Assessing the effects of pseudo-absences on predictive distribution model performance
Assessing the effects of pseudo-absences on predictive distribution model performance
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DOI：
10.1016/j.ecolmodel.2007.08.010
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发表时间：
2008-02-10
影响因子：
3.1
通讯作者：
Lobo, Jorge M.
中科院分区：
文献类型：
--
作者：
Chefaoui, Rosa M.;Lobo, Jorge M.
利用地图集、博物馆藏品和数据库中的存在数据对物种分布进行建模是一项挑战。在本文中，我们比较了七个程序来生成伪缺失数据，这反过来又被用来生成GLM-logistic回归模型时，可靠的缺失数据不可用。我们使用随机选择的伪缺席或通过存在的方法（ENFA和MDE）来模拟一个受威胁的地方性伊比利亚蛾物种（Graellsia isabelae）的分布。结果表明，伪缺失选择方法极大地影响了解释变异的百分比;准确性措施的分数，最重要的是，分布估计的约束程度。当我们从存在数据建立的最佳环境区域进一步提取伪缺失时，生成的模型获得了更好的准确性分数，并且过度预测增加。当环境因素以外的变量影响物种的分布时（即，非平衡状态）并且不存在关于缺失的精确信息时，随机选择伪缺失或从类似于物种存在数据的环境地点中选择伪缺失产生最受约束的预测分布图，因为伪缺失可以位于环境适宜的区域内。这项研究表明，如果我们没有可靠的缺席数据，伪缺席选择的方法强烈的条件下获得的模型，产生不同的模型预测之间的梯度潜在和实现的分布。(c)2007 Elsevier B. V.保留所有权利。
Modelling species distributions with presence data from atlases, museum collections and databases is challenging. In this paper, we compare seven procedures to generate pseudo-absence data, which in turn are used to generate GLM-logistic regressed models when reliable absence data are not available. We use pseudo-absences selected randomly or by means of presence-only methods (ENFA and MDE) to model the distribution of a threatened endemic Iberian moth species (Graellsia isabelae). The results show that the pseudo-absence selection method greatly influences the percentage of explained variability; the scores of the accuracy measures and, most importantly, the degree of constraint in the distribution estimated. As we extract pseudo-absences from environmental regions further from the optimum established by presence data, the models generated obtain better accuracy scores, and over-prediction increases. When variables other than environmental ones influence the distribution of the species (i.e., non-equilibrium state) and precise information on absences is non-existent, the random selection of pseudo-absences or their selection from environmental localities similar to those of species presence data generates the most constrained predictive distribution maps, because pseudo-absences can be located within environmentally suitable areas. This study shows that if we do not have reliable absence data, the method of pseudo-absence selection strongly conditions the obtained model, generating different model predictions in the gradient between potential and realized distributions. (c) 2007 Elsevier B.V. All rights reserved.