Skip to main content

Bootstrap Evaluation of a Young Douglas-Fir Height Growth Model for the Pacific Northwest

Buy Article:

$21.50 plus tax (Refund Policy)

We evaluated the stability of a complex regression model developed to predict the annual height growth of young Douglas-fir. This model is highly nonlinear and is fit in an iterative manner for annual growth coefficients from data with multiple periodic remeasurement intervals. The traditional methods for such a sensitivity analysis either involve laborious math or rely on prior knowledge of parameter behavior. To achieve our goals, we incorporate a bootstrap approach to obtain estimates of the distribution of predicted height growth for any set of input variables. This allows for a sensitivity analysis with knowledge of the probability of a given outcome. The bootstrap distributions should approximate the variation we might expect from running the model on numerous independent datasets. From the variation in the model parameters, we are able to produce ranges of height growth prediction error falling under a given probability of occurrence. By evaluating these ranges under several combinations of input variables that represent extreme situations, we are able to visualize the stability of the model under each situation. Each of the four components of the model can be investigated separately, which allows us to determine which components of the model might benefit from reformulation. In this case we find that the model is less stable in extremely high site index, especially under low vegetation competition. Other than the computing time involved with the bootstrap, most of the analysis is fairly quick and easy to perform.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: density; non-linear model; sensitivity analysis; vegetation

Document Type: Research Article

Publication date: 2010-12-01

More about this publication?
  • Important Notice: SAF's journals are now published through partnership with the Oxford University Press. Access to archived material will be available here on the Ingenta website until March 31, 2018. For new material, please access the journals via OUP's website. Note that access via Ingenta will be permanently discontinued after March 31, 2018. Members requiring support to access SAF's journals via OUP's site should contact SAF's membership department for assistance.

    Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
  • Submit a Paper
  • Membership Information
  • Author Guidelines
  • Podcasts
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more