Genetic control of propagation traits in a single Corymbia torelliana x Corymbia variegata family

Genetic control of vegetative propagation traits was described for a second-generation, outbred, intersectional hybrid family (N = 208) derived from two species, Corymbia torelliana (F. Muell.) K.D. Hill & L.A.S. Johnson and Corymbia variegata (F. Muell.) K.D. Hill & L.A.S. Johnson, which contrast for propagation characteristics and in their capacity to develop lignotubers. Large phenotypic variances were evident for rooting and most other propagation traits, with significant proportions attributable to differences between clones (broad-sense heritabilities 0.2-0.5). Bare root assessment of rooting rate and root quality parameters tended to have the highest heritabilities, whereas rooting percentage based on root emergence from pots and shoot production were intermediate. Root biomass and root initiation had the lowest heritabilities. Strong favourable genetic correlations were found between rooting percentage and root quality traits such as root biomass, volume, and length. Lignotuber development on a seedling was associated with low rooting and a tendency to poor root quality in cuttings and was in accord with the persistence of species parent types due to gametic phase disequilibrium. On average, nodal cuttings rooted more frequently and with higher quality root systems, but significant cutting type x genotype interaction indicated that for some clones, higher rooting rates were obtained from tips. Low germination, survival of seedlings, and rooting rates suggested strong hybrid breakdown in this family.

The accuracy of varietal selection using factor analytic models for multi-environment plant breeding trials

Modeling of cultivar x trial effects for multienvironment trials (METs) within a mixed model framework is now common practice in many plant breeding programs. The factor analytic (FA) model is a parsimonious form used to approximate the fully unstructured form of the genetic variance-covariance matrix in the model for MET data. In this study, we demonstrate that the FA model is generally the model of best fit across a range of data sets taken from early generation trials in a breeding program. In addition, we demonstrate the superiority of the FA model in achieving the most common aim of METs, namely the selection of superior genotypes. Selection is achieved using best linear unbiased predictions (BLUPs) of cultivar effects at each environment, considered either individually or as a weighted average across environments. In practice, empirical BLUPs (E-BLUPs) of cultivar effects must be used instead of BLUPs since variance parameters in the model must be estimated rather than assumed known. While the optimal properties of minimum mean squared error of prediction (MSEP) and maximum correlation between true and predicted effects possessed by BLUPs do not hold for E-BLUPs, a simulation study shows that E-BLUPs perform well in terms of MSEP.

Genetic control of wheat quality: interactions between chromosomal regions determining protein content and composition, dough rheology, and sponge and dough baking properties

While the genetic control of wheat processing characteristics such as dough rheology is well understood, limited information is available concerning the genetic control of baking parameters, particularly sponge and dough (S&D) baking. In this study, a quantitative trait loci (QTL) analysis was performed using a population of doubled haploid lines derived from a cross between Australian cultivars Kukri x Janz grown at sites across different Australian wheat production zones (Queensland in 2001 and 2002 and Southern and Northern New South Wales in 2003) in order to examine the genetic control of protein content, protein expression, dough rheology and sponge and dough baking performance. The study highlighted the inconsistent genetic control of protein content across the test sites, with only two loci (3A and 7A) showing QTL at three of the five sites. Dough rheology QTL were highly consistent across the 5 sites, with major effects associated with the Glu-B1 and Glu-D1 loci. The Glu-D1 5 + 10 allele had consistent effects on S&D properties across sites; however, there was no evidence for a positive effect of the high dough strength Glu-B1-al allele at Glu-B1. A second locus on 5D had positive effects on S&D baking at three of five sites. This study demonstrated that dough rheology measurements were poor predictors of S&D quality. In the absence of robust predictive tests, high heritability values for S&D demonstrate that direct selection is the current best option for achieving genetic gain in this product category.

Corymbia leaf oils, latitude, hybrids and herbivory: a test using common-garden field trials

Foliar oils, particularly monoterpenes, can influence the susceptibility of plants to herbivory. In plants, including eucalypts, monoterpenes are often associated with plant defence. A recent analysis revealed an increase in foliar oil content with increasing latitudinal endemism, and we tested this pattern using three eucalypt taxa comprising a latitudinal replacement cline. We also examined the relative concentrations of two monoterpenes (alpha-pinene and 1,8-cineole), for which meta-analyses also showed latitudinal variation, using hybrids of these three taxa with Corymbia torelliana. These, and pure C. torelliana, were then assessed in common-garden field plots for the abundance and distribution of herbivory by four distinct herbivore taxa. Differing feeding strategies among these herbivores allowed us to test hypotheses regarding heritability of susceptibility and relationships to alpha-pinene and 1,8-cineole. We found no support for an increase in foliar oil content with increasing latitude, nor did our analysis support predictions for consistent variation in alpha-pinene and 1,8-cineole contents with latitude. However, herbivore species showed differential responses to different taxa and monoterpene contents. For example, eriophyid mites, the most monophagous of our censused herbivores, avoided the pure species, but fed on hybrid taxa, supporting hypotheses on hybrid susceptibility. The most polyphagous herbivore (leaf blister sawfly Phylacteophaga froggatti) showed no evidence of response to plant secondary metabolites, while the distribution and abundance patterns of Paropsis atomaria showed some relationship to monoterpene yields.

Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.

Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.