Our employees Jaime Nickeson, Pete Ma, and Joanne Nightingale have been working on a project at NASA Goddard under HBS Contract involving Honeybees. For the past few years they have been working with Dr. Wayne Esaias, of code 614.5, on this project utilizing satellite data layers to model and predict the potential movement of Africanized honey bees (AHB) and uses European (domestic) honeybees in a network of hives placed on scales to detect trends in nectar flows.
The timing of honeybee nectar flow and blooming dates are vitally important to agricultural produce and affects our natural ecosystems as well. Our network of scale hives captures this record by measuring the weight of beehives during the year. HoneyBeeNet has detected dramatic shifts of nectar flow timing in the Mid-Atlantic and other regions of the US, building a more robust baseline upon which to predict how the bees and their interaction with plants will respond to climate change. The goal is to expand the network, in both time and space, to help us address these questions. The HoneyBeeNet scale hive network has grown since its inception in 2007, from about 20 sites, in a handful of states, clustered around Maryland, to nearly 100 sites in 31 states today.
Sigma staff members have worked closely with Dr. Esaias and collaborators at the USGS to develop the infrastructure and scientific basis for improving the predicted expansion of the AHB in the U.S. using habitat suitability models. Often referred to as the ‘Killer Bee’ the AHB threatens to interfere with beekeeping industry, still recovering from the effects of Colony Collapse Disorder in recent years. Using NASA’s MODIS vegetation phenology data products, the team has helped the USGS and Colorado State University to develop modeling tools for resource managers to map the AHB in the US. The Sigma/GSFC team provided expertise in bees, GIS, and remote sensing to develop a new tool for bee managers located at Colorado State, that allows users to submit data on AHB presence/absence and run species distribution models on using climate and NASA satellite data layers.