Wednesday, August 6, 2008 - 9:20 AM

COS 53-5: Long-term effects of industrial history on the forest flora of southeastern Ohio

Krysta E. Hougen and Glenn R. Matlack. Ohio University

Background/Question/Methods

Throughout the Appalachian region, remnant effects from century old industrial iron production may be inhibiting plant communities from returning to their pre-industrial species composition. Current knowledge of regeneration in post-industrial areas results from studies of sites < 50 years old, despite the prevalence of abandoned 19th century industrial remains. The purpose of this project is to determine differences in plant community composition on two charcoal-iron furnace sites in southeastern Ohio, and if those differences can be linked to specific disturbances. These sites were abandoned 120 years ago and thereby provide invaluable insight into the potential for forests to regenerate to pre-disturbed conditions.

To implement this study, 120 plots (4 m2 each) were established on various remnant industrial disturbances types (e.g. logging roads, charcoal production pits, waste dumps, and the immediate furnace area (within 200 meters)) as well as undisturbed, control areas. The vegetation within each study plot was surveyed in the summer and fall of 2007 and spring 2008. Additionally, soil tests examining heavy metals and organic compound composition were performed to further describe differences between plots.

Results/Conclusions

Results indicate differences in community composition between industrial disturbed and control sites, with some disturbance types (i.e. waste dumps) exhibiting more profound differences. In general, disturbed areas have a greater number of species and indigenous forest species define the control plots. Preliminary results suggest differences can be most attributed to soil properties. This study argues that residual effects from century old industrial activities are still impacting the plant community and identifying the most influential disturbances will better enable us to predict the best actions for restoring present day industrial sites.