Evaluating ensemble forecasts of plant species distributions under climate change
Evaluating ensemble forecasts of plant species distributions under climate change
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DOI：
10.1016/j.ecolmodel.2013.07.006
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发表时间：
2013-09-24
影响因子：
3.1
通讯作者：
Mynsberge, Alison R.
中科院分区：
文献类型：
--
作者：
Crimmins, Shawn M.;Dobrowski, Solomon Z.;Mynsberge, Alison R.
物种分布模型(SDMS)通常用于评估潜在物种在气候变化下的范围转移或灭绝风险。有人建议，通过避免单一建模方法中固有的偏差或预测误差，使用集合预测，其中使用各种模型算法来生成共识预测，比使用单独的SDMS更可取。虽然有几项研究使用交叉验证或数据分割方法评估了集合预测的性能，但很少有研究通过使用时间独立的模式验证数据来评估气候变化下的集合预测的预测精度。我们使用5种SDM方法对20世纪30年代加州145种维管植物的分布进行了一致的预测，并将它们的预测与目前的分布进行了对比，时间跨度约为75年。当用部分模型训练数据进行评估时，共识预测非常准确，平均AUC值为0.97。假阳性和假阴性错误率也很低，表现出与随机森林模型相似的性能。然而，当用时间独立的数据进行评估时，共识预测的准确性与广义线性模型和广义加性模型相似，平均AUC值为0.83。我们的结果表明，当使用数据划分方法时，共识预测所表现出的高水平预测精度可能不能反映其在预测时间独立数据时的性能。我们认为，共识预测可能不是预测未来气候变化下物种分布的最佳方法，因为与更容易对模型结构进行生态解释的传统建模方法相比，它们在新的时间域可能不提供更好的预测精度。(C)2013爱思唯尔B.V.保留所有权利。
Species distributions models (SDMs) are commonly used to assess potential species' range shifts or extinction risk under climate change. It has been suggested that the use of ensemble forecasts, where a variety of model algorithms are used to generate consensus predictions, are preferred to individual SDMs by avoiding bias or prediction error inherent in a single modeling approach. Whereas several studies have assessed the performance of ensemble predictions using cross-validation or data-partitioning approaches, few studies have assessed the predictive accuracy of ensemble forecasts under climate change by using temporally independent model validation data. We used five SDM approaches to develop consensus forecasts of distributions of 145 vascular plant species from California in the 1930s and tested their projections against current distributions, a span of approximately 75 years. When evaluated with a portion of the model training data, consensus forecasts were highly accurate with an average AUC value of 0.97. False positive and false negative error rates were also low, exhibiting similar performance to random forest models. However, when evaluated with temporally independent data, the accuracy of consensus forecasts was similar to that of generalized linear and generalized additive models, with an average AUC value of 0.83. Our results suggest that the high levels of predictive accuracy exhibited by consensus forecasts when using data partitioning approaches may not reflect their performance when predicting temporally independent data. We contend that consensus forecasts may not represent the best approach for predicting species distributions under future climatic change, as they may not provide superior predictive accuracy in novel temporal domains compared to traditional modeling approaches that more readily lend themselves to ecological interpretation of model structure. (C) 2013 Elsevier B.V. All rights reserved.