Safeners such as metcamifen and benoxacor are widely used in maize to enhance the selectivity of herbicides through the induction of key detoxifying enzymes, notably cytochrome P450 monooxygenases (CYPs). Using a combination of transcriptomics, proteomics, and functional assays, the safener-inducible CYPs responsible for herbicide metabolism in this globally important crop have been identified. A total of 18 CYPs belonging to clans 71, 72, 74, and 86 were safener-induced, with the respective enzymes expressed in yeast and screened for activity toward thiadiazine (bentazon), sulfonylurea (nicosulfuron), and triketone (mesotrione and tembotrione) chemistries. Herbicide metabolism was largely restricted to family CYP81A members from clan 71, notably CYP81A9, CYP81A16, and CYP81A2. Quantitative transcriptomics and proteomics showed that CYP81A9/CYP81A16 were dominant enzymes in safener-treated field maize, whereas only CYP81A9 was determined in sweet corn. The relationship between CYP81A sequence and activities were investigated by splicing CYP81A2 and CP81A9 together as a series of recombinant chimeras. CYP81A9 showed wide ranging activities toward the three herbicide chemistries, while CYP81A2 uniquely hydroxylated bentazon in multiple positions. The plasticity in substrate specificity of CYP81A9 toward multiple herbicides resided in the second quartile of its N terminal half. Further phylogenetic analysis of CYP81A9 showed that the maize enzyme was related to other CYP81As linked to agrochemical metabolism in cereals and wild grasses, suggesting this clan 71 CYP has a unique function in determining herbicide selectivity in arable crops.

This paper describes a model which links four levels in an ecological hierarchy using a series of matrices. The four levels are landscape, land cover type, community and species. Each matrix quantifies the probabilistic associations between entities in two adjacent levels in the hierarchy. A landscape classification (1 km resolution) provides a spatial element to the model enabling the distributions of species to be predicted and presented as maps within a geographical information system (GIS). Implementation of the model in Northern England is described. The distributions of 579 species of plants were predicted and compared with data from independent field surveys. The predicted distributions were found to be accurate for 59% of species. The distributions of rare and non-native (introduced) species of plant were relatively poorly predicted. The potential of this approach to model plant species distributions is discussed.

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