Invasive species, often recognized as ecosystem engineers, can dramatically alter geomorphic processes and landforms. Our review shows that the biogeomorphic impacts of invasive species are common, but variable in magnitude or severity, ranging from simple acceleration or deceleration of preexisting geomorphic processes to landscape metamorphosis. Primary effects of invasive flora are bioconstruction and bioprotection, whereas primary effects of invasive fauna are bioturbation, bioerosion, and bioconstruction. Landwater interfaces seem particularly vulnerable to biogeomorphic impacts of invasive species. Although not different from biogeomorphic impacts in general, invasive species are far more likely to lead to major geomorphic changes or landscape metamorphosis, which can have long-lasting impacts. In addition, invasive species can alter selection pressures in both macroevolution and microevolution by changing geomorphic processes. However, the differing timescales of biological invasions, landscape evolution, and biological evolution complicate assessment of the evolutionary impacts of invasive organisms.

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This paper reports on the deployment of a predictive model that combines spatial analysis and fuzzy logic modeling to translate expert archeological knowledge into predictive surfaces. Analytic predictive archeological models have great utility for state departments of transportation, and some states have invested millions of dollars in such models. However, classic statistical modeling approaches often require too much data and create questions about whether areas are categorized as low probability because (a) there are no sites or (b) no surveys have been conducted there. However, this process can build robust models around typically sparse archeological data and is not subject to spatial bias. These models are intended to lower overall project costs by identifying corridors with a lower probability of having archeological sites, not to supplant field surveys once a corridor has been chosen. Five influencing factors were defined by archeologists and were calculated with the ArcGIS platform. The archeologists then informed a fuzzy logic induction process that was mapped to output probability functions. These data were geocoded into ArcGIS output surfaces that showed the probability of encountering artifacts. The predictive results were tested through a blind control protocol against cleansed archeological data. These models were shown to perform as well as or better than traditional statistical models and required much less data. The Kentucky implementation includes the superior predictive coverage and, more important, a suite of tools to allow the ArcGIS-competent archeologist to design and execute new modeling routines or to build new models. The availability of higher-quality geographic information systems data will also allow archeologists to update the model.

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