Novel three-step pseudo-absence selection technique for improved species distribution modelling.
Novel three-step pseudo-absence selection technique for improved species distribution modelling.
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DOI：
10.1371/journal.pone.0071218
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Ikeda T
中科院分区：
文献类型：
--
作者：
Senay SD;Worner SP;Ikeda T
空间分布模型（SDM）的伪缺失选择是正在进行的调查的主题。许多技术仍在开发中，关于其有效性的报告各不相同。由于存在和不存在数据的质量对于相关SDM预测的可接受的准确性是关键的，因此确定用于消除SDM的伪不存在的适当方法是至关重要的。当前用于生成伪缺失点的主要方法是：1）从背景数据随机生成伪缺失位置; 2）在距记录的存在点的限定地理距离内生成伪缺失位置;以及3）在环境与存在点不同的区域中选择的伪缺失位置。需要一种既考虑地理范围又考虑环境要求的方法，以产生在空间和生态上平衡的伪缺失点。我们使用一种新的三步方法，满足空间和生态原因，为什么目标物种可能会发现一个特定的地理位置不合适。步骤1包括建立物种存在点周围的地理范围，根据不同距离处环境变量重要性的分析，从中选择伪缺失点。这一步给出了一个生态上有意义的解释的空间范围的背景数据，而不是使用任意半径。步骤2确定在步骤1中指定的距离内与存在点在环境上不同的位置。步骤3执行K-均值聚类，以通过取步骤2中识别的环境最不相似的类中的聚类的质心来将潜在的伪缺失的数量减少到期望的集合。通过考虑空间，生态和环境方面，三步方法确定适当的伪缺失点的相关SDM。我们说明了这种方法，通过预测新西兰的亚洲虎蚊（白纹伊蚊）和西方玉米根虫（玉米根萤叶甲）的潜在分布。
Pseudo-absence selection for spatial distribution models (SDMs) is the subject of ongoing investigation. Numerous techniques continue to be developed, and reports of their effectiveness vary. Because the quality of presence and absence data is key for acceptable accuracy of correlative SDM predictions, determining an appropriate method to characterise pseudo-absences for SDM’s is vital. The main methods that are currently used to generate pseudo-absence points are: 1) randomly generated pseudo-absence locations from background data; 2) pseudo-absence locations generated within a delimited geographical distance from recorded presence points; and 3) pseudo-absence locations selected in areas that are environmentally dissimilar from presence points. There is a need for a method that considers both geographical extent and environmental requirements to produce pseudo-absence points that are spatially and ecologically balanced. We use a novel three-step approach that satisfies both spatial and ecological reasons why the target species is likely to find a particular geo-location unsuitable. Step 1 comprises establishing a geographical extent around species presence points from which pseudo-absence points are selected based on analyses of environmental variable importance at different distances. This step gives an ecologically meaningful explanation to the spatial range of background data, as opposed to using an arbitrary radius. Step 2 determines locations that are environmentally dissimilar to the presence points within the distance specified in step one. Step 3 performs K-means clustering to reduce the number of potential pseudo-absences to the desired set by taking the centroids of clusters in the most environmentally dissimilar class identified in step 2. By considering spatial, ecological and environmental aspects, the three-step method identifies appropriate pseudo-absence points for correlative SDMs. We illustrate this method by predicting the New Zealand potential distribution of the Asian tiger mosquito (Aedes albopictus) and the Western corn rootworm (Diabrotica virgifera virgifera).
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