Modeling a spatially restricted distribution in the Neotropics: How the size of calibration area affects the performance of five presence-only methods
Modeling a spatially restricted distribution in the Neotropics: How the size of calibration area affects the performance of five presence-only methods
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DOI：
10.1016/j.ecolmodel.2009.10.009
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发表时间：
2010-01-24
影响因子：
3.1
通讯作者：
Alexandrino, Joao
中科院分区：
文献类型：
--
作者：
Giovanelli, Joao G. R.;de Siqueira, Marinez Ferreira;Alexandrino, Joao
在这里，我们研究物种分布模型的新热带区无尾类限制在巴西大西洋森林热点的喜雨地区。我们通过GPS实地调查，并使用五种建模方法（BIOCLIM，域，OM-GARP，SVM和MAXENT）和选定的生物气候和地形变量来模拟物种分布的树蛙Hypsiboas bischoffi（无尾目：雨蛙科）的已知发生。模型首先使用两个校准区域进行训练：巴西大西洋森林（BAF）和整个南美洲（SA）。所有建模方法均显示出良好的预测能力和准确性，平均AUC范围为0.77（BIOCLIM/BAF）至0.99（MAXENT/SA）。MAXENT和SVM是这里测试的方法中最准确的仅存在方法。除了用SA校准的SVM模型外，所有模型与BAF校准的模型相比都预测了更大的分布区域。OM-GARP显著高估了在SA校准的模型的物种分布，预测面积比其他SDM预测的面积大10（6）km（2）左右。随着校准区域（和环境空间）的增加，OM-GARP预测遵循与校准区域增加相关的环境空间变化，而MAXENT模型在校准区域之间更加一致。MAXENT是唯一一种在校准区域检索一致预测的方法，同时允许一些过度预测，这一结果可能与其他空间受限生物的分布建模相关。(C)2009 Elsevier B. V.保留所有权利。
We here examine species distribution models for a Neotropical anuran restricted to ombrophilous areas in the Brazilian Atlantic Forest hotspot. We extend the known occurrence for the treefrog Hypsiboas bischoffi (Anura: Hylidae) through GPS field surveys and use five modeling methods (BIOCLIM, DOMAIN, OM-GARP, SVM, and MAXENT) and selected bioclimatic and topographic variables to model the species distribution. Models were first trained using two calibration areas: the Brazilian Atlantic Forest (BAF) and the whole of South America (SA). All modeling methods showed good levels of predictive power and accuracy with mean AUC ranging from 0.77 (BIOCLIM/BAF) to 0.99 (MAXENT/SA). MAXENT and SVM were the most accurate presence-only methods among those tested here. All but the SVM models calibrated with SA predicted larger distribution areas when compared to models calibrated in BAF. OM-GARP dramatically overpredicted the species distribution for the model calibrated in SA, with a predicted area around 10(6) km(2) larger than predicted by other SDMs. With increased calibration area (and environmental space), OM-GARP predictions followed changes in the environmental space associated with the increased calibration area, while MAXENT models were more consistent across calibration areas. MAXENT was the only method that retrieved consistent predictions across calibration areas, while allowing for some overprediction, a result that may be relevant for modeling the distribution of other spatially restricted organisms. (C) 2009 Elsevier B.V. All rights reserved.