Foraging Behaviour of Damage-causing Birds in Table Grape Vineyards in the Orange River Valley, South Africa

The foraging behaviour of damage-causing birds in table grape vineyards was examined in the Orange River valley,Northern Cape Province, South Africa during the summer harvest season from November 2001 to January 2002.Based on a sample of 300 foraging acts observed, it was found that mixed-feeder species fed more on grapes duringthe first month of the harvest (November), when only about 13% of the vineyards bore ripe grapes, than duringthe subsequent two months (December and January). During the latter two months, insects were a more preferredfood item, while grape foraging declined significantly despite the increased availability of grapes, suggesting a shiftin their dietary preferences during the course of the harvesting season. A daily bimodal feeding pattern was alsodetected, with birds feeding more regularly on grapes and other food items from early to late morning and in the lateafternoon, with a marked decrease during midday. There was also a preference for feeding from the top rather thanfrom the side or bottom of bunches. There were clear differences in foraging strategies among the most commonlyrecorded species when these were not feeding on grapes. Mixed feeders fed on invertebrates by gleaning vine barkand foliage, hawking, or foraging in ground litter. Granivorous species generally foraged for plant matter, such asseeds, on the ground, but also foraged on grapes during the early stages of the harvest season (November)

Longitudinal vehicle dynamics : a comparison of physical and data-driven models under large-scale real-world driving conditions

Mathematical models of vehicle dynamics will form essential components of future autonomous vehicles. They may be used within inverse or forward control loops, or within predictive learning systems. Often, nonlinear physical models are used in this context, which, though conceptually simple (especially for decoupled, longitudinal dynamics), may be computationally costly to parameterise and also inaccurate if they omit vehicle-specific dynamics. In this study we sought to determine the relative merits of a commonly used nonlinear physical model of vehicle dynamics versus data-driven models in large-scale real-world driving conditions. To this end, we compared the performance of a standard nonlinear physical model with a linear state-space model and a neural network model. The large-scale experimental data was obtained from two vehicles; a Lancia Delta car and a Jeep Renegade sport utility vehicle. The vehicles were driven on regular, public roads, during normal human driving, across a range of road gradients. Both data-driven models outperformed the physical model. The neural network model performed best for both vehicles; the state-space model performed almost as well as the neural network for the Lancia Delta, but fell short for the Jeep Renegade whose dynamics were more strongly nonlinear. Our results suggest that the linear data-driven model gives a good trade-off in accuracy and simplicity, whilst the neural network model is most accurate and is extensible to more nonlinear operating conditions, and finally that the widely used physical model may not be the best choice for control design

GSBS News, Spring 2015

Cover image by artist Syd Moen; Benefactor News: Smithville fundraiser supports GSBS fellow; New endowment to support MD/PhD Program; Coming full circle: developing and promoting a Core Curriculum; Celebrating 50 Years of Excellence: GSBS Alumni All-Stars shine at Super Panel and 2013 Reunion; Faculty Membership Report; Faculty News: Dr. Vicki Knutson retires; GSBS Outreach Council hosts community Science Night; Student News; 2013-2014 Student Awards; Alumni News; Message to Alumn