Modeling change in forest biomass across the eastern US

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• 作者：Erin M. Schliep ; Alan E. Gelfand ; James S. Clark…
• 关键词：Allometric equations ; Bayesian hierarchical model ; Cumulative uncertainty ; Forest biomass
• 刊名：Environmental and Ecological Statistics
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：23
• 期：1
• 页码：23-41
• 全文大小：1,293 KB
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• 作者单位：Erin M. Schliep (1)
  Alan E. Gelfand (1)
  James S. Clark (1) (2)
  Kai Zhu (2) (3) (4)
  
  1. Department of Statistical Sciences, Duke University, Durham, NC, USA
  2. Nicholas School of the Environment, Duke University, Durham, NC, USA
  3. Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA
  4. Department of Biology, Stanford University, Stanford, CA, USA
  
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Life Sciences
  Ecology
  Statistics
  Mathematical Biology
  Evolutionary Biology
  
• 出版者：Springer Netherlands
• ISSN：1573-3009

文摘

Predictions of above-ground biomass and the change in above-ground biomass require attachment of uncertainty due the range of reported predictions for forests. Because above-ground biomass is seldom measured, there have been no opportunities to obtain such uncertainty estimates. Standard methods involve applying an allometric equation to each individual tree on sample plots and summing the individual values. There is uncertainty in the allometry which leads to uncertainty in biomass at the tree level. Due to interdependence between competing trees, the uncertainty at the plot level that results from aggregating individual tree biomass in this way is expected to overestimate variability. That is, the variance at the plot level should be less than the sum of the individual variances. We offer a modeling strategy to learn about change in biomass at the plot level and model cumulative uncertainty to accommodate this dependence among neighboring trees. The plot-level variance is modeled using a parametric density-dependent asymptotic function. Plot-by-time covariate information is introduced to explain the change in biomass. These features are incorporated into a hierarchical model and inference is obtain within a Bayesian framework. We analyze data for the eastern United States from the Forest Inventory and Analysis (FIA) Program of the US Forest Service. This region contains roughly 25,000 FIA monitored plots from which there are measurements of approximately 1 million trees spanning more than 200 tree species. Due to the high species richness in the FIA data, we combine species into plant functional types. We present predictions of biomass and change in biomass for two plant functional types. Keywords Allometric equations Bayesian hierarchical model Cumulative uncertainty Forest biomass