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

Content Provider	Springer Nature Link
Author	Rathbun, Stephen L. Black, Bryan
Copyright Year	2006
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.
Starting Page	427
Ending Page	448
Page Count	22
File Format	PDF
ISSN	13528505
Journal	Environmental and Ecological Statistics
Volume Number	13
Issue Number	4
e-ISSN	15733009
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2006-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bayesian hierarchical spatial model Poisson point process Conditional autoregressive model MCMC algorithm Ecology Evolutionary Biology Mathematical Biology in General Statistics
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Environmental Science Statistics, Probability and Uncertainty

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