Estimating tree growth from complex forest monitoring data

Abstract

Understanding tree growth as a function of tree size is important for a multitude of ecological and management applications. Determining what limits growth is of central interest, and forest inventory permanent plots are an abundant source of long-term information but are highly complex. Observation error and multiple sources of shared variation (spatial plot effects, temporal repeated measures, and a mosaic of sampling intervals) make these data challenging to use for growth estimation. We account for these complexities and incorporate potential limiting factors (tree size, competition, and resource supply) into a hierarchical state-space model. We estimate the diameter growth of white fir (Abies concolor) in the Sierra Nevada of California from forest inventory data, showing that estimating such a model is feasible in a Bayesian framework using readily available modeling tools. In this forest, white fir growth depends strongly on tree size, total plot basal area, and unexplained variation between individual trees. Plot-level resource supply variables (representing light, water, and nutrient availability) do not have a strong impact on inventory-size trees. This approach can be applied to other networks of permanent forest plots, leading to greater ecological insights on tree growth.

Keywords

Abies concolor; competition intensity; hierarchical model; individual variation; Markov chain Monte Carlo; OpenBUGS; permanent plots; state-space model

Published in

Ecological Applications
2013, volume: 23, number: 6, pages: 1288-1296
Publisher: WILEY-BLACKWELL

SLU Authors

• Knape, Jonas

  • University of California Berkeley

UKÄ Subject classification

Forest Science

Publication identifier

• DOI: https://doi.org/10.1890/12-0504.1

Permanent link to this page (URI)

https://res.slu.se/id/publ/84031