51,99 €
inkl. MwSt.
Versandkostenfrei*
Versandfertig in über 4 Wochen
  • Broschiertes Buch

Bumble bees are significant pollinators of many plants, and are particularly attracted to mass-flowering crops such as Oilseed Rape, which they cross-pollinate. The assessment of pollinator-mediated cross-pollination events could be important when considering containment strategies of genetically modified (GM) crops, such as GM varieties of Oilseed Rape, but requires a landscape-scale understanding of pollinator movements.In this book, a simulation approach to understanding landscape-scale bumble bee movements is presented. HARVEST is a computer simulation that uses Artificial Intelligence…mehr

Produktbeschreibung
Bumble bees are significant pollinators of many plants, and are particularly attracted to mass-flowering crops such as Oilseed Rape, which they cross-pollinate. The assessment of pollinator-mediated cross-pollination events could be important when considering containment strategies of genetically modified (GM) crops, such as GM varieties of Oilseed Rape, but requires a landscape-scale understanding of pollinator movements.In this book, a simulation approach to understanding landscape-scale bumble bee movements is presented. HARVEST is a computer simulation that uses Artificial Intelligence techniques and Agent Based Modelling to predict the landscape-scale foraging patterns of Bumble Bees. The details of the model are explained, and the model is validated at the small-patch scale to demonstrate its capabilities. The model is then used to make landscape-scale predictions of the potential impact of Bumble Bees in a landscape containing both GM and non-GM crops. Potential GM containment strategies are also reviewed.
Autorenporträt
Dr Daniel Chalk, PhD : Studied Computer Science at University of Exeter, before achieving a PhD in Biosciences at the institution. Currently working as an Associate Research Fellow in Applied Operational Research in Healthcare with the Peninsula Medical School. Applying Operational Research techniques to improve delivery in the NHS.