Internship Overview: The Changing Landscapes Initiative

First, I would like to recognize Dr. Iara Lacher and Craig Fergus, as well as the entire Ecology Lab for making the long drive to the Smithsonian Conservation Biology Institute in Front Royal, VA (a 1 hour commute in the morning and 1 hour commute in the evening, every Wednesday, from January to May) well worth the gas. Dr. Lacher and Craig are two wonderfully passionate and witty ecologists that never failed to brighten my day with their dedication, drive, and dad jokes. While I was not an official Smithsonian intern this semester (I was a Changing Landscapes Initiative volunteer, working for internship credit towards my GIS minor), Dr. Lacher and Craig made sure to make this as meaningful an experience as possible.

Over the course of 16 weeks, I was able to familiarize myself with raster data, as well as explore the applications of predictive modeling in advising resource management decisions on local and regional levels. As an environmental scientist, my goal is to translate ecological theory and assessments in a way that allows the general public to understand the implications of their actions on an individual level, as well as community scale. In addition to that goal, I want to be able to inform citizens of the options they have to alter their choices as consumers and producers – for which this internship has taught me considerable approaches for achieving.

During my internship, I examined distributions of the following variables for a six counties and one city (i.e., Clarke County, Fauquier County, Frederick County, Loudoun County, Rappahannock County, Warren County, and Winchester City) during the years 2001 and 2011: percent of individuals employed in the agricultural industry, percent of population that have an associate’s degree or higher, median income, percent of population that was a person of color, population density, poverty percent, distance to water bodies, elevation, geology, land use, soil productivity, protected land areas, protected lands gap status, protected lands ownership status, average annual precipitation, riparian habitat distribution, slope, maximum annual temperature, minimum annual temperature, and travel time to developed areas. My job was to translate this raw data into a visualization (i.e., a map, graph, or table) using ArcMap and Microsoft Excel that illustrate the distribution of each variable across the seven regions. These maps and accompanying statistics act as tools for the Changing Landscapes Initiative to explain the current state, as well as the change in these variables over the past 10 years to various stakeholder groups such as energy companies, developers, and land use managers. The data that were compiled from the U.S. census were divided by census block groups. Until this internship, I had not dealt with census block groups. This is a vital grouping tool to understand, for my future career as an aquatic ecologist, as I hope to examine the influence human populations have on aquatic ecosystem health. I now know that census block groups are a meaningful way to examine the potential impacts of human factors on ecosystems. Beyond utilizing these maps as an explanatory tool, the data I worked with were then incorporated into predictive models using the software, Dinamica.

Prior to the beginning of my internship, my supervisor designed and executed five future scenarios in Dinamica Modeling software that illustrate the status of the natural resources and biodiversity of northwestern Virginia fifty years from now, dependent on the state of the variables listed above. Each scenario depicts a future driven primarily by changes in population size and the political will to either opportunistically change or strategically plan for changes that promote the economy and well being of the community. An example of products I made utilizing this data, were two maps illustrating the types of land cover that accounted for the greatest magnitude of change within each 12-digit hydrologic unit code (HUC-12) area, for each predictive model. This method, HUC-12, was another new way of spatially segmenting data that I had not encountered, and will be useful in my work assessing factors that influence watershed dynamics. The Dinamica model gave output values for the new areas covered by each land cover type. Then, I calculated the change in area for each type and identified which land cover types changed the most utilizing queries in ArcMap. I also had the opportunity to develop a Dinamica Walkthrough guide for a professional workshop surrounding predictive modeling, hosted at the Smithsonian Conservation Biology Institute, based on a basic description of the program.

Deciding how to present each of the data types in a map that accurately represented the data allowed me to further developed by map presentation and data processing skills that will be crucial when I begin presenting my own research to stakeholder groups invested in the projects I work on as an ecologist. While the majority of my internship revolved around developing maps to illustrate the changes that occurred over the past 10 years, some days I would get to work on projects that dealt with data presentation even further down the line. An example of this work is the Project Overview guide that I was able to edit, in such a way that made the presentation of the goals of the Changing Landscapes Initiative, the summary of the work already done, and the summary of the work to be done in the future, clear and easy to understand for the general public. Data presentation is a skill that I have always prided myself in, but having the opportunity to work with real data surrounding a local topic that I can see the impacts of for myself has made the possibility of pursuing my own ecosystem assessment projects more feasible.