Modeling Advance Oak Reproduction at Landscape Scale: The Relative Importance of Abiotic and Biotic Factors

Author:

Rittenhouse Joshua W1ORCID,Leites Laura P2ORCID

Affiliation:

1. The Pennsylvania State University, Department of Ecosystem Science and Management , 214 Forest Resources Building, University Park, PA 18602 , USA

2. The Pennsylvania State University, Department of Ecosystem Science and Management , 312 Forest Resources Building, University Park, PA 18602 , USA

Abstract

Abstract Establishing advance oak reproduction in the understory of mature oak forests before an overstory removal is critical to regenerate mixed-oak forests (Quercus spp.). Many studies have been conducted at the stand scale; however, studies at landscape scale can be particularly useful to guide silvicultural management investments. We developed a model of advance reproduction abundance at landscape scale using inventory, climate, topography, and soils data from ninety-seven mixed-oak forests located in Pennsylvania, USA. A negative binomial model was used to predict the abundance of oak seedlings between 15.2 and 91.4 cm in height and with a root collar diameter of 0.64 to 1.9 cm (established oak seedlings). This model had a Nagelkerke’s pseudo R2 of 58%. Deviance partitioning methods indicate that climate and overstory influence explain similar proportions of total deviance, but climate contributes a higher proportion of nonoverlapping information (22% versus 18%). Overstory basal area of two common oak species were positively associated with seedling abundance, whereas biotic stressors were negatively correlated. Biotic stressors showed considerable overlap with information provided by climate and overstory influences. The results of this study provide a reference to guide silvicultural management at a landscape scale.

Funder

Pennsylvania Department of Conservation and Natural Resources

U.S. Department of Agriculture

National Institute of Food and Agriculture

Publisher

Oxford University Press (OUP)

Subject

Ecological Modeling,Ecology,Forestry

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