2026-07-15 –, Thomas Swain Room
The Ecosystem Services Market Consortium (ESMC) is expanding its agricultural sustainability programs to include biodiversity outcomes across the United States. To support this effort, we developed a Python-based Biodiversity Metric Module that estimates biodiversity gains associated with agricultural best management practices. The module integrates national land cover data, species occurrence records, protected lands datasets, and soil microbial biomass information within a unified geospatial workflow to generate standardized biodiversity unit estimates. This presentation outlines the ecological framework, computational architecture, and lessons learned while scaling biodiversity assessment across thousands of spatially explicit agricultural fields.
The Ecosystem Services Market Consortium (ESMC) works with agricultural producers across the United States to incentivize sustainable land management. As biodiversity increasingly becomes part of climate and sustainability reporting frameworks, ESMC identified the need for a consistent, scalable method to quantify biodiversity gains associated with agricultural best management practices. Unlike carbon accounting, biodiversity does not reduce to a single stock or flux. It reflects habitat condition, ecological function, spatial connectivity, and recovery through time. Estimating biodiversity change across working agricultural landscapes requires both a sound ecological foundation and robust computational design.
To meet this need, we developed the Biodiversity Metric Module, a Python-based tool that supports biodiversity quantification within ESMC’s Monitoring, Reporting, and Verification platform. The module evaluates agricultural fields using a structured ecological framework and it calculates biodiversity units by integrating five interacting components: habitat quality, functional diversity, conservation context, habitat size, and time-dependent ecological response.
We derived habitat quality from national land cover datasets (CDL, NLCD) and translated land cover classes into ecological condition scores using structured parameter tables aligned with program objectives. We represented functional diversity by analyzing species occurrence records (GBIF) to characterize ecological guild presence for plants, insects, and birds, and we supplemented those data with publicly available soil microbial biomass datasets. The model evaluates landscape context by calculating proximity to protected areas to reflect conservation priority and connectivity. It accounts for habitat size by incorporating the spatial footprint of each management practice. Time-dependent response functions model ecological recovery following practice implementation. The system computes biodiversity units for baseline and practice-change conditions and quantifies net biodiversity gain as their difference.
We operationalized this framework by integrating publicly available, geospatial datasets at multiple spatial resolutions, including raster and vector. Scientific Python tools support spatial processing, numerical computation, and reproducible data management throughout the workflow (e.g. geopandas, rasterio, rasterstats).
We deployed the Biodiversity Metric Module as a Flask-based application within the broader Monitoring, Reporting, and Verification platform architecture already in place. The application accepts spatial field boundaries and management attributes as inputs, executes geospatial and numerical workflows, and returns standardized geospatial outputs. This design enables consistent evaluation across fields while maintaining clear separation between ecological logic and user interface components.
Throughout development, I focused on raster processing, habitat quality scoring, and integration of species occurrence data within spatial buffers. Aligning national land cover rasters at differing resolutions required deliberate aggregation and consistency checks across baseline and practice-change scenarios. Developing both the habitat quality and species function scores reinforced the importance of explicitly documenting ecological assumptions and spatial bias. These challenges highlighted the importance of modular design and transparent assumptions when building biodiversity metrics at national scale.
The development of this tool highlights broader challenges in applied biodiversity modeling, including data limitations, spatial bias in species occurrence records, and temporal mismatches between ecological processes and national-scale datasets. It also demonstrates how scientific Python enables integration of diverse environmental data into scalable, reproducible, geospatial workflows. As biodiversity accounting continues to evolve, computational frameworks like this will play an increasingly important role in connecting ecological science with large-scale environmental decision-making.
Hannah Ferriby is an Environmental Data Scientist at Tetra Tech based out of the Lansing, MI area. She received her BSE in Environmental Engineering from the University of Michigan and her MS in Biosystems Engineering from Michigan State University. She specializes in geospatial and remote sensing analysis with a focus on water quality applications. Her work ranges from cyanobacteria harmful algal bloom forecasting to numeric nutrient water quality criteria development to biodiversity metric creation. Hannah is newer to coding in Python but has extensive background in R and JavaScript (Google Earth Engine).