Modelling temperature, photoperiod and vernalization responses of Brunonia australis (Goodeniaceae) and Calandrinia sp (Portulacaceae) to predict flowering time

Crop models for herbaceous ornamental species typically include functions for temperature and photoperiod responses, but very few incorporate vernalization, which is a requirement of many traditional crops. This study investigated the development of floriculture crop models, which describe temperature responses, plus photoperiod or vernalization requirements, using Australian native ephemerals Brunonia australis and Calandrinia sp. A novel approach involved the use of a field crop modelling tool, DEVEL2. This optimization program estimates the parameters of selected functions within the development rate models using an iterative process that minimizes sum of squares residual between estimated and observed days for the phenological event. Parameter profiling and jack-knifing are included in DEVEL2 to remove bias from parameter estimates and introduce rigour into the parameter selection process. Development rate of B. australis from planting to first visible floral bud (VFB) was predicted using a multiplicative approach with a curvilinear function to describe temperature responses and a broken linear function to explain photoperiod responses. A similar model was used to describe the development rate of Calandrinia sp., except the photoperiod function was replaced with an exponential vernalization function, which explained a facultative cold requirement and included a coefficient for determining the vernalization ceiling temperature. Temperature was the main environmental factor influencing development rate for VFB to anthesis of both species and was predicted using a linear model. The phenology models for B. australis and Calandrinia sp. described development rate from planting to VFB and from VFB to anthesis in response to temperature and photoperiod or vernalization and may assist modelling efforts of other herbaceous ornamental plants. In addition to crop management, the vernalization function could be used to identify plant communities most at risk from predicted increases in temperature due to global warming.

Prediction of breeding values for average fruit weight in mango using a multivariate individual mixed model

Mango is an important horticultural fruit crop and breeding is a key strategy to improve ongoing sustainability. Knowledge of breeding values of potential parents is important for maximising progress from breeding. This study successfully employed a mixed linear model methods incorporating a pedigree to predict breeding values for average fruit weight from highly unbalanced data for genotypes planted over three field trials and assessed over several harvest seasons. Average fruit weight was found to be under strong additive genetic control. There was high correlation between hybrids propagated as seedlings and hybrids propagated as scions grafted onto rootstocks. Estimates of additive genetic correlation among trials ranged from 0.69 to 0.88 with correlations among harvest seasons within trials greater than 0.96. These results suggest that progress from selection for broad adaptation can be achieved, particularly as no repeatable environmental factor that could be used to predict G x E could be identified. Predicted breeding values for 35 known cultivars are presented for use in ongoing breeding programs.

Effect of water quality and season on the population dynamics of Cabomba caroliniana in subtropical Queensland, Australia

Cabomba caroliniana is a submersed aquatic macrophyte that originates from the Americas and is currently invading temperate, subtropical, and tropical freshwater habitats around the world. Despite being a nuisance in many countries, little is known about its ecology. We monitored C. caroliniana populations in three reservoirs in subtropical Queensland, Australia, over 5.5 years. Although biomass, stem length, and plant density of the C. caroliniana stands fluctuated over time, they did not exhibit clear seasonal patterns. Water depth was the most important environmental factor explaining C. caroliniana abundance. Plant biomass was greatest at depths from 2–4 m and rooted plants were not found beyond 5 m. Plant density was greatest in shallow water and decreased with depth, most likely as a function of decreasing light and increasing physical stress. We tested the effect of a range of water physico-chemical parameters. The concentration of phosphorus in the water column was the variable that explained most of the variation in C. caroliniana population parameters. We found that in subtropical Australia, C. caroliniana abundance does not appear to be affected by seasonal conditions but is influenced by other environmental variables such as water depth and nutrient loading. Therefore, further spread will more likely be governed by local habitat rather than climatic conditions.

Effect of water quality and season on the population dynamics of Cabomba caroliniana in subtropical Queensland, Australia

Cabomba caroliniana is a submersed aquatic macrophyte that originates from the Americas and is currently invading temperate, subtropical, and tropical freshwater habitats around the world. Despite being a nuisance in many countries, little is known about its ecology. We monitored C. caroliniana populations in three reservoirs in subtropical Queensland, Australia, over 5.5 years. Although biomass, stem length, and plant density of the C. caroliniana stands fluctuated over time, they did not exhibit clear seasonal patterns. Water depth was the most important environmental factor explaining C. caroliniana abundance. Plant biomass was greatest at depths from 2–4 m and rooted plants were not found beyond 5 m. Plant density was greatest in shallow water and decreased with depth, most likely as a function of decreasing light and increasing physical stress. We tested the effect of a range of water physico-chemical parameters. The concentration of phosphorus in the water column was the variable that explained most of the variation in C. caroliniana population parameters. We found that in subtropical Australia, C. caroliniana abundance does not appear to be affected by seasonal conditions but is influenced by other environmental variables such as water depth and nutrient loading. Therefore, further spread will more likely be governed by local habitat rather than climatic conditions.

Flying-Fox Species Density-A Spatial Risk Factor for Hendra Virus Infection in Horses in Eastern Australia

Hendra virus causes sporadic but typically fatal infection in horses and humans in eastern Australia. Fruit-bats of the genus Pteropus (commonly known as flying-foxes) are the natural host of the virus, and the putative source of infection in horses; infected horses are the source of human infection. Effective treatment is lacking in both horses and humans, and notwithstanding the recent availability of a vaccine for horses, exposure risk mitigation remains an important infection control strategy. This study sought to inform risk mitigation by identifying spatial and environmental risk factors for equine infection using multiple analytical approaches to investigate the relationship between plausible variables and reported Hendra virus infection in horses. Spatial autocorrelation (Global Moran’s I) showed significant clustering of equine cases at a distance of 40 km, a distance consistent with the foraging ‘footprint’ of a flying-fox roost, suggesting the latter as a biologically plausible basis for the clustering. Getis-Ord Gi* analysis identified multiple equine infection hot spots along the eastern Australia coast from far north Queensland to central New South Wales, with the largest extending for nearly 300 km from southern Queensland to northern New South Wales. Geographically weighted regression (GWR) showed the density of P. alecto and P. conspicillatus to have the strongest positive correlation with equine case locations, suggesting these species are more likely a source of infection of Hendra virus for horses than P. poliocephalus or P. scapulatus. The density of horses, climate variables and vegetation variables were not found to be a significant risk factors, but the residuals from the GWR suggest that additional unidentified risk factors exist at the property level. Further investigations and comparisons between case and control properties are needed to identify these local risk factors.