Modeling annualized occurrence, frequency, and composition of ingrowth using mixed-effects zero-inflated models and permanent plots in the Acadian Forest Region of North America

• Published in: Canadian journal of forest research Vol. 41; no. 10; pp. 2077 - 2089
• Main Authors: Li, Rongxia, Weiskittel, Aaron R, Kershaw, John A. Jr
• Format: Journal Article
• Language: English
• Published: Ottawa, ON NRC Research Press, National Research Council Canada 01.10.2011; NRC Research Press; National Research Council of Canada; Canadian Science Publishing NRC Research Press
• Subjects: basal area; Biological and medical sciences; forest growth; forest trees; Forestry; Forests; Forests and forestry; Fundamental and applied biological sciences. Psychology; hardwood; Measurement; Plant growth; Poisson distribution; prediction; probability; Probability distribution; regression analysis; Species composition; species diversity; Stochastic models; Stochastic processes; tree and stand measurements; Trees; North America; America; United States; Acadian forest; Growth; Occurrence frequency; Continuous inventory; Modeling; Forestry
• ISSN: 0045-5067; 1208-6037
• DOI: 10.1139/x11-117

Forest tree ingrowth is a highly variable and largely stochastic process. Consequently, predicting occurrence, frequency, and composition of ingrowth is a challenging task but of great importance in long-term forest growth and yield model projections. However, ingrowth data often require different statistical techniques other than traditional Gaussian regression, because these data are often bounded, skewed, and non-normal and commonly contain a large fraction of zeros. This study presents a set of regression models based on discrete Poisson and negative binomial probability distributions for ingrowth data collected from permanent sample plots in the Acadian Forest Region of North America. Models considered here include regular Poisson, zero-inflated Poisson (ZIP), zero-altered Poisson (ZAP; hurdle Poisson), regular negative binomial (NB), zero-inflated negative binomial (ZINB), and zero-altered negative binomial (ZANB; hurdle NB). Plot-level random effects were incorporated into each of these models. The ZINB model with random effects was found to provide the best fit statistics for modeling annualized occurrence and frequency of ingrowth. The key explanatory variables were stand basal area per hectare, percentage of hardwood basal area, number of trees per hectare, a measure of site quality, and the minimum measured diameter at breast height of each plot. A similar model was developed to predict species composition. All models showed logical behavior despite the high variability observed in the original data.