Spatial bias in the GBIF database and its effect on modeling species' geographic distributions
Spatial bias in the GBIF database and its effect on modeling species' geographic distributions
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DOI：
10.1016/j.ecoinf.2013.11.002
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发表时间：
2014-01-01
影响因子：
5.1
通讯作者：
Schwanghart, Wolfgang
中科院分区：
文献类型：
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作者：
Beck, Jan;Boeller, Marianne;Schwanghart, Wolfgang
物种分布建模与标本分布记录数据库相结合，是解决生物地理学和生态学中分布数据有限问题的一种方法。全球生物多样性信息设施（GBIF）是一个整理数字化收集和调查数据的门户网站，是最大的分布记录在线提供商。然而，由于采样、数据存储和动员的不均匀，所有分布式数据库都存在空间偏倚。这种偏见在GBIF中尤为明显，各国在资助和数据共享方面的差异导致对GBIF的贡献存在巨大差异。我们以一种常见的欧亚蝴蝶（Aglais urticae）作为样本分类群，提供了证据表明，由于GBIF分布记录的空间聚类，范围模型质量正在下降。此外，我们表明，这种模型质量的损失将被忽视的标准方法的模型质量评价。通过对瑞士物种分布的模型预测进行评估，我们将完整数据的分布模型与子抽样程序以记录数量为代价消除空间偏差的数据进行比较，而不是记录的空间范围。我们表明，空间偏差较小的数据产生更好的预测模型，即使它们基于较少的输入数据。因此，我们的次采样程序可能是一种合适的方法来减少空间偏差对物种分布模型的影响。我们的研究结果警告了物种分布模型在分布数据库中的自动化应用（正如已经提倡和实施的那样），因为内部模型评估并没有显示模型质量随着空间偏差的增加而下降（相反），而专家评估则明显下降。(C) 2013 Elsevier B.V.版权所有
Species distribution modeling, in combination with databases of specimen distribution records, is advocated as a solution to the problem of distributional data limitation in biogeography and ecology. The global biodiversity information facility (GBIF), a portal that collates digitized collection and survey data, is the largest online provider of distribution records. However, all distributional databases are spatially biassed due to uneven effort of sampling, data storage and mobilization. Such bias is particularly pronounced in GBIF, where nation-wide differences in funding and data sharing lead to huge differences in contribution to GBIF.We use a common Eurasian butterfly (Aglais urticae) as an exemplar taxon to provide evidence that range model quality is decreasing due to the spatial clustering of distributional records in GBIF. Furthermore, we show that such loss of model quality would go unnoticed with standard methods of model quality evaluation. Using evaluations of model predictions of the Swiss distribution of the species, we compare distribution models of full data with data where a subsampling procedure removes spatial bias at the cost of record numbers, but not of spatial extent of records. We show that data with less spatial bias produce better predictive models even though they are based on less input data. Our subsampling routine may therefore be a suitable method to reduce the impact of spatial bias to species distribution models.Our results warn of automatized applications of species distribution models to distributional databases (as has been advocated and implemented), as internal model evaluation did not show the decline of model quality with increased spatial bias (but rather the opposite) while expert evaluation clearly did. (C) 2013 Elsevier B.V. All rights reserved.