Patterns of exotic plant invasions in Pennsylvania’s Allegheny National Forest using intensive Forest Inventory and Analysis plots

Abstract

Intensive Forest Inventory and Analysis (FIA) plot data collected in the Allegheny National Forest (ANF), Pennsylvania, between 1999 and 2006 were evaluated for their ability to predict ANF’s vulnerability to invasion by exotic plants. A total of 26 variables classified by biotic, abiotic, or disturbance characteristics were examined. Likelihood of colonization by invasive exotic and non-invasive exotic plants was analyzed using a logistic regression model. Approximately, 11% of the 449 species documented in these plots were exotic, which is higher than has been found in other northeastern forested plots. Only 1% of the ANF flora was invasive exotic plants and these were at low abundance, confirming that most invasions are still at an early stage of establishment. Sites richer in native or non-invasive exotic plants and with more alkaline soils were more likely to be invaded. Younger forests, forests with non-forest patches present, and forests rich in native species were more likely to be colonized by exotic (invasive or non-invasive) plants. Frangula alnus, which is starting to spread locally, differed from the other invasive exotic species in terms of its association with high sapling density to tree density ratios, high soil nitrogen levels, and the presence of fire. Variables representing mortality due to beech bark disease and distance to the nearest exotic planting manifested counterintuitive results. In both cases, the combined occurrence of mortality due to beech bark disease or a close (less than 500 m away) known propagule source and the presence of an invasive or non-invasive exotic plant was rare. We encourage increased use of intensive sampling for FIA in the U.S.A. and similar monitoring programs in other countries, but suggest adding a step to the plot selection phase that would allow forest-wide or regional stratified sampling of typically coarse-scale variables, such as historic or predicted defoliation or fire events, and forest or land type. A more accurate picture of the importance of disturbance variables in defining forest vulnerability to plant invasion may be achieved.