Modeling spatial patterns of longleaf pine needle dispersal using long-term data

Journal Article · 27 September 2023 · Fire Ecology (Online)

DOI: https://doi.org/10.1186/s42408-023-00209-z · OSTI ID:2006691

Blaydes, Suzanne H. ; Cannon, Jeffery B.; Aubrey, Doug P.

Abstract Background Predicting patterns of fire behavior and effects in frequent fire forests relies on an understanding of fine-scale spatial patterns of available fuels. Leaf litter is a significant canopy-derived fine fuel in fire-maintained forests. Litter dispersal is dependent on foliage production, stand structure, and wind direction, but the relative importance of these factors is unknown. Results Using a 10-year litterfall dataset collected within eighteen 4-ha longleaf pine ( Pinus palustris Mill.) plots varying in canopy spatial pattern, we compared four spatially explicit models of annual needle litter dispersal: a model based only on basal area, an overstory abundance index (OAI) model, both isotropic and anisotropic litter kernel models, and a null model that assumed no spatial relationship. The best model was the anisotropic model (R ²  = 0.656) that incorporated tree size, location, and prevailing wind direction, followed by the isotropic model (R ²  = 0.612), basal area model (R ²  = 0.488), OAI model (R ²  = 0.416), and the null model (R ²  = 0.08). Conclusions As with previous studies, the predictive capability of the litter models was robust when internally validated with a subset of the original dataset (R ²  = 0.196–0.549); however, the models were less robust when challenged with an independent dataset (R ²  = 0.122–0.319) from novel forest stands. Our model validation underscores the need for rigorous tests with independent, external datasets to confirm the validity of litter dispersal models. These models can be used in the application of prescribed fire to estimate fuel distribution and loading, as well as aid in the fine tuning of fire behavior models to better understand fire outcomes across a range of forest canopy structures.

• Sponsoring Organization: USDOE
• Grant/Contract Number: EM0004391
• OSTI ID: 2006691
• Journal Information: Fire Ecology (Online), Journal Name: Fire Ecology (Online) Journal Issue: 1 Vol. 19; ISSN 1933-9747
• Publisher: Springer Science + Business MediaCopyright Statement
• Country of Publication: United States
• Language: English