Using topographic wetness index in vegetation ecology: does the algorithm matter

Questions: How important is the choice of flow routing algorithm with respect to application of topographic wetness index (TWI) in vegetation ecology? Which flow routing algorithms are preferable for application in vegetation ecology? Location: Forests in three different regions of the Czech Republi...

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Published inApplied vegetation science Vol. 13; no. 4; pp. 450 - 459
Main Authors Kopecky, Martin, Cizkova, Stepanka
Format Journal Article
LanguageEnglish
Published Oxford, UK Blackwell Publishing Ltd 01.10.2010
Blackwell Publishing, Ltd
Wiley Subscription Services, Inc
Subjects
Algorithms
botanical composition
Catchment area
Correlation coefficient
Czech Republic
Digital elevation model (DEM)
Digital elevation models
Ecological modeling
Ecology
Flow
Flow routing algorithm
flow routing algorithms
Forest communities
Forest ecology
Forest vegetation
geomorphology
Geomorphometry
Hydrological modeling
Landscape ecology
Moisture content
Network management systems
plant communities
plant ecology
Soil moisture
Soil water
soil water content
Species composition
species diversity
Temperate forest
temperate forests
Terrain analysis
Topographic parameters
topographic wetness index
Topography
Vegetation
Czech Republic
Online AccessGet full text
ISSN1402-2001
1654-109X
DOI10.1111/j.1654-109x.2010.01083.x

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Abstract Questions: How important is the choice of flow routing algorithm with respect to application of topographic wetness index (TWI) in vegetation ecology? Which flow routing algorithms are preferable for application in vegetation ecology? Location: Forests in three different regions of the Czech Republic. Methods: We used vegetation data from 521 georeferenced plots, recently sampled in a wide range of forest communities. From a digital elevation model, we calculated 11 variations of TWI for each plot with 11 different flow routing algorithms. We evaluated the performance of differently calculated TWI by (1) Spearman rank correlation with average Ellenberg indicator values for soil moisture, (2) Mantel correlation coefficient between dissimilarities of species composition and dissimilarities of TWI and (3) the amount of variation in species composition explained by canonical correspondence analysis. Results: The choice of flow routing algorithm had a considerable effect on the performance of TWI. Correlation with Ellenberg indicator values for soil moisture, Mantel correlation coefficient and explained variation doubled when the appropriate algorithm was used. In all regions, multiple flow routing algorithms performed best, while single flow routing algorithms performed worst. Conclusions: We recommend the multiple flow routing algorithms of Quinn et al. and Freeman for application in vegetation ecology.
AbstractList Questions: How important is the choice of flow routing algorithm with respect to application of topographic wetness index (TWI) in vegetation ecology? Which flow routing algorithms are preferable for application in vegetation ecology? Location: Forests in three different regions of the Czech Republic. Methods: We used vegetation data from 521 georeferenced plots, recently sampled in a wide range of forest communities. From a digital elevation model, we calculated 11 variations of TWI for each plot with 11 different flow routing algorithms. We evaluated the performance of differently calculated TWI by (1) Spearman rank correlation with average Ellenberg indicator values for soil moisture, (2) Mantel correlation coefficient between dissimilarities of species composition and dissimilarities of TWI and (3) the amount of variation in species composition explained by canonical correspondence analysis. Results: The choice of flow routing algorithm had a considerable effect on the performance of TWI. Correlation with Ellenberg indicator values for soil moisture, Mantel correlation coefficient and explained variation doubled when the appropriate algorithm was used. In all regions, multiple flow routing algorithms performed best, while single flow routing algorithms performed worst. Conclusions: We recommend the multiple flow routing algorithms of Quinn et al. and Freeman for application in vegetation ecology.
Questions: How important is the choice of flow routing algorithm with respect to application of topographic wetness index (TWI) in vegetation ecology? Which flow routing algorithms are preferable for application in vegetation ecology? Location: Forests in three different regions of the Czech Republic. Methods: We used vegetation data from 521 georeferenced plots, recently sampled in a wide range of forest communities. From a digital elevation model, we calculated 11 variations of TWI for each plot with 11 different flow routing algorithms. We evaluated the performance of differently calculated TWI by (1) Spearman rank correlation with average Ellenberg indicator values for soil moisture, (2) Mantel correlation coefficient between dissimilarities of species composition and dissimilarities of TWI and (3) the amount of variation in species composition explained by canonical correspondence analysis. Results: The choice of flow routing algorithm had a considerable effect on the performance of TWI. Correlation with Ellenberg indicator values for soil moisture, Mantel correlation coefficient and explained variation doubled when the appropriate algorithm was used. In all regions, multiple flow routing algorithms performed best, while single flow routing algorithms performed worst. Conclusions: We recommend the multiple flow routing algorithms of Quinn et al. and Freeman for application in vegetation ecology.
Questions: How important is the choice of flow routing algorithm with respect to application of topographic wetness index (TWI) in vegetation ecology? Which flow routing algorithms are preferable for application in vegetation ecology? Location: Forests in three different regions of the Czech Republic. Methods: We used vegetation data from 521 georeferenced plots, recently sampled in a wide range of forest communities. From a digital elevation model, we calculated 11 variations of TWI for each plot with 11 different flow routing algorithms. We evaluated the performance of differently calculated TWI by (1) Spearman rank correlation with average Ellenberg indicator values for soil moisture, (2) Mantel correlation coefficient between dissimilarities of species composition and dissimilarities of TWI and (3) the amount of variation in species composition explained by canonical correspondence analysis. Results: The choice of flow routing algorithm had a considerable effect on the performance of TWI. Correlation with Ellenberg indicator values for soil moisture, Mantel correlation coefficient and explained variation doubled when the appropriate algorithm was used. In all regions, multiple flow routing algorithms performed best, while single flow routing algorithms performed worst. Conclusions: We recommend the multiple flow routing algorithms of Quinn et al. and Freeman for application in vegetation ecology.
Author Čížková, Štěpánka
Kopecký, Martin
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Svenning, J.-C., Kinner, D.A., Stallard, R.F., Engelbrecht, B.M.J. & Wright, S.J. 2004. Ecological determinism in plant community structure across a tropical forest landscape. Ecology 85: 2526-2538.
Dyer, J.M. 2009. Assessing topographic patterns in moisture use and stress using a water balance approach. Landscape Ecology 24: 391-403.
Hutchinson, M.F. 1989. A new procedure for gridding elevation and stream line data with automatic removal of spurious pits. Journal of Hydrology 106: 211-232.
Moore, I.D., Grayson, R.B. & Ladson, A.R. 1991. Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrologic Processes 5: 3-30.
Holmgren, P. 1994. Multiple flow direction algorithms for runoff modelling in grid based elevation models: an empirical evaluation. Hydrological Processes 8: 327-334.
Cairns, D.M. 2001. A comparison of methods for predicting vegetation type. Plant Ecology 156: 3-18.
Costa-Cabral, M. & Burges, S.J. 1994. Digital elevation model networks (DEMON): a model of flow over hillslopes for computation of contributing and dispersal areas. Water Resources Research 30: 1681-1692.
Summerell, G.K., Dowling, T.I., Wild, J.A. & Beale, G. 2004. FLAG UPNESS and its application for mapping seasonally wet to waterlogged soils. Australian Journal of Soil Research 42: 155-162.
Taverna, K., Urban, D.L. & McDonald, R.I. 2005. Modeling landscape vegetation pattern in response to historic land-use: a hypothesis-driven approach for the North Carolina Piedmont, USA. Landscape Ecology 20: 689-702.
Bader, M.Y. & Ruijten, J.J.A. 2008. A topography-based model of forest cover at the alpine tree line in the tropical Andes. Journal of Biogeography 35: 711-723.
McCune, B. & Keon, D. 2002. Equations for potential annual direct incident radiation and heat load. Journal of Vegetation Science 13: 603-606.
Pierce, K.B., Lookingbill, T.R. & Urban, D.L. 2005. A simple method for estimating potential relative radiation (PRR) for landscape-scale vegetation analysis. Landscape Ecology 20: 137-147.
Samek, V. 1964. Lesní společenstva Českého Krasu (Forest communities of the Bohemian Karst). NakladatelstvíČeskoslovenské akademie věd, Praha, CZ (in Czech).
Wilson, J.P., Lam, C.S. & Deng, Y.X. 2007. Comparison of performance of flow-routing algorithms used in Geographic Information Systems. Hydrological Processes 21: 1026-1044.
Gallant, J.C. & Dowling, T.I. 2003. A multiresolution index of valley bottom flatness for mapping depositional areas. Water Resources Research 39: 1347-1360.
Zinko, U., Seibert, J., Dynesius, M. & Nilsson, C. 2005. Plant species numbers predicted by a topography based groundwater-flow index. Ecosystems 8: 430-441.
Johnson, C.E. & Barton, C.C. 2004. Where in the world are my field plots? Using GPS effectively in environmental field studies. Frontiers in Ecology and Environment 2: 475-482.
O'Callaghan, J.F. & Mark, D.M. 1984. The extraction of drainage networks from digital elevation data. Computer Vision, Graphic and Image Processing 28: 328-344.
Quinn, P., Beven, K., Chevallier, P. & Planchon, O. 1991. The prediction of hillslope flow paths for distributed hydrological modeling using digital terrain models. Hydrological Processes 5: 59-80.
Park, S.J., Rüecker, G.R., Agyare, W.R., Akramhanov, A., Kim, M. & Vlek, P.L.G. 2009. Influence of grid cell size and flow routing algorithm on soil-landform modelling. Journal of the Korean Geographical Society 44: 122-145.
Kopecký, M. & Vojta, J. 2009. Land use legacies in post-agricultural forests in the Doupovské Mountains, Czech Republic. Applied Vegetation Science 12: 251-260.
Freeman, G.T. 1991. Calculating catchment area with divergent flow based on a regular grid. Computers and Geosciences 17: 413-422.
Wise, S.M. 2007. Effect of differing DEM creation methods on the results from a hydrological model. Computers & Geosciences 33: 1351-1365.
Parolo, G., Rossi, G. & Ferrarini, A. 2008. Toward improved species niche modelling: Arnica montana in the Alps as a case study. Journal of Applied Ecology 45: 1410-1418.
Allen, R.B., Peet, R.K. & Baker, W.L. 1991. Gradient analysis of latitudinal variation in Southern Rocky Mountain forests. Journal of Biogeography 18: 123-139.
Sørensen, R., Zinko, U. & Seibert, J. 2006. On the calculation of the topographic wetness index: evaluation of different methods based on field observations. Hydrology and Earth System Sciences 10: 101-112.
Piedallu, C. & Gégout, J. 2008. Efficient assessment of topographic solar radiation to improve plant distribution models. Agriculture and Forest Meteorology 148: 1696-1706.
Beven, K.J. & Kirkby, M.J. 1979. A physically based, variable contributing area model of basin hydrology. Hydrologic Science Bulletin 24: 43-69.
Ellenberg, H., Weber, H.E., Düll, R., Wirth, V., Werner, W. & Paulissen, D. 1992. Zeigerwerte von Pflanzen in Mitteleuropa. Scripta Geobotanica 18: 1-258.
Grabs, T., Seibert, J., Bishop, K. & Laudon, H. 2009. Modeling spatial patterns of saturated areas: a comparison of the topographic wetness index and a dynamic distributed model. Journal of Hydrology 373: 15-23.
Loucks, O.L. 1962. Ordinating forest communities by means of environmental scalars and phytosociological indices. Ecological Monographs 32: 137-166.
Moody, A. & Meentemeyer, R.K. 2001. Environmental factors influencing spatial patterns of woody plant diversity in chaparral, Santa Ynez Mountains, California. Journal of Vegetation Science 12: 41-52.
Pan, F., Peters-Lidard, C.D., Sale, M.J. & King, A.W. 2004. A comparison of geographical information system-based algorithms for computing the TOPMODEL topographic index. Water Resources Research 40: 1-11.
Kubát, K., Hrouda, L., Chrtek, J. Jr., Kaplan, Z., Kirschner, J. & Stěpánek, J. (eds.), 2002. Key to the flora of the Czech Republic. Academia, Prague, CZ (in Czech).
Zhou, Q. & Liu, X. 2002. Error assessment of grid-based flow routing algorithms used in hydrological models. International Journal of Geographical Information Science 16: 819-842.
Parviainen, M., Luoto, M., Ryttari, T. & Heikkinen, R.K. 2008. Modelling the occurrence of threatened plant species in taiga landscapes: methodological and ecological perspectives. Journal of Biogeography 35: 1888-1905.
Zevenbergen, L.W. & Thorne, C.R. 1987. Quantitative analysis of land surface topography. Earth Surface Processes and Landforms 12: 47-56.
Dirnböck, T., Hobbs, R.J., Lambeck, R.J. & Caccetta, P.A. 2002. Vegetation distribution in relation to topographically driven processes in southwestern Australia. Applied Vegetation Science 5: 147-158.
Iverson, L.R., Dale, M.E., Scott, C.T. & Prasad, A. 1997. A GIS-derived integrated moisture index to predict forest composition and productivity of Ohio forests (U.S.A.). Landscape Ecology 12: 331-348.
Lookingbill, T.R. & Urban, D.L. 2004. An empirical approach towards improved spatial estimates of soil moisture for vegetation analysis. Landscape Ecology 19: 417-433.
Evans, J.S. & Cushman, S.A. 2009. Gradient modeling of conifer species using random forests. Landscape Ecology 24: 673-683.
Parker, A.J. 1982. The topographic relative moisture index: an approach to soil moisture assessment in mountain terrain. Physical Geography 3: 160-168.
Bunn, A.G., Waggoner, L.A. & Graumlich, L.J. 2005. Topographic mediation of growth in high elevation foxtail pine (Pinus balfournia Grev. et Balf.) forests in the Sierra Nevada, USA. Global Ecology and Biogeography 14: 103-114.
Erskine, R.H., Green, T.R., Ramirez, J.A. & MacDonald, L.H. 2006. Comparison of grid-based algorithms for computing upslope contributing area. Water Resources Research 42: W09416.
Van Niel, K.P., Laffan, S.W. & Lees, B.G. 2004. Effect of error in the DEM on environmental variables for predictive vegetation modelling. Journal of Vegetation Science 15: 747-756.
Endreny, T.A. & Wood, E.F. 2003. Maximizing spatial congruence of observed and DEM-delineated overland flow networks. International Journal of Geographical Information Science 17: 699-713.
Fairfield, J. & Leymarie, P. 1991. Drainage networks from grid digital elevation models. Water Resources Research 27: 709-717.
McDonald, R.I. & Urban, D.L. 2004. Forest edges and tree growth rates in the North Carolina Piedmont. Ecology 85: 2258-2266.
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– reference: Grabs, T., Seibert, J., Bishop, K. & Laudon, H. 2009. Modeling spatial patterns of saturated areas: a comparison of the topographic wetness index and a dynamic distributed model. Journal of Hydrology 373: 15-23.
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Snippet Questions: How important is the choice of flow routing algorithm with respect to application of topographic wetness index (TWI) in vegetation ecology? Which...
Questions: How important is the choice of flow routing algorithm with respect to application of topographic wetness index (TWI) in vegetation ecology? Which...
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SubjectTerms Algorithms
botanical composition
Catchment area
Correlation coefficient
Czech Republic
Digital elevation model (DEM)
Digital elevation models
Ecological modeling
Ecology
Flow
Flow routing algorithm
flow routing algorithms
Forest communities
Forest ecology
Forest vegetation
geomorphology
Geomorphometry
Hydrological modeling
Landscape ecology
Moisture content
Network management systems
plant communities
plant ecology
Soil moisture
Soil water
soil water content
Species composition
species diversity
Temperate forest
temperate forests
Terrain analysis
Topographic parameters
topographic wetness index
Topography
Vegetation
Title Using topographic wetness index in vegetation ecology: does the algorithm matter
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