Weed species richness, density and relative abundance on farms in the subtropical grain region of Australia

Weed management is one of the most important economic and agronomic issues facing farmers in Australia's grain regions. Weed species occurrence and abundance was monitored between 1997 and 2000 on 46 paddocks (sites) across 18 commercial farms located in the Northern Grain Region. The sites generally fell within 4 disjunct regions, from south to north: Liverpool Plains, Moree, Goondiwindi and Kingaroy. While high species richness was found (139 species or species groups), only 8 species occurred in all 4 regions and many (56 species) only occurred at 1 site or region. No species were observed at every site but 7 species (Sonchus spp., Avena spp., Conyza spp., Echinochloa spp., Convolvulus erubescens, Phalaris spp. and Lactuca serriola) were recorded on more than 70% of sites. The average number of species observed within crops after treatment and before harvest was less than 13. Species richness tended to be higher in winter pulse crops, cotton and in fallows, but overall was similar at the different sampling seasons (summer v. winter). Separate species assemblages associated with the Goondiwindi and Kingaroy regions were identified by correspondence analysis but these appeared to form no logical functional group. The species richness and density was generally low, demonstrating that farmers are managing weed populations effectively in both summer and winter cropping phases. Despite the apparent adoption of conservation tillage, an increase in opportunity cropping and the diversity of crops grown (13) there was no obvious effect of management practices on weed species richness or relative abundance. Avena spp. and Sonchus spp. were 2 of the most dominant weeds, particularly in central and southern latitudes of the region; Amaranthus spp. and Raphanus raphanistrum were the most abundant species in the northern part of the region. The ubiquity of these and other species shows that continued vigilance is required to suppress weeds as a management issue.

Raw data for "Linkage disequilibrium estimation of effective population size with immigrants from divergent populations: a case study on Spanish mackerel (Scomberomorus commerson)."

Abstract of Macbeth, G. M., Broderick, D., Buckworth, R. & Ovenden, J. R. (In press, Feb 2013). Linkage disequilibrium estimation of effective population size with immigrants from divergent populations: a case study on Spanish mackerel (Scomberomorus commerson). G3: Genes, Genomes and Genetics. Estimates of genetic effective population size (Ne) using molecular markers are a potentially useful tool for the management of endangered through to commercial species. But, pitfalls are predicted when the effective size is large, as estimates require large numbers of samples from wild populations for statistical validity. Our simulations showed that linkage disequilibrium estimates of Ne up to 10,000 with finite confidence limits can be achieved with sample sizes around 5000. This was deduced from empirical allele frequencies of seven polymorphic microsatellite loci in a commercially harvested fisheries species, the narrow barred Spanish mackerel (Scomberomorus commerson). As expected, the smallest standard deviation of Ne estimates occurred when low frequency alleles were excluded. Additional simulations indicated that the linkage disequilibrium method was sensitive to small numbers of genotypes from cryptic species or conspecific immigrants. A correspondence analysis algorithm was developed to detect and remove outlier genotypes that could possibly be inadvertently sampled from cryptic species or non-breeding immigrants from genetically separate populations. Simulations demonstrated the value of this approach in Spanish mackerel data. When putative immigrants were removed from the empirical data, 95% of the Ne estimates from jacknife resampling were above 24,000.

Supermodels: sorghum and maize provide mutual insight into the genetics of flowering time

Nested association mapping (NAM) offers power to dissect complex, quantitative traits. This study made use of a recently developed sorghum backcross (BC)-NAM population to dissect the genetic architecture of flowering time in sorghum; to compare the QTL identified with other genomic regions identified in previous sorghum and maize flowering time studies and to highlight the implications of our findings for plant breeding. A subset of the sorghum BC-NAM population consisting of over 1,300 individuals from 24 families was evaluated for flowering time across multiple environments. Two QTL analysis methodologies were used to identify 40 QTLs with predominately small, additive effects on flowering time; 24 of these co-located with previously identified QTL for flowering time in sorghum and 16 were novel in sorghum. Significant synteny was also detected with the QTL for flowering time detected in a comparable NAM resource recently developed for maize (Zea mays) by Buckler et al. (Science 325:714-718, 2009). The use of the sorghum BC-NAM population allowed us to catalogue allelic variants at a maximal number of QTL and understand their contribution to the flowering time phenotype and distribution across diverse germplasm. The successful demonstration of the power of the sorghum BC-NAM population is exemplified not only by correspondence of QTL previously identified in sorghum, but also by correspondence of QTL in different taxa, specifically maize in this case. The unification across taxa of the candidate genes influencing complex traits, such as flowering time can further facilitate the detailed dissection of the genetic control and causal genes.

The use of genetic correlations to evaluate associations between SNP markers and quantitative traits

Open-pollinated progeny of Corymbia citriodora established in replicated field trials were assessed for stem diameter, wood density, and pulp yield prior to genotyping single nucleotide polymorphisms (SNP) and testing the significance of associations between markers and assessment traits. Multiple individuals within each family were genotyped and phenotyped, which facilitated a comparison of standard association testing methods and an alternative method developed to relate markers to additive genetic effects. Narrow-sense heritability estimates indicated there was significant additive genetic variance within this population for assessment traits ( h ˆ 2 =0.28to0.44 ) and genetic correlations between the three traits were negligible to moderate (r G = 0.08 to 0.50). The significance of association tests (p values) were compared for four different analyses based on two different approaches: (1) two software packages were used to fit standard univariate mixed models that include SNP-fixed effects, (2) bivariate and multivariate mixed models including each SNP as an additional selection trait were used. Within either the univariate or multivariate approach, correlations between the tests of significance approached +1; however, correspondence between the two approaches was less strong, although between-approach correlations remained significantly positive. Similar SNP markers would be selected using multivariate analyses and standard marker-trait association methods, where the former facilitates integration into the existing genetic analysis systems of applied breeding programs and may be used with either single markers or indices of markers created with genomic selection processes.

Two distinct classes of QTL determine rust resistance in sorghum

Background: Agriculture is facing enormous challenges to feed a growing population in the face of rapidly evolving pests and pathogens. The rusts, in particular, are a major pathogen of cereal crops with the potential to cause large reductions in yield. Improving stable disease resistance is an on-going major and challenging focus for many plant breeding programs, due to the rapidly evolving nature of the pathogen. Sorghum is a major summer cereal crop that is also a host for a rust pathogen which occurs in almost all sorghum growing areas of the world, causing direct and indirect yield losses in sorghum worldwide, however knowledge about its genetic control is still limited. In order to further investigate this issue, QTL and association mapping methods were implemented to study rust resistance in three bi-parental populations and an association mapping set of elite breeding lines in different environments. Results: In total, 64 significant or highly significant QTL and 21 suggestive rust resistance QTL were identified representing 55 unique genomic regions. Comparisons across populations within the current study and with rust QTL identified previously in both sorghum and maize revealed a high degree of correspondence in QTL location. Negative phenotypic correlations were observed between rust, maturity and height, indicating a trend for both early maturing and shorter genotypes to be more susceptible to rust. Conclusions: The significant amount of QTL co-location across traits, in addition to the consistency in the direction of QTL allele effects, has provided evidence to support pleiotropic QTL action across rust, height, maturity and stay-green, supporting the role of carbon stress in susceptibility to rust. Classical rust resistance QTL regions that did not co-locate with height, maturity or stay-green QTL were found to be significantly enriched for the defence-related NBS-encoding gene family, in contrast to the lack of defence-related gene enrichment in multi-trait effect rust resistance QTL. The distinction of disease resistance QTL hot-spots, enriched with defence-related gene families from QTL which impact on development and partitioning, provides plant breeders with knowledge which will allow for fast-tracking varieties with both durable pathogen resistance and appropriate adaptive traits.