Historical bias in biodiversity inventories affects the observed environmental niche of the species
Historical bias in biodiversity inventories affects the observed environmental niche of the species
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DOI：
10.1111/j.0030-1299.2008.16434.x
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发表时间：
2008-06-01
期刊：
影响因子：
3.4
通讯作者：
Baselga, Andres
中科院分区：
文献类型：
--
作者：
Hortal, Joaquin;Jimenez-Valverde, Alberto;Baselga, Andres
众所周知，来自历史清单的生物多样性数据存在重要的地理和分类偏差。因此，目前关于大多数物种分布的知识可能是不完整和有偏见的。我们评估了历史生物多样性数据中的偏差可能如何影响对该物种环境生态位的描述，并以马德里屎甲虫分布的详尽数据为例进行了研究。我们描述了调查的历史过程，并将这些历史数据与详尽调查的结果进行了比较，识别了不同时期历史调查中的环境偏差，并随时间评估了历史数据提供的物种环境生态位的完整性。西班牙内战等事件影响了调查的速度和范围，但自1970年以来的详尽工作提供了到1998年该地区的很好的覆盖范围，尽管不完整。尽管如此，历史数据中的偏见导致了对许多重要物种的生态位的有限了解。尽管近一半的物种在1998年的数据中有100%的生态位被覆盖，但大约三分之一的物种的生态位不到75%，近四分之一的物种不到50%，由于缺乏数据，18种物种不得不被排除在分析之外。我们的结果指出，来自非标准化清单的数据往往不能完全描述大多数物种的环境反应。因此，我们强调指出，目前的物种分布预测模型存在一些局限性，因为基于关于物种环境生态位的部分信息的模型结果将受到影响。因此，在构建物种分布预测图之前，必须评估现有数据中的偏差，并在从这些图中得出结论或保护策略时考虑到这一点。
It is well known that biodiversity data from historical inventories presents important geographic and taxonomic biases. Due to this, current knowledge on the distribution of most species could be incomplete and biased. We assess how the biases in historical biodiversity data might affect the description of the environmental niche of the species, using exhaustive data on the distribution of dung beetles in Madrid as a case study. We describe the historical process of survey and compare such historical data with the results of an exhaustive survey, identifying the environmental biases in the historical surveys during different periods, and assessing the completeness of the environmental niche of the species provided by historical data through time. Events like the Spanish Civil War affect the tempo and spread of surveys, but the exhaustive work since 1970 provides a good, though incomplete, coverage of the region by 1998. In spite of this, the biases in historical data result in a limited knowledge about the niche of an important number of species. Although nearly a half of the species had the 100% of their niche covered by data in 1998, roughly a third had less than 75%, nearly a fourth less than 50%, and 18 species had to be excluded from the analyses due to the lack of data. Our results point out that data from non-standardized inventories often provide an incomplete description of the environmental responses of most species. Due to this, we highlight that currently predictive models of species distributions present some limitations, since the results of models based in partial information about the environmental niche of the species will be compromised. Therefore, the biases in the available data Must be evaluated before constructing predictive maps of species distributions, and taken into account when drawing conclusions or conservation strategies from these maps.