The Gulf Sturgeon Acipenser oxyrinchus desotoi is an anadromous species that inhabits Gulf of Mexico coastal waters from Louisiana to Florida and is listed as threatened under the U.S. Endangered Species Act. Seasonal cues (e.g., freshwater discharge) determine the timing of spawning and migration and may influence the availability of critical habitat during winter months in six estuaries. Large information gaps, especially related to critical estuarine habitat for juveniles, hinder recovery efforts to protect these habitats and assess risks from emerging threats. Using Apalachicola Bay, Florida, as a model system, we developed and analyzed a preliminary Bayesian network model so that we could identify knowledge gaps (i.e., where expert knowledge was lacking) and data gaps (i.e., where data were unavailable) that limit the ability to assess the quantity of critical estuarine habitat for juvenile Gulf Sturgeon. The model hypothesized habitat availability per winter month in estuarine habitat under alternative scenarios of river discharge and length of the winter foraging season. A search for geospatial data sets revealed that the largest gap involved salinity, temperature, and oxygen (i.e., water condition) monitoring data, with data available only for Apalachicola Bay. For the Apalachicola Bay model, data gaps prevented the development of 53% of water condition geospatial data sets and a sensitivity analysis showed that water condition data most limited the ability to predict habitat availability. Expert knowledge was low, and conditional certainty scores showed that the relationships with the lowest certainty were abiotic suitability and habitat availability. Reducing information gaps could aid the development of a model that is appropriate for informing management. Future efforts could prioritize the expansion of water monitoring within critical habitat estuaries and predicting abiotic suitability and habitat availability. Bayesian network models can easily incorporate prior and new information for complex systems. Thus, our model could be updated as future research and monitoring efforts close these information gaps.

中文翻译：

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识别信息不足，以预测海湾Gulf鱼的冬季觅食栖息地

海湾St鱼是一种有害性物种，栖息于路易斯安那州至佛罗里达州的墨西哥湾沿岸水域，并被美国濒危物种法列为受威胁物种。季节线索（例如淡水排放）决定产卵和迁移的时间，并可能影响六个河口冬季月份关键栖息地的可用性。巨大的信息鸿沟，特别是与重要的少年河口栖息地有关的信息，阻碍了为保护这些栖息地和评估新出现的威胁而进行的恢复工作。我们使用佛罗里达州的Apalachicola Bay作为模型系统，开发并分析了初步的贝叶斯网络模型，以便我们可以识别知识差距（即缺乏专家知识的地方）和数据差距（即 无法获得数据的地方），这限制了评估海湾Gulf鱼幼鱼关键河口栖息地数量的能力。该模型假设在河流量和冬季觅食季节长度的替代情景下，河口栖息地每个冬季月份的栖息地可利用性。对地理空间数据集的搜索显示，最大的差距涉及盐度，温度和氧气（即水状况）监测数据，而这些数据仅适用于阿巴拉契科拉湾。对于Apalachicola Bay模型，数据缺口阻止了53％的水状况地理空间数据集的开发，敏感性分析表明，水状况数据最限制了预测栖息地可用性的能力。专家知识不足 条件确定性得分显示，确定性最低的关系是非生物适应性和栖息地可利用性。减少信息鸿沟可以帮助开发适合于通知管理人员的模型。未来的工作可以优先考虑在关键栖息地河口范围内扩大水监测范围，并预测非生物适应性和栖息地可用性。贝叶斯网络模型可以轻松合并复杂系统的现有信息和新信息。因此，随着未来的研究和监测工作弥合这些信息鸿沟，我们的模型可能会得到更新。未来的工作可以优先考虑在关键栖息地河口范围内扩大水监测范围，并预测非生物适应性和栖息地可用性。贝叶斯网络模型可以轻松合并复杂系统的现有信息和新信息。因此，随着未来的研究和监测工作弥合这些信息鸿沟，我们的模型可能会得到更新。未来的工作可以优先考虑在关键栖息地河口范围内扩大水监测范围，并预测非生物适应性和栖息地可用性。贝叶斯网络模型可以轻松合并复杂系统的现有信息和新信息。因此，随着未来的研究和监测工作弥合这些信息鸿沟，我们的模型可能会得到更新。