Landscape Ecology

, Volume 20, Issue 6, pp 689–702

Modeling Landscape Vegetation Pattern in Response to Historic Land-use: A Hypothesis-driven Approach for the North Carolina Piedmont, USA

  • Kristin Taverna
  • Dean L. Urban
  • Robert I. McDonald
Research Article

Abstract

Current methods of vegetation analysis often assume species response to environmental gradients is homogeneously monotonic and unimodal. Such an approach can lead to unsatisfactory results, particularly when vegetation pattern is governed by compensatory relationships that yield similar outcomes for various environmental settings. In this paper we investigate the advantages of using classification tree models (CART) to test specific hypotheses of environmental variables effecting dominant vegetation pattern in the Piedmont. This method is free of distributional assumptions and is useful for data structures that contain non-linear relationships and higher-order interactions. We also compare the predictive accuracy of CART models with a parametric generalized linear model (GLM) to determine the relative strength of each predictive approach. For each method, hardwood and pine vegetation is modeled using explanatory topographic and edaphic variables selected based on historic reconstructions of patterns of land use. These include soil quality, potential soil moisture, topographic position, and slope angle. Predictive accuracy was assessed on independent validation data sets. The CART models produced the more accurate predictions, while also emphasizing alternative environmental settings for each vegetation type. For example, relic hardwood stands were found on steep slopes, highly plastic soils, or hydric bottomlands – alternatives not well captured by the homogeneous GLM. Our results illustrate the potential utility of this flexible modeling approach in capturing the heterogeneous patterns typical of many ecological datasets.

Keywords

CART Classification tree Generalized linear models GLM Logistic regression Vegetation modeling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ashe, W.W. 1897

    Forest of North Carolina

    Pinchot, G.Ashe, W.W. eds. Timber Trees and Forests of North CarolinaBulletin No. 6North Carolina Geological SurveyWinston, North Carolina, USA
    Google Scholar
  2. Bio, A.M.F., Alkemade, R., Barendregt, A. 1998Determining alternative models for vegetation response analysis: a non-parametric approachJ. Veget. Sci.9516Google Scholar
  3. Brady, N.C., Weil, R.R. 2002The Nature and Properties of Soils13Prentice HallNew Jersey, USAGoogle Scholar
  4. Braun, E.L. 1950Deciduous Forests of Eastern North AmericaHafner Publishing CompanyNew York, USAGoogle Scholar
  5. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J. 1984Classification and Regression Trees. The Wadsworth Statistics/Probability SeriesChapman & Hall Inc.New York, USAGoogle Scholar
  6. Brown, D.G. 1994Predicting vegetation types at treeline using topography and biophysical disturbance variablesJ. Veget. Sci.5641656Google Scholar
  7. Chambers, J.M., Hastie, T.J. 1992Statistical Models in S. Wadsworth & Brooks/ColePacific GroveCalifornia, USAGoogle Scholar
  8. Christensen, N.L., Peet, R.K. 1981

    Secondary forest succession on the North Carolina piedmont

    West, D.Shugart, H.Botkin, D. eds. Forest Succession: Concept and ApplicationsSpringer-VerlagNew York, USA230245
    Google Scholar
  9. Coile, T.S. 1948Relation of Soil Characteristics to Site Index of Loblolly and Shortleaf Pines in the Lower Piedmont Region of North CarolinaBulletin. No. 13Duke University School of ForestryDurham, North Carolina, USAGoogle Scholar
  10. Daniels, R.B., Buol, S.W., Kleiss, H.J., Ditzler, C.A. 1999Soil Systems in North CarolinaTechnical Bulletin 314North Carolina State UniversityRaleigh, North Carolina, USAGoogle Scholar
  11. De’Ath, G., Fabricius, K.E. 2000Classification and regression trees: a powerful yet simple technique for ecological data analysisEcology8131783192Google Scholar
  12. Draper, D. 1995Assessment and propagation of model uncertainty (with discussion)J. R. Soc. Ser. B574597Google Scholar
  13. Fielding, A.H., Bell, J.F. 1997A review of methods for the assessment of prediction errors in conservation presence/absence modelsEnviron. Conserv.243849CrossRefGoogle Scholar
  14. Franklin, J. 1995Predictive vegetation mapping: geographic modeling of biospatial patterns in relation to environmental gradientsProg. Phys. Geogr.19474499Google Scholar
  15. Franklin, J. 1998Predicting the distribution of shrub species in southern California from climate and terrain-derived variablesJ. Veget. Sci.9733748Google Scholar
  16. Guisan, A., Zimmerman, N.E. 2000Predictive habitat distribution models in ecologyEcol. Model.135147186CrossRefGoogle Scholar
  17. Hand, D.J. 1997Construction and Assessment of Classification RulesJohn Wiley & Sons LtdChichesterGoogle Scholar
  18. Healy, R.G. 1985Competition for Land in the American South: AgricultureHuman Settlementand the EnvironmentConservation FoundationWashington, DC, USAGoogle Scholar
  19. Hosmer, D.W., Lemeshow, S. 2000Applied Logistic RegressionWileyNew York, USAGoogle Scholar
  20. Kutner, M.H. 1996

    Logistic regression, Poisson regression and generalized linear models

    Neter, J.Kutner, M.H.Nachtseim, C.J.Wasserman, W. eds. Applied Linear Statistical Models, 4th edIrwin, Times Mirror Higher Education Group, Inc.Chicago, Illinois, USA
    Google Scholar
  21. Linnet, K., Brandt, E. 1986Assessing diagnostic tests once an optimal cutoff point has been selectedClin. Chem.3213411346PubMedGoogle Scholar
  22. Maunz, S.J. 2002Interactions of stream channel geometry, riparian species distribution and land cover in an urban watershedUniversity of North CarolinaChapel Hill, North Carolina, USA88MA thesisGoogle Scholar
  23. McDonald, R.I., Peet, R.K., Urban, D.L. 2002Environmental correlates of oak decline and red maple increase in the North Carolina PiedmontCastanea678495Google Scholar
  24. McNemar, Q. 1947Note on the sampling error of the difference between correlated proportions or percentagesPsychometrika12153157Google Scholar
  25. Moore, I.D., Gryson, R.B., Ladson, A.R. 1991aDigital terrain modeling: a review of hydrological, geomorphological, and biological applicationsHydrol. Processes5330Google Scholar
  26. Moore, I.D., Lee, B.G., Davey, S.M. 1991bA new method for predicting vegetation distributions using decision tree analysis in a geographic information systemEnviron. Manage.155971Google Scholar
  27. Morisette, J.T., Khorram, S., Mace, T. 1999Land-cover change detection enhanced with generalized linear modelsInt. J. Remote Sens.2027032721CrossRefGoogle Scholar
  28. National Soil Information System (NASIS). June 2003. Digital Soil Survey Area Attribute Tables, Electronic Media. Natural Resources Conservation ServiceU.S. Department of AgricultureOrangeDurham and Wake Counties, North Carolina, USA.Google Scholar
  29. North Carolina Climate Office. 2003. North Carolina State University, Raleigh, North Carolina, USA.Google Scholar
  30. Oosting, H.J. 1942An ecological analysis of the plant communities of PiedmontNorth CarolinaAm. Midland Nat.281126Google Scholar
  31. Parker, A.J. 1982The topographic relative moisture index: an approach to soil-moisture assessment in mountain terrainPhys. Geogr.3160168Google Scholar
  32. Peet, R.K. 1992

    Community structure and ecosystem function

    Glenn-Lewin, D.C.Peet, R.K.Veblen, T.T. eds. Plant Succession: Theory and PredictionChapman and HallLondon102151
    Google Scholar
  33. Peet, R.K., Christensen, N.L. 1980Hardwood forest vegetation of the North Carolina Piedmont. Veröffentlichungen Geobotanik Institut ETHStiftung Rübel691439Google Scholar
  34. Schneider, L.C., Pontius, R.G. 2001Modeling land-use change in the Ipswich watershedMassachusetts, USAAgric. Ecosyst. Environ.858394CrossRefGoogle Scholar
  35. Trimble, S.W. 1974Man-induced Soil Erosion on the Southern Piedmont 1700–1970Soil Conservation Society of AmericaAnkeny, Iowa, USA180Google Scholar
  36. Urban, D.L. 2002

    Classification and regression trees

    McCune, B.Grace, J.B. eds. Analysis of Ecological CommunitiesMjM Software DesignGleneden Beach, Oregon, USA221231
    Google Scholar
  37. Urban, D., Goslee, S., Pierce, K., Lookingbill, T. 2002Extending community ecology to landscapesEcoscience9200212Google Scholar
  38. Vayssières, M.P., Plant, R.E., Allen-Diaz, B.H. 2000Classification trees: an alternative non-parametric approach for predicting species distributionsJ. Veget. Sci.11679694Google Scholar
  39. Wear, D.N., Bolstad, P. 1998Land-use changes in southern Appalachian landscapes: spatial analysis and forecast evaluationEcosystems1575594CrossRefGoogle Scholar
  40. White, P.S., White, R.D. 1996

    Old growth oak and oak-hickory forests

    Davis, M.B. eds. Eastern Old-Growth ForestsIsland PressWashington, DC, USA178198
    Google Scholar
  41. Wolock, D.M., McCabe, J. 1995Comparison of single and multiple flow direction algorithms for computing topographic parameters in TOPMODELWater Resour. Res.3113151324CrossRefGoogle Scholar
  42. Yee, T.W., Mitchell, N.D. 1991Generalized additive models in plant ecologyJ. Veget. Sci.2587602Google Scholar

Copyright information

© Springer 2005

Authors and Affiliations

  • Kristin Taverna
    • 1
    • 3
  • Dean L. Urban
    • 2
  • Robert I. McDonald
    • 2
  1. 1.Curriculum in EcologyUniversity of North CarolinaChapel HillUSA
  2. 2.Nicholas School of the Environment and Earth SciencesDuke UniversityDurhamUSA
  3. 3.Virginia Department of Conservation and RecreationDivision of Natural HeritageRichmondUSA

Personalised recommendations