The effects of species' range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact?
The effects of species' range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact?
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DOI：
10.1111/j.0021-8901.2004.00943.x
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发表时间：
2004-10-01
影响因子：
5.7
通讯作者：
Rogers, DJ
中科院分区：
文献类型：
--
作者：
McPherson, JM;Jetz, W;Rogers, DJ
1.保护科学家和资源管理人员越来越多地采用经验分布模型来帮助决策。然而，这些模型并不是对所有物种都同样可靠，范围大小会影响它们的性能。我们研究了这种效应在多大程度上反映了范围大小对用于训练和测试模型的数据的样本大小和采样率（代表物种存在的样本比例）的影响所产生的统计伪影。我们的分析使用了模拟数据和经验分布模型的32种鸟类特有的南非，莱索托和斯威士兰。用逻辑回归或非线性判别分析建立模型，并用四种模型准确性指标进行评估：灵敏度、特异性、Cohen's kappa和受试者工作特征（ROC）曲线的曲线下面积（AUC）。利用傅立叶处理后的卫星影像资料，以环境指数作为预测因子。我们首先遵循传统的建模实践，以说明范围大小可能会影响模型的性能，当采样率反映物种的自然流行。然后，我们证明了这种影响主要是人为的。模型评估过程中可能出现统计学伪影，因为Cohen的kappa系统地响应患病率的变化。相比之下，AUC在很大程度上不受影响，因此是模型性能的更可靠的度量。在模型拟合过程中也会出现统计伪影。Logistic回归和判别分析都对训练数据的样本大小和抽样率敏感。当样本量较大且患病率中等时，两者的表现最好。合成与应用。物种的生态特性可能会影响分布模型的性能。然而，统计人为因素可能会混淆旨在确定这些特征的比较研究的结果。为了减轻人为影响，我们建议仔细报告抽样流行率，AUC作为准确性的衡量标准，并在比较研究中固定抽样流行率的中间水平。
1. Conservation scientists and resource managers increasingly employ empirical distribution models to aid decision-making. However, such models are not equally reliable for all species, and range size can affect their performance. We examined to what extent this effect reflects statistical artefacts arising from the influence of range size on the sample size and sampling prevalence (proportion of samples representing species presence) of data used to train and test models.2. Our analyses used both simulated data and empirical distribution models for 32 bird species endemic to South Africa, Lesotho and Swaziland. Models were built with either logistic regression or non-linear discriminant analysis, and assessed with four measures of model accuracy: sensitivity, specificity, Cohen's kappa and the area under the curve (AUC) of receiver-operating characteristic (ROC) plots. Environmental indices derived from Fourier-processed satellite imagery served as predictors.3. We first followed conventional modelling practice to illustrate how range size might influence model performance, when sampling prevalence reflects species' natural prevalences. We then demonstrated that this influence is primarily artefactual. Statistical artefacts can arise during model assessment, because Cohen's kappa responds systematically to changes in prevalence. AUC, in contrast, is largely unaffected, and thus a more reliable measure of model performance. Statistical artefacts also arise during model fitting. Both logistic regression and discriminant analysis are sensitive to the sample size and sampling prevalence of training data. Both perform best when sample size is large and prevalence intermediate.4. Synthesis and applications. Species' ecological characteristics may influence the performance of distribution models. Statistical artefacts, however, can confound results in comparative studies seeking to identify these characteristics. To mitigate artefactual effects, we recommend careful reporting of sampling prevalence, AUC as the measure of accuracy, and fixed, intermediate levels of sampling prevalence in comparative studies.