Using GIS to model how climate change and land use will affect the abundance of common ragweed

The big picture of the project I am working in is to model how climate change and difference in land use will affect the allergenic potency of Artemisia artemissifolia, better known as common ragweed. This is the first year of a four-year study, so creating a database that will work for the rest of the project is indispensable. I am working on developing part of a geodatabase containing a myriad of GIS shape files, from “all roads” layers to layers containing parcel owner information and population densities.

Using GIS, my team has created a layer that combines three different land cover categories (Forest, Open and Developed) with three different averaged temperatures. This combination gives us nine categories (E.g. Open, Low Temperature). We have up to 10 plots created randomly in each category, and I have created maps that facilitate us access to our plots by collecting detailed information such as addresses and land use. We have surveyed most of our plots in Massachusetts, New York, Connecticut, and New Jersey, and we expect to go to Vermont and New Hampshire in the next few weeks. 

Once we are at our plots, we also look for factors, other than temperature and land cover, that might affect ragweed distribution at a finer scale. For example, we’ve been in plots under the same category, but completely different land uses. One might be a baseball field and the other an agricultural field. Or even if both are agricultural fields, plowing practices and distance to right-of-ways might affect ragweed population per plot, so we have to be careful when collecting data to pay attention to those details. 

Building a bridge between environmental and social factors is essential to understand how ragweed will affect human populations in the future, and that is exactly what I am trying to do. With the collected data, I am going to do a spatial distribution model showing ragweed abundance using the characteristics mentioned before as parameters, and linking them to socio-economic variables and human population density.