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首页> 外文期刊>Ecological indicators >Reciprocal extrapolation of species distribution models between two islands - Specialists perform better than generalists and geological data reduces prediction accuracy
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Reciprocal extrapolation of species distribution models between two islands - Specialists perform better than generalists and geological data reduces prediction accuracy

机译:在两个岛屿之间进行物种分布模型的倒数外推-专家的表现要优于通才,并且地质数据降低了预测的准确性

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This study aims to test the extrapolation effects of species distribution models (SDM) using three groups of predictor variables: climate, relief and geology (bedrock type). We highlight potential ecological differences for selected taxa, regarding both generalists and specialists in terms of edaphic conditions. We used distributional data of 12 woody species shared by two large Mediterranean islands (Crete and Sicily) to calibrate Maxent models of their potential distribution. We trained models with data from Crete and extrapolated to Sicily and vice versa. We tested ten proxies for the three variable groups and compared AUC values as a measure of model performance. Extrapolation of SDMs worked fairly well across species and islands for those models including climatic and relief data, while those including geological information performed worse for the tested species. Edaphic generalists performed less well compared to edaphic specialists. The latter performed best with climate data included in models and more poorly with only geological data. This may be due to differences in bedrock type affinities of species between the two islands.
机译:这项研究旨在使用三组预测变量测试物种分布模型(SDM)的外推效应:气候,起伏和地质(基岩类型)。我们针对通才条件方面的通才和专家,重点介绍了选定分类单元的潜在生态差异。我们使用了两个大型地中海岛屿(克里特岛和西西里岛)共有的12种木本物种的分布数据来校准其潜在分布的Maxent模型。我们使用来自克里特岛的数据对模型进行了训练,并推断到西西里岛,反之亦然。我们测试了三个变量组的十个代理,并比较了AUC值作为模型性能的度量。在包括气候和地形数据在内的那些模型中,SDM的外推在整个物种和岛屿上的效果都很好,而那些包含地质信息的模型对被测物种的表现则较差。与专业专家相比,精通专家的表现较差。后者在模型中包含的气候数据中表现最佳,而在仅地质数据中则表现较差。这可能是由于两个岛屿之间物种的基岩类型亲和力不同。

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