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首页> 外文期刊>North American Journal of Fisheries Management >Evaluation of Bayesian Networks for Predicting Spawning Habitat Quality of Chinook Salmon in Data-Poor Watersheds
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Evaluation of Bayesian Networks for Predicting Spawning Habitat Quality of Chinook Salmon in Data-Poor Watersheds

机译：贝叶斯网络评估贝塞尔网络，以预测数据贫困流域中奇努克鲑鱼的产卵栖息地素质

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摘要

California's native salmonid populations are in decline, as evidenced by the 2008 fishing closures on one historically abundant species, Chinook Salmon Oncorhynchus tshawytscha. One major impact on spring-run Chinook Salmon within the Central Valley has been the modification of natal rivers. Bayesian networks are one modeling method that could help researchers to understand these systems and direct restoration efforts. We constructed a Bayesian network for Deer Creek, in Tehama County, to assess its utility as a tool for guiding restoration of spawning habitats for spring-run Chinook Salmon. We applied this network on a riffle-pool subreach scale to determine the suitability of each reach for spawning, indicated by the probability of redd presence. To evaluate the network we conducted sensitivity analyses and thereby determined the influence of each variable and the degree to which each variable informed the probability of redd presence. Sensitivity analyses were run for networks trained with two different stream alignments, one derived from the National Hydrography Dataset from the U.S. Geological Survey and one derived from tracing aerial imagery. We also conducted a Mann-Whitney test comparing redd densities from subreaches predicted to be good with those predicted to be poor for four fish passage condition scenarios. Of the four scenarios we modeled with the network, three exhibited significantly higher redd densities in subreaches designated as good by the network. Our results indicate that Bayesian networks can be used to predict habitat use and prioritize restoration in a data-poor northern California watershed.
机译：加州的本土鲑鱼种群正在衰落，这是由2008年捕鱼封闭在历史上丰富的物种，Chinook Salmon Oncorhynchus Tshawytscha的捕鱼封闭所证明。在中央山谷内春天陆桐鲑鱼的一个重大影响一直是纳塔尔河流的修改。贝叶斯网络是一种建模方法，可以帮助研究人员了解这些系统和直接恢复努力。我们为鹿河县建造了贝叶斯网络，以评估其实用性，作为一个工具，以指导春季陆克鲑鱼的产卵栖息地的工具。我们在Riffle-Pool SubReach Scale上应用了这个网络，以确定每个覆盖范围的适用性，以REDD存在的概率表示。为了评估网络，我们进行了灵敏度分析，从而确定了每个变量的影响以及每个变量通知REDD存在概率的程度。为具有两个不同流对准的网络运行敏感性分析，其中一个来自美国地质调查的国家水文数据集，一个来自追踪空中图像的人。我们还进行了一个曼诺 - 惠特尼测试，比较了预测的子弹中的redd密度，预计为四种鱼类通道条件方案预测的那些。在我们与网络建模的四种方案中，三个在网络指定为良好的子场中的冗长密度显着更高。我们的结果表明，贝叶斯网络可用于预测栖息地使用，并在数据贫困地中的北加州流域中优先考虑恢复。

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