GIS is the backbone of many scientific investigations and fieldwork. Spatial analysis is so much more than making maps; it allows us to link data across many variables to a point in time and space. It’s a crucial resume item for many fields these days.
The problem is that doing spatial analysis with ArcGIS takes up a lot of storage, a lot of processing power, and a lot of patience. Files get corrupted, geoprocessing tools run for 15 hours and then break, and inconsistent data entry causes headaches for analysis. I had a little GIS experience from college, but had never taken part in big spatial analysis projects.
The New Mexico State Office has been running a Rare Plants Monitoring program for three years. This program, supported by the ERNMD and New Mexico Natural Heritage Program, aims to gather consistent population monitoring on rare plants around the state. In 2017 at the program’s inception, six species were being monitored. In 2019, we’re aiming to monitor ten species across New Mexico. For many of these species, there’s little hope. Between resource extraction, habitat fragmentation, and climate change, too many subpopulations are disappearing, and reproductive effort is compromised.
The Pecos Sunflower (Helianthus paradoxus) is not such a hopeless case. This annual sunflower loves seeps and sandy banks along the Rio Grande and Pecos River. Some populations fluctuate between hundreds and hundreds of thousands of individuals year-to-year due to a robust seed bank, and this habitat isn’t threatened by oil and gas development. The major threat to this sunflower is water table depletion that dries up the cienegas that it depends on. The NMSO Rare Plants Monitoring crew will be monitoring known populations and searching for new ones this field season, and this project begins with a lot of GIS prep work.
By comparing the habitats (Ecosites, soil, and landform data) of known populations to the combinations of factors that exist in the landscape, we can model potential habitats for Pecos Sunflower. After generating multiple models that weight different variables different amounts, we just need to see which are on BLM lands and head to the field to find them. Simple, right? Wrong. Even bringing in the right data from data.gov can be a whole-day task, and processes often take 20 hours to run. Imagine a photo of New Mexico with a 10 meter resolution, and then imagine comparing dozens of those photos, pixel by pixel, to generate more. Lots can go wrong, and sometimes you don’t know until much later.
This process has been frustrating, enlightening, and very interesting, and I’m very thankful I’ve got the resources to learn more about this crucial system in my first month at the BLM. These skills are transferable almost anywhere, and it’s exciting to get so much hands-on experience in GIS early in my career.
BLM — New Mexico State Office