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A Mixed-Effects Heterogeneous Negative Binomial Model for Postfire Conifer Regeneration in Northeastern California, USA

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Abstract:

Many western USA fire regimes are typified by mixed-severity fire, which compounds the variability inherent to natural regeneration densities in associated forests. Tree regeneration data are often discrete and nonnegative; accordingly, we fit a series of Poisson and negative binomial variation models to conifer seedling counts across four distinct burn severities and three forest types 10 years after the 23,000-ha Storrie Fire, a large mixed-severity fire in northern California. Despite the accessibility and power of the zero-inflated negative binomial mixture model, a flexible heterogeneous negative binomial model offered a superior fit. Incorporation of a random stand effect further improved model performance. A parametric bootstrap analysis was conducted to examine seedling distributions and stand stocking. Mean simulated seedling densities had an expansive range (272‐29,257 ha−1). Stocking analyses suggest a high probability of deficient conifer coverage in the majority of lower-elevation high-severity burn stands. In addition, models were fit to fir and pine seedling counts. Only a minority of postfire stands were likely to be stocked in the pine-only analysis. These models will help land managers prioritize limited resources for artificial reforestation in mixed-severity burned landscapes.
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  • 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.
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