Earth observing satellites are a major research tool for spatially explicit ecosystem nowcasting and forecasting. However, there are practical challenges when integrating satellite data into usable real-time products for stakeholders. The need of forecast immediacy and accuracy means that forecast systems must account for missing data and data latency while delivering a timely, accurate and actionable product to stakeholders. This is especially true for species that have legal protection. Acipenser oxyrinchus oxyrinchus (Atlantic Sturgeon) were listed under the United States Endangered Species Act in 2012, which triggered immediate management action to foster population recovery and increase conservation measures. Building upon an existing research occurrence model, we developed an Atlantic Sturgeon forecast system in the Delaware Bay, U.S.A. To overcome missing satellite data due to clouds and produce a three-day forecast of ocean conditions, we implemented Data Interpolating Empirical Orthogonal Functions (DINEOF) on daily observed satellite data. We applied the Atlantic Sturgeon research model to the DINEOF output and found that it correctly predicted Atlantic Sturgeon telemetry occurrences over 90% of the time within a three-day forecast. A similar framework has been utilized to forecast harmful algal blooms, but to our knowledge, this is the first time a species distribution model has been applied to DINEOF gap-filled data to produce a forecast product for fishes. To implement this product into an applied management setting, we worked with state and federal organizations to develop real-time and forecasted risk maps in the Delaware River Estuary for both state level managers and commercial fishers. An automated system creates and distributes these risk maps to subscribers’ mobile devices, highlighting areas that should be avoided to reduce interactions. Additionally, an interactive web interface allows users to plot historic, current, future, and climatological risk maps as well as the underlying model output of Atlantic Sturgeon occurrence. The mobile system and web tool provide both stakeholders and managers real-time access to estimated occurrences of Atlantic Sturgeon, enabling conservation planning and informing fisher behavior to reduce interactions with this endangered species while minimizing impacts to fisheries and other projects.

This dataset and code is based on the linked article (Breece et al. 2018) and satellite data from the MODIS-Aqua sensor for SST and ocean color estimates and Visible Infrared Imaging Radiometer suite (VIIRS) on board Suomi-NPP, launched in 2011 in a combined effort between NASA and the National Oceanographic and Atmospheric Administration to extend and improve MODIS data products into the foreseeable future through continued launches of similar sensors on future satellites through the Joint Polar Satellite System (https://www.jpss.noaa.gov/launch.html).

To properly use this dataset the user must use program R and the associated packages in the code. To expand on the example climatology contained within the user would be required to download and process satellite data from VIIRS. The associated manuscript and Breece et al. 2018 are very informative in the production, use, and function of this dataset.