day, J. (2015). Global effects of land use on local terrestrial biodiversity. Nature, 520(7545), 45.

    Google Scholar 

  • Novaković, J. D., Veljović, A., Ilić, S. S., Papić, Ž, & Milica, T. (2017). Evaluation of classification models in machine learning. Theory and Applications of Mathematics & Computer Science, 7(1), 39–46.

    Google Scholar 

  • O’Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673–690.

    Google Scholar 

  • Oliveira, S., Oehler, F., San-Miguel-Ayanz, J., Camia, A., & Pereira, J. M. (2012). Modeling spatial patterns of fire occurrence in mediterranean Europe using multiple regression and random forest. Forest Ecology and Management, 275, 117–129.

    Google Scholar 

  • Ondier, J. O., Okach, D. O., Onyango, J. C., & Otieno, & D.O. . (2019). Interactive influence of rainfall manipulation and livestock grazing on species diversity of the herbaceous layer community in a humid savannah in Kenya. Plant Diversity, 41(3), 198–205.

    Google Scholar 

  • Ozdemir, A., & Altural, T. (2013). A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. Journal of Asian Earth Sciences, 64, 180–197.

    Google Scholar 

  • Payne, R., Harding, S.A., Murray, D.A., Souta, D.M., Baird, D.B., Glaser, A.I., & Webster, R. (2012). A guide to regression, nonlinear and generalized linear models in GenStat. VSN International: Hemel Hempstead, UK.

  • Pereira, H. M., Ferrier, S., Walters, M., Geller, G. N., Jongman, R. H. G., Scholes, R. J., & Coops, N. C. (2013). Essential biodiversity variables. Science, 339(6117), 277–278.

    Google Scholar 

  • Pertille, R. H., Sachet, M. R., Guerrezi, M. T., & Citadin, I. (2019). An R package to quantify different chilling and heat models for temperate fruit trees. Computers and Electronics in Agriculture, 167, 105067.

    Google Scholar 

  • Pouteau, R., Meyer, J. Y., Taputuarai, R., & Stoll, B. (2012). Support vector machines to map rare and endangered native plants in Pacific islands forests. Ecological Informatics, 9, 37–46.

    Google Scholar 

  • Rahimian Boogar, A., Salehi, H., Pourghasemi, H. R., & Blaschke, T. (2019). Predicting habitat suitability and conserving Juniperus spp. habitat using SVM and maximum entropy machine learning techniques. Water, 11(10), 2049.

    Google Scholar 

  • Ramsay, R. R., Popovic-Nikolic, M. R., Nikolic, K., Uliassi, E., & Laura Bolognesi, M. (2018). A perspective on multi-target drug discovery and design for complex diseases. Clinical and Translational Medicine, 7, 1–14.

    Google Scholar 

  • Ramsay, J. O., & Silverman, B. W. (2007). Applied functional data analysis: Methods and case studies. Springer.

    Google Scholar 

  • Rausch, J. R., & Kelley, K. (2009). A comparison of linear and mixture models for discriminant analysis under nonnormality. Behavior Research Methods, 41(1), 85–98.

    Google Scholar 

  • Ravindra, K., Rattan, P., Mor, S., & Aggarwal, A. N. (2019). Generalized additive models: Building evidence of air pollution, climate change and human health. Environment International, 132, 104987.

    Google Scholar 

  • Razzaghi-Abyaneh, M., Shams-Ghahfarokhi, M., & Rai, M. (2013). Antifungal plants of Iran: An insight into ecology, chemistry, and molecular biology Antifungal metabolites from plants (pp. 27–57). Springer.

    Google Scholar 

  • Reineking, B., & Schröder, B. (2006). Constrain to perform: Regularization of habitat models. Ecological Modelling, 193, 675–690.

    Google Scholar 

  • Remya, K., Ramachandran, A., & Jayakumar, S. (2015). Predicting the current and future suitable habitat distribution of Myristica dactyloides Gaertn. Using MaxEnt model in the Eastern Ghats India. Ecological Engineering, 82, 184–188.

    Google Scholar 

  • Rengstorf, A. M., Grehan, A., Yesson, C., & Brown, C. (2012). Towards high-resolution habitat suitability modeling of vulnerable marine ecosystems in the deep-sea: Resolving terrain attribute dependencies. Marine Geodesy, 35(4), 343–361.

    Google Scholar 

  • Ripley, B. (2002). Modern applied statistics with S 4ed. New York: Springer-Verlag.

    Google Scholar 

  • Rodríguez-Garrido, B., Balseiro-Romero, M., Kidd, P. S., & Monterroso, C. (2020). Effect of plant root exudates on the desorption of hexachlorocyclohexane isomers from contaminated soils. Chemosphere, 241, 124920.

    Google Scholar 

  • Rösch, P., Harz, M., Peschke, K. D., Ronneberger, O., Burkhardt, H., Schüle, A., & Motzkus, H. W. (2006). On-line monitoring and identification of bioaerosols. Analytical Chemistry, 78(7), 2163–2170.

    Google Scholar 

  • Rossi, M., & Reichenbach, P. (2016). LAND-SE: A software for statistically based landslide susceptibility zonation, version 1.0. Geoscientific Model Development, 9, 3533–3543.

    Google Scholar 

  • Rotllan-Puig, X., & Traveset, A. (2021). Determining the minimal background area for species distribution models: MinBAR package. Ecological Modelling, 439, 109353.

    Google Scholar 

  • Rupprecht, F., Oldeland, J., & Finckh, M. (2011). Modelling potential distribution of the threatened tree species Juniperus oxycedrus: How to evaluate the predictions of different modelling approaches? Journal of Vegetation Science, 22(4), 647–659.

    Google Scholar 

  • Sadraei, H., Asghari, G. R., Hajhashemi, V., Kolagar, A., & Ebrahimi, M. (2001). Spasmolytic activity of essential oil and various extracts of Ferula gummosa Boiss on ileum contractions. Phytomedicine, 8(5), 370–376.

    Google Scholar 

  • Saha, A., & Basak, B. B. (2019). Scope of value addition and utilization of residual biomass from medicinal and aromatic plants. Industrial Crops and Products, 145, 111979.

    Google Scholar 

  • Salazar, F., Toledo, M. Á., Oñate, E., & Suárez, B. (2016). Interpretation of dam deformation and leakage with boosted regression trees. Engineering Structures, 119, 230–251.

    Google Scholar 

  • Sayyah, M., Mandgary, A., & Kamalinejad, M. (2002). Evaluation of the anticonvulsant activity of the seed acetone extract of Ferula gummosa Boiss. Against seizures induced by pentylenetetrazole and electroconvulsive shock in mice. Journal of Ethnopharmacology, 82(2–3), 105–109.

    Google Scholar 

  • Scherrer, D., D’Amen, M., Fernandes, R. F., Mateo, R. G., & Guisan, A. (2018). How to best threshold and validate stacked species assemblages? Community optimization might hold the answer. Methods in Ecology and Evolution, 9(10), 2155–2166.

    Google Scholar 

  • Schmid, U., Roesch, P., Krause, M., Harz, M., Popp, J., & Baumann, K. (2009). Gaussian mixture discriminant analysis for the single-cell differentiation of bacteria using micro-Raman spectroscopy. Chemometrics and Intelligent Laboratory, 96(2), 159–171.

    Google Scholar 

  • Schmitt, S., Pouteau, R., Justeau, D., de Boissieu, F., & Birnbaum, P. (2017). SSDM: An r package to predict distribution of species richness and composition based on stacked species distribution models. Methods in Ecology and Evolution, 8(12), 1795–1803.

    Google Scholar 

  • Serrano, N. B., Sánchez, A. S., Lasheras, F. S., Iglesias-Rodríguez, F. J., & Valverde, G. F. (2020). Identification of gender differences in the factors influencing shoulders, neck and upper limb MSD by means of multivariate adaptive regression splines (MARS). Applied Ergonomics, 82, 102981.

    Google Scholar 

  • Sharma, S. K., Misra, S. K., & Singh, J. B. (2019). The role of GIS-enabled mobile applications in disaster management: A case analysis of cyclone Gaja in India. International Journal of Information Management, 51, 102030.

    Google Scholar 

  • Shataee, S. H., Weinaker, H., & Babanejad, M. (2011). Plot-level forest volume estimation using airborne laser scanner and TM Data, comparison of boosting and random forest tree regression algorithms. Environmental Sciences Proceedings, 7, 68–73.

    Google Scholar 

  • Somodi, I., Lepesi, N., & Botta-Dukat, Z. (2017). Prevalence dependence in model goodness measures with special emphasis on true skill statistics. Ecology and Evolution, 7, 863–872.

    Google Scholar 

  • Srinet, R., Nandy, S., & Patel, N. R. (2019). Estimating leaf area index and light extinction coefficient using Random Forest regression algorithm in a tropical moist deciduous forest, India. Ecological Informatics, 52, 94–102.

    Google Scholar 

  • Sterlacchini, S., Ballabio, C., Blahut, J., Masetti, M., & Sorichetta, A. (2011). Spatial agreement of predicted patterns in landslide susceptibility maps. Geomorphology, 125(1), 51–61.

    Google Scholar 

  • Stevović, S., & Nestorović, Ž. (2016). Impact of environment GIS modeling on sustainable water systems management. Procedia Engineering, 162, 293–300.

    Google Scholar 

  • Subasi, A., Jukic, S., & Kevric, J. (2019). Comparison of EMD, DWT and WPD for the localization of epileptogenic foci using random forest classifier. Measurement, 146, 846–855.

    Google Scholar 

  • Syfert, M. M., Smith, M. J., & Coomes, D. A. (2013). The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models. PloS one, 8(2), e55158.

    Google Scholar 

  • Tedesco, P. A., Beauchard, O., Bigorne, R., Blanchet, S., Buisson, L., Conti, L., & Jézéquel, C. (2017). A global database on freshwater fish species occurrence in drainage basins. Scientific Data, 4, 170141.

    Google Scholar 

  • Tessarolo, G., Lobo, J. M., Rangel, T. F., & Hortal, J. (2021). High uncertainty in the effects of data characteristics on the performance of species distribution models. Ecological Indicators, 121, 107147.

    Google Scholar 

  • Thuiller, W., Araújo, M. B., & Lavorel, S. (2003). Generalized models vs classification tree analysis: predicting spatial distributions of plant species at different scales. Journal of Vegetation Science, 14(5), 669–680.

    Google Scholar 

  • Thuiller, W., & Münkemüller, T. (2010). Habitat suitability modeling Effects of climate change on birds (pp. 77–85). Oxford University Press.

    Google Scholar 

  • Trigila, A., Frattini, P., Casagli, N., Catani, F., Crosta, G., Esposito, C., Ladanza, C., Lagomarsino, D., Scarascia Mugnozza, G., Segoni, S., Spizzichino, D., Tofani, V., & Lari, S. (2013). Landslide susceptibility mapping at national scale: The Italian case study. Landslide Science and Practice, 1, 287–295.

    Google Scholar 

  • van den Burg, M. P., Van Belleghem, S. M., & Villanueva, C. N. D. J. (2020). The continuing march of Common Green Iguanas: arrival on mainland Asia. Journal for Nature Conservation, 57, 125888.

    Google Scholar 

  • Vanam, M. K., Jiwani, B. A., Swathi, A., & Madhavi, V. (2021). High performance machine learning and data sciencebased implementation using Weka. Materials Today: Proceedings.

  • Velazco, S. J. E., Ribeiro, B. R., Laureto, L. M. O., & Júnior, P. D. M. (2020). Overprediction of species distribution models in conservation planning: A still neglected issue with strong effects. Biological Conservation, 252, 108822.

    Google Scholar 

  • Vilar, L., Woolford, D. G., Martell, D. L., & Martín, M. P. (2010). A model for predicting human-caused wildfire occurrence in the region of Madrid Spain. International Journal of Wildland Fire, 19(3), 325–337.

    Google Scholar 

  • Vorpahl, P., Elsenbeer, H., Märker, M., & Schröder, B. (2012). How can statistical models help to determine driving factors of landslides? Ecological Modelling, 239, 27–39.

    Google Scholar 

  • Vu, D. H., Muttaqi, K. M., & Agalgaonkar, A. P. (2015). A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables. Applied Energy, 140, 385–394.

    Google Scholar 

  • Wang, G., Huang, Y., Wei, Y., Zhang, W., Li, T., & Zhang, Q. (2019). Inner Mongolian grassland plant phenological changes and their climatic drivers. Science of the Total Environment, 683, 1–8.

    Google Scholar 

  • Wang, G., Zhong, L., Zhou, S., Liu, Q., Li, Q., Fu, Q., & Li, X. (2018). Jet breaking tools for natural gas hydrate exploitation and their support technologies. Natural Gas Industry, 5(4), 312–318.

    Google Scholar 

  • Wang, L., Sawada, K., & Moriguchi, S. (2011). Landslide susceptibility mapping by using logistic regression model with neighborhood analysis: A case study in Mizunami City. International Journal of Geomate, 1, 99–104.

    Google Scholar 

  • Wang, G., Wang, C., Guo, Z., Dai, L., Wu, Y., Liu, H., & Xue, F. (2020). Integrating Maxent model and landscape ecology theory for studying spatiotemporal dynamics of habitat: Suggestions for conservation of endangered Red-crowned crane. Ecological Indicators, 116, 106472.

    Google Scholar 

  • Wei, B., Wang, R., Hou, K., Wang, X., & Wu, W. (2018). Predicting the current and future cultivation regions of Carthamus tinctorius L using MaxEnt model under climate change in China. Global Ecology and Conservation, 16, e00477.

    Google Scholar 

  • Xie, W., Wei, W., & Cui, Q. (2019). The impacts of climate change on the yield of staple crops in Chinese: A meta-analysis. Chinese Journal of Population, Resources and Environment, 29(1), 79–85.

    Google Scholar 

  • Xing, J., Wang, H., Luo, K., Wang, S., Bai, Y., & Fan, J. (2019). Predictive single-step kinetic model of biomass devolatilization for CFD applications: A comparison study of empirical correlations (EC), artificial neural networks (ANN) and random forest (RF). Renewable Energy, 136, 104–114.

    Google Scholar 

  • Xu, H., & Soares, C. G. (2020). Manoeuvring modelling of a containership in shallow water based on optimal truncated nonlinear kernel-based least square support vector machine and quantum-inspired evolutionary algorithm. Ocean Engineering, 195, 106676.

    Google Scholar 

  • Xu, D., Zhuo, Z., Wang, R., Ye, M., & Pu, B. (2019). Modeling the distribution of Zanthoxylum armatum in China with MaxEnt modeling. Global Ecology and Conservation, 19, e00691.

    Google Scholar 

  • Xu, Y., Zhou, Y., Sekula, P., & Ding, L. (2021). Machine learning in construction: From shallow to deep learning. Developments in the Built Environment, 56, 100045.

    Google Scholar 

  • Yang, J., Chen, W. Y., Fu, Y., Yang, T., Luo, X. D., Wang, Y. H., & Wang, Y. H. (2020). Medicinal and edible plants used by the Lhoba people in Medog County, Tibet China. Journal of Ethnopharmacology, 249, 112430.

    Google Scholar 

  • Yang, X. Q., Kushwaha, S. P. S., Saran, S., Xu, J., & Roy, P. S. (2013). Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Ecological Engineering, 51, 83–87.

    Google Scholar 

  • Yassin, N. I., Omran, S., El Houby, E. M., & Allam, H. (2018). Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review. Computer Methods and Programs in Biomedicine, 156, 25–45.

    Google Scholar 

  • Yi, Y. J., Cheng, X., Yang, Z. F., & Zhang, S. H. (2016). Maxent modeling for predicting the potential distribution of endangered medicinal plant (H. riparia Lour) in Yunnan China. Ecological Engineering, 92, 260–269.

    Google Scholar 

  • Youcefi, M. N., Bouhoun, M. D., Kemassi, A., & El-Hadj, M. D. O. (2019). Relationship between topography and the distribution of matorral plant species in the Saharan Atlas: Case of Djebel Amour Algeria. Acta Ecologica Sinica, 40(3), 237–246.

    Google Scholar 

  • Yuan, H. S., Wei, Y. L., & Wang, X. G. (2015). Maxent modeling for predicting the potential distribution of Sanghuang, an important group of medicinal fungi in China. Fungal Ecology, 17, 140–145.

    Google Scholar 

  • Zhang, X., Su, C., Liu, X., Liu, Z., Liang, X., Zhang, Y., & Feng, Y. (2020). Effect of plantgrowth-promoting rhizobacteria on phytoremediation efficiency of Scirpus triqueter in pyrene-Ni co-contaminated soils. Chemosphere, 241, 125027.

    Google Scholar 

  • Zhang, K., Yao, L., Meng, J., & Tao, J. (2018). Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Science of the Total Environment, 634, 1326–1334.

    Google Scholar 

  • Zhao, S., Zhang, S., Liu, J., Wang, H., Zhu, J., Li, D., & Zhao, R. (2021). Application of machine learning in intelligent fish aquaculture: A review. Aquaculture, 5, 736724.

    Google Scholar 

  • Zheng, G., Yang, P., Zhou, H., Zeng, C., Yang, X., He, X., & Yu, X. (2019). Evaluation of the earthquake induced uplift displacement of tunnels using multivariate adaptive regression splines. Computers and Geotechnics, 113, 103099.

    Google Scholar 

  • Zheng, L., & Yu, P. (2018). Biological relevance testing. Package BRT. https://cran.r-project.org/web/packages/brt/brt.pdf

  • Zou, M., Sun, C., Liang, S., Sun, Y., Li, D., Li, L., & Xia, W. (2019). Fisher discriminant analysis for classification of autism spectrum disorders based on folate-related metabolism markers. Journal of Nutritional Biochemistry, 64, 25–31.

    Google Scholar 

Download references

Acknowledgments

This research was supported by the College of Agriculture at Shiraz University (Grant No. 99GRC1M271143).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Majid Mohammady or Hamid Reza Pourghasemi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohammady, M., Pourghasemi, H.R., Yousefi, S. et al. Modeling and Prediction of Habitat Suitability for Ferula gummosa Medicinal Plant in a Mountainous Area. Nat Resour Res 30, 4861–4884 (2021). https://doi.org/10.1007/s11053-021-09940-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-021-09940-3

Keywords

Navigation