Neolema ogloblini- An agent in the biological control of Tradescantia
Tradescantia (Tradescantia fluminensis) is the worst weed in New Zealand. By smothering and shading out seedlings, Tradescantia prevents forest regeneration. Current control methods are ineffective and simultaneously cause harm to native forest. In 2011 Neolema ogloblini, a Brazilian beetle was introduced into New Zealand as a biological control for Tradescantia. To be successful in New Zealand, a country with different environmental factors, the beetles’ ranges of preference (temperature and light intensity) had to be investigated. A gender specific trait also identified, to enable desired sex ratios within founding populations to be selected. [18] This would ensure that the beetles are not released in areas of physiological stress, and can be optimised to have the greatest impact on Tradscantia. To establish how the intensity of light affects the distribution and amount of Tradescantia eaten by N.ogloblini a choice chamber investigation was conducted. Different layers of shade cloth provided a range of light intensities 150-3450Lux (likely to be found under forest canopy where Tradescantia is problematic). Thirty beetles of a range of sizes and approximately same maturity were randomly distributed through the chambers. Each chamber contained a shoot of Tradescantia with 5 leaves. After a 24hour period the number of beetles in each chamber were counted and the amount of surface area of the leaves eaten measured. The effect of temperature on the amount of leaf surface area eaten was investigated by selecting 90 beetles of a range of sizes and withholding food for 24hours. Five beetles were placed in each of three containers containing two leaves. Each trial container was precooled/warmed to the test temperature before the beetles were added. Leaves of a similar size, shape, mass and maturity were used. All leaves were genetically identical and collected from the same location. Sets of three containers were held in the dark at the following temperatures for 24hours: 9°C, 15°C, 20°C, 25°C, 30°C and 35°C. The surface area of leaf eaten at each temperature (mm2) was calculated. Lastly, microscopic dissections were conducted, using 32 beetles ranging in size, to establish if length (measured from the top of the head to the base of the abdomen) could be used as a phenotypic marker to identify beetle gender. While only a very weak positive relationship between increasing light intensity and the number of beetles was found a significantly higher area of leaf was eaten at a light intensity of 3450Lux compared to 150Lux. The amount of leaf area eaten is significantly reduced at temperatures of 15˚C and below, and significantly increased at 35˚C. There is no significant difference in the amount of leaf area eaten when comparing temperatures between 20-30˚C. Females have on average a larger body length (median=4.92mm) than the males (median=4.215mm). Therefore, sites with warmer temperatures in dappled light conditions (3450Lux) should be prioritised for the release of N.ogloblini, as this is the location in New Zealand at which their use as a biological control will be optimised. Beetle length can be confidently used to select desired gender ratios.
Geographic Belts for Hurricane Landfall Location Prediction
When predicting a hurricane’s landfall location, small improvements in accuracy result in large savings of lives, property, and money. The project’s purpose was to apply a breakthrough method that can predict the geographic location of a hurricane’s landfall with high accuracy. Researchers have known for a long time that there are strong correlations between a hurricane’s landfall location and the geographic regions its track passes through. However, no methods have been developed to mathematically and explicitly describe these correlations. Consequently, the correlations can only serve to meteorologists as vague guidelines for their guestimates and are not usable in making practical forecasts. By studying the correlations and performing numerical optimization on historical hurricane data, this research discovered a set of geographic belt regions in the Gulf of Mexico that can be used as landfall location predictors. When a hurricane passes through any one of these belt lines, a prediction can be made by extending the hurricane’s moving direction vector towards land – the intersection point of this extension line with the coastline is the predicted landfall location. This prediction method is simple and straightforward. It only uses basic measurements from meteorological satellites: the hurricane’s real-time locations and moving directions. In conclusion, when compared to existing methods, the predictive belt method (PBM) created in this research provides a landfall location forecast with higher accuracy. Verification with historical hurricane data demonstrated that the PBM’s average error is less than 50% of the National Hurricane Center models’ error.