Evidence of biotic resistance to invasions in forests of the Eastern USA
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Detecting biotic resistance to biological invasions across large geographic areas may require acknowledging multiple metrics of niche usage and potential spatial heterogeneity in associations between invasive and native species diversity and dominance.
Determine (1) if native communities are resistant to biological invasions at macroscales; (2) the metrics that best quantify biotic resistance at these scales; and (3) the degree to which the direction and magnitude of invader-native associations vary with scale and/or location.
Using a mixed-effects modeling framework to account for potential sub-regional and cross-scale variability in invader-native associations, we modeled the species richness and cover of invasive plants in 42,626 plots located throughout Eastern USA forests in relationship to plot-level estimates of native tree biomass, species richness, and evolutionary diversity.
We found (1) native tree biomass and evolutionary diversity, but not species richness, to be negatively associated with invader establishment and dominance, and thus indicative of biotic resistance; (2) evidence that evolutionary diversity limits invader dominance more than it does invader establishment; (3) evidence of greater invasion resistance in parts of the agriculturally-dominated Midwest and in and around the more-contiguous forests of the Appalachian Mountains; and (4) the magnitude to which native tree biomass and evolutionary diversity limit invasion varies across the ranges of these metrics.
These findings illustrate the improved understanding of biotic resistance to invasions that is gained by accounting for sub-regional variability in ecological processes, and underscores the need to determine the factors leading to spatial heterogeneity in biotic resistance.
KeywordsBig data Biomass FIA Program Invasive plants Macrosystems Niche Evolutionary diversity Taxonomic diversity
We would like to thank the many FIA workers who collected the data used in this investigation; Beth Schulz, Andrew Gray, and Chris Witt for helping to compile the data; Johanna Desprez and Jarrod Doucette for GIS-related advice; and two anonymous reviewers for their constructive criticisms and suggestions on how to improve this paper. This research was supported by NSF Macrosystems Biology Grant # 1241932 and by a Research Joint Venture Agreement 13-JV-11330110-043 and Cost Share Agreement 14-CS-11330110-042 between the USDA Forest Service and North Carolina State University.
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