Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
  • BCO-DMO Processing Description
  • Problem Description
• Data Files
• Related Publications
• Related Datasets
• Parameters
• Project Information
• Funding

No raw data is included in this dataset. For information pertaining to the collection methods for the data used to generate this dataset, refer to methods sections of linked datasets

The satellite-scale habitat data processing workflow uses a combination of Bash and Python scripts to calculate habitat metrics and spatial relationships across multiple scales. Designed for use on a Slurm-based high-performance computing cluster, the workflow efficiently handles large spatial datasets through parallelized processing. It is divided into two main phases: preprocessing and scale-specific calculations.

The preprocessing phase, managed by the preprocess_satscale.sh Bash script and executed through the satscale_preprocessing_241022.py Python script, prepares habitat shapefiles and site data for subsequent analyses. Habitat polygons from google2022.shp are reprojected to EPSG:32615 and assigned numerical identifiers for efficient processing. Water and mudflat polygons are consolidated into a single "Water" multipolygon for streamlined analyses. The processed habitat polygons are saved as GeoPackage files (habitat_poly.gpkg, water_poly.gpkg), while site data from drop_field.csv is georeferenced, reprojected, and saved as pffw_sites.gpkg. Wind data from CO-OPS_8761724_met.csv is used to calculate fetch distances for each site based on the prevailing wind direction, determined from average wind vectors. These fetch distances are then integrated into the site dataset.

The scale-specific calculations phase, managed by the permutations_satscale.sh Bash script and the satscale_permutations_241022.py Python script, calculates habitat metrics for various combinations of buffer distances (100–1000 meters) and edge distances (1, 3, 5 meters). Buffers are generated around each site, and habitat polygons are clipped to these buffers to calculate land-to-water ratios, edge proportions (e.g., mangrove and marsh edge lengths), and classifications of sites into "mangrove," "marsh," or "mixed" categories based on habitat thresholds. Fetch distance is also included as a metric for wind and wave exposure. The outputs for each buffer and edge combination are saved as CSV files in the output/satscale directory, with filenames structured as google2022_edge[distance]_buf[distance].csv.

To improve efficiency, the workflow employs a Slurm job array, enabling parallel processing of multiple scale permutations. The Python scripts utilize geopandas for spatial processing and implement memory management to handle large datasets effectively. This workflow plays a critical role in calculating fine-scale habitat metrics required for analyzing species-habitat relationships in estuarine environments. The calculated metrics, including edge proportions, land-to-water ratios, and fetch distances, serve as key predictors for ecological models, supporting analyses of habitat suitability and the impacts of habitat changes such as mangrove encroachment. By optimizing calculations across scales, the workflow ensures accurate and efficient processing, making it integral to the project’s success.

* created zip files per phase: preprocessing phase, calculations and output files.

NSF Award Abstract:
Coastal marshes provide a suite of vital functions that support natural and human communities. Humans frequently take for granted and exploit these ecosystem services without fully understanding the ecological feedbacks, linkages, and interdependencies of these processes to the wider ecosystem. As demands on coastal ecosystem services have risen, marshes have experienced substantial loss due to direct and indirect impacts from human activity. The rapidly changing coastal ecosystems of Louisiana provide a natural experiment for understanding how coastal change alters ecosystem function. This project is developing new metrics and tools to assess food web variability and test hypotheses on biodiversity and ecosystem function in coastal Louisiana. The research is determining how changing habitat configuration alters the distribution of energy across the seascape in a multitrophic system. This work is engaging students from the University of Louisiana Lafayette and Dillard University in placed-based learning by immersing them in the research and local restoration efforts to address land loss and preserve critical ecosystem services. Students are developing a deeper understanding of the complex issues facing coastal regions through formal course work, directed field work, and outreach. Students are interacting with stakeholders and managers who are currently battling coastal change. Their directed research projects are documenting changes in coastal habitat and coupling this knowledge with the consequences to ecosystems and the people who depend on them. By participating in the project students are emerging with knowledge and training that is making them into informed citizens and capable stewards of the future of our coastal ecosystems, while also preparing them for careers in STEM. The project is supporting two graduate students and a post-doc.

The transformation and movement of energy through a food web are key links between biodiversity and ecosystem function. A major hurdle to testing biodiversity ecosystem function theory is a limited ability to assess food web variability in space and time. This research is quantifying changing seascape structure, species diversity, and food web structure to better understand the relationship between biodiversity and energy flow through ecosystems. The project uses cutting edge tools and metrics to test hypotheses on how the distribution, abundance, and diversity of key species are altered by ecosystem change and how this affects function. The hypotheses driving the research are: 1) habitat is a more important indirect driver of trophic structure than a direct change to primary trophic pathways; and 2) horizontal and vertical diversity increases with habitat resource index. Stable isotope analysis is characterizing energy flow through the food web. Changes in horizontal and vertical diversity in a multitrophic system are being quantified using aerial surveys and field sampling. To assess the spatial and temporal change in food web resources, the project is combining results from stable isotope analysis and drone-based remote sensing technology to generate consumer specific energetic seascape maps (E-scapes) and trophic niche metrics. In combination these new metrics are providing insight into species’ responses to changing food web function across the seascape and through time.

This project is jointly funded by Biological Oceanography and the Established Program to Stimulate Competitive Research (EPSCoR).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.