Field-evolved resistance to four modes of action of herbicides in a single kochia (Kochia scoparia L. Schrad.) population.

BACKGROUND: Evolution of multiple herbicide resistance in weeds is a serious threat to weed management in crop production. Kochia is an economically important broadleaf weed in the U.S. Great Plains. This study aimed to confirm resistance to four sites of action of herbicides in a single kochia (Kochia scoparia L. Schrad.) population from a crop field near Garden City (GC), Kansas, and further determine the underlying mechanisms of resistance. RESULTS: One-fourth of the GC plants survived the labeled rate or higher of atrazine [photosystem II (PSII) inhibitor], and the surviving plants had the Ser-264 to Gly mutation in the psbA gene, the target site of atrazine. Results showed that 90% of GC plants survived the labeled rate of dicamba, a synthetic auxin. At least 87% of the plants survived up to 72 g a.i. ha(-1) of chlorsulfuron [acetolactate synthase (ALS) inhibitor], and analysis of the ALS gene revealed the presence of Pro-197 to Thr and/or Trp-574 to Lue mutation(s). Most GC plants also survived the labeled rate of glyphosate [5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) inhibitor), and the resistant plants had 5-9 EPSPS gene copies (relative to the ALS gene). CONCLUSION: We confirm the first case of evolution of resistance to four herbicide sites of action (PSII, ALS and EPSPS inhibitors and synthetic auxins) in a single kochia population, and target-site-based mechanisms confer resistance to atrazine, glyphosate and chlorsulfuron.

An affinity-effect relationship for microbial communities in plant-soil feedback loops.

Feedback loops involving soil microorganisms can regulate plant populations. Here, we hypothesize that microorganisms are most likely to play a role in plant-soil feedback loops when they possess an affinity for a particular plant and the capacity to consistently affect the growth of that plant for good or ill. We characterized microbial communities using whole-community DNA fingerprinting from multiple "home-and-away" experiments involving giant ragweed (Ambrosia trifida L.) and common sunflower (Helianthus annuus L.), and we looked for affinity-effect relationships in these microbial communities. Using canonical ordination and partial least squares regression, we developed indices expressing each microorganism's affinity for ragweed or sunflower and its putative effect on plant biomass, and we used linear regression to analyze the relationship between microbial affinity and effect. Significant linear affinity-effect relationships were found in 75 % of cases. Affinity-effect relationships were stronger for ragweed than for sunflower, and ragweed affinity-effect relationships showed consistent potential for negative feedback loops. The ragweed feedback relationships indicated the potential involvement of multiple microbial taxa, resulting in strong, consistent affinity-effect relationships in spite of large-scale microbial variability between trials. In contrast, sunflower plant-soil feedback may involve just a few key players, making it more sensitive to underlying microbial variation. We propose that affinity-effect relationship can be used to determine key microbial players in plant-soil feedback against a low "signal-to-noise" background of complex microbial datasets.