Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models
Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models
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DOI：
10.1890/02-5364
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发表时间：
2004-04-01
影响因子：
5
通讯作者：
Danin, A
中科院分区：
文献类型：
--
作者：
Kadmon, R;Farber, O;Danin, A
取样偏差是动植物分布记录中的一种普遍现象。然而，基于这些记录的模型通常忽略了数据收集中的偏差对模型预测准确性的潜在影响。本研究旨在调查路边偏差（生物多样性数据库中最常见的偏差来源之一）对生物气候模型生成的预测地图准确性的影响。利用以色列129种木本植物的分布数据，我们检验了以下假设：（1）收集的关于以色列木本植物分布的数据存在路边偏差，（2）这种偏差影响模型预测的准确性，（3）以色列的道路网在气候条件方面存在偏差，（4）路边偏差对模式预测的影响取决于道路网络地理分布中气候偏差的大小。正如预期的那样，道路附近植物观测的频率始终大于空间随机分布的预期频率。这种偏差在距离道路500-2000米处最为明显，但在更大的尺度上也具有统计学意义。基于近路观测的预测地图的准确性低于基于非道路或“校正”观测（校正路边偏差的观测）的预测地图。然而，这些差异的幅度是非常低的，这表明，即使在物种观测分布的强烈偏见并不一定会恶化生物气候模型生成的预测地图的准确性。对数据的进一步分析表明，以色列的道路网络在温度方面相对无偏，在降雨条件方面只有微弱的偏差。总体结果是一致的假设，路边偏差模型预测的影响取决于气候偏差的道路网络的地理分布的大小。我们讨论了生物多样性数据库中偏差校正的一些理论和实践考虑。
Sampling bias is a common phenomenon in records of plant and animal distribution. Yet, models based on such records usually ignore the potential implications of bias in data collection on the accuracy of model predictions. This study was designed to investigate the effect of roadside bias, one of the most common sources of bias in biodiversity databases, on the accuracy of predictive maps produced by bioclimatic models. Using data on the distribution of 129 species of woody plants in Israel, we tested the following hypotheses: (1) that data collected on woody plant distribution in Israel suffer from roadside bias, (2) that such bias affects the accuracy of model predictions, (3) that the road network of Israel is biased with respect to climatic conditions, and (4) that the impact of roadside bias on model predictions depends on the magnitude of climatic bias in the geographic distribution of the road network.As expected, the frequency of plant observations near roads was consistently greater than that expected from a spatially random distribution. This bias was most pronounced at distances of 500-2000 m from roads, but it was statistically significant also at larger scales. Predictive maps based on near-road observations were less accurate than those based on off-road or "rectified" observations (observations corrected for roadside bias). However, the magnitude of these differences was extremely low, indicating that even a strong bias in the distribution of species observations does not necessarily deteriorate the accuracy of predictive maps generated by bioclimatic models. Further analysis of the data indicated that the road network of Israel is relatively unbiased in terms of temperature, and only weakly biased in terms of rainfall conditions. The overall results are consistent with the hypothesis that the impact of roadside bias on model predictions depends on the magnitude of climatic bias in the geographic distribution of the road network. We discuss some theoretical and practical considerations of bias correction in biodiversity databases.