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Estimating tree growth from complex forest monitoring data
Author(s) -
Eitzel Melissa,
Battles John,
York Robert,
Knape Jonas,
de Valpine Perry
Publication year - 2013
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1890/12-0504.1
Subject(s) - basal area , tree (set theory) , ecology , forest inventory , forest dynamics , sampling (signal processing) , competition (biology) , forest management , environmental science , computer science , mathematics , biology , mathematical analysis , filter (signal processing) , computer vision
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.