Plant Ecology

, Volume 191, Issue 1, pp 85–94 | Cite as

Mapping pre-European settlement vegetation at fine resolutions using a hierarchical Bayesian model and GIS

  • Hong S. He
  • Daniel C. Dey
  • Xiuli Fan
  • Mevin B. Hooten
  • John M. Kabrick
  • Christopher K. Wikle
  • Zhaofei Fan
Original Paper

Abstract

In the Midwestern United States, the General Land Office (GLO) survey records provide the only reasonably accurate data source of forest composition and tree species distribution at the time of pre-European settlement (circa late 1800 to early 1850). However, GLO data have two fundamental limitations: coarse spatial resolutions (the square mile section and half mile distance between quarter corner and section corner) and point data format, which are insufficient to describe vegetation that is continuously distributed over the landscape. Thus, geographic information system and statistical inference methods to map GLO data and reconstruct historical vegetation are needed. In this study, we applied a hierarchical Bayesian approach that combines species and environment relationships and explicit spatial dependence to map GLO data. We showed that the hierarchical Bayesian approach (1) is effective in predicting historical vegetation distribution, (2) is robust at multiple classification levels (species, genus, and functional groups), (3) can be used to derive vegetation patterns at fine resolutions (e.g., in this study, 120 m) when the corresponding environmental data exist, and (4) is applicable to relatively moderate map sizes (e.g., 792 × 763 pixels) due to the limitation of computational capacity. Our predictions of historical vegetation from this study provide a quantitative and spatial basis for restoration of natural floodplain vegetation. An important assumption in this approach is that the current environmental covariates can be used as surrogates for the historical environmental covariates, which are often not available. Our study showed that terrain and soil covariates least affected by past natural and anthropogenic alternations can be used as covariates for GLO vegetation mapping.

Keywords

GLO GIS Hierarchical Bayesian models Presettlement vegetation Missouri 

References

  1. Albert J, Chib S (1993) Bayesian analysis of binary and polychotomous response data. J Am Stat Assoc 88:669–679CrossRefGoogle Scholar
  2. Batek MJ, Rebertus AJ, Schroeder, WA, Haithcoat TL, Compas E, Guyette RP (1999) Reconstruction of early nineteenth century vegetation and fire regimes in the Missouri Ozarks. J Biogeogr 26:397–412CrossRefGoogle Scholar
  3. Black BA, Ruffner CM, Abrams MD (2006) Native American influences on the forest composition of the Allegheny Plateau, northwest Pennsylvania. Can J Forest Res 36:1266–1275CrossRefGoogle Scholar
  4. Bolliger J, Schulte LA, Burrows SN, Sickley TA, Mladenoff DJ (2004) Assessing ecological restoration potentials of Wisconsin (USA) using historical landscape reconstructions. Restor Ecol 12:124–142CrossRefGoogle Scholar
  5. Bolliger J, Mladenoff DJ (2005) Quantifying spatial classification uncertainties of the historical Wisconsin landscape (USA). Ecography 28:141–156CrossRefGoogle Scholar
  6. Bourdo EA (1956) A review of the General Land Office Survey and of its use in quantitative studies of former forests. Ecology 37:754–768CrossRefGoogle Scholar
  7. Bragg TB, Tatschl AK (1977) Changes in flood-plain vegetation and lands use along the Missouri River from 1826 to 1972. Environ Manage 4:348–353Google Scholar
  8. Brown DG (1998) Mapping historical forest types in Baraga County Michigan, USA as fuzzy sets. Plant Ecol 134:97–111CrossRefGoogle Scholar
  9. Burns RM, Honkala BH (1990) Silvics of North America, Vol. 2. Hardwoods. Agriculture handbook 654. US Department of Agriculture Forest Service, Washington, DC, 877 ppGoogle Scholar
  10. Dey D, Burhans D, Kabrick J, Root B, Grabner J, Gold M (2000) The Missouri River floodplain: history of oak forests and current restoration efforts. Glade 3:2–4Google Scholar
  11. Dyer JM (2001) Using witness trees to assess forest change in southeastern Ohio. Can J Forest Res 31:1708–1718CrossRefGoogle Scholar
  12. Franklin J (1995) Predictive vegetation mapping: geographic modeling of biospatial patterns in relation to environmental gradients. Progress Phys Geogr 19:474–499Google Scholar
  13. Gutzwiller KJ (ed) (2002) Applying landscape ecology in biological conservation. Springer-Verlag, New YorkGoogle Scholar
  14. He HS, Mladenoff DJ, Sickley T, Gutensburgen GG (2000) GIS interpolation of witness tree records (1839–1866) of northern Wisconsin. J Biogeogr 27:1031–1042CrossRefGoogle Scholar
  15. Hooten MB (2001) Modeling the distribution of ground flora on large spatial domains in the Missouri Ozarks. Master’s thesis, University of Missouri, Columbia, MO, USAGoogle Scholar
  16. Hooten MB, Larsen DR, Wikle CK (2003) Predicting the spatial distribution of ground flora on large domains using a hierarchical Bayesian model. Landscape Ecol 18:487–502CrossRefGoogle Scholar
  17. Hosmer DW, Lemeshow S (2000) Applied logistic regression. Wiley, New York, USAGoogle Scholar
  18. Lichstein J, Simmons T, Shriner S, Fanzreb K (2002) Spatial autocorrelation and autoregressive models in ecology. Ecol Monogr 72:446–463Google Scholar
  19. Manies KL, Mladenoff DJ (2000) Testing methods to produce landscape-scale presentment vegetation maps from the U. S. public land survey records. Landscape Ecol 15:741–754CrossRefGoogle Scholar
  20. Manies KL, Mladenoff DJ, Nordheim EV (2001) Surveyor bias in forest data of the U. S. General Land Office records for northern Wisconsin. Can J Forest Res 31:1719–1730CrossRefGoogle Scholar
  21. McDonald RI, Dean LU (2006) Spatially varying rules of landscape change: lessons from a case study. Landscape Urban Plan 74:7–20CrossRefGoogle Scholar
  22. Mladenoff DJ, Dahir SE, Nordheim EV, Schulte LA, Guntenspergen GG (2002) Narrowing historical uncertainty: probabilistic classification of ambiguously identified tree species in historical forest survey data. Ecosystems 5:539–553Google Scholar
  23. Nelson JC, (1997) Presettlement vegetation patterns along the 5th Principal Meridian, Missouri territory, 1815. Am Midland Nat 137:79–94CrossRefGoogle Scholar
  24. Nigh TA, Schroeder WA (2002) Atlas of Missouri ecoregions. Missouri Department of Conservation Publication, 212 ppGoogle Scholar
  25. Porter S (1998) Modeling historic woody vegetation in the lower Ozarks of Missouri. Master’s Thesis. University of Missouri, Columbia, MO, USAGoogle Scholar
  26. Pontius RG, Schneider LC (2001) Land-cover change model validation by an ROC method for the Ipswich Watershed, Massachusetts, USA. Agricult Ecosyst Environ 85:239–248CrossRefGoogle Scholar
  27. Royle JA, Wikle CK (2005) Efficient statistical mapping of avian count data. Ecol Environ Stat 12:225–243CrossRefGoogle Scholar
  28. Schulte LA, Mladenoff DJ, Burrows SN, Sickley TA, Nordheim EV (2005) Spatial controls of Pre-Euroamerican wind and fire in Wisconsin (USA) forests: a multiscale assessment. Ecosystems 8:73–94CrossRefGoogle Scholar
  29. USDA Natural Resources Conservation Service (1995) Soil Survey Geographic (SSURGO) Data Base, Data Use Information. USDA Natural Resources Conservation Service, Miscellaneous Publication 1527, 31 ppGoogle Scholar
  30. Wikle CK (2002) spatial modeling of count data: a case study in modeling breeding bird survey data on large spatial domains. In: Spatial cluster modelling. Chapman and Hall/CRC, London/Boca Raton, FL, pp 199–209Google Scholar
  31. Wikle CK (2003) Hierachical Baysian models for predicting the spread of ecological processes. Ecology 84:1382–1394CrossRefGoogle Scholar
  32. Zimmermann N, Kienast F (1999) Predictive mapping of alpine grasslands in Switzerland: species versus community approach. J Veget Sci 10:469–482CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Hong S. He
    • 1
  • Daniel C. Dey
    • 2
  • Xiuli Fan
    • 1
  • Mevin B. Hooten
    • 3
  • John M. Kabrick
    • 2
  • Christopher K. Wikle
    • 4
  • Zhaofei Fan
    • 1
  1. 1.School of Natural ResourcesUniversity of Missouri-ColumbiaColumbiaUSA
  2. 2.US Forest Service, North Central Research StationColumbiaUSA
  3. 3.Department of Mathematics and StatisticsUtah State UniversityLoganUSA
  4. 4.Department of StatisticsUniversity of Missouri-ColumbiaColumbiaUSA

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