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, USA. To overcome missing satellite data due to clouds and produce a 3-d 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 3-d 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.

中文翻译：

---

一种基于卫星的移动预警系统，以减少与濒危物种的互动

地球观测卫星是空间明确的生态系统临近预报和预测的主要研究工具。然而，在将卫星数据整合到利益相关者可用的实时产品中时，存在实际挑战。对预测即时性和准确性的需求意味着预测系统必须考虑丢失的数据和数据延迟，同时向利益相关者提供及时、准确和可操作的产品。对于受法律保护的物种尤其如此。Acipenser oxyrinchus oxyrinchus（大西洋鲟鱼）于 2012 年被列入美国濒危物种法案，这引发了立即的管理行动，以促进种群恢复和增加保护措施。基于现有的研究发生模型，我们在美国特拉华湾开发了大西洋鲟鱼预报系统。为了克服由于云造成的卫星数据丢失并生成海洋状况的 3 维预测，我们在日常观测的卫星数据上实施了数据插值经验正交函数 (DINEOF)。我们将大西洋鲟鱼研究模型应用于 DINEOF 输出，发现它在 3-d 预测中正确预测大西洋鲟鱼遥测事件的发生率超过 90%。类似的框架已被用于预测有害藻华，但据我们所知，这是第一次将物种分布模型应用于 DINEOF 填充数据以生成鱼类预测产品。为了将该产品应用到应用管理环境中，我们与州和联邦组织合作，为州级管理人员和商业渔民开发特拉华河河口的实时和预测风险地图。自动化系统创建这些风险图并将其分发到用户的移动设备，突出显示应避免的区域以减少交互。此外，交互式网络界面允许用户绘制历史、当前、未来和气候风险图以及大西洋鲟鱼发生的基础模型输出。移动系统和网络工具为利益相关者和管理人员提供实时访问估计的大西洋鲟发生率，