COS 93-10 - Characterizing canopy gap dynamics using hemispherical photography in emerald ash borer-impacted forests

Thursday, August 11, 2011: 11:10 AM
9AB, Austin Convention Center
Wendy S. Klooster, Entomology, The Ohio State University/ OARDC, Columbus, OH, Catherine P. Herms, Horticulture and Crop Science, Ohio State University/ OARDC, Wooster, OH, Kathleen S. Knight, Northern Research Station, USDA Forest Service, Delaware, OH, Daniel A. Herms, Entomology, The Ohio State University / OARDC, Wooster, OH and John Cardina, Horticulture and Crop Science, The Ohio State University/ OARDC, Wooster, OH

Ash (Fraxinus species) mortality due to emerald ash borer (EAB; Agrilus planipennis) infestation may drastically alter the structure and composition of native forests. Canopy gaps resulting from dead and fallen trees can impact understory microclimates and ecological interactions. This in turn may result in altered successional trajectories, and potentially facilitate the establishment and growth of invasive plant species. From 2008 through 2010 we have taken hemispherical photographs at 5 to 20 points per plot in 129 plots within 7 different Michigan State Parks or Metroparks. Our objectives were to characterize variation among points within a plot, plots within a transect, transects within a park, among parks, and over time. We also compared canopy gap fraction with ash mortality, and understory plant growth, abundance, and composition over time.


Gap fraction for a subset of plots in 2009 ranged from 0.5 to 39.3%. Photographs taken in 2010 had a median gap fraction of 6.73%; this is a slight decline from the plots in 2009, which had a median of 10.0%. Apparent decrease in median % gap fraction from 2009 to 2010 indicates possible initial stages of canopy closure as trees grow up or outward to fill in the gaps. However, initial closure may be reversed as standing dead ash trees fall, potentially creating even larger canopy gaps. Within-plot variation, analyzed for a subset of plots sampled in 2010, showed a minimum variation of 3.0% gap fraction, while the maximum was 9.4%. The median values among plots ranged from 3.7% to 9.1% gap fraction. Within-transect variation was even smaller, with a maximum variation of 5.0% A preliminary analysis of variance on a subset of the data attributed the majority of variation (75%) to differences among photographs within plots.

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