Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.
Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.
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DOI：
10.1371/journal.pone.0097122
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Secondi J
中科院分区：
文献类型：
--
作者：
Fourcade Y;Engler JO;Rödder D;Secondi J
MAXENT现在是一种常用的物种分布模型（SDM）工具，被保护从业者用来根据一组记录和环境预测因子来预测物种的分布。然而，用于训练模型的物种发生数据集在地理空间上往往存在偏差，因为整个研究区域的采样努力不均匀。这种偏差可能是导致模型严重不准确的一个原因，并可能导致不正确的预测。虽然已经提出了一些抽样偏差校正方法，但没有共识的指导方针来解释它。在此，我们比较了五种偏差校正方法在三个物种发生数据集上的表现：一个来自土地覆盖图的“虚拟”数据集，以及龟（Chrysemys picta）和火蜥蜴（Plethodon aceus）的两个实际数据集。我们对这些数据集进行了四种类型的抽样偏差，对应于潜在的经验偏差类型。我们对有偏样本应用了五种校正方法，并将分布模型的输出与无偏数据集进行比较，以评估每种方法的整体校正性能。结果表明，校正初始抽样偏差的方法的能力因偏差类型、偏差强度和物种的不同而有很大差异。然而，简单系统的记录抽样在测试的条件范围内始终是表现最好的，而其他方法在大多数情况下表现较差。初始条件对校正性能的强烈影响突出了进一步研究的必要性，以制定一个循序渐进的指南来解释抽样偏差。然而，这种方法在纠正抽样偏差方面似乎是最有效的，在大多数情况下都应该被推荐。
MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one “virtual” derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases.
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