'Rubygem' strawberry

'Rubygem', a new short-day strawberry (Fragaria xananassa Duch.), produces high yields of moderately firm, attractive well-flavored fruit from late autumn through early spring in the strawberry-growing district in Southeast Queensland. 'Rubygem' is recommended for trial in areas with mild winter climates, especially where rainfall is unlikely and a well-flavored berry is required.

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.

Research into crown rot and wilt diseases of strawberries in Queensland

Plant losses due to fungal diseases in strawberry (Fragaria × ananassa Duch.) can potentially cause total loss of production. Three fungal pathogens, Fusarium oxysporum f. sp. fragariae, Colletotrichum gloeosporioides and Macrophomina phaseolina, cause similar crown rot and wilt symptoms in strawberries in Queensland. Since the withdrawal of methyl bromide in 2005, no effective chemical control of any of the three pathogens has been available. This study aims at identifying sources of plant genetic resistance that can be used in the breeding program to develop resistant cultivars for use as part of an integrated disease management plan in commercial strawberry production. Results from glasshouse pathogenicity and screening trials on the three pathogens showed that when breeding for resistance against a pathogen, resistance to other pathogens also needs to be considered, e.g., cultivar 'Festival' is resistant to F. oxysporum f. sp. fragariae, but is highly susceptible to C. gloeosporioides. The cultivars 'Earlisweet', 'Kabarla' and 'Phenomenal', two seedling clones and four DAFF breeding lines were resistant to C. gloeosporioides. Cultivar 'Suncoast Delight' showed the most promising level of resistance to M. phaseolina. These cultivars, breeding lines and seedling selections have been made available for incorporation into the crossing program to support the Queensland strawberry breeding program.