Defining edge gradients using plant species composition in oak-hickory forest patches

Abstract

Forest conservation is greatly enhanced with an increased understanding of the transitional nature of edges. Edge characteristics control ecological flows into the forest that may influence its structure and composition. Most forests in east-central Illinois exist as small patches, making the region an ideal location for studying edge effects. The objectives of this study were to identify the mechanisms and processes that determine gradients of plant composition and to develop a predictive model of edge effects using plant species composition as an indicator of edge influence. A data set was gathered to relate understory forest species composition to several environmental and forest structure variables. These data were analyzed in several multiple regressions to find the most important variables controlling community responses. The study system had a sharply defined edge gradient, with vegetation cover, species richness, and diversity highest at the edge and decreasing dramatically 15-30 meters into the forest. Percent canopy cover, distance from the edge, and plot slope were the most important predictors of edge influence for most community responses. Contrary to previous studies, edge aspect had no effect on the edge response of the community. Canopy cover controlled light intensity on the forest floor, and was strongly related to the understory community. Distance from the forest edge reflected the plot landscape position, and plot slope reflected the mediating effects of microtopography on plant growth, where total cover did not change with distance on plots with steep slopes. Besides providing a useful tool for predicting the effect of the edge on understory plant composition, this study also found microtopography to be an important controller in this system, which has previously been overlooked in the study of edge effects.

Date of Award	2006
Original language	American English
Awarding Institution	Eastern Illinois University
Supervisor	Scott J. Meiners (Supervisor)