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Title: Forecasting long-term acorn production with and without oak decline using forest inventory data
Author: Greenberg, Cathryn H.; Keyser, Chad E.; Rathburn, Leah C.; Rose, Anita K.; Fearer, Todd M.; McNab, Henry W.;
Source: Forest Science Journal 60(2):222–230
Publication Series: Scientific Journal (JRNL)
Description: Acorns are important as wildlife food and for oak regeneration, but production is highly variable, posing a challenge to forest managers targeting acorn production levels. Forest managers need tools to predict acorn production capability tailored to individual landscapes and forest management scenarios, adjusting for oak mortality and stand development over time. We implemented published predictive models of average annual acorn production by five oak species common to the eastern United States in the Forest Vegetation Simulator and used forest inventory data to estimate long-term acorn production on the Bent Creek Experimental Forest watershed, with and without oak decline. Under a no-management scenario, simulations forecasted a 58% increase in average annual acorn production by 2062 without oak decline but a 17% decrease with oak decline. Forecasts were influenced by the initial abundance and basal area of different oak species on the landscape and stand dynamics over time. Simulations indicated that heavy oak mortality with regeneration failure could substantially affect acorn production over the long-term by reducing the proportion of mature canopy oaks and relative abundance of oak species. Forest Vegetation Simulator ACORN provides a powerful tool for long-term acorn production planning that can be tailored to individual landscapes and forest management scenarios to predict average annual number and mass of acorns.
Keywords: acorn, Forest Vegetation Simulator, hard mast, oak decline, upland hardwood forest
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Greenberg, Cathryn H.; Keyser, Chad E.; Rathburn, Leah C.; Rose, Anita K.; Fearer, Todd M.; McNab, Henry W. 2013. Forecasting long-term acorn production with and without oak decline using forest inventory data. Forest Science Journal 60(2):222–230.
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