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Predicting distributions of seven bitterling fishes in northern Kyushu, Japan

机译:预测日本九州北部七种苦涩鱼的分布

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The distributions of seven bitterling species and subspecies-Tanakia lanceolata, T. limbata, Acheilognathus tabira nakamurae, A. rhombeus, Rhodeus ocellatus kurumeus, R. ocellatus ocellatus, and R. atremius atremius-in northern Kyushu were predicted using generalized linear models (GLMs) in order to provide information helpful for conserving native bitterlings and preventing the expansion of alien bitterling species. Predictions were made according to the following procedure: (1) a set of GLMs for each species was formulated using environmental data from 710 sites that were derived using digital maps and GIS software, from which the best fit model for each species was selected using the Akaike information criterion for predicting the fish occurrence, (2) model performance was evaluated based on the receiver-operating characteristics (ROC) analysis using occurrence and environmental data from 362 sites, and (3) potential distributions of the bitterling were analyzed using the best fit models and environmental data for 1,272 sites, of which 200 data points without occurrence data were prepared. The best fit models revealed that 4-6 environmental factors were important in predicting seven bitterling distributions, which was supported by the area under the ROC curve (AUC) values of these fishes ranging from 0.753 to 0.927. The AUC values in model evaluation were significantly greater than 0.5 for six fishes, suggesting the moderate accuracies of these best fit models for predicting the fish distributions. These predictive models can be used for evaluating potential native bitterling richness and the potential distribution expansion of an alien subspecies.
机译:九州北部使用广义线性模型预测了七种苦味物种和亚种-圆叶草(Tanakia lanceolata),芒草(T. limbata),油菜纲(Acheilognathus tabira nakamurae),菱形拟南芥(Rhodeus ocellatus kurumeus,R。 ),以提供有助于保护本地苦味和防止外来苦味物种扩展的信息。根据以下程序进行预测:(1)使用数字地图和GIS软件得出的710个站点的环境数据,为每个物种制定了一套GLM,然后使用以下方法为每个物种选择最合适的模型: Akaike信息准则可预测鱼的发生,(2)使用来自362个地点的发生和环境数据,基于接收者操作特征(ROC)分析,评估模型性能,(3)使用最佳方法分析苦味的潜在分布1,272个地点的拟合模型和环境数据,其中准备了200个没有发生数据的数据点。最佳拟合模型显示,4-6个环境因素对于预测7种苦味分布很重要,这些鱼的ROC曲线下面积(AUC)值介于0.753至0.927之间,这支持了这一点。在模型评估中,六种鱼的AUC值显着大于0.5,这表明这些最佳拟合模型在预测鱼的分布方面具有中等精度。这些预测模型可用于评估潜在的本地苦涩丰富度和外来亚种的潜在分布范围。

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