Modeling and spatial prediction of pre-settlement patterns of forest distribution using witness tree data

Stephen L. Rathbun, Bryan Black

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

At the time of European settlement, land surveys were conducted progressively westward throughout the United States. Outside of the original 13 colonies, surveys generally followed the Public Land Survey system in which trees, called witness trees, were regularly recorded at 1 mi by 1 mi grid intersections. This unintentional sampling provides insight into the composition and structure of pre-European settlement forests, which is used as baseline data to assess forest change following settlement. In this paper, a model for the Public Land Surveys of east central Alabama is developed. Assuming that the locations of trees of each species are realized from independent Poisson processes whose respective log intensities are linear functions of environmental covariates (i.e., elevation, landform, and physiographic province), the species observed at the survey grid intersections are independently sampled from a generalized logistic regression model. If all 68 species found in the survey were included, the model would be highly over-parameterized, so only the distribution of the most common taxon, pines, will be considered at this time. To assess the impact of environmental factors not included in the model, a hidden Gaussian random field shall be added as a random effect. A Markov Chain Monte Carlo algorithm is developed for Bayesian inference on model parameters, and for Bayes posterior prediction of the spatial distribution of pines in east central Alabama.

Original languageEnglish (US)
Pages (from-to)427-448
Number of pages22
JournalEnvironmental and Ecological Statistics
Volume13
Issue number4
DOIs
StatePublished - Dec 2006
Externally publishedYes

Keywords

  • Bayesian hierarchical spatial model
  • Conditional autoregressive model
  • MCMC algorithm
  • Poisson point process

ASJC Scopus subject areas

  • Statistics and Probability
  • General Environmental Science
  • Statistics, Probability and Uncertainty

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