Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent
Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent
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DOI：
10.1111/j.2007.0906-7590.05102.x
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发表时间：
2007-08-01
期刊：
影响因子：
5.9
通讯作者：
Eaton, Muir
中科院分区：
文献类型：
--
作者：
Peterson, A. Townsend;Papes, Monica;Eaton, Muir
我们比较了两种常用的物种生态位建模算法GARP和Maxent的预测成功率，以挑战算法的通用性，即能够预测物种在广泛的未采样区域的分布，这里称为可转移性。两种算法的结果截然不同——Maxent模型在低阈值下重建了物种的总体分布，但较高的Maxent预测水平反映了对输入数据的过度拟合；另一方面，GARP模型成功地预测了大多数物种的分布潜力，但代价是增加了（至少是明显的）佣金误差。受试者工作特征（ROC）检验在识别模型方面很弱，这些模型能够从非采样区域预测到广泛的未采样区域。这种可转移性显然是建模算法的新挑战，并且需要与密集采样景观预测不同的质量-在这种情况下，Maxent仅在非常低的阈值下可转移，输入数据中的偏差和差距可能经常影响基于较高Maxent阈值的结果，需要仔细解释模型结果。
We compared predictive success in two common algorithms for modeling species' ecological niches, GARP and Maxent, in a situation that challenged the algorithms to be general - that is, to be able to predict the species' distributions in broad unsampled regions, here termed transferability. The results were strikingly different between the two algorithms - Maxent models reconstructed the overall distributions of the species at low thresholds, but higher predictive levels of Maxent predictions reflected overfitting to the input data; GARP models, on the other hand, succeeded in anticipating most of the species' distributional potential, at the cost of increased (apparent, at least) commission error. Receiver operating characteristic (ROC) tests were weak in discerning models able to predict into broad unsampled areas from those that were not. Such transferability is clearly a novel challenge for modeling algorithms, and requires different qualities than does predicting within densely sampled landscapes - in this case, Maxent was transferable only at very low thresholds, and biases and gaps in input data may frequently affect results based on higher Maxent thresholds, requiring careful interpretation of model results.