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Modelling Groundwater-dependent Vegetation Patterns Using Ensemble Learning : Volume 4, Issue 5 (02/10/2007)

By Peters, J.

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Book Id: WPLBN0003986923
Format Type: PDF Article :
File Size: Pages 31
Reproduction Date: 2015

Title: Modelling Groundwater-dependent Vegetation Patterns Using Ensemble Learning : Volume 4, Issue 5 (02/10/2007)  
Author: Peters, J.
Volume: Vol. 4, Issue 5
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2007
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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C. Verhoes, N. E., Baets, B. D., Samson, R., & Peters, J. (2007). Modelling Groundwater-dependent Vegetation Patterns Using Ensemble Learning : Volume 4, Issue 5 (02/10/2007). Retrieved from http://cn.ebooklibrary.org/


Description
Description: Department of Forest and Water Management, Ghent University, Coupure links 653, 9000 Gent, Belgium. Vegetation patterns arise from the interplay between intraspecific and interspecific biotic interactions and from different abiotic constraints and interacting driving forces and distributions. In this study, we constructed an ensemble learning model that, based on spatially distributed environmental variables, could model vegetation patterns at the local scale. The study site was an alluvial floodplain with marked hydrologic gradients on which different vegetation types developed. The model was evaluated on accuracy, and could be concluded to perform well. However, model accuracy was remarkably lower for boundary areas between two distinct vegetation types. Subsequent application of the model on a spatially independent data set showed a poor performance that could be linked with the niche concept to conclude that an empirical distribution model, which has been constructed on local observations, is incapable to be applied beyond these boundaries.

Summary
Modelling groundwater-dependent vegetation patterns using ensemble learning

Excerpt
Austin, M. P.: Spatial prediction of species distribution: an interface between ecological theory and statistical modelling, Ecol. Modell., 157(2–3), 101–118, 2002.; Baird, A. J. and Wilby, R. L. (Eds.): Eco-hydrology: Plants and Water in Terrestrial and Aquatic Environments, Routledge, London, 1999.; Bio, A. M. F., De Becker, P., De Bie, E., Huybrechts, W., and Wassen, M.: Prediction of plant species distribution in lowland river valleys in Belgium: modelling species response to site conditions, Biodiversity and Conservation, 11, 2189–2216, 2002.; Breiman, L.: Random forests, Machine Learning, 45, 5–32, 2001.; Breiman, L. and Cutler, A.: http://www.stat.berkeley.edu/-users/-Breiman/-RandomForests, 2005.; Breiman, L., Friedman, J. H., Olsehen, R. A., and Stone, C. J.: Classification and Regression Trees, Chapman and Hall, New York, 1984.; Cohen, J.: A coefficient of agreement for nominal scales, Educational and Psychological Measurement, 20, 37–46, 1960.; Dall'O', M., Kluge, W., and Bartels, F.: FEUWAnet: a multibox water level and lateral exchange model for riperian wetlands, J. Hydrol., 250, 40–62, 2001.; De Becker, P., Hermy, M., and Butaye, J.: Ecohydrological characterization of a groundwater-fed alluvial floodplane mire, Applied Vegetation Science, 2, 215–228, 1999.; De Becker, P. and Huybrechts, W.: De Doode Bemde – Ecohydrologische Atlas, Institute of Nature Conservation, Brussels, Belgium, 2000. (In Dutch); De Jongh, I. L. M., Verhoest, N. E. C., and De Troch, F. P.: Analysis of a 105-year time series of precipitation observed at Uccle, Belgium, Int. J. Climatol., 26, 2023–2039, 2006.; Edwards Jr., T. C., Cutler, D. R., Zimmerman, N. E., Geiser, L., Moisen, G. G.: Effect of sample survey design on the accuracy of classification tree models in species distribution models, Ecol. Modell., 199, 132–141, 2006.; Engel, V., Jobby, E. G., Steiglitz, M., Williams, M., and Jackson, R. B.: Hydrological consequences of Eucalyptus afforestation in the Argentine Pampas, Water Resour. Res., 41, W10409, doi:10.1029/2004WR003761, 2005.; Famiglietti, J. S., Rudnicki, J. W., and Rodell, M.: Variability in surface moisture content along a hillslope transect: Rattlesnake Hill, Texas, J. Hydrol., 210(1–4), 259–281, 1998.; Fawcett, T.: An introduction to ROC analysis, Pattern Recognition Lett., 27, 861–874, 2006.; Fisher, J. and Acreman, M. C.: Wetland nutrient removal: a review of the evidence, Hydrol. Earth Syst. Sci., 8(4), 673–685, 2004.; Franklin, J.: Predictive vegetation mapping: geographic modelling of bio-spatial patterns in relation with environmental gradients, Progress in Physical Geography, 19, 474–499, 1995.; Guisan, A. and Zimmerman, N. E.: Predictive habitat distribution models in ecology, Ecol. Modell., 135(2–3), 147–186, 2000.; Guisan, A. and Thuiller, W.: Predicting species distribution: offering more than simple habitat models, Ecology Lett., 8, 993–1009, 2005.; Hill, M. O.: TWINSPAN – a FORTRAN program for arranging multivariate data in an ordered two-way table by classification of the individuals and attributes, Cornell University, Ithaca, 1979.; Hill, A. R.: Nitrate removal in stream riparian zones, Journal of Environmental Quality, 25(4), 743–755, 1996.; Hosmer, D. W. and Lemeshow, S.: Applied Logistic Regression, 2nd ed., New York, Chichester, Wiley, 2000.; Hutchinson, G. E.: Concluding remarks, Cold Spring Harbor Symposia on Quantitative Biology, 22(2), 415–427, 1957.; Huybrechts, W. and De Becker, P.: De Snoekengracht - Ecohydrologische Atlas, Institute of Nature Conservation, Brussels, Belgium, 1999. (In Dutch); Jaccard, P.: The distribution of the flora of the alpine zone, New Phytologist, 11, 37–50, 1912.; Jongman, R. H. G., Ter Braak, C. J. F., and Tongeren, O. F. R. V. (Eds.): Data Analysis i

 
 



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