Exotic weeds can interfere with neighboring species by releasing allelochemicals that either directly inhibit growth and distribution of associated species or affect them indirectly by disrupting their interaction with soil organisms, such as arbuscular mycorrhizal fungi (AMF). The allelopathic potential of tall hedge mustard was assessed using aqueous root and shoot extracts in seed germination and radicle growth bioassays. Aqueous tall hedge mustard root and shoot extracts strongly inhibited seed germination and growth of bluebunch wheatgrass, Idaho fescue, and spotted knapweed, but had minimal autotoxicity. Chemical analysis of tall hedge mustard tissues revealed the presence of two major glucosinolates—isopropyl and sec-butyl glucosinolate. The degradation products of these glucosinolates (isopropyl isothiocyanate and sec-butyl isothiocyanate) were identified in dichloromethane extracts of tall hedge mustard aqueous root and shoot extracts. Commercially available isopropyl isothiocyanate and sec-butyl isothiocyanate inhibited seed germination and radicle growth, suggesting their role in the allelopathic potential of tall hedge mustard. Tall hedge mustard aqueous extracts and isothiocyanates incorporated into an agar medium inhibited the spore germination of the AMF, Glomus intraradices. Tall hedge mustard infestations were also found to reduce the AMF inoculum potential of soil under field conditions. The results from this study show that tall hedge mustard produces chemicals that can inhibit the growth of neighboring plant species and their AMF associates.

Remote sensing technology is a tool for detecting invasive species affecting forest, rangeland, and pasture environments. This article provides a review of the technology, and algorithms used to process remotely sensed data when detecting weeds and a working example of the detection of spotted knapweed and babysbreath with a hyperspectral sensor. Spotted knapweed and babysbreath frequently invade semiarid rangeland and irrigated pastures of the western United States. Ground surveys to identify the extent of invasive species infestations should be more efficient with the use of classified images from remotely sensed data because dispersal of an invasive plant may have occurred before the discovery or treatment of an infestation. Remote sensing data were classified to determine if infestations of spotted knapweed and babysbreath were detectable in Swan Valley near Idaho Falls, ID. Hyperspectral images at 2-m spatial resolution and 400- to 953-nm spectral resolution with 12-nm increments were used to identify locations of spotted knapweed and babysbreath. Images were classified using the spectral angle mapper (SAM) algorithm at 1, 2, 3, 4, 5, and 10° angles. Ground validation of the classified images established that 57% of known spotted knapweed infestations and 97% of known babysbreath infestations were identified through the use of hyperspectral imagery and the SAM algorithm.

Invasive weed managers are presented with a complicated and ever-enlarging set of management alternatives. Identifying the optimal weed management strategy for a given set of conditions requires predicting how candidate strategies will affect plant community composition. Although field experiments have advanced our ability to predict postmanagement composition, extrapolation problems limit the prediction accuracy achieved by interpreting treatment means as predictions. Examples of extrapolation problems include nonlinear relationships between competing plants, site-to-site variation in plant population growth rates, and the carrying capacities of desired species and weeds. Our objective was to develop a model that improves predictions of weed management outcomes by overcoming a subset of these problems. To develop the model, we used data from two field experiments in which four Kentucky bluegrass, six western wheatgrass, and six invasive plant (i.e., leafy spurge) densities were combined in field plots. Graphs of our model's predictions vs. observed field experiment data indicate that the model predicted the data accurately. Our model may improve predictions of plant community response to invasive weed management actions.

Spotted knapweed is an invasive mycorrhizal weed prevalent in the Pacific Northwest of the United States. Little is known about the effects of spotted knapweed or its management methods on soil quality and soil structure. This study compared soils from spotted knapweed–infested areas with areas where spotted knapweed is being managed using several herbicides and mechanical treatments. We measured concentrations of glomalin, a glycoprotein produced by arbuscular mycorrhizal fungi (AMF), that is correlated with soil aggregate stability, AMF hyphal length, and percent water-stable aggregates (WSA) in soils from managed and unmanaged areas. Areas with high knapweed density (unmanaged areas) generally had lower glomalin concentrations and AMF hyphal lengths compared with areas receiving chemical and combined mechanical–chemical management treatments. Total glomalin was significantly negatively correlated with percent knapweed cover. However, WSA was high (70 to 80%) in soils from all management treatments and was not affected by knapweed cover. Our results suggest that spotted knapweed does not have negative effects on soil quality from our study site, likely because of the high aggregate stability of the soils in the area. However, Centaurea maculosa may have negative effects on soil quality in soils with lower aggregate stability.