Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Data-intensive modeling of forest dynamics

Jean F. Liénard, Dominique Gravel, Nikolay S. Strigul
doi: https://doi.org/10.1101/005009
Jean F. Liénard
aDepartment of Mathematics, Washington State University Vancouver, Washington, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Dominique Gravel
bDépartement de Biologie, Universit du Québec à Rimouski, Québec, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nikolay S. Strigul
aDepartment of Mathematics, Washington State University Vancouver, Washington, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: nick.strigul@vancouver.wsu.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Forest dynamics are highly dimensional phenomena that are not fully understood theoretically. Forest inventory datasets offer unprecedented opportunities to model these dynamics, but they are analytically challenging due to high dimensionality and sampling irregularities across years. We develop a data-intensive methodology for predicting forest stand dynamics using such datasets. Our methodology involves the following steps: 1) computing stand level characteristics from individual tree measurements, 2) reducing the characteristic dimensionality through analyses of their correlations, 3) parameterizing transition matrices for each uncorrelated dimension using Gibbs sampling, and 4) deriving predictions of forest developments at different timescales. Applying our methodology to a forest inventory database from Quebec, Canada, we discovered that four uncorrelated dimensions were required to describe the stand structure: the biomass, biodiversity, shade tolerance index and stand age. We were able to successfully estimate transition matrices for each of these dimensions. The model predicted substantial short-term increases in biomass and longer-term increases in the average age of trees, biodiversity, and shade intolerant species. Using highly dimensional and irregularly sampled forest inventory data, our original data-intensive methodology provides both descriptions of the short-term dynamics as well as predictions of forest development on a longer timescale. This method can be applied in other contexts such as conservation and silviculture, and can be delivered as an efficient tool for sustainable forest management.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted May 02, 2015.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Data-intensive modeling of forest dynamics
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Data-intensive modeling of forest dynamics
Jean F. Liénard, Dominique Gravel, Nikolay S. Strigul
bioRxiv 005009; doi: https://doi.org/10.1101/005009
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Data-intensive modeling of forest dynamics
Jean F. Liénard, Dominique Gravel, Nikolay S. Strigul
bioRxiv 005009; doi: https://doi.org/10.1101/005009

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Ecology
Subject Areas
All Articles
  • Animal Behavior and Cognition (2440)
  • Biochemistry (4803)
  • Bioengineering (3340)
  • Bioinformatics (14724)
  • Biophysics (6658)
  • Cancer Biology (5188)
  • Cell Biology (7455)
  • Clinical Trials (138)
  • Developmental Biology (4378)
  • Ecology (6904)
  • Epidemiology (2057)
  • Evolutionary Biology (9943)
  • Genetics (7357)
  • Genomics (9550)
  • Immunology (4583)
  • Microbiology (12730)
  • Molecular Biology (4960)
  • Neuroscience (28422)
  • Paleontology (199)
  • Pathology (810)
  • Pharmacology and Toxicology (1400)
  • Physiology (2031)
  • Plant Biology (4521)
  • Scientific Communication and Education (980)
  • Synthetic Biology (1305)
  • Systems Biology (3922)
  • Zoology (731)