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Using hedgerows as model linkages to examine non-native plant patterns

Agriculture Ecosystems & Environment

Abstract

Non-native plant distribution and community composition, along with an array of environmental factors, were examined in 31 hedgerows, an archetypal class of conservation linkage, in the northern part of California's Central Valley. Row crop, orchard, and vineyard agriculture dominate this area, and hedgerows have been popular for well over a decade. Seven groups of explanatory data (environmental, historical, landscape, management, spatial, structural, and biological) were used to determine the strongest correlates of spatially-explicit patterns of non-native plants within and immediately surrounding hedgerows. In 15 hedgerows, a field experiment tested the effect of degree of shading on non-native plant diversity and cover. The results of this project showed that: (1) Hedgerows harbored a flora of non-native plants richer than the surrounding matrix and that invasion was spatially structured. (2) Edges were more invaded than interiors in terms of both non-native richness and percent cover. (3) Differences between edges and interiors were likely due to shade. (4) Community-level patterns were most strongly correlated with the environmental, historical, structural and/or landscape explanatory variables. (5) Matrix types affected the non-native. plant community in different ways, and the direction of those relationships was influenced by plant dispersal mode. This research revealed that hedgerows can function as barriers to plant invasion if managed appropriately. Results supported the idea that these features may function as invasion conduits but perhaps not as major sources for invasion into agricultural fields. Specific recommendations are made regarding key factors (management, site, and species characteristics) influencing invasion, with particular emphasis on the role of shade, matrix characteristics, and plant dispersal mode. (C) 2014 Elsevier B.V. All rights reserved.

Author(s): Wilkerson, ML

Journal: Agriculture Ecosystems & Environment

Year: 2014

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