Spatial filtering to reduce sampling bias can improve the performance of ecological niche models
Spatial filtering to reduce sampling bias can improve the performance of ecological niche models
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DOI：
10.1016/j.ecolmodel.2013.12.012
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发表时间：
2014-03-10
影响因子：
3.1
通讯作者：
Anderson, Robert P.
中科院分区：
文献类型：
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作者：
Boria, Robert A.;Olson, Link E.;Anderson, Robert P.
本研究采用空间滤波的发生数据的目的是减少过度拟合的生态位模型（ENMs）的抽样偏差。地理空间中的抽样偏差导致在环境空间中也可能有偏差的地点。如果是这样，模型可能会过度拟合这些偏差。作为解决这个问题的初步测试，我们使用Maxent，生物气候变量，和发生地点的广泛分布的马达加斯加tenrec，Microgale cowani（Tenrecidae：Oryzorictinae）。我们使用三个不同的数据集：未过滤的，空间过滤的，和稀薄的未过滤的地方，这个物种的非生物适宜区建模。为了量化过拟合和模型性能，我们计算了评估AUC、校准和评估AUC之间的差异（=AUC（diff））和遗漏率。用过滤数据集制作的模型显示出比其他两套模型更低的过拟合和更好的性能，具有更低的遗漏率和AUC（diff），以及更高的AUC（evaluation）。此外，对于三个评估指标，稀疏的未过滤数据集的表现优于未过滤数据集，这可能是因为较大的数据集加强了偏差。这些结果表明，发生地点的空间过滤，可能会让地理学家产生更好的模型。(C)2014爱思唯尔有限公司版权所有。
This study employs spatial filtering of occurrence data with the aim of reducing overfitting to sampling bias in ecological niche models (ENMs). Sampling bias in geographic space leads to localities that may also be biased in environmental space. If so, the model can overfit to those biases. As a preliminary test addressing this issue, we used Maxent, bioclimatic variables, and occurrence localities of a broadly distributed Malagasy tenrec, Microgale cowani (Tenrecidae: Oryzorictinae). We modeled the abiotically suitable area of this species using three distinct datasets: unfiltered, spatially filtered, and rarefied unfiltered localities. To quantify overfitting and model performance, we calculated evaluation AUC, the difference between calibration and evaluation AUC (=AUC(diff)), and omission rates. Models made with the filtered dataset showed lower overfitting and better performance than the other two suites of models, having lower omission rates and AUC(diff), and a higher AUC(evaluation). Additionally, the rarefied unfiltered dataset performed better than the unfiltered one for three evaluation metrics, likely because the larger one reinforced the biases. These results indicate that spatial filtering of occurrence localities may allow biogeographers to produce better models. (C) 2014 Elsevier B.V. All rights reserved.