Flight directionality of Tribolium castaneum soon after take-off under glasshouse and field conditions

Flight directionality of the rust-red flour beetle, Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae), was investigated under glasshouse and field conditions using sticky traps placed around dense experimental infestations of T. castaneum derived from field-collected samples. Although beetles of this species are known to fly quite readily, information on flight of beetles away from grain resources is limited. Under still glasshouse conditions, T. castaneum does not demonstrate strong horizontal or vertical trajectories in their initial flight behaviour. Flight was significantly directional in half of the replicates, but trapped beetles were only weakly concentrated around the mean direction of flight. In the field, by contrast, emigration of T. castaneum was strongly directional soon after flight initiation. The mean vector lengths were generally >0.5 which indicates that trapped beetles were strongly concentrated around the calculated mean flight direction. A circular-circular regression of mean flight vs. mean downwind direction suggested that flight direction was generally correlated with downwind direction. The mean height at which T. castaneum individuals initially flew was 115.4 ± 7.0 cm, with 58.3% of beetles caught no more than 1 m above the ground. The height at which beetles were trapped did not correlate with wind speed at the time of sampling, but the data do indicate that wind speed significantly affected T. castaneum flight initiation, because no beetles (or very few; no more than three) were trapped in the field when the mean wind speed was above 3 m s−1. This study thus demonstrates that wind speed and direction are both important aspects of flight behaviour of T. castaneum, and therefore of the spatio-temporal dynamics of this species.

Estimation of light interception in research environments: A joint approach using directional light sensors and 3D virtual plants applied to sunflower (Helianthus annuus) and Arabidopsis thaliana in natural and artificial conditions

Light interception is a major factor influencing plant development and biomass production. Several methods have been proposed to determine this variable, but its calculation remains difficult in artificial environments with heterogeneous light. We propose a method that uses 3D virtual plant modelling and directional light characterisation to estimate light interception in highly heterogeneous light environments such as growth chambers and glasshouses. Intercepted light was estimated by coupling an architectural model and a light model for different genotypes of the rosette species Arabidopsis thaliana (L.) Heynh and a sunflower crop. The model was applied to plants of contrasting architectures, cultivated in isolation or in canopy, in natural or artificial environments, and under contrasting light conditions. The model gave satisfactory results when compared with observed data and enabled calculation of light interception in situations where direct measurements or classical methods were inefficient, such as young crops, isolated plants or artificial conditions. Furthermore, the model revealed that A. thaliana increased its light interception efficiency when shaded. To conclude, the method can be used to calculate intercepted light at organ, plant and plot levels, in natural and artificial environments, and should be useful in the investigation of genotype-environment interactions for plant architecture and light interception efficiency. This paper originates from a presentation at the 5th International Workshop on Functional–Structural Plant Models, Napier, New Zealand, November 2007.

Healthy hardwoods. A field guide to pests, diseases and nutritional disorders in subtropical hardwoods.

Healthy hardwoods: A field guide to pests, diseases and nutritional disorders in subtropical hardwoods can be used to help identify the common damaging insects, fungi and nutritional disorders in young eucalypt (Eucalyptus and Corymbia species) plantations in subtropical eastern Australia. This guide includes photographs of each insect, fungus and nutritional disorder and the damage they cause, along with a brief description to aid identification. A brief host list for insects and fungi, including susceptibility and occurrence, is provided as a guide only. A hand lens will be useful, especially to identify fungi. Although it is possible to identify insects and fungi from these photographs, laboratory examination will sometimes be necessary. For example, microscopes and culturing media might be used to identify fungi. Information about four exotic pests and diseases has also been included in the Biosecurity threats chapter. Potentially, these would have a severe impact on plantation and natural forests if introduced into Australia. To prevent establishment of these pests, early detection and identification is crucial. If an exotic insect or disease is suspected, then an immediate response is required. Usually, the first response will be to contact the nearest Australian Quarantine and Inspection Service office or forestry agency to seek advice.

Networks of stored grain diseases and pests: Strategies for sampling and mitigation

Pathogens and pests of stored grains move through complex dynamic networks linking fields, farms, and bulk storage facilities. Human transport and other forms of dispersal link the components of this network. A network model for pathogen and pest movement through stored grain systems is a first step toward new sampling and mitigation strategies that utilize information about the network structure. An understanding of network structure can be applied to identifying the key network components for pathogen or pest movement through the system. For example, it may be useful to identify a network node, such as a local grain storage facility, through which grain from a large number of fields will be accumulated and move through the network. This node may be particularly important for sampling and mitigation. In some cases more detailed information about network structure can identify key nodes that link two large sections of the network, such that management at the key nodes will greatly reduce the risk of spread between the two sections. In addition to the spread of particular species of pathogens and pests, we also evaluate the spread of problematic subpopulations, such as subpopulations with pesticide resistance. We present an analysis of stored grain pathogen and pest networks for Australia and the United States.