title: 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.
authors.affiliation: Department of Environmental Science, Policy, and Management, University of California, Berkeley, California 94720-3114, USA. mveitzel@berkeley.edu
authors.forename: Melissa
authors.initials: M
authors.lastname: Eitzel
authors.forename: John
authors.initials: J
authors.lastname: Battles
authors.forename: Robert
authors.initials: R
authors.lastname: York
authors.forename: Jonas
authors.initials: J
authors.lastname: Knape
authors.forename: Perry
authors.initials: P
authors.lastname: de Valpine
mesh_terms: D002140:California; D004784:Environmental Monitoring / Q000379:methods*; D014197:Trees / Q000254:growth & development*